Analysis and Recognition of Animals in Zoo Using Transform based techniques
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Abstract
An object recognition system identifies an object which is under test by comparing different features of the test and training database images. This task is difficult for computers, however for humans object recognition is effortless and instantaneous. The various applications for object recognition are in the fields of Medicine, Communications, Military Intelligence, Bioinformatics and many others. It is the ability to perceive an object’s physical properties such as shape, color and texture and apply semantic attributes to the object, which includes the understanding of its use, previous experience with the object and how it relates to others. Algorithmic description of this task for implementation on machines has been very difficult. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different viewpoints, in many different sizes and scales or even when they are translated or rotated. Many approaches to this task have been implemented over decades, but still this is an open area of research. There are standard databases available for research in the area of object recognition. In this proposed work, the images of the different animals are downloaded from https://www.rocq.inria.fr/gamma/gamma/download/ANIMALS/index0.php and 3D Meshes Research Database. These images are to be converted into binary form, by using thresholding method. In the next step, distinguishable features which may be Zone based, Transform based or Statistical in nature are to be extracted from these images. Using these features, animals (in the present work) can be recognized by using various classifiers such as SVM, Neural Networks and NNC (Nearest Neighborhood Classifier) namely ‘Euclidean Distance’. The execution time of the simulation would also be compared for all the transforms implemented in the work. The results obtained would be compared with the existing results of similar work
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