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Description: The research paper What is Digital Image Processing Technical Document for Students speaks about Digital Image Processing. The research paper suggests that the image processing procedures depend on  storage, transmission and restoration of image using minimum number of bits without any noticeable trade off in the clarity of the image. Image processing is divided into three major operations.

They are:

1.      Image Compression

2.      Image enhancement and restoration

3.      Measurement extraction.

The research paper elucidates all these major operations of Digital Image Processing at leangth. The purpose of image processing is to digitize an image and make it capable of being stored in a computer disk or CD-ROM. This digitization procedure can be done by a scanner, or by a video camera connected to a frame grabber board in a computer. Once the image has been digitized, it can be operated upon by various image processing operations. The defects that creep in while digitizing can be rectified using the image enhancement and restoration technique. An image with poor contrast can be rectified using an image histogram. The research paper comments on image acquisition which often leads to image degradation. The Wiener filter is a solution to the restoration problem based upon the hypothesized use of a linear filter and the minimum mean-square (or rms) error criterion.  

Conclusion: The research paper concludes on a note that the domain of digital image processing is gaining prominence like never before. There could be greater audio, video quality of pictures produced in the future. New tools to enhance the quality will sooner be included in the domain. Audio stream contain extremely valuable data, whose contents is also very rich and diverse. The combination of audio and image techniques, will definitely generate interesting results, and very likely improve the quality of the present analysis. Digital image processing has a long way to go.

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Description: The research paper Retrieval of Trademark Images comments on retrieval of trademark images based on shape. The research paper talks about the need to have shape feature that is able to capture the human perceptual similarity in a better way. To achieve this, an automatic retrieval algorithm needs to be developed and it’s a very difficult task. Considering the amount of time and effort that goes into a manual retrieval, the automatic retrieval algorithm seems to be a better option in many ways.

Image Processing:

 In the research paper is suggested a method for trademark image database retrieval based on object shape information that would supplement traditional text based retrieval system. The proposed system achieves both the desired efficiency and accuracy using a two stage hierarchy: in the first stage simple and easily computable shape features are used to quickly browse through the database to generate a moderate number of possible retrievals when a query is presented and in the second stage, images from the first stage are screened using a deformable template matching process to discard spurious matches.  It is suggested in the research paper that textual features such as filenames, captions, keywords have been used to annotate and retrieve images, but research says that there are many problems associated with these traditional methods. Various schemes have been proposed for shape representation and retrieval. These include shape representation using polygonal approximation of shape and matching using the polygonal vertices, image representation on the basis of strings , comparing the images using the hausdorff distance .  

Conclusion: The research paper concludes upon a note that an effective trademark retrieval algorithm has been developed. Efficiency can be improved by designing a two tier hierarchical retrieval system.

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Description: The research Seminar on Image Processing – Pattern Matching comments on image processing and how pattern matching is done during image processing. Wherein number of calculations for a normalized correlation factor can be reduced thus increasing the speed and processing of pattern matching in digital image processing. In  the  pattern  matching  processing  when  an  insensitive  region  is  set  in  a template image, values  (the sum and the squared sum)  obtained  during  a  previous  operation for  a  prior  search  target  sub-image  are  employed  to  perform  calculations  for  a  current search  target  sub-image ,  relative  to  coordinate  sequence  code  that  indicates  the  start  point  and  the  end  point  of  a  sequence  of  pixels  having values  of  “1.”

What is pattern matching:

The research paper suggests that pattern matching is the method of checking the constituents within a given pattern. In contrast to pattern recognition. This method facilitates easy specification of a pattern. In pattern matching there are either sequences or tree structures. Pattern matching is used to test whether things have a desired structure, to find relevant structure, to retrieve the aligning parts, and to substitute the matching part with something else.

The research paper suggests a point that pattern matching is a powerful tool for syntax analysis. The main idea of pattern matching comes from the SNOBOL4 language (see the wonderful book THE SNOBOL4 PROGRAMMING LANGUAGE by R. E. Griswold, J. F. Poage and I. P. Polonsky). Some of the pattern expression atoms and statements were taken from there. One can find that patterns are very similar to the Backus-Naur forms. Comparing with the regular expressions (used by grep and egrep in UNIX) patterns are more powerful, but slower in matching.

Conclusion: The research paper concludes on a note that pattern matching is a perfect tool invented for image processing. The practical benefits of the tool are many.

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Description: The research paper Printing and Image Processing Technical Presentation talks about Printing and Image Processing. The research paper suggests that there have been giant strides made in the field of digital image processing since its inception in early 1960s. The algorithms used in image processing are varied and the user need not restrict himself to a method. This choosing from an array of algorithms gives edge to digital image processing over other traditional methods. Not just this digital image processing is very cheap too. The process of image processing is usually undertaken to enhance the quality of pictures taken and help interpret the significance and other aspects pertaining to information/data and other such things of the image.

Printing and Image Processing:

 It is suggested in the research paper that in the domain of electrical engineering and computer science, image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. Image processing usually refers to digital image processing, but optical and analog image processing are also possible.

 What is face detection:

Face detection can be regarded as a specific case of object-class detection; in object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars. Face detection can be regarded as a more general case of face localization; in face localization, the task is to find the locations and sizes of a known number of faces and it is usually one most of the time.

Conclusion: The research paper concludes on a note that Fuzzy logic could play an important role in enhancing the quality of pictures and determining other aspects of digital image processing.

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Description: The research paper Digital Image Processing Btech Technical Document speaks about Digital Image Processing. The research paper explains what digital image processing is. It suggests that digital image processing is a technique that enhances the quality of images. Digital image processing also helps interpret signals and information from the images. Digital processing of images and digital images are the order of the day not just owing to the quality precision but also owing to its cost effectiveness. Digital image processing is undertaken to filter the images captured over a digital camera.

Digital Image Processing:

 Digital image processing utilizes an array of algorithms that help snubbing the unnecessary noise in order to enhance the image quality. Image Editors provide the means for altering and improving images in a number of times. They accept images in large variety of image formats. The other features of this technology is image size alteration, cropping on images, removal of noise and unwanted elements, merging of images, color adjustments and image filtering. Image filtering is yet another comprehensive task undertaken during digital image processing. This involves a lot many CPU cycles. Digital images can be classified according to the number and nature of those samples Each pixel of an 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.

• Binary                                              
• Grayscale
• Color                                              
• False color
• Multi color                                       
• Thematic
• Picture function. The research paper also speaks extensively on ‘image editing’.

 Conclusion: The research paper concludes on a note suggesting that ‘digital image processing’ has taken over other traditional methods of processing owing to its accuracy, and cost effectiveness. Speedy processing is one other attribute of digital image processing. The research paper suggests that distributed image filtering is faster than centralized image filtering.

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Description: The research paper Astronomical Images and Printing Techniques Technical Doc speaks about astronomical images and the techniques adopted to print them. There has been giant strides made in the field of digital image processing and the current astronomical image processing methods and techniques have transcended beyond the traditional forms. Two methods have called for a breakthrough research. First it is the availability of high quality, multi wavelength, narrow band data that surpasses the sensitivity of human eye, secondly availability of very sophisticated software that enables capture of minutest astronomical data with precision. Each data set is intensity-scaled and colorized independently, creating an immense parameter space that can be used to assemble the image. Since such images are intended for data visualization, scaling and color schemes must be chosen that best illustrate the science.

Generating a color image goes by the following steps:

1. Intensity scaling and projection of data into gray scale images.
2. Importing these images as layers into IP software pack.
3. Intensity rescaling to improve contrast.
4. Assigning a color to each layer.
5. Fine-tuning the image.
6. Preparing the image for an electronic distribution.

One of the most critical steps in the image generation is also the first step after the data are reduced. Each data set must first be projected into a gray-scale image. In this research paper the terminology of ‘‘data set’’ is used when referring to the original, reduced data, and ‘‘image’’ is used when referring to a data set that has been projected into an image.

Conclusion: The research paper concludes over a note saying that technology has grown by leaps and bounds and capturing of astronomical data is not a far- fetched dream as it used to be. There are many sophisticated techniques in vogue that have taken over the traditional forms. The astronomical images thus produced help in major scientific interpretations and illustrations.

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Description: The research paper Image Processing – Printing Technical CSE Abstract speaks about the domain of Image Processing- Printing specifically. Lately digital image processing has gained a tremendous footage. Images are processed to make them convey the inherent message and also to store, transmit and represent autonomous machine perception. Digital image processing is done by a digital computer that takes into consideration several elements of the image at the unit level, also considering the position and value. Digital image processing is not easy that it sounds to be. There are many external influences impeding the smooth flow of the process time and again. The images generated are analog in nature, processing converts the analog images to digital images via transfer functions. A transfer function maps the pixel values from the CCD to the available brightness values in the imaging software all the images so far have been plotted using linear transfer functions

What is a digital image: A digital image is picture which is divided into a grid of “pixels” (picture elements). Each pixel is defined by three numbers(x, y, z), and displayed on a computer screen.

The research paper suggests that every raw image has image and noise. A person involved in digital image processing keeps the image and removes the sound. A transfer function maps the pixel values from the CCD to the available brightness values in the imaging software all the images so far have been plotted using linear transfer functions. The research paper suggests that basically an image signal has varying frequencies.

Conclusion: The research paper concludes on a note suggesting what unsharp masking is. It says that unsharp masking consists of a 3 – step process. Firstly it is to make a copy of the image where each pixel is the average of the group of pixels surrounding it, so that the large features are not disturbed, but the small ones are blurred (this is the unsharp mask). Next, the pixel values of the original image are multiplied by a constant (“A”), and then the pixel values of the unsharp mask are subtracted from this one or more times (“B”). In this way, the large features are not changed by much, but the small ones are enhanced. And then finally a low – pass filter is applied to the result.

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