We present a novel method to robustly and efficiently detect moving object, even under the complexity background, such as illumination changes, long shadows etc. This work is disti...
One of the most widely used approaches in the context of object recognition across illumination changes consists in comparing the images by means of the intersection between invar...
We address the problem of variational optical flow for video processing applications that need fast operation and robustness to drastic variations in illumination. Recently, a sol...
We propose a change detection method which is robust against illumination change and requires little background learning as a result of using texture based features. We propose Pe...
Illumination change is one of most important and difficult problems which prevent from applying face recognition to real applications. For solving this, we propose a method to comp...
Face tracking in realistic environments is a difficult problem due to pose variations, occlusions of objects, illumination changes and cluttered background, among others. The paper...
Pose and illumination changes from picture to picture are two main barriers toward full automatic face recognition. In this paper, a novel method to handle both pose and lighting c...
Abstract. The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although it is important to adapt the mean-shift kernel to handle changes in il...
— A major challenge in the path of widespread use of mobile robots is the ability to function autonomously, learning useful features from the environment and using them to adapt ...
Abstract. The estimation of parametric global motion has had a significant attention during the last two decades, but despite the great efforts invested, there are still open issu...