In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. In this paper proposed method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor Classification (KNN) to enhance the writer identification accuracy. After feature extraction can be clustered by K-means algorithm to standardize the number of features the feature extraction and feature clustering called to gather Bag of Word (BOW), it converts arbitrary number of image feature to uniform length feature vector the proposed method experimented using (IFN/ENIT) database. The experiment result is (96.666) recognition rate.
Texture Characterization of Bone radiograph images (TCB) is a challenge in the osteoporosis diagnosis organized for the International Society for Biomedical Imaging. The objective of this paper is to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis. In this paper, we propose a Bone Texture Characterization method based on texture features (Segmentation-based Fractal Texture Analysis (SFTA), Basic Texture and Gabor filters) and compare these resulted features with HOG features for 2D bone structure evaluation. The classification experiments are tested with linear SVM and decision tree classifiers. The classification performance for HOG features are always higher than other texture features, and show excellent classification performance compared to other existing methods.
General TermsImage mining, Artificial Intelligence.
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