Foreground Extraction Combining Graph Cut and Histogram Shape Analysis

Interactive Foreground Extraction using graph cut

The topic of interactive image segmentation has received considerable attention in the computer vision community in the last decade. Today, this topic is very mature and commercial products exist which feature advanced research solutions. This means that interactive image segmentation is today probably one of the most used computer vision technologies world-wide. In this chapter we review one c...

Feature Extraction Combining Spe and Cepstral Histogram Equaliz

This work is mainly focused on showing experimental results using a combination of two methods for noise compensation which are shown to be complementary: classical spectral subtraction algorithm and histogram equalization. While spectral subtraction is focused on the reduction of the additive noise in the spectral domain, histogram equalization is applied in the cepstral domain to compensate t...

Improved Cut-Based Foreground Identification

Automatic content based schemes, as opposed to those with human endeavor, have become important as users attempt to organize massive data presented in the form of multimedia data such as images, and home or movie videos. One important goal, be it in shot understanding, or scene detection, or compression, is the ability to find foreground pixels. This higher level task is best realized using a g...

Noise Robust Surface Reconstruction by Combining PU and Graph-cut

Region-based Foreground Extraction

We propose a region-based method to extract foreground regions from colour video sequences. The foreground region is decided by voting with scores from background subtraction to the sub-regions by graphbased segmentation. Experiments show that the proposed algorithm improves on conventional approaches especially in strong shadow regions.