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Geometric theory Detector for uncharacteristic occasion Detection in Crowded Scenes

Abstract

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively

Geometric theory Detector for uncharacteristic occasion Detection in Crowded Scenes pdf

 

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Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques,Automated Localization of Optic Disc in Retinal Images,Localization and segmentation of optic disc in retinal images ,classification and detection of diabetic retinopathy,vessel segmentation algorithm,A Semi-automated System for Optic Nerve Head Segmentation,A precise retinotopic map of primate striate cortex generated