A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval

We introduce a shape descriptor that extracts keypoints from binary images and automatically detects the salient ones among them. The proposed descriptor operates as follows: First, the contours of the image are detected and an image transformation is used to generate background information. Next, pixels of the transformed image that have specific characteristics in their local areas are used to extract keypoints. Afterwards, the most salient keypoints are automatically detected by filtering out redundant and sensitive ones. Finally, a feature vector is calculated for each keypoint by using the distribution of contour points in its local area. The proposed descriptor is evaluated using public datasets of silhouette images, handwritten math expressions, hand-drawn diagram sketches, and noisy scanned logos. Experimental results show that the proposed descriptor compares strongly against state of the art methods, and that it is reliable when applied on challenging images such as fluctuated handwriting and noisy scanned images. Furthermore, we integrate our descripto

Object Tracking using Generalized Gradient Vector Flow

The aim of an object tracker is to generate the trajectory of an object over time by locating its position in every frame of the video. In this research, we present an object contour tracking approach using Generalized Gradient Vector Flow (GGVF). GGVF active contour, or snake, is a dynamic curve that moves within an image domain to capture desired image features. Mostly, GGVF is not sensitive to initial conditions and converges to the optimal contour. Given an initial contour near the object in the first video frame, GGVF can iteratively converge to an optimal object boundary. In each video frame thereafter, the resulting contour in the previous video frame is taken as initialization so the algorithm consists of two steps. In the first step, the initial contour is applied to the desired object in first video frame. The resulting contour is taken as initialization of the second step, which applies GGVF to current video frame. To evaluate the tracking performance, we applied the algorithm to several real world video sequences. Experimental results are provided

Contour-based object detection as dominant set computation

10.1016/j.patcog.2011.11.010Pattern Recognition4551927-1936PTNR