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digital image processing full report
Post: #16
Presented by:

.doc  image processingg.doc (Size: 692 KB / Downloads: 115)
Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. In image steganography the information is hidden exclusively in images. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. Generation of stego images containing hidden messages using LSB is a very common and most primitive method of steganography. In this method, the least significant bit of some or all of the bytes inside an image is changed. With a well-chosen image, one can even hide the message in the least as well as second to least significant bit and still not see the difference. The present paper compares these two schemes. Good conclusions are drawn from the experimental results.
Steganography is the art and science of writing hidden messages in such a way that no one apart from the intended recipient knows of the existence of the message. Unlike cryptography, where the existence of the message is clear, but the meaning is obscured, the steganographic technique
strives to hide the very presence of the message itself from an observer.
The word steganography is derived from the Greek words “stegos” meaning “cover” and “grafia” meaning “writing” defining it as “covered writing”.
Steganography simply takes one piece of information and hides it within another. Computer files (images, sounds recordings, even disks) contain unused or insignificant areas of data. Steganography takes advantage of these areas, replacing them with information. One can replace the least significance bit of the original file (audio/image) with the secret bits and the resultant cover will not be distorted. It is not to keep others from knowing the hidden information, but it is to keep others from thinking that the information even exists. If a steganography method causes someone to suspect that there is secret information in the carrier medium, then this method fails. The noise or any modulation induced by the message should not change the characteristics of the cover and should not produce any kind of distortion. The paper is organized as follows. The II section gives methodology. The III section gives the types of LSB techniques. IV section gives the experimental results followed by conclusions.
LSB is a simple approach for embedding information in an image. In this scheme the hidden message will be inserted in LSB’s of the image.
When using a 24-bit image, a bit of each of the red, green and blue colour components can be used, since they are each represented by a byte. In other words, one can store 3 bits in each pixel. An 800 × 600 pixel image, can thus store a total amount of 1,440,000 bits or 180,000 bytes of embedded data.
For example a grid for 3 pixels of a 24-bit image can be as follows:
(00101101 00011100 11011100)
(10100110 11000100 00001100)
(11010010 10101101 01100011)
When the number 200, which binary representation is 11001000, is embedded into the least significant bits of this part of the image, the resulting grid is as follows:
(00101101 00011101 11011100)
(10100110 11000101 00001100)
(11010010 10101100 01100011)
Although the number was embedded into the first 8 bytes of the grid, only the 3 underlined bits needed to be changed according to the embedded message. On average, only half of the bits in an image will need to be modified to hide a secret message using the maximum cover size. Since there are 256 possible intensities of each primary colour, changing the LSB of a pixel results in small changes in the intensity of the colours. These changes cannot be perceived by the human eye -thus the message is successfully hidden.
With a well-chosen image, one can even hide the message in the least as well as second to least significant bit and still not see the difference.
Post: #17
presented by

.doc  05. Digital Image Processing.doc (Size: 572.5 KB / Downloads: 103)
Medical imaging is a field which researches and develops tools and technology to acquire, manipulate and archive digital images which are used by the dimensional function , f, that takes an input two spatial coordinates x and y and returns a value f(x, y). The value f(x, y) is a gray level of the image at that point. The gray level is also called the intensity. Digital images are a discretized partition of the spatial images into small cells which are referred to as pixels – picture elements. Digital image processing is a field for processing digital images using a digital computer. Processing of digital images include operations involving digital images such as acquisition, storage, retrieval, translation, compression, etc.
Quantifying disease progression of patients with early stage Rheumatoid Arthritis (RA) presents special challenges. Establishing a robust and reliable method that combines the ACR criteria with bone and soft-tissue measurement techniques, would make possible the diagnosis of early RA and/or the monitoring of the progress of the disease. In this paper and automated, reliable and robust system that combines the ACR criteria with radiographic absorptiometry based bone and soft tissue density measurement techniques is presented. The system is comprised of an image digitization component and automated image analysis component. Radiographs of the hands and the calibration wedges are acquired and digitized following a standardized procedure. The image analysis system segments the relevant joints into soft-tissue and bone regions and computes density values of each of these regions relative to the density of the reference wedges. Each of the joints is also scored by trained radiologists using the well established ACR criteria. The results of this work indicate that use of standardized imaging procedures and robust image analysis techniques can significantly improve the reliability of quantitative measurements for rheumatoid arthritis assessment. Furthermore, the methodology has the potential to be clinically used in assessing disease condition of early stage RA subjects.
Keywords: Automated hand image analysis, Hand image segmentation, Radiographic absorptiometry, Rheumatoid arthritis
Conventional examination of the hand radiographs is well established as a diagnostic as well as an outcome measuree in Rheumatoid Arthritis (RA). It is readily available and has been correlated with measures of disease activity and function. X-ray changes are, however, historical rather than predictive, and there is significant observer variation in quantifying erosive changes. The earliest radiographic changes seen in the hand are soft-tissue swelling symmetrically around the joints involved, juxta-articular osteoporosis and erosion of the ‘bare’ areas of bone (i.e. areas lacking articular cartilage).These changes help to confirm the presence of an inflammatory process.
The presence of early soft-tissue swelling is easily recognized on plain radiographs but not readily quantified. Although the presence of early osteoporosis is recognized in the affected hand, a mild osteoporosis may be extremely subtle to the eyes. The recognition of the changes in soft-tissue and bone density is subjective and is known to vary from assessor to assessor. Therefore, attention has been focused on the more objective erosion and joint narrowing assessment. Use of magnetic resonance (MR) technique has been shown to sensitively detect early local edema and inflammation prior to a positive finding on plain film radiographs. However, MR is an expensive examination and may not be used as a routine technique.
Presently, radiographs of the hands and wrists are employed to assess disease progression. The parameters used to determine progression are the changes in erosions and joint-space narrowing observed on the radiographs. There are some problems with both of these parameters. First, both erosion and joint narrowing are not the earliest changes in RA and further they may be substantially irreversible. Second, these two changes may occur independently of each other. Third, there is a tremendous variability in erosive disease: some patients never develop erosions; some go into spontaneous remission of their erosive disease; and for some, the progression is relentless. Fourth, joint-space or cartilage loss may be caused by either the disease itself or by mechanical stress. Present scoring methods require that any degree of joint-space loss be recorded as a progressive change due to RA.
Quantitative techniques currently available may provide a new approach in monitoring disease progression in patients with RA. Adoption of these techniques may have implications for the management of patients with RA and for possible detection of the disease at an early stage.
1.1. Hand bone densitometry
Considerable advances have been made over the past two decades in developing radiological techniques for assessing bone density. However; all of these techniques have been utilized on aging-related osteoporosis, a pathological change involving general bone mineral reduction. Owing to the wide availability of DXA, recently published research describes the use of Bone Mineral Density (BMD) measurements in the hands of patients with chronic RA. Most published observations on RA have examined BMD changes, focusing on only the general bone loss around the joints. Quantification of the difference of bone loss between the juxta-articular bone and the shaft of tubular bones in the hands could be a sensitive index for quantitative analysis of RA patients. Hand BMD measurements offer an observer independent and reproducible means of quantifying the cumulative effects of local and systemic inflammation. The technique could be of use in the assessment of patients with early RA, in whom conventional measures of disease are not helpful until disease is (irreversibly) more advanced.
1.2. Hand radiographic absorptiometry
In conventional Radiographic Absorptiometry, radiographs of the hand are acquired with reference wedges placed on the films. The films are and subsequently analyzed using an optical densitometer. The resulting density values computed by the densitometer are calibrated relative to that of the reference wedge and are expressed in arbitrary units.
Recent improvements in hardware and software available for digital image processing have led to the quantitative assessment of radiological abnormalities in diagnostic radiology. Such improvements have also enabled introduction of several radiographic absorptiometry techniques. One such technique uses centralized analysis of hand radiographs and averages the BMD of the second to fourth middle phalanges. Another technique developed in Japan uses the diaphysis of the second metacarpal to determine BMD. A third technique developed in Europe measures the diaphysis and proximal metaphysics of the second middle phalanx. Based on published short-term precision errors, computer-assisted Radiographic Absorptiometry appears to be suitable for the measurements of the BMD of phalanges and metacarpals, and is used in several hundred centers worldwide.
In this work we present preliminary results of an ongoing research work aimed at developing an automated radiographic absorptiometry system for the assessment and monitoring of both BMD and soft tissue swelling in early stage RA. This paper focuses on the reproducibility and accuracy of the methodology being developed. The paper is organized as follows: the next section provides an overview of the image acquisition procedure. In section 3 the image analysis algorithms used in this work are presented. In section 4 we present results obtained by analyzing the data collected in a small reproducibility study involving 10 normal subjects.
One key factor influencing the outcome of any radiographic absorptiometry technique is the standardization of the image acquisition technique. Variability in acquisition parameters can significantly affect the measured values. In order to carry out this work, a standard image acquisition protocol was defined. This protocol has been successfully used in earlier large scale multinational phase 3 clinical trials for Rheuatoid Arthrtis related drugs. Radiographs of the left and right hands are taken one
at a time. Templates were developed to guide the positioning of the hand with respect to the center of the x-ray beam. The X-ray beam was centered between the 2nd and 3rd metacarpo-phalangeal joints and angled at 90° to the film surface. This results in a tangential image of the joints. Improper beam centering generally results in overlapping joint margins. The X-ray exposure parameters were maintained constant for all subjects. All normal subjects were imaged at the same clinic at UCSF. In addition to providing a template for hand positioning, two sets of calibration wedges were also provided to the clinic. Each set of wedges consisted of one Acrylic wedge, for soft tissue and one Aluminum wedge for bone tissue. These wedges were custom designed for the purposes of this research work.
3.Enhancement Image and Restoration
The image at the left of Figure 1 has been corrupted by noise during the digitization process. The 'clean' image at the right of Figure 1 was obtained by applying a median filter to the image.
An image with poor contrast, such as the one at the left of Figure 2, can be improved by adjusting the image histogram to produce the image shown at the right of Figure 2.
One of the major difficulties in analyzing hand radiograph images is the high level of noise present in the images. Additionally the trabecular texture of the hand in the vicinity of the joints increases the noise in edge maps of this regions. Use of non-standard acquisition protocol can add additional challenges at it can result in further degradation of image quality. This last challenge is minimized in this work, as a standard image acquisition protocol is followed. Given a particular application varying degrees of accuracy in anatomy segmentation can be considered acceptable. For instance in detecting joint-space narrowing there is a need for accurate and reliable determination of the joint-space of any finger and the bone edges in this region. However, accurate delineation of the bone edges in the vicinity of the joint is not as relevant. Depending upon the application there can be additional constraints on performance issues as well. In an application for which off-line processing of the data is acceptable, more sophisticated algorithms can be employed. This particular application requires that the overall process be fast, accurate and reproducible enough for on-line processing. Accurate estimation of the bone edges in the middle shaft and in the joint vicinity of high relevance in this work. This is primarily because the disease progression follows different patterns in the joint area as compared to the middle phalange area. Also, the manifestations of the disease symptoms in its early stage have different effects on soft-tissue and bone as well, which require reliable segmentation of these two types of tissue at different time points. The algorithm for hand segmentation can be outlined into the following main stages:
• Hand outline delineation
• Joint identification
• Bone outline delineation
• Segmentation of soft tissue and bone
The first stage of the algorithm has been well studied in the literature and is not described here. The second stage can be more challenging, especially when dealing with hands of patients in advanced stage of disease progression. As this system will be applied to a patient population that is in their early disease stage it is expected that the joints will be well defined. The system provided to the radiologists allows them to adjust the location of the automatically identified joints. Results presented in this work were obtained by having the radiologists place control points to identify the joints, rather than having them automatically computed by the system.
3.1. Control point placement
A simple user interface was provided to enable placement of control points on the joints. This was primarily done to investigate the sensitivity of the system to the initial control point positioning, which in an automated system would invariably be the same for the same image. The user placed 16 control points on each image. These joints are show in Figure 4. In addition to placing the control points for the joints, the control points for the two wedges are also placed by the radiologists. For each wedge, six control points are placed with four at the corners and two in the middle. Once all the control points are placed, the remaining steps of the generalized algorithm stated above are carried out autonomously. The middle phalange or cortex control points are computed automatically and are located at the middle of straight line connecting the two joints, one above and one below the middle phalange. The diameters of the circular regions of interest placed around each joint are computed proportionally to the length
Post: #18

.doc  DIGITAL IMAGE PROCESSING1.doc (Size: 520 KB / Downloads: 103)

By using Digital Image Processing we enhance the digital images and extracting information and features from the image. Digital Image Processing has become the most common form of image processing, and it is generally used because it is not only the most versatile method, but also the cheapest. This is one type of image processing and it used for editing the digital images which are taken from the digital cameras. This technology is more useful in the investigation in Crime Branch. Digital Image Processing has the advantages as a wider range of algorithm to be applied to the input data and can avoid the problems such as build-up of notice and signal distortion during processing. For this the NASA and WE military have developed advanced computer software. By using this software improve the clarity of and amount of detail visible in still and video images.
The main feature of this technology is Digital Image Editing. Image Editors are provide the means for altering and improving images in an all most endless number of time. They accept images in large variety of image formats. The other features of this technology are Image Size Alteration, Cropping on Image, Removal of Notice and unwanted elements, merging of images and finally color adjustments.
And in this paper we present categories of digital image processing, Image Compression, Image viewing and image types, and digital image editing, and finally advantages and disadvantages of digital image processing.
Digital Image Processing is concerned with acquiring and processing of an image. In simple words an image is a representation of a real scene, either in black and white or in color, and either in print form or in a digital form i.e., technically a image is a two-dimensional light intensity function. In other words it is a data intensity values arranged in a two-dimensional form like an array, the required property of an image can be extracted from processing an image. Image is typically by stochastic models. It is represented by AR model. Degradation is represented by MA model.
Image Processing
Image processing is enhancing image or extracting information or features from an image. Any activity that transforms an input image into an output image. The manipulation and alteration of images using computer software.
Digital Image Processing
Digital image processing is the use of computer to perform on. Digital image processing has the same advantages (over analog image processing) as has (over analog signal processing) -- it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing.
Digital Image
A digital image is a representation of a two-dimensional a finite set of values, called picture elements or. Typically, the pixels are stored in computer memory as one or a two-dimensional array of small integers. These values are often transmitted or stored in a form.
Digital images can be by a variety of input devices and techniques, such as, scanners, coordinate-measuring machines, seismographic profiling, airborne radar, and more.
It is an image that was acquired through scanners or captured from digital cameras. The most common kind of digital image processing is digital image editing.
Because of the computational load of dealing with images containing millions of pixels, digital image processing was largely of academic interest until the 1970s, when dedicated hardware became available that could process images in real time, for some dedicated problems such as television standards conversion. As general-purpose computers became faster, they started to take over the role of dedicated hardware for all but the most specialized and compute-intensive operations.
With the fast computers and signal processors available in the 2000s, digital image processing has become the most common form of image processing, and is generally used because it is not only the most versatile method, but also the cheapest.
Digital Processing Of Camera Images
Images taken by popular digital cameras often need processing to improve their quality; distinct advantage digital cameras have over film cameras. The digital image processing is done by special software programs that manipulate the images is many ways. This process is performed in a "digital darkroom", which is not really a darkroom as it is accomplished via a computer and keyboard.
Reasons for Introducing Digital Image Processing
Figure 1: Polarization by filters
Few types of evidence are more incriminating than a photograph or videotape that places a suspect at a crime scene, whether or not it actually depicts the suspect committing a criminal act. Ideally, the image will be clear, with all persons, settings, and objects reliably identifiable. Unfortunately, though, that is not always the case, and the photograph or video image may be grainy, blurry, of poor contrast, or even damaged in some way.
In such cases, investigators may rely on computerized technology that enables digital processing and enhancement of an image. The U.S. government, and in particular, the military, the FBI, and the National Aeronautics and Space Agency (NASA), and more recently, private technology firms, have developed advanced computer software that can dramatically improve the clarity of and amount of detail visible in still and video images. NASA, for example, used digital processing to analyze the video of the Challenger incident.
How Can We Process An Image?
The first step in digital image processing is to transfer an image to a computer, digitizing the image and turning it into a computer image file that can be stored in a computer's memory or on a storage medium such as a hard disk or CD-ROM. Digitization involves translating the image into a numerical code that can be understood by a computer. It can be accomplished using a scanner or a video camera linked to a frame grabber board in the computer.
The computer breaks down the image in to thousands of pixels. Pixels are the smallest component of an image. They are the small dots in the horizontal lines across a television screen. Each pixel is converted into a number that represents the brightness of the dot. For a black-and-white image, the pixel represents different shades between total black and full white. The computer can then adjust the pixels to enhance image quality.
Categories Of Digital Image Processing:
The Three main categories of digital image processing are:
Image Compression is a mathematical technique used to reduce the amount of computer memory needed to store a digital image. The computer discards (rejects) some information, while retaining sufficient information to make the image pleasing to the human eye.
Enhancement Image enhancement techniques can be used to modify the brightness and contrast of an image, to remove blurriness, and to filter out some of the noise. Using mathematical equations called algorithms, the computer applies each change to either the whole image or targets a particular portion of the image.
For example, global contrast enhancement would affect the entire image, whereas local contrast enhancement would improve the contrast of small details, such as a face or a license plate on a vehicle. Some algorithms can remove background noise without disturbing the key components of the image.
Measurement Extraction is used to gather useful information from an enhanced image.
Image Viewing
The user can utilize different program to see the image. The GIF, JPEG and PNG images can be seen simply using a web browser because they are the standard internet image formats. The SVG format is more and more used in the web and is a standard W3C format.
Image Types
Digital images can be classified according to the number and nature of those samples: The term digital image is also applied to data associated to points scattered over a three-dimensional region, such as produced by tomography equipment. In that case, each datum is called a voxel.
Types Of Images
1. Binary Image: A binary image is a digital image that has only two possible values for each pixel. Binary images are also called bi-level or two-level. A binary image is usually stored in memory as a bitmap, a packed array of bits. A binary image is also a compiled version of source code in Linux and Unixes
2. Gray Scale: In computing, a grayscale or grayscale digital image is an image in which the value of each pixel is a single sample. Grayscale images are distinct from black-and-white images, which in the context of computer imaging are images with only two colors, black and white; grayscale images have many shades of gray in between. In most contexts other than digital imaging, however, the term "black and white" is used in place of "grayscale";
For example, photography in shades of gray is typically called "black-and-white photography". The term monochromatic in some digital imaging contexts is synonymous with grayscale, and in some contexts synonymous with black-and-white.
3. Color Image: A (digital) color image is a digital image that includes color information for each pixel. For visually acceptable results, it is necessary (and almost sufficient) to provide three samples (color channels) for each pixel, which are interpreted as coordinates in some color space. The RGB color space is commonly used in computer displays, but other spaces such as YUV, HSV, and are often used in other contexts. Color Image Representation: A color image is usually stored in memory as a raster map, a two-dimensional array of small integer triplets; or (rarely) as three separate raster maps.
Post: #19
Presented by
Arunachalam. PL

.pptx  360868_634055933651740000.pptx (Size: 3.04 MB / Downloads: 112)
Image processing involves processing or altering an existing image in a desired manner.
The next step is obtaining an image in a readable format.
The Internet and other sources provide countless images in standard formats.
Image processing are of two aspects..
improving the visual appearance of images to a human viewer
preparing images for measurement of the features and structures present.
Since the digital image is “invisible” it must be prepared for viewing on one or more output device (laser printer,monitor,etc)
The digital image can be optimized for the application by enhancing or altering the appearance of structures within it (based on: body part, diagnostic task, viewing preferences,etc)
It might be possible to analyze the image in the computer and provide cues to the radiologists to help detect important/suspicious structures (e.g.: Computed Aided Diagnosis, CAD)
Scientific instruments commonly produce images to communicate results to the operator, rather than generating an audible tone or emitting a smell.
Space missions to other planets and Comet Halley always include cameras as major components, and we judge the success of those missions by the quality of the images returned.
Image-to-image transformations
Image-to-information transformations
Information-to-image transformations
Enhancement (make image more useful, pleasing)
Egg. deblurring ,grid line removal
(scaling, sizing , Zooming, Morphing one object to another).
Image statistics (histograms)
Histogram is the fundamental tool for analysis and image processing
Image compression
Image analysis (image segmentation, feature extraction, pattern recognition)
computer-aided detection and diagnosis (CAD)
Decompression of compressed image data.
Reconstruction of image slices from CT or MRI raw data.
Computer graphics, animations and virtual reality (synthetic objects).
The process of obtaining an high resolution (HR) image or a sequence of HR images from a set of low resolution (LR) observations.
HR techniques are being applied to a variety of fields, such as obtaining
improved still images
high definition television,
high performance color liquid crystal display (LCD) screens,
video surveillance,
remote sensing, and
medical imaging.
Conversion from RGB (the brightness of the individual red, green, and blue signals at defined wavelengths) to YIQ/YUV and to the other color encoding schemes is straightforward and loses no information.
Y, the “luminance” signal, is just the brightness of a panchromatic monochrome image that would be displayed by a black-and-white television receiver
• Most computers use color monitors that have much higher resolution than a television set but operate on essentially the same principle.
• Smaller phosphor dots, a higher frequency scan, and a single progressive scan (rather than interlace) produce much greater sharpness and color purity.
• Multiple images may constitute a series of views of the same area, using different wavelengths of light or other signals.
• Examples include the images produced by satellites, such as
– the various visible and infrared wavelengths recorded by the Landsat Thematic Mapper™, and
– images from the Scanning Electron Microscope (SEM) in which as many as a dozen different elements may be represented by their X-ray intensities.
– These images may each require processing.
A general-purpose computer to be useful for image processing, four key demands must be met: high-resolution image display, sufficient memory transfer bandwidth, sufficient storage space, and sufficient
computing power.
A 32-bit computer can address
up to 4GB of memory(RAM).
• Adobe Photoshop
• Corel Draw
• Serif Photoplus
Post: #20

.doc  JPEG.doc (Size: 4.25 MB / Downloads: 150)
1.1 Introduction:
A digital image is a representation of a two-dimensional signal using ones and zeros (binary). Depending on whether or not the image resolution is fixed, it may be of vector or raster type. Without qualifications, the term "digital image" usually refers to raster images also called bitmap images
An image may be defined as a two-dimensional function f(x ,y ) where x and y are spatial(plane) co-ordinates, and the amplitude of f at any pair of coordinates(x, y) called the intensity or gray level of the image at that point. When x, y and the amplitude values of f are all finite, discrete quantities, We call the image a digital image.
Raster image types:
Each pixel of a raster image is typically associated to a specific 'position' in some 2D region, and has a value consisting of one or more quantities (samples) related to that position. Digital images can be classified according to the number and nature of those samples:
• binary
• grayscale
• 1color
• false-color
• multi-spectral
• thematic
• picture function
The term digital image is also applied to data associated to points scattered.
1.2 Digital Image Processing:
The field of digital image processing refers to processing digital images by Means of a digital computer. A digital image is composed of a finite number of elements, each of which has a particular location and value. This elements are referred to as picture elements, image elements, and pixels. Pixels is the term most widely used to denote the elements of digital image.
1.2.1 Fundamental Steps In Digital Image Processing:
Image Enhancement:

Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured or simply to highlight certain features of interest in an image.
A familiar example of enhancement is when we increase the contrast of an image because,it is important to keep in mind that enhancement is a very subjective area of image processing.
Image Restoration:
Image Restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration techniques tend to be based on mathematical or probabilistic models of image Degradation.
Color Image Processing:
Color image processing is an area that has been gaining in importance because of the significant Increase In the use of digital image over the internet.
Wavelets are the foundation for representing images in various degrees of resolution. In particular, this will be used for image data compression and for pyramidal representation, in which images are subdivided successively into smaller Regions.
As the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it. Although storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. Image
compression is used familiar to most users of computers in the form of image file extensions, Such as the file extension used I the JPEG(joint photographic experts group).
1.2.2 Applications of Digital Image Processing:
• Remote sensing via satellites & space crafts
• Image transmission &storage for business applications
• Medical processing
• Radar & sonar image processing
• Robotics
Exampl1es of fields that use digital image processing
• gama ray imaging
• X-ray imaging
• Imaging in ultra violet band
• Imaging in visible & infrared bands
• Imaging in the microwave bands
• Imaging in radio bands
1.3 Aim of the project:
The main aim of any encoder scheme is to compress the number of data bits that could be transmitted into the channel. Mainly jpeg encoder tries to remove three types of redundancies that occur in an image namely coding redundancy, psychovisual redundancy and inter pixel redundancies.
1.4 Problem statement:
The main problem with jpeg encoder is that it can be applied to stationary images. How ever we need in certain circumstances to apply it for non stationary images where we need to lie upon discrete wavelet transforms.
Chapter 1 presents introduction to image processing, fundamental steps, applications and example areas image processing. Finally aim and problem statements are discussed.
Chapter 2 provides the literature survey on fourier transform, discrete fourier transform and fast fourier transform and their properties.
Chapter 3 provides the various concepts of image compression, types of image compression, proposed model block diagram and explanation of that block diagram.
Chapter 4 focuses the Implementation of jpeg encoder module wise such as DCT, quantiser, entropy encoding .
Chapter 5 presents the some of results obtained after application of input images and corresponding reconstructed images.
Chapter 6 gives Applications and usage of JPEG Encoder
Chapter 7 gives summary of the work carried, this includes conclusions, performance analysis.
Chapter 8 represents scope for future work.
Chapter 9 References.
Appendix A focuses on the VLSI Design Flow.
Appendix B Source code
2.1 Introduction to Fourier Transform:

In, the Fourier transform (often abbreviated FT) is an operation that transforms one complex-valued function of a real variable into another. In such applications as signal processing, the domain of the original function is typically time and is accordingly called the time domain. That of the new function is frequency, and so the Fourier transform is often called the frequency domain representation of the original function. It describes which frequencies are present in the original function. This is analogous to describing a chord of music in terms of the notes being played. In effect, the Fourier transform decomposes a function into oscillatory functions. The term Fourier transform refers both to the frequency domain representation of a function, and to the process or formula that "transforms" one function into the other.
The Fourier transform and its generalizations are the subject of Fourier analysis. In this specific case, both the time and frequency domains are unbounded linear continua. It is possible to define the Fourier transform of a function of several variables, which is important for instance in the physical study of wave motion and optics. It is also possible to generalize the Fourier transform on discrete structures such as finite groups, efficient computation of which through a fast Fourier transform is essential for high-speed computing.
There are several common conventions for defining the Fourier transform of an integrable function ƒ : R → C (Kaiser 1994). This article will use the definition:
for every real number ξ.
When the independent variable x represents time (with SI unit of seconds), the transform variable ξ represents frequency (in hertz). Under suitable conditions, ƒ can be reconstructed from by the inverse transform
for every real number x.
Introduction: The motivation for the Fourier transform comes from the study of Fourier series. In the study of Fourier series, complicated periodic functions are written as the sum of simple waves mathematically represented by sines and cosines. Due to the properties of sine and cosine it is possible to recover the amount of each wave in the sum by an integral. In many cases it is desirable to use Euler's formula, which states that e2πiθ = cos 2πθ + i sin 2πθ, to write Fourier series in terms of the basic waves e2πiθ. This has the advantage of simplifying many of the formulas involved and providing a formulation for Fourier series that more closely resembles the definition followed in this article. This passage from sines and cosines to complex exponentials makes it necessary for the Fourier coefficients to be complex valued. The usual interpretation of this complex number is that it gives you both the amplitude (or size) of the wave present in the function and the phase (or the initial angle) of the wave. This passage also introduces the need for negative "frequencies". If θ were measured in seconds then the waves e2πiθ and e−2πiθ would both complete one cycle per second, but they represent different frequencies in the Fourier transform. Hence, frequency no longer measures the number of cycles per unit time, but is closely related.
Post: #21

.doc  Image Processing - 1.doc (Size: 306 KB / Downloads: 154)
This paper presents a digital image processing based finite element method for the two-dimensional mechanical analysis of geo-materials by actually taking into account their material inhomogeneities and microstructures. The proposed method incorporates the theories and techniques of digital image processing, the principles of geometry vectorization and the techniques of automatic finite element mesh generation into the conventional finite element methods. Digital image techniques are used to acquire the inhomogeneous distributions of geo-materials including soils,rocks, asphalt concrete and cement concrete in digital format. Digital
image processing algorithms are developed to identify and classify the main homogeneous material types and their distribution structures that form the inhomogeneity of a geomaterial in the image. The vectorized digital images are used as inputs for finite element mesh generations using automatic mesh generation techniques. Lastly, the conventional finite element methods are
employed to carry out the computation and analysis of geomechanical problems by taking into account the actual internal inho-mogeneity of the geomaterial. Using asphalt concrete as an example, the paper gives a detailed explanation of the proposed digital image processing based finite element method.
Digital image processing (DIP) is the term applied to convert video pictures into a digital form, and apply various mathematical algorithms to extract significant information from the picture. This information may be characteristics of cracks on a material surface, the microstructure of inhomogeneous soils and rocks and other man-made geo-materials, texture of sea ice or the
angularties and shapes of granular materials. While digital image processing has been widely used in a range of engineering topics in recent years, a literature survey indicates that the incorporation of digital image pro-cessing into computational geomechanical methods
such as finite element methods (FEM) is very limited .This paper is intended to present an innovative digital image processing based finite element method for the mechanical analysis of geomaterials by taking into account their actual inhomogeneities and micro-structures. It is noted that the DIP based FEM pro-posed in this paper is for two-dimensional finite element analysis. Using the stereoscopic logical alternation principle, it is believed that the proposed method can be
extended to three-dimensional finite element analysis .
*Digital images and discrete function
microstructure of a geomaterial. A cylindrical asphalt concrete (AC) sample is used for the illustration. In general, field cores or laboratory pre-pared AC or rock samples can be cut with a circular masonry saw into multiple vertical or horizontal plane cross-sections. The fresh cross-sections are then photo-graphed with eithe a conventional came a o a digital camera. A scale is placed beneath the section fo DIP
scaling and calibration. If a conventional camera is used, the photographs can be digitized using a scanner and digital image stored in a desktop computer.The digital image consists of a rectangula a ray of image elements or pixels. Each pixel is the intersection area of any horizontal scanning line with the vertical scanning line. These lines all have an equal width h.At each pixel, the image brightness is sensed and assigned with an integer value that is named as the gray level.For the mostly used 56 gray images and binary images,their gray levels have the integer interval from 0 to 55 and from 0 to 1 respectively. As a result, the digital image can be expressed as a discrete function f(i,j)in the i and j Cartesian coordinate system below.Digital image
Post: #22
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.doc  PROJECT REPORT.doc (Size: 1.01 MB / Downloads: 122)
The purpose of our project is to identify a tumor from a given MRI scan of a brain using digital image processing techniques.
The part of the image that has the tumor has more intensity in that portion and we can make our assumptions about the radius of the tumor in the image,these are the basic things considered in the algorithm.First of all some image enhancement and noise reduction techniques are used to enhance the image quality,after that some morphological operations are applied to detect the tumor in the image.The morphological operations are basically applied on some assumptions about the size and shape of the tumor and in the end the tumor is mapped onto the original gray scale image with 255 intensity to make visible the tumor in the image.The algorithm has been tried on a number of different images from different angles and has always given the correct desired result.
A tumor or tumor is the name for a neoplasm or a solid lesion formed by an abnormal growth of cells (termed neoplastic) which looks like a swelling.Tumor is not synonymous with cancer. A tumor can be benign, pre-malignant or malignant, whereas cancer is by definition malignant.

A benign tumor is a tumor that lacks all three of the malignant properties of a cancer. Thus, by definition, a benign tumor does not grow in an unlimited, aggressive manner, does not invade surrounding tissues, and does not spread to non-adjacent tissues (metastasize). Common examples of benign tumors include moles and uterine fibroids.
Malignancy (from the Latin roots mal- = "bad" and -ignis = "fire") is the tendency of a medical condition, especially tumors, to become progressively worse and to potentially result in death. It is characterized by the properties of anaplasia, invasiveness, and metastasis. Malignant is a corresponding adjectival medical term used to describe a severe and progressively worsening disease. The term is most familiar as a description of cancer.
A precancerous condition (or premalignant condition) is a disease, syndrome, or finding that, if left untreated, may lead to cancer. It is a generalized state associated with a significantly increased risk of cancer.
Magnetic resonance imaging (MRI), or nuclear magnetic resonance imaging (NMRI), is primarily a medical imaging technique used in radiology to visualize detailed internal structure and limited function of the body. MRI provides much greater contrast between the different soft tissues of the body than computed tomography (CT) does, making it especially useful in neurological (brain), musculoskeletal, cardiovascular, and oncological (cancer) imaging. Unlike CT, MRI uses no ionizing radiation. Rather, it uses a powerful magnetic field to align the nuclear magnetization of (usually) hydrogen atoms in water in the body. Radio frequency (RF) fields are used to systematically alter the alignment of this magnetization. This causes the hydrogen nuclei to produce a rotating magnetic field detectable by the scanner. This signal can be manipulated by additional magnetic fields to build up enough information to construct an image of the body.
The part of the image containing the tumor normally has more intensity then the other portion and we can assume the area, shape and radius of the tumor in the image. We have used these basic conditions to detect tumor in our code and the code goes through th following steps:
Post: #23
hi, I am searchig for a topic for computer vision project, I can't select it. I want to it be related with biological signal processing, coulde naybody help me?Undecided
Post: #24
Image processing operations deals with the storage, transmission and restoration of the image using minimum number of bits without any noticeable tradeoff in the clarity of the image. Image processing operations are divided into three major categories, Image Compression, Image Enhancement and Restoration, and Measurement Extraction. Image compression is familiar to most people. It involves reducing the amount of memory needed to store a digital image. Where as the Image Enhancement and Restoration deals with the retrieval of the image back.
This paper deals with the Image Enhancement and Restoration which helps in the image restoration with maximum clarity and enhancement of the image quality. The first section describes what Image Enhancement and Restoration is and the second section tells us about the techniques used for the Image Enhancement and Restoration and, final section describes the advantages and disadvantage of using these techniques.
Post: #25

.doc  IMAGE PROCESSING.doc (Size: 203 KB / Downloads: 80)
Image processing is one of the most powerful technologies that will shape science and engineering in the twenty first century. In the broadest sense, image processing is any form of information processing for which both the input and output are images, such as photographs or frames of video. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it.
Signal processing is the processing, amplification and interpretation of signals and deals with the analysis and manipulation of signals.
A few decades ago, image processing was done largely in the analog domain, chiefly by optical devices. These optical methods are still essential to applications such as holography because they are inherently parallel; however, due to the significant increase in computer speed, these techniques are increasingly being replaced by digital image processing methods.
Digital image processing techniques are generally more versatile, reliable, and accurate; they have the additional benefit of being easier to implement than their analog counterpart. Today, hardware solutions are commonly used in video processing systems. However, commercial image processing tasks are more commonly done by software running on conventional personal computers.
Image resolution describes the detail an image holds. The term applies equally to digital images, film images, and other types of images. Higher resolution means more image detail.
Image resolution can be measured in various ways. Basically, resolution quantifies how close lines can be to each other and still be visibly resolved. Resolution units can be tied to physical sizes or to the overall size of a picture. Furthermore, line pairs are often used instead of lines. A line pair is a pair of adjacent dark and light lines, while lines count both dark lines and light lines.
The term resolution is often used as a pixel count in digital imaging None of these pixel resolutions are true resolutions, but they are widely referred to as such; they serve as upper bounds on image resolution.
Below is an illustration of how the same image might appear at different pixel resolutions, if the pixels were poorly rendered as sharp squares (normally, a smooth image reconstruction from pixels would be preferred, but for illustration of pixels, the sharp squares make the point better?)
The goal of edge detection is to mark the points in a digital image at which the luminous intensity changes sharply. Edge detection is a research field within image processing and computer vision, in particular within the area of feature extraction. Edge detection of an image reduces significantly the amount of data and filters out information that may be regarded as less relevant, preserving the important structural properties of an image.
The red, green, and blue color channels of a photograph. The fourth image is a composite.
• Geometric transformations such as enlargement, reduction, and rotation
• Color corrections such as brightness and contrast adjustments, quantization, or conversion to a different color space
• Registration (or alignment) of two or more images
• Combination of two or more images, e.g. into an average, blend, difference, or image composite
• Interpolation, demosaicing, and recovery of a full image from a RAW image format.
• Segmentation of the image into regions
• Image editing and digital retouching
• Extending dynamic range by combining differently exposed images.
and many more.
Besides static two-dimensional images, the field also covers the processing of time-varying signals such as video and the output of tomographic equipment. Some techniques, such as morphological image processing, are specific to binary or grayscale images.
The objective of compression is to reduce the data volume and achieve reproduction of the original data without any perceived loss in data quality. The neighbouring pixels are correlated and therefore contain redundant information in most images. The foremost task then is to find less correlated representation of the image. Two fundamental concepts of compression are redundancy and irrelevancy reduction. Reduction is a characteristic related to the factors such as predictability, randomness and smoothness in the data. Redundancy reduction aims at removing duplication from the image while irrelevancy reduction omits parts of the signal that will not be noticed by the signal receiver. In general, three types of redundancy can be identified: spatial redundancy that exists between adjacent frames in a sequence of images. Image compression aims removing the spatial and spectral redundancies as much as possible.
Digital imaging depends have been continuously going up both due to size of the image and its resolution. Storage of picture data has become a growing need in any application. A simple gray scale image of 512x512 pixels will need a storage array of 256 bytes assuming that the pixel information is 8 bit wide (0-255 representing white to black on a 256 discrete scale)
A 35mm slide if digitized with a solution of about 12 microns will need 18 megabytes of data storage. In general, picture frame data compression when can be separated into
• Lossy compression
• Lossless compression
In lossy compression schemes, the compressed image contains degradation relative to the original image while the compression achieved much higher compression than the lossless compression because it completely discards redundant information. Lossy encoding is base don the concept of compromising the accuracy of the reconstructed image in exchange for increased compression. If the resulting distortion (which may or may not be visually apparent) can be tolerated, the increase in the compression can be significant.
Lossy image compression is useful in applications such as broad cast television, video conferencing and facsimile transmission, in which a certain amount of error is an acceptable tradeoff for increased compression performances. Lossy compression usually prohibited for legal reasons
In lossless compression schemes, the compressed image is numerically identical to the original image while the compression can only achieve a modest amount. In numerous applications error free compressions is the only acceptable means of data reductions. The need for error free compression is motivated by the intended use or nature of the image under consideration. They normally provide compression ratios of 2-10. Moreover they are equally applicable to both binary and gray scale images. This technique generally is composed of two relatively independent operations:
(1) Devising an alternative representation of the image in which its interpixel redundancies are reduced.
(2) Coding the representation to eliminate coding redundancies.
Post: #26
to get information about the topic morphological image processing full report,ppt and related topic please refer the link bellow
Post: #27

.ppt  DIGITAL IMAGE PROCESSING2.ppt (Size: 1.99 MB / Downloads: 77)

Digital image processing is the technology of applying a number of computer algorithms to process digital images. The outcomes of this process can be either images or a set of representative characteristics or properties of the original images.

The application of digital image processing have been commonly found in robotics/intelligent systems, medical imaging, remote sensing, photography and forensices.

What Is Digital Image Processing

An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point.

When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image.

Digital Image Processing refers to processing digital images by means of a digital computer.

Low-level processes involve primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images.

Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects.

The Origins Of Digital Image Processing

One of the first applications of digital images was in the newspaper industry, when pictures were first sent by submarine cable between London and New York.

Introduction of the Bartlane cable picture transmission system in the early 1920s reduced the time required to transport a picture across the Atlantic from more than a week to less than three hours. Specialized printing equipment coded pictures for cable transmission and then reconstructed them at the receiving end.

A finite number of elements, each of which has a particular location and Value.
Post: #28
to get information about the topic digital signal processing full report ,ppt and related topic refer the link bellow
Post: #29
digital image processing

.doc  DIGITAL IMAGE PROCESSING.doc (Size: 158 KB / Downloads: 47)

What is a Diamond ?

Diamond is a natural gem known to mankind from prehistoric times and has been praised for centuries as a gemstone of exceptional beauty, brilliance and lustre. It is a symbol of richness and power. Graphite and Diamond are two allotropic forms of Carbon. In diamond, each Carbon atom is covalently single bonded to four other Carbon atoms in a tetrahedral manner.

The Impostors :

As Impostor is any gem that claims to be as good or better than, just like, as hard, more beautiful than, but cheaper than a diamond. The “Impostors” come in two groups, the stimulants and the synthetics. A stimulant is something that looks similar to a diamond but does not have the same properties (weight, specific gravity, refractive index, hardness, etc). Zircon is a natural stone (stimulant) often used to imitate diamonds. A synthetic is a man-made diamond that has all properties of a natural diamond. Cubic Zircon is a synthetic stone. Zircon is a substitute for diamond and Glass is a substitute for Zircon.

3. The pavilion(the bottom)
The size of the table, the symmetry of the facets, the thickness of the girdle, and the angle of the pavilion must all work together to give the diamond the sparkle that is wanted. The light enters the diamond through the crown, splits into white and colures light, bounces off the facets of the pavilion back up through thee crown, where it could be seen as a ‘Sparkle’. To achieve the maximum sparkle – that magic combinations of brilliance and fire – the diamond must be well cut and cut in the proper proportion. The typical diamond is cut with 58 facets, 33 on the crown and 25 on the pavilion. On a well- proportioned stone, these facets will be uniform and symmetrical. If they are not, the diamonds ability to refract light will suffer.


Dispersion is the breaking up of white light into spectral colors. Prisms show that a beam of white light is composed of different light rays, each with its own wavelength. Each different wavelength is perceived as a different colour. Each ray of light has its own wavelength, direction of travel, and intensity.
Post: #30
to get information about the topic 3D Image Processing in Medical Imaging related topic refer the link bellow

Marked Categories : digital image prcessing, seminar topics for digital image processing, digital image processing, ppt on matric and topological properties of digital images, ppt topicson dip, ppt topics on digital image processing, free download seminar topics on digital image processing, abstract on disital immage prossesing, seminar topics on digital image processing, digital signal processing doc, image processing report, image processing, computer vision image processing seminar topics, paper presentation of digital image processing, image processing projects, seminar topics for image processing, digital image processing topics, latest seminar papers on image processing,

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