In this paper we present a redundancy reduction based approach for computational bottomup visual saliency estimation. In contrast to conventional methods, our approach determines the saliency by filtering out redundant contents instead of measuring their significance. To analyze the redundancy of self-repeating spatial structures, we propose a non-local self-similarity based procedure. The resu...

The bottom-up contribution to the allocation of exogenous attention is a saliency map, whose neural substrate is hard to identify because of possible contamination by top-down signals. We obviated this possibility using stimuli that observers could not perceive, but that nevertheless, through orientation contrast between foreground and background regions, attracted attention to improve a locali...

Computational model for object-based visual attention is a hot research field. A computational model of object-based visual attention is proposed in this paper. This model mainly includes three key technologies which are perceptual objects extraction, attention value calculation of perceptual objects and focus of attention shift. Firstly spatial visual saliency of each pixel is calculated and a...

In this study, we propose a novel method to measure bottom-up saliency maps of natural images. In order to eliminate the influence of top-down signals, backward masking is used to make stimuli (natural images) subjectively invisible to subjects, however, the bottom-up saliency can still orient the subjects attention. To measure this orientation/attention effect, we adopt the cueing effect parad...

This paper presents a novel image retargeting approach for ranging cameras. The proposed approach first extracts three feature maps: depth map, saliency map, and gradient map. Then, the depth map and the saliency map are used to separate the main contents and the background and thus compute a map of saliency objects. After that, the proposed approach constructs an importance map which combines ...

Salient region detection in images is very useful for image processing applications like image compressing, image segmentation, object detection and recognition. In this paper, an improved approach to detect salient region is presented. The proposed method can generate a robust saliency map and extract salient regions with precise boundaries. In the proposed method, local saliency, global salie...

General Presentation of the Research Domain The focus of the REVES research group is on image and sound synthesis for virtual environments. Our research is on the development of new algorithms to treat complex scenes in real time, both for image rendering (for example the capture and rendering of trees using an image-based technique [1]) or for sound (for example using perceptual masking and cl...

The computer model of visual attention derives an interest or saliency map from an input image in a process that encompasses several data combination steps. While several combination strategies are possible, not all perform equally well. This paper compares main cue combination strategies by measuring the performance of the considered models with respect to human eye movements. Six main combina...

In this paper we propose an effective non-rigid object tracking method based on spatial-temporal consistent saliency detection. In contrast to most existing trackers that use a bounding box to specify the tracked target, the proposed method can extract the accurate regions of the target as tracking output, which achieves better description of the non-rigid objects while reduces background pollu...