Cancer is uncontrolled growth of cells. Breast Cancer is the uncontrolled growth of cells in the breast region. Breast cancer is the second leading cause of cancer deaths in women today. Early detection of the cancer can reduce mortality rate. Early detection of Breast Cancer can be achieved using Digital Mammography, typically through detection of characteristic masses and/or microcalcifications. A Mammogram is an x-ray of the breast tissue which is designed to identify abnormalities. Studies have shown that radiologists can miss the detection of a significant proportion of abnormalities in addition to having high rates of false positives. Therefore, it would be valuable to develop a computer aided method for mass/tumor classification based on extracted features from the Region Of Interest (ROI) in mammograms. ROI has to be segmented from the digital mammogram using the Segmentation techniques. Pattern recognition in image processing requires the extraction of features from regions of the image, and the processing of these features with a pattern recognition algorithm. We consider the feature extraction part of this processing, with a focus on the problem of tumor detection in digital mammography. Features are nothing but observable patterns in the image which gives some information about the image. For every Pattern Classification problem, the most important stage is Feature Extraction. The accuracy of the classification depends on the Feature Extraction stage. The different features that can be extracted for a digital mammogram are: Texture Features, Statistical Features, Structural Features. In this paper, we are able to calculate Texture, Statistical and Structural Features. We have used MATLAB for extracting the tumors from input mammogram and for calculating various features.

No abstract

Automatic face detection is a challenging task for computer vision and pattern recognition applications such as video surveillance and traffic monitoring. During the last few years, subspace methods have been proposed for visual learning and recognition which are sensitive to variations in illumination, pose and occlusion. To overcome these problems, the authors have proposed a method that combines block‐based tensor locality preservative projection (TLPP) with Adaboost algorithm which improves the accuracy of face detection. In the proposed algorithm Adaboost modular TLPPs (AMTLPPs), the face image is divided into overlapping small blocks and these block features are given to TLPP to extract the features where TLPP take data directly in the form of tensors as input. AMTLPP algorithm selects the optimal block features from the large set of the block features which forms the weak classifiers and are combined to form the strong classifier. A number of assessments are conducted for YouTube celebrity, McGill face dataset and also on collected video sequences of an own dataset recorded under indoor, outdoor, day, sunset and crowded environment. Experimental results show that the proposed approach is effective and efficient.

No abstract

No abstract

No abstract

No abstract

No abstract