CHAPTER 1
INTRODUCTION
1.1 INTRODUCTION:
Image segmentation plays a vital role in Image Analysis and computer vision which is considered as the obstruction in the development of image processing technology, Image segmentation has been the subject of intensive research and a wide variety of segmentation techniques has been reported in the last two decades. Image segmentation is a classical and fundamental problem in computer vision. It refers to partitioning an image into several disjoint subsets such that each subset corresponds to a meaningful part of the image. As an integral step of many computer vision problems, the quality of segmentation output largely influences the performance of the whole vision system. In general terms, image segmentation divides an image into related sections or regions, consisting of image pixels having related data feature values. It is an essential issue since it is the first step for image understanding and any other, such as feature extraction and recognition, heavily depends on its results. Segmentation algorithms are based on two significant criteria: the homogeneity of a region and the discontinuity
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The role of segmentation is to subdivide the objects in an image; in case of medical image segmentation the aim is to:
• Study anatomical structure
• Identify Region of Interest i.e. locate tumor, lesion and other abnormalities
• Measure tissue volume to measure growth of tumor (also decrease in size of tumor with treatment)
• Help in treatment planning prior to radiation therapy; in radiation dose calculation
Automatic segmentation of medical images is a difficult task as medical images are complex in nature and rarely have any simple linear feature. Further, the output of segmentation algorithm is affected due to
• partial volume effect.
• intensity
During World War II, after the Japanese bombing of Pearl Harbor, Japanese Americans in the western United States were forced into internment camps because the government felt as though the Japanese were dangerous if they were not relocated. These camps were usually in poor condition and in deserted areas of the nation. The Japanese were forced to make the best of their situation and thus the adults farmed the land and tried to maximize leisure while children attempted to enjoy childhood. The picture of the internee majorettes, taken by internee and photographer Toyo Miyatake, shows sixteen girls standing on bleachers while posing in front of the majestic Sierra Nevada mountain range and desolate Manzanar background. Their faces show mixed expressions of happiness, sadness and indifference, and their attire is elegant and American in style. With the image of these smiling girls in front of the desolate background, Miyatake captures an optimistic mood in times of despair. Though this photograph is a representation of the Manzanar internment camp and, as with most representations, leaves much unsaid, the majorette outfits and smiling faces give a great deal of insight on the cooperative attitudes of Japanese Americans and their youth's desire to be Americanized in this time.
Surgery is the oldest type of treatment for cancer. In its earlier use, surgery was not as successful as it is today. This was due to the difficulties involved with the anesthesias, excessive blood loss, respiratory distress, and a number of other complications. Tumors were removed, but metastases had formed, and the cancer returned quickly. In modern day surgery, namely laser surgery, these difficulties do not occur as readily. There are four basic ways to use surgery. One way is a diagnostic biopsy. This is a technique that involves removing part of the tumor, in order to test for malignancy. Another technique is surgery that removes the primary tumor. A third technique is removal of lymphatic tumors and metastases, which can help reduce the recurrence of cancer. The fourth technique is a combination of surgeries to detect and treat a malignant or benign tissue mass (1).
She put it on, leaving her clothing in the bath-house. But when she was there beside the sea, absolutely alone, she cast the unpleasant, pricking garments from her, and for the first time in her life she stood naked in the open air, at the mercy of the sun, the breeze that beat upon her, and the waves that invited her.
The movie Shock Doctrine revolves around the concept of the same name. The film begins by discussing psychological research on the effects of shock therapy. It is evident that a person under extreme stress and anxiety commonly experienced during a crisis functions and performs inadequately. It is noted that the studies are conducted by a man by the name of Milton Friedman, from the University of Chicago; the studies took place in the past, and some of the subjects are still recovering in the aftermath. From this research, interrogation techniques were learned and the concept of the shock doctrine was formed. Essentially through causing a crisis, the population of a country can be shocked into complying with accepting laws that favors the United States and capitalism. This theory coexists with Friedman’s belief in that government regulation is bad, and through a crisis a country would better itself with deregulation. The video uses Chile as an example and shows how America allowed a crisis to occur in Chile, through coups, interrogations and subterfuge. In the end a new government is formed that allows capitalism. Unfortunately afterwards violence and riots occur, as the rich gain most of the wealth and poverty rises. In addition to Chile, Argentina, Russia and even Iraq underwent the shock doctrine. Almost in every account, poverty rises and violence ends up erupting. The movie ends by showing how the US was in the process of the shock doctrine, and still is but the population has taken notice. Protests such as Occupy Wall Street are some of the initiatives necessary to bring awareness to the problems of class inequalities in order to prevent capitalism from benefitting the rich and increasing the wealth gap among the classes.
In order to increase the algorithm accuracy for little cropped image, it might be interesting
character segmentation and recognition. Deskewing the input image is then a crucial step in the
The denotation of the image which I can see in the frame is a bible
Image enhancement is a methodology including changing the pixels' power of the information picture with the goal that the yield picture ought to subjectively looks better [1]. The motivation behind picture improvement is to enhance the interpretability or recognition of data held in the picture for human viewers, or to give a "finer" info for other mechanized picture preparing frameworks. Contrast improvement is a valuable strategy for preparing investigative pictures, to enhance subtle elements in pictures that are over or under uncovered. Contrast upgrade enhances the detectable quality of items in the scene by upgrading the shine distinction between articles and their experiences. A high-difference picture compasses the full extend of light black level values consequently; a low complexity picture could be changed into a high-differentiation picture by remapping or extending the ash level values such that the histogram compasses the full run. Contrast enhancements are regularly executed as a difference stretch emulated by a tonal improvement, in spite of the fact that these could both be performed in one stage. A complexity stretch enhances the shine contrasts consistently over the element extent of the picture, while tonal improvements enhance the brilliance contrasts in the shadow (dark), midtone (grey hairs), or highlight (bright) locales at the cost of the brightness contrasts in alternate districts
After the initial pre processing steps of smoothening and removal of noise, the edge strength is calculated by taking the gradient of the image. For the purpose of edge detection in an image, the Sobel operator first performs a 2-D spatial gradient measurement with the help of convolution masks. The convolution masks used is of the size 3X3, where one is used to calculate the horizontal gradient(Gx) while the other is used to calculate the vertical gradient(Gy). Then, the approximate absolute edge strength can be calculated at each point. The masks used for the convolution process is as shown
...ting the disparity map was based on belief propagation and mean shift segmentation [19]. The disparity map and the reference image (JI_L) are segmented into some objects. The objects and the average disparity of these objects are denoted by O_(JI_L)^i & d_(JI_L)^i, respectively, i = 1,2,…,m. If d_(JI_L)^i is in [D_b,D_f ], O_(JI_L)^i is regarded as the main content, O_(JI_L)^i∈O_maipart.If d_(JI_L)^iis not in [D_b,D_f ], O_(JI_L)^i is regarded as the background, O_(JI_L)^i∈O_background. That is,
Clustering algorithms can be categorized based on their cluster model. The most appropriate clustering algorithm for a particular problem often needs to be chosen experimentally. It should be designed for one kind of models has no chance on a data set that contains a radically different kind of models. For example, k-means cannot find non-convex clusters. Difference between classification and clustering are two common data mining techniques for finding hidden patterns in data. While the classification and clustering is often me...
One of the important areas in information technology is computer graphics. The use of computer graphics is very important because it can help users in daily work efficiently and properly (Yuwaldi, 2000). Among the most important in the field of computer graphics is geometric transformation. With the process of geometric transformation, an object can be manipulated (Yaglom, 2009). Examples of object manipulation in computer graphics are translation, rotation, reflection and scaling. An object can be manipulated in accordance with the requirements of users such as object rotation on normal images like JPEG and bitmap. Geometric transformation can also be performed on digital medical images. This study aims to produce techniques and algorithms that can be used to implement the transformation of objects such as rotation and reflection on medical images.
Pattern recognition is the science of making inferences from perceptual data, using tools from statistics, probability, computational geometry, machine learning, signal processing, and algorithm design. Thus, it is of central importance
Segmentation is another technique for non-contiguous storage allocation. it's totally different from paging as pages ar physical in nature and thus ar of fastened size, whereas segments ar logical divisions of a program and thus ar of variable size. it's a memory management theme that supports the user read of memory instead of system read of memory as in paging. In segmentation we have a tendency to divide the logical address area into totally different segments. the final division will be: main program, set of subroutines, procedures, functions and set of information structure. every section features a name and length that is loaded into physical memory because it is. For simplicity, the sections ar referred by a segment variety, instead of section name. Thus, a logical address consists of 2 tuples:
The task of handwriting recognition is the transcription of handwritten data into a digital format. The goal is to process handwritten data electronically with the same or nearly the same accuracy as humans (Gunter, n.d). Basically, handwriting can be divided into two categories, cursive script and printed handwriting both with different ways of recognition (off-line or on-line). Accuracy is the main problem in handwriting recognition for both categories because of the similar strokes and shapes some letters may possess. The software may have an inaccurate recognition of the letter, considering the possibility of the handwriting being illegible or some other factors. One notable problem that makes this task difficult especially in cursive handwriting recognition is the fact that there may be no obvious character boundaries (the start and end of a character), compared to printed handwriting, it does not have gaps or spaces between each letter to know the start and stop of recognition per character. This problem makes the recognizer prone to errors that lead to inaccurate