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In this paper, an image processing algorithm is presented for the interpretation of the Taiwanese Sign Language, which is one of the sign languages used by the majority of the deaf community. The process involves two layer classifications. At first, coarse classification is done according to detection of hand motion and tracking the hand location and second classification is based on key frame selection and hand shape recognition of key frames. Motion history image and Fourier descriptor are used for motion direction recognition and key frame selection respectively. Generic cosine descriptor (GCD) has been proposed for feature extraction of hand postures. GCD is invariant to scale, translation and rotation of hand shapes. Our system tests 15 different hand gestures of 10 persons. The experimental results show that our system can achieve 100% recognition rate for test persons.

Background:Breast cancer is one of the most encountered cancers in women. Detection and classification of the cancer into malignant or benign is one of the challenging fields of the pathology.Objectives:Our aim was to classify the mammogram data into normal and abnormal by ensemble classification method.Patients and Methods:In this method, we first extract texture features from cancerous and normal breasts, using the Gray-Level Co-occurrence Matrices (GLCM) method. To obtain better results, we select a region of breast with high probability of cancer occurrence before feature extraction. After features extraction, we use the maximum difference method to select the features that have predominant difference between normal and abnormal data sets. Six selected features served as the classifying tool for classification purpose by the proposed ensemble supervised algorithm. For classification, the data were first classified by three supervised classifiers, and then by simple voting policy, we finalized the classification process.Results:After classification with the ensemble supervised algorithm, the performance of the proposed method was evaluated by perfect test method, which gave the sensitivity and specificity of 96.66% and 97.50%, respectively.Conclusions:In this study, we proposed a new computer aided diagnostic tool for the detection and classification of breast cancer. The obtained results showed that the proposed method is more reliable in diagnostic to assist the radiologists in the detection of abnormal data and to improve the diagnostic accuracy.