Biomedical imaging plays an essential role in analyzing and diagnosing diseases related to the brain. There is a considerable variation in brain medical datasets, such as some CT scan datasets containing a patient table along with the brain object, whereas others only have the brain. In the first said category, the worthless undesirable tables are needed to be removed for further processing. On the other hand, no such removal techniques are required to be applied for the second case. This article has computed the classification performance of the Support Vector Machine(SVM) and Back Propagation algorithm(BPNN). The proposed method has been trained with 661 images and tested with 165 images. And the performance matrices show that both classifiers have the almost same level of accuracy, i.e., for SVM, average accuracy is 98.58% and for BPNN, it is 98.11%.

An analysis on SVM and BPNN for CT Patient Table Detection using Distance-verses-angle Signature Feature

The shape of an object has always been a key attribute through which humans have been able to distinguish and identify them. Object shape recognition deals with creating an automated computer-based approach to correctly identify the type of object in an image or video. Object shape recognition is an important pre-processing step for many applications such as recognition of a tiny cell under a microscope to outliner detection in an intelligent surveillance system. Although object shape recognition has seen a lot of progress in the past few years and many systems have been proposed, there is still a massive scope for improvement as a myriad of factors which include variation in object instances through pose and appearance, environmental factors such as the degree of occlusion, and lighting tend to cause failures in the existing system. In order to combat the above mentioned short-comings of the existing systems, a system has been developed that detects the contour of the object which serves as a major factor in the recognition process, and the objects whose contours are found are then

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Object Recognition using Shape Features

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Object Recognition using Shape Features