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Computer vision imitates human vision ability to perceive real world. Computer vision has proven its part from autonomous navigation on martin surface to histopathology images recognition. In practice, histopathologists examine microscopic imagery of diseased tissue and diagnose on the bases of malignancy, likewise a computer assisted diagnosis (CAD) system is devised for histopathology images recognition on the basis of visual features. This paper describes the design methodology, workflow and test results of locating an object in a cluttered scene. In this paper speeded up robust features are used for feature extraction and matching. After object detection, Random sample consensus is used for removing outliers to refine results. This CAD system provides a heuristic approach to assist histopathologists in diagnostics. Keywords–computer vision; surf; ransac; computer assisted diagnostics; histopathology

Object Detection in a Cluttered Scene Using SURF for Computer Assisted Histopathology