IST LIVE

Content related to "close-up-detection"

(1) Skin Detection from Different Color Spaces for Model-based Face Detection

Skin and face detection has many important applications in intelligent human-machine interfaces, reliable video surveillance and visual understanding of human activities. In this paper, we propose an efficient and effective method for frontal-view face detection based on skin detection and knowledge-based modeling. Firstly, skin pixels are modeled by using supervised training, and boundary conditions are then extracted for skin segmentation. Faces are further detected by shape filtering and knowledge-based modeling. Skin results from different color spaces are compared. In addition, experimental results have demonstrated our method robust in successful detection of skin and face re-gions even with variant lighting conditions and poses.

(1) Real-time and Automatic Close-up Retrieval from Compressed Videos

In this paper, we propose a thorough scheme, by virtue of camera zooming descriptor with two-level threshold, to automatically retrieve close-ups directly from MPEG compressed videos based on camera motion analysis. In the retrieval process, we build camera-motion-based semantic retrieval. To improve the coverage of the proposed scheme, we investigate close-up retrieval in all kinds of videos. Extensive experiments illustrate that the proposed scheme provides promising retrieval results under real-time and automatic application scenario.

(1) Recognition of JPEG Compressed Face Images Based on AdaBoost

This paper presents an advanced face recognition system based on AdaBoost algorithm in the JPEG compressed domain. First, the dimensionality is reduced by truncating some of the block-based DCT coefficients and the nonuniform illumination variations are alleviated by discarding the DC coefficient of each block. Next, an improved AdaBoost.M2 algorithm which uses Euclidean Distance(ED) to eliminate non-effective weak classifiers is proposed to select most discriminative DCT features from the truncated DCT coefficient vectors. At last, the LDA is used as the final classifier. Experiments on Yale face databases show that the proposed approach is superior to other methods in terms of recognition accuracy, efficiency, and illumination robustness.

(1) Camera Motion Analysis towards Semantic-based Video Retrieval in Compressed Domain

To reduce the semantic gap between low-level visual features and the richness of human semantics, this paper proposes new algorithms, by virtue of the combined camera motion descriptors with multi-threshold, to automatically retrieve the semantic concepts, i.e., close-up, and panorama, directly in MPEG compressed domain based on camera motion analysis. Extensive experiments illustrate that the proposed algorithms provide promising retrieval results under real-time application scenario and without human intervention

(1) Object recognition with deformable feature graphs

A fundamental question in invariant object recognition is that of representation. This chapter reviews object representation based on deformable graphs that describe particular views of an object as a spatial constallation of image features. These representations are particular useful in situations of high clutter and partial occlusions. We demonstrate the benfits of these representations in three recognition applications: face analysis, hand gesture recognition and the interpretation of cluttered scenes composed of mutible partly occluded objects. We conclude by discussing current trends and open challenges.