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Scale Invariant Feature Transform (SIFT) is widely used in vision systems for various applications such as object detection and face recognition. In SIFT, threshold is applied to determine local extrema (keypoint selection) and global extrema (keypoint refinement). Next, descriptor matching is performed with selected keypoints. This paper presents a new method of adaptive thresholding which improves keypoint selection in SIFT. The value of adaptive threshold depends upon the average regional intensity of an image. Experimental results show that our method is robust for matching the keypoints among the images with illumination differences. Our new adaptive threshold technique for keypoint selection reduces false matches and shows significantly improved performance in experimental results.

Hand gestures can be used for natural and intuitive human-computer interaction. Our new method combines existing techniques of skin color based ROI segmentation and Viola-Jones Haar-like feature based object detection, to optimize hand gesture recognition for mouse operation. A mouse operation has two parts, movement of cursor and clicking using the right or left mouse button. In this paper, color is used as a robust feature to first define a Region of Interest (ROI). Then within this ROI, hand postures are detected by using Haar-like features and AdaBoost learning algorithm. The AdaBoost learning algorithm significantly speeds up the performance and constructs an accurate cascaded classifier by combining a sequence of weak classifiers.