Ofer Miller

Color image segmentation based on adaptive local thresholds

Abstract The goal of still color image segmentation is to divide the image into homogeneous regions. Object extraction, object recognition and object-based compression are typical applications that use still segmentation as a low-level image processing. In this paper, we present a new method for color image segmentation. The proposed algorithm divides the image into homogeneous regions by local thresholds. The number of thresholds and their values are adaptively derived by an automatic process, where local information is taken into consideration. First, the watershed algorithm is applied. Its results are used as an initialization for the next step, which is iterative merging process. During the iterative process, regions are merged and local thresholds are derived. The thresholds are determined one-by-one at different times during the merging process. Every threshold is calculated by local information on any region and its surroundings. Any statistical information on the input images is not given. The algorithm is found to be reliable and robust to different kind of images.

Fast gradient methods based global motion estimation for video compression

This paper presents a fast global motion estimation (GME) algorithm based on gradient methods (GM), which can be used for real-time applications, such as MPEG-4 video compression. Our approach improves existing state-of-the-art GME algorithms by introducing two major modifications: first, only a small subset (down-to 3%) of the original image pixels is used in the estimation process. Second, a warp-free formulation of the basic GM is derived, further decreasing the computational complexity. Experimental results show that a 20 fold computation complexity reduction is achieved, without compromising the GME accuracy and compression efficiency.

Objects based change detection in a pair of gray-level images

The goal of the presented change detection algorithm is to extract objects that appear in only one of two input images. A typical application is surveillance, where a scene is captured at different times of the day or even on different days. In this paper we assume that there may be a significant noise or illumination differences between the input images. For example, one image may be captured in daylight while the other was captured during night with an infrared device. By using a connectivity analysis along gray-level technique, we extract significant blobs from both images. All the extracted blobs are candidates to be classified as changes or part of a change. Then, the candidate blobs from both images are matched. A blob from one image that does not satisfy the matching criteria with its corresponding blob from the other image is considered as an object of change. The algorithm was found to be reliable, fast, accurate, and robust even under extreme changes in illumination and some distortion of the images. The performance of the algorithm is demonstrated using real images. The worst-case time complexity of the algorithm is almost linear in the image size. Therefore, it is suitable for real-time applications.

Robust phase correlation

Phase-correlation (PC) is a computationally efficient method for two and three dimensional translation estimation. We present a projection operator, which significantly improves the accuracy and robustness of the PC scheme. The operator projects the estimated correlation function into the space of correlation functions resulting from a certain range of translations, while rejecting components which are unrelated to the estimated motion. Thus, the registration accuracy is improved by an order of magnitude, especially in the registration of noisy images and volumes. In addition, this approach is shown to be complementary with other subpixel phase correlation based techniques.

Tracking of moving objects based on graph edges similarity

This paper suggests a novel contour-based algorithm for tracking moving objects in a video sequence. The algorithm uses the segmentation results of the input frames, which are represented by two region adjacency graphs (RAG) data structures. Based on the image segmentation result, the objects contour is divided into subcurves while junctions of the contour are derived. The junctions are being matched in a search area that is the RAG edges of the consecutive frame. Each pair of matched junctions may be connected by several of paths (edges) that are candidates to represent the tracked contour. Using algorithm for finding the k-shortest paths between two nodes these paths are obtained. Then, the construction of the tracked contour is done by a matching process between edges, which are the subcurves, and sets of candidate paths. The use of RAG to construct the tracked contour enables an accurate representation of the moving object while preserving efficient complexity.