Foreground Segmentation with Tree-Structured Sparse RPCA.

Background subtraction is a fundamental video analysis technique that consists of creation of a background model that allows distinguishing foreground pixels. We present a new method in which the image sequence is assumed to be made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse matrix. The decomposition task is then solved using our approximated Robust Principal Component Analysis (ARPCA) method which is an extension to the RPCA that can handle camera motion and noise.

Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning.

Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, several issues still remain unsolved for generating vivid and detail-preserving personal sketch portraits. For example, quite a few artifacts may exist in synthesizing hairpins and glasses, and textural details may be lost in the regions of hair or mustache.

Histology image retrieval in optimised multi-feature spaces.

Content based histology image retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content based image retrieval, feature combination plays a key role. It aims at enhancing the descriptive power of visual features corresponding to semantically meaningful queries.

Scene-oriented hierarchical classification of blurry and noisy images.

A system for scene-oriented hierarchical classification of blurry and noisy images is proposed. It attempts to simulate important features of the human visual perception. The underlying approach is based on three strategies: extraction of essential signatures captured from a global context, simulating the global pathway; highlight detection based on local conspicuous features of the reconstructed image, simulating the local pathway; and hierarchical classification of extracted features using probabilistic techniques.

A comparison between two robust techniques for segmentation of blood vessels.

Image segmentation plays an important role in image analysis. According to several authors, segmentation terminates when the observer's goal is satisfied. For this reason, a unique method that can be applied to all possible cases does not yet exist.

Rate-distortion optimized motion-compensated prediction for packet loss resilient video coding.

A rate-distortion optimized motion-compensated prediction method for robust video coding is proposed. Contrasting methods from the conventional literature, the proposed approach uses the expected reconstructed distortion after transmission, instead of the displaced frame difference in motion estimation. Initially, the end-to-end reconstructed distortion is estimated through arecursive per-pixel estimation algorithm.