Titlebook: Image Feature Detectors and Descriptors; Foundations and Appl Ali Ismail Awad,Mahmoud Hassaballah Book 2016 Springer International Publishi

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Image Feature Extraction Accelerationed by regular imagers is combined with distributed memory supporting concurrent processing. Custom circuitry is added per pixel in order to accelerate image feature extraction right at the focal plane. Specifically, the proposed sensing-processing chips aim at the acceleration of two flagships algor

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Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Featuresion method in order to get a final registered version. As a pre-processing step, speckle noise removal is performed on radar images in order to reduce the number of false detections. In a similar fashion, optical images are also processed by sharpening and enhancing edges in order to get more accura

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Intelligent Detection of Foveal Zone from Colored Fundus Images of Human Retina Through a Robust Com the current algorithm. This was followed by applying gradient vector flow (GVF) based active contour technique in order to extract the boundary of the foveal region. The algorithm was applied on a several retinal images acquired from different persons with a very good success rate. The present work

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OMIT

Application of Texture Features for Classification of Primary Benign and Primary Malignant Focal LivROIs using six feature extraction methods namely, FOS, GLCM, GLRLM, FPS, Gabor and Laws’ features. Three texture feature vectors (TFVs) i.e., TFV1 consists of texture features computed from IROIs, TFV2 consists of texture ratio features (i.e., texture feature value computed from IROI divided by text

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Application of Statistical Texture Features for Breast Tissue Density ClassificationM based CAD system designs for two-class and three-class breast tissue density classification using mammographic images. It is observed that for two-class breast tissue density classification, the highest classification accuracy of 94.4 % is achieved using only the first 10 principal components (PCs

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Hrishikesh Bhaumik,Siddhartha Bhattacharyya,Susanta Chakraborty