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Image processing by manish myst, ssgbcoet
 

Image processing by manish myst, ssgbcoet

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by - Manish Myst

by - Manish Myst
B.E E&C 2011 batch
shri sant gadge baba college of engg & tech, bhusawal SSGBCOET

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    Image processing by manish myst, ssgbcoet Image processing by manish myst, ssgbcoet Document Transcript

    • Click Here & Upgrade Expanded Features PDF Unlimited PagesDocumentsComplete IMAGE PROCESSING Mr.J.P.Patil Mr.R.D.Badgujar Mr.M.L.Patel Lecturer, Lecturer, Lecturer, RCPIT,Shirpur RCPIT,Shirpur RCPIT,Shirpur patiljitendra@rediffmail.com ravi_badgujar@rediffmail.com Mob.:9423193448 Mob.:-9881804224 Mob.:9372092305 ABSTRACT Image and Speech processing are used to be a single unified field in the early sixties and seventies. Today , it has expanded and diversified into several branches based on mathematical tools as well as applications. For instance there are separate topics dealing with fuzzy IP, morphological IP knowledge based IP etc. Similarly several topics deal with diverse application specific tools for remote sensing industrial vision and so forth. Image analysis issue such as segmentation, edge/line detection, feature extraction, image description and pattern recognition have been covered in great deal and all the state- of-art concepts have been discussed in many papers. The main motivation for extracting the content of information is the accessibility problem. A problem that is even more relevant for dynamic multimedia data, which also have to be searched and retrieved. While content extraction techniques are reasonably developed for text, video data still is essentially opaque. Its richness and complexity suggests that there is a long way to go in extracting video features, and the implementation of more suitable and effective processing procedures is an important goal to be achieved. 1. INTRODUCTION computer easier. Virtual reality, the technology of interacting with a computer Image Processing is development of the art using all of the human senses, will also and technique of producing images known as contribute to better human and computer photographs. Photography is so much a part of interfaces. Standards for virtual-reality life today that the average person may program languages—for example, Virtual encounter more than 1000 camera images a Reality Modeling language (VRML)—are day. Photographs preserve personal memories currently in use or are being developed for the (family snapshots) and inform us of public World Wide Web. events (news photos). They provide a means of identification (drivers license photos) and Synchronization of Image and Speech of glamorization (movie-star portraits); views Processing plays a very important role in this of far-off places on Earth (travel photographs) fairy world. Other, exotic models of and in space (astral photographs); as well as computation are being developed, including microscopic scenes from inside the human biological computing that uses living body (medical and scientific photos). Many organisms, molecular computing that uses specialized commercial categories, including molecules with particular properties, and fashion, product, and architectural computing that uses deoxyribonucleic acid photography, also fit under the broad umbrella (DNA), the basic unit of heredity, to store data that defines photographys function in the and carry out operations. These are examples world today. of possible future computational platforms that, so far, are limited in abilities or are Speech Processing improved speech strictly theoretical. Scientists investigate them recognition will make the operation of a because of the physical limitations of
    • Click Here & Upgrade Expanded Features PDF Unlimited PagesDocumentsComplete miniaturizing circuits embedded in silicon. 4.1.2 Erosion :- There are also limitations related to heat generated by even the tiniest of transistors. Erosion is the process of eliminating all the boundary points from an object, leaving the object smaller in area by one pixel all around its perimeter. If it narrows to less than three 3. BACKGROUND pixels thick at any point, it will become Content of image includes resolution, disconnected (into two objects) at that point. It color, intensity, and texture. Image resolution is useful for removing from a segmented is just the size of image in term of display image objects that are too small to be of pixels. Color is represented using RGB color interest. model in computer. For each pixel on the screen, there are three bytes (R,G,B color Shrinking is an special kind of erosion in component) to represent its color. Each color that single-pixel objects are left intact. This is component is in the range of 0 to 255. useful when the total object count must be Intensity is the gray level information of pixels preserved. represented by one byte. The intensity value is Thinning is another special kind of in the range of 0 to 255. Texture characterizes erosion. It is implemented in a two-step local variations of image color or intensity. process. The first step will mark all candidate Although texture-based methods has been pixels for removal. The second step actually widely used in computer vision and graphics, removes those candidates that can be removed there is no single commonly accepted without destroying object connectivity. definition of texture. Each texture analysis method defines texture according to its own 4.1.3 Dilation :- model. We consider texture as a symbol of local color or intensity variation. Image Dilation is the process of incorporating regions that are detected to have a similar into the object all the background pixels that texture have similar pattern of local variation touch it, leaving it larger in area by that of color or intensity. amount. If two objects are separated by less than three pixels at any point, they will 4. BASIS IMAGE PROCESSING: become connected (merged into one object) at 4.1 THEORY OF IMAGE PROCESSING that point. It is useful for filling small holes in Modern digital technology has made it segmented objects. possible to manipulate multi-dimensional signals with systems that range from simple Thickening is a special kind of dilation. It digital circuits to advanced parallel computers. is implemented in a two-step process. The first The goal of this manipulation can be divided step marks all the candidate pixels for into three categories: addition. The second step adds those * Image Processing image in -> image out candidates that can be added without merging * Image Analysis image in -> measurements objects. out 4.1.4 Opening :- * Image Understanding image in -> high-level description out The process of erosion followed by Common Image Processing techniques : dilation is called opening. It has the effect of eliminating small and thin objects, breaking 4.1.1 Dithering :- objects at thin points, and generally smoothing the boundaries of larger objects without Dithering is a process of using a pattern of significantly changing their area. solid dots to simulate shades of gray. Different shapes and patterns of dots have been 4.1.5 Closing :- employed in this process, but the effect is the same. When viewed from a great enough The process of dilation followed by distance that the dots are not discernible, the erosion is called closing. It has the effect of pattern appears as a solid shade of gray. filling small and thin holes in objects, connecting nearby objects, and generally
    • Click Here & Upgrade Expanded Features PDF Unlimited PagesDocumentsComplete smoothing the boundaries of objects without between black and white. To improve the significantly changing their area. ability to differentiate, special lighting techniques must often be employed. It should 4.1.6 Filtering :- be pointed out that the above method of using a histogram is only one of a large number of Image filtering can be used for noise ways to threshold an image. Such a method is reduction, image sharpening, and image said to use a global threshold for an entire smoothing. By applying a low-pass or high- image. When it is not possible to find a single pass filter to the image, the image can be threshold or an entire image, an approach is to smoothed or sharpened respectively. Low pass partition the total image into smaller filter is used to reduce the amplitude of high- rectangular areas and determine the threshold frequency components. Simple low pass filters or each window being analyzed. Images of a applies local averaging. The gray level at each weld pool in real time were taken and digitized pixel is replaced with the average of the gray using thresholding technique. The images levels in a square or rectangular neighborhood. were thresholded at various threshold values Gaussian Low pass Filter applies Fourier and also at the optimum value to show the transform to the image. High pass filter is used importance of choosing an appropriate to increase the amplitude of high-frequency threshold. components 4.2 IMAGE ANALYSIS FEATURE EXTRACTION: Image techniques are used to enhance, We have seen analysis or any visual improve, or otherwise alter an image and to pattern reorganization problem, the camera prepare it for image analysis. takes the picture of scene and passes the picture to a feature extractor, whose purpose The various techniques employed in image is data reduction by measuring certain features processing and analysis are: or properties that distinguish objects or their 1. Image data reduction parts. Feature extraction usually, is associated with another method called feature selection. 2. Segmentation The objective of feature selection and extraction techniques is to reduce this 3. Feature extraction dimensionality. The objective of feature extraction is to 4. Object recognition represent an object in compact way that facilities image analysis task in terms of SEGMENTATION algorithmic simplicity and computationally Segmentation is the generic name for the efficiency. number of different techniques that divide the image into segments of its constituents. In OBJECT RECOGNITION segmentation, the objective is to group areas of an image having similar characteristics or The most difficult part of image features into distinct entities representing parts processing is object recognition. Although of the image. One of the most important there are many image segmentation algorithms techniques which this papers deals with is that can segment image into regions with some thresholding. continuous feature, it is still very difficult to THRESHOLDING recognize objects from these regions. Thresholding is a binary conversion There are several reasons for this. technique in which each pixel is converted into First, image segmentation is an ill-posed task a binary value either black or white. This is and there is always some degree of uncertainty accomplished by utilizing a frequency in the segmentation result. Second, an object histogram of the image and establishing what may contain several regions and how to intensity (gray level) is to be the border connect different regions is another problem.
    • Click Here & Upgrade Expanded Features PDF Unlimited PagesDocumentsComplete At present, no algorithm can segment general images into objects automatically with high accuracy. In the case that there is some a prior knowledge about the foreground objects or background scene, the accuracy of object recognition could be pretty good. Usually the image is first segmented into regions according to the pattern of color or texture. Then separate regions will be grouped to form objects. The grouping process is important for the success of object recognition. Full automatically grouping only occurs when the a prior knowledge about the foreground objects or background scene exists. In the other cased, human interaction may be required to achieve good accuracy of object recognition 5. DEMOS This is a demo showing different image processing techniques. Here is the ORIGINAL image, taken from the photo "Robin Jeffers at Ton House" (1927) by Edward Weston. QUANTIZATION LOW PASS FILTERING Here is the image with only Here is the image filtered 5 grayscale shades; the original this filter is a 3-by-3 mean filter has 184 shades. - notice how it smoothes the Note how much detail is retained the texture of the image while with only 5 shades blurring out the edges LOW PASS FILTERING II EDGE DETECTION Here is the image filtered This filter is a 2-dimensional Notice the difference between the Laplacian (actually the negative Images from the two filters? of the Laplacian) - notice how it brings out the edges in the image Here is the image with every 3rd pixel sampled, and the intermediate pixels filled in with the sampled values. Note the blocky appearance of the new image.
    • Click Here & Upgrade Expanded Features PDF Unlimited PagesDocumentsComplete 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 image is stored only as a set of pixels with RGB values in computer. The computer knows nothing about the meaning of these pixel values. The content of an image is quite clear for a person. However, it is not so easy for a computer. For example, it is a piece of cake to recognize yourself in an image, even in a crowd. But this is extremely difficult for computer. The preprocessing is to help the EDGE DETECTION II computer to understand the content of image. This is the Laplacian filter with the original What is the so-called content of image? Here image added back in – notice how it brings out content means features of image or its objects the edges in the image while maintaining the such as color, texture, resolution, and motion. underlying grey scale information. Object can be viewed as a meaningful component in an image. For example, a moving car, a flying bird, a person are all objects. There are a lot of techniques for image processing. This chapter starts with an introduction to general image processing techniques and then talks about video processing techniques. The reason we want to introduce image processing first is that image processing techniques can be used on video if we treat each picture of a video as a still image. 7. APPLICATIONS 6. IMAGE PROCESSING VERSUS REAL-TIME MEASUREMENT OF IMAGE ANALYSIS TRAFFIC QUEUE PARAMETERS BY USING IMAGE PROCESSING Image processing relates to the TECHNIQUES preparation of an image for latter analysis and use. Images captured by a camera or a similar The real-time measurement of traffic technique (e.g. by a scanner) are not queue parameters are required in many traffic necessarily in a form that can be used by situations such as accident and congestion image analysis routines. Some may need monitoring and adjusting the timings of the improvement to reduce noise, others may need traffic lights. So far the reported image to be simplified, and still others may need to processing methods have been targeted for be enhanced, altered, segmented, filtered, etc. measuring simple traffic parameters. In this Image processing is the collection of routines paper we describe image processing and techniques that improve, simplify, techniques together with the results to measure enhance, or otherwise alter an image. Image the queue traffic parameters in real-time. The analysis is the collection of processes in which proposed queue detection algorithm consists of a captured image that is prepared by image a motion detection and vehicle detection processing is analyzed in order to extract operation, both based on extracting edges of information about the image and to identify the scene. The results show that the reposed objects or facts about the object or its algorithms are able to measure various queue environment. parameters such as queue detection, length of In a sophisticated image processing the queue, period of the occurrence of the system it should be possible to apply specific queue, slope of the queue etc. image processing operations to selected
    • Click Here & Upgrade Expanded Features PDF Unlimited PagesDocumentsComplete BAW-Project - Digital Image Processing The signals are usually processed in a digital representation whereby speech Aim of this project is to investigate the processing can be seen as the intersection of effects that lead to structural changes in river digital signal processing and natural language embankments. Changes on the microscopic processing. scale can eventually course complete destabilization of shore fortifications. We Speech processing can be divided in the study the microscopic movement that occurs at following categories: boundaries between sediment layers (or geotextiles) due to hydraulic pressure changes. Speech recognition, which deals with Towards this end endoscopes are used to analysis of the linguistic content of a speech gather images from within the sediment. The signal. images are in turn analyzed by digital image Speaker recognition, where the aim is to sequence analysis techniques which yield recognize the identity of the speaker. information on the frequency of motion and occurring velocity fields. Another aspect of Enhancement of speech signals, e.g. noise our research is the estimation of flow fields reduction, through sediment layers which again can be done using endoscopes in conjunction with Speech coding for compression and image processing techniques. transmission of speech. See also telecommunication. Remote sensing Natural resources survey and Voice analysis for medical purposes, such management; estimation related to agriculture, as analysis of vocal loading and dysfunction of hydrology, forestry, mineralogy; urban the vocal cords. planning; environment and pollution control; Speech synthesis: the artificial synthesis of cartography, registration of satellite images speech, which usually means computer with terrain maps; monitoring traffic along generated speech. roads, docks, air fields; etc. Speech compression is important in the Bio-medical telecommunications area for increasing the amount of info which can be transferred, ECG, EEG, EMG analysis; cytological, stored, or heard, for a given set of time and histological and stereological applications; space constraints. automated radiology and pathology, X-ray images analysis; mask screening of medical Speech can be described as an act of images such as chromosome slides for producing voice through the use of the vocal detection various diseases mammograms, folds and vocal apparatus to create a linguistic cancers, smears, CAP, MRI, PET, SPECT, act designed to convey information. USG and other tomography images. Various types of linguistic acts where the Military Applications audience consists of more than one individual, including public speaking, oration, and Missile guidance and detection; target quotation. identification; navigation of pilot less vehicles; reconnaissance; and range finding; etc. The physical act of speaking, primarily through the use of vocal cords to produce voice. See phonology and linguistics for more detailed information on the physical act of 8. SPEECH PROCESSING speaking. Speech processing is the study of speech However, speech can also take place signals and the processing methods of these inside ones head, known as intrapersonal signals. communication, for example, when one thinks or utters sounds of approval or disapproval. At
    • Click Here & Upgrade Expanded Features PDF Unlimited PagesDocumentsComplete a deeper level, one could even consider subconscious processes, including dreams where aspects of oneself communicate with each other (see Sigmund Freud), as part of intrapersonal communication, even though most human beings do not seem to have direct access to such communication. Speech recognition (in many contexts also known as automatic speech recognition, computer speech recognition or erroneously as Voice Recognition) is the process of converting a speech signal to a sequence of words, by means of an algorithm implemented as a computer program. Speech recognition applications that have emerged over the last years include voice dialing (e.g., Call home), call routing (e.g., I would like to make a collect call), simple data entry (e.g., entering a credit card number), and preparation of structured documents (e.g., a radiology report). Voice recognition or speaker recognition is a related process that attempts to identify the person speaking, as opposed to what is being said. CONCLUSION So, these were some of the primitive processing operations which are applied on the captured Image. Not all the operations are necessary; actually it depends on our need. Speech Processing improved speech recognition will make the operation of a computer easier. Virtual reality, the technology of interacting with a computer using all of the human senses, will also contribute to better human and computer interfaces. Standards for Virtual-reality program languages REFERENCES : 1. ACM Transaction on graphics 2. Digital Image Processing and Analysis- B.Chanda, D.Dutta Maujmder 3. http://www.google.com 4. www.howstuffworks.com 5. http://www.baw.de