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18-10-2010, 02:23 PM
Post: #9
RE: digital image processing full report


.ppt  image processing.ppt (Size: 792 KB / Downloads: 192)


“Morphing” is an interpolation technique used to create a series of intermediate objects from two objects.
“The face - morphing algorithm” automatically extracts feature points on the face and morphing is performed.
This algorithm is proposed by Mr. M.Biesel within Bayesian framework to do automatic face morphing.

Pre – Processing

removing the noisy backgrounds

clipping to get a proper facial image, and

scaling the image to a reasonable size. 
20-10-2010, 12:13 PM
Post: #10
RE: digital image processing full report

.ppt  DIP.ppt (Size: 580 KB / Downloads: 173)
digital image processing full report



Two dimensional representation of values.
These are called “PIXELS”.
Pixels are stored in computer memory.


The processing done by using Computer software.

Avoids build-up of noise and signal distortion

How can we process an Image?

Transfer image to a computer
Digitize the image
* Digitization – translating image into numerical code understood by computer.
Processing can be done through software programs in a “Digital Dark-room”
Image is broken down into thousands of pixels
01-11-2010, 11:05 AM
Post: #11
RE: digital image processing full report

.doc  project report.doc (Size: 464.5 KB / Downloads: 296)
image processing full report


Over the last two decades, we have witnessed an explosive growth in both the diversity of techniques and the range of applications of image processing. However, the area of color image processing is still not covered, despite having become common place, with consumers choosing the convenience of color imaging over traditional grayscale imaging. With advances in image sensors, digital TV, image databases, and video and multimedia systems, and with the proliferation of color printers, color image displays, DVD devices, and especially digital cameras and image-enabled consumer electronics, color image processing appears to have become the main focus of the image-processing research community. Processing color images or, more generally, processing multichannel images, such as satellite images, color filter array images, microarray images, and color video sequences, is a nontrivial extension of the classical grayscale processing. Recently, there have been many color image processing and analysis solutions, and many interesting results have been reported concerning filtering, enhancement, restoration, edge detection, analysis, compression, preservation, manipulation, and evaluation of color images. The surge of emerging applications, such as single-sensor imaging, color-based multimedia, digital rights management, art, and biomedical applications, indicates that the demand for color imaging solutions will grow considerably in the next decade[4].
06-01-2011, 03:16 PM
Post: #12
RE: digital image processing full report

.ppt  Image Processing.ppt (Size: 1.53 MB / Downloads: 191)

Alok K. Watve

Applications of image processing
Gamma ray imaging
X-ray imaging
Multimedia systems
Satellite imagery
Flaw detection and quality control
And many more…….

Fundamental Steps in digital image processing
Image acquisition
Image enhancement(gray or color images)
Wavelet and multi-resolution processing
Morphological processing
Representation & description
Object recognition
Image enhancement in spatial domain

Binary images
Only two colors
Gray images
A range of colors(not more than 256) from black to white
Color images
Contain several colors(as many as 224)

07-03-2011, 03:43 PM
Post: #13
RE: digital image processing full report

.doc  vamsi1.doc (Size: 253 KB / Downloads: 223)
In the era of multimedia and Internet, image processing is a key technology.
Image processing is any form of information processing for which the input is an image, such as photographs or frames of video; the output is not necessarily an image, but can be for instance a set of features of the image.
Image processing is of two types Analog image processing and digital image processing. Digital image processing has the same advantages over analog image processing as digital signal processing 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. But the cost of analog image processing was fairly high compared to digital image processing.
Analog image can be converted to a digital image which can be processed in greater aspects, having greater advantages affordably and the processes such as sampling, quantization, Image acquisition, Image Segmentation of converting analog image to a digital image is explained in this report.
Image processing has a very good scope in the fields of Signal-processing aspects of image processing, imaging systems, and image scanning, display and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, and color reproduction.
Many of the techniques of digital image processing, or digital picture processing as it was often called, were developed in the 1960s at the Jet Propulsion Laboratory, MIT, Bell Labs, University of Maryland, and a few other places, with application to satellite imagery, wirephoto standards conversion, medical imaging, videophone, character recognition, and photo enhancement. But the cost of processing was fairly high with the computing equipment of that era. In the 1970s, digital image processing proliferated, when cheaper computers and dedicated hardware became available. Images could then be processed 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 image processing is the use of computer algorithms to perform image processing on digital images. Digital image processing has the same advantages over analog image processing as digital signal processing 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.
We will restrict ourselves to two-dimensional (2D) image processing although most of the concepts and techniques that are to be described can be extended easily to three or more dimensions.
We begin with certain basic definitions. An image defined in the "real world" is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y). An image may be considered to contain sub-images sometimes referred to as regions-of-interest, ROIs, or simply regions. This concept reflects the fact that images frequently contain collections of objects each of which can be the basis for a region. In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition.
The amplitudes of a given image will almost always be either real numbers or integer numbers. The latter is usually a result of a quantization process that converts a continuous range (say, between 0 and 100%) to a discrete number of levels. In certain image-forming processes, however, the signal may involve photon counting which implies that the amplitude would be inherently quantized. In other image forming procedures, such as magnetic resonance imaging, the direct physical measurement yields a complex number in the form of a real magnitude and a real phase.
It is a 2D function f(x, y) where x and y are spatial co-ordinates and f (Amplitude of function) is the intensity of the image at x, y. Thus, an image is a 2-dimensional function of the co-ordinates x, y.
If x, y and amplitude of f are all discrete quantities, then the image is called Digital Image. Digital image is a collection of elements called pixels, where each pixel has a specific co-ordinate value and a particular gray-level. Processing of this image using a digital computer is called Digital Image Processing. E.g. Fingerprint Scanning Handwriting Recognition System Face recognition system Biometric scanning used for authentication in Modern pen drives. The effect of digitization is shown in Figure 1.
The 2D continuous image a(x, y) is divided into N rows and M columns. The intersection of a row and a column is termed a pixel. The value assigned to the integer coordinates [m,n] with {m=0,1,2,...,M-1} and {n=0,1,2,...,N-1} is a[m,n]. In fact, in most cases a(x, y)--which we might consider to be the physical signal that impinges on the face of a 2D sensor--is actually a function of many variables including depth (z), color ( ), and time (t). Unless otherwise stated, we will consider the case of 2D, monochromatic, static images in this
12-03-2011, 03:05 PM
Post: #14
RE: image processing full report

.doc  IMAGE PROCESSING.doc (Size: 366.5 KB / Downloads: 171)

In thispaper, the basics of capturing an image, image processing to modify and enhance the image are discussed. There are many applications for Image Processing like surveillance, navigation, and robotics. Robotics is a very interesting field and promises future development so it is chosen as an example to explain the various aspects involved in Image Processing .
The various techniques of Image Processing are explained briefly and the advantages and disadvantages are listed. There are countless different routines that can be used for variety of purposes. Most of these routines are created for specific operations and applications. However, certain fundamental techniques such as convolution masks can be applied to many classes of routines. We have concentrated on these techniques, which enable us to adapt, develop, and use other routines and techniques for other applications. The advances in technology have created tremendous opportunities for visual system and image processing. There is no doubt that the trend will continue into the future.
Image Processing :

Image processing pertains to the alteration and analysis of pictorial information. Common case of image processing is the adjustment of brightness and contrast controls on a television set by doing this we enhance the image until its subjective appearing to us is most appealing. The biological system (eye, brain) receives, enhances, and dissects analyzes and stores mages at enormous rates of speed.
Basically there are two-methods for processing pictorial information. They are:
1. Optical processing
2. Electronic processing.
Optical processing uses an arrangement of optics or lenses to carry out the process. An important form of optical image processing is found in the photographic dark room.
Electronic image processing is further classified as:
1. Analog processing
2. Digital processing.
Analog processing:
These ple of this kind is the control of brightness and contrast of television image. The television signal is a voltage level that varies In amplitude to represent brightness through out the image by electrically altering these signals , we correspondingly alter the final displayed image appearance.
Digital image processing:
Processing of digital images by means of digital computer refers to digital image processing. Digital images are composed of finite number of element of which has a particular location value. Picture elements, image elements, and pixels are used as elements used for digital image processing.
Digital Image Processing is concerned with 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, 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.
Other form is orthogonal series expansion. Image processing system is typically non-casual system. Image processing is two dimensional signal processing. Due to linearity Property, we can operate on rows and columns separately. Image processing is vastly being implemented by “Vision Systems” in robotics. Robots are designed, and meant, to be controlled by a computer or similar devices. While “Vision Systems” are most sophisticated sensors used in Robotics. They relate the function of a robot to its environment as all other sensors do.
“Vision Systems” may be used for a variety of applications, including manufacturing, navigation and surveillance.
Some of the applications of Image Processing are:
1.Robotics. 3.Graphics and Animations.
2.Medical Field. 4.Satellite Imaging.
19-03-2011, 10:19 AM
Post: #15
RE: Image Processing and Embedding
presented by:
Ranjith & Waquas

.pptx  1112Image.pptx (Size: 529.35 KB / Downloads: 144)
Introduction to Image Processing
What is an Image?

An Image is an Array, or a Matrix, of square pixels (Picture elements) arranged in Columns and Rows.
 There are two groups of Images
 Vector Graphics (or line art)
 Bitmaps (Pixel based images)
 There are two groups of Colors
 Fourier Transform : a Review
 Fourier Transform Basic Functions
Image Enhancements
 Image Enhancement techniques:
Spatial Domain Methods
Frequency Domain Methods
 Spatial (time) domain techniques are techniques that operate directly on pixels.
 Frequency domain techniques are based on the modifying the Fourier Transform of an Image.
Frequency Domain Filtering
 Edges and transitions (e.g., Noise) in an image contribute significantly to High – frequency content of Fourier Transform.
 Low frequency contents in the Fourier Transform are responsible to the general appearance of the image over smooth areas.
 Blurring (Smoothing) is achieved by attenuating range of High – frequency components of Fourier Transform.
Embedded Image Processing System on FPGA
The Design of an Embedded Image Processing System (called DIPS) on FPGA is presented. DIPS is based on the Xilinix MicroBlaze 32 – bit soft processor core and implemented in Spartan – 3.
Today, embedded systems can be Microcontroller-based, DSP based, ASIC based, or FPGA based Systems. Xilinix, a FPGA vendor has provided the MicroBlaze 32 – bit soft processor core which is licensed as part of Xilinix Embedded Development Kit.
Overview of the Xilinix MicroBlaze
 The MicorBlaze soft processor is a 32 – bit Architecture.
 The Backbone of the architecture is a single – issue, 3 stage pipeline with 32 general purpose registers, Arithmetic Logic Units (ALU), a shift units, and two levels of Interrupt.
 Two Memory interfaces of MicroBlaze Processor
 Local Memory Bus (LMB)
 Xilinix Cache Link (XCL)
 Fast Simplex Link (FSL)
 The Local Memory Bus is provides a Low latency storage such as interrupt and exception handler
 The Xilinix Cache Link is a High performance point – to – point connection to an external memory controller.
 The Fast Simplex Link is a simple, yet powerful, yet point – to – point interfaces that connects User – Developed co-processors to the MicroBlaze Processor pipeline.
Image Processing Vs Computer Graphics
 There generally is a bit of confusion in recognising the difference between the fields of Image processing and Computer graphics.
 This two topics will be entirely different, almost the opposite of each other. And a com. graphics is involved with image synthesis, and not recognition or Analysis, as in the case of Image processing.
 Morphing used in advertisements could be said to be the most commonly witnessed computer graphics technique.
 Input to an Image processing is always a real image formed via some physical phenomenon such as Scanning, filming, Etc.
 Imaging professionals, scientists, and engineers who use image processing as a tool and wish to develop a deeper understanding and create custom solutions to imaging problems in their field.
 IT professionals wanting a self-study course featuring easily adaptable code and completely worked out examples enabling them to be productive right away.
 Image processing using all Programming Languages like,
C, C++, Java, Etc.
• It is used for all fields like, Medical, all Web standards, Etc.
• The visual system of a single human being does more image processing than the entire world’s supply of supercomputers.
21-03-2011, 11:29 AM
Post: #16
RE: image processing full report
Presented by:

.doc  image processingg.doc (Size: 692 KB / Downloads: 109)
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.
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