Multiple Hypothesis Video Segmentation (MHVS) is a method for the unsupervised photometric segmentation of video sequences. MHVS segments arbitrarily long video streams by considering only a few frames at a time, and handles the automatic creation, continuation and termination of labels with no user initialization or supervision. The process begins by generating several pre-segmentations per fr...

We propose a novel approach for segmentation of human objects, including face and body, in image sequences. Object segmentation is important for achieving a high compression ratio in modern video coding techniques, e.g., MPEG-4 and MPEG-7, and human objects are usually the main parts in the video streams of multimedia applications. Existing segmentation methods apply simple criteria to detect h...

We have developed an original approach for content-based video indexing and retrieval. By introducing a causal Gibbsian modeling of the spatio-temporal distribution of appropriate local motion-related measurements, we have designed a general and efficient statistical framework for non parametric motion modeling, motion recognition and classification, and motion segmentation. It is exploited for...

This paper addresses the issue of segmentation and recognition of text embedded in video sequences from their associated text image sequence extracted by a text detection module. To this end, we propose a probabilistic algorithm based on Bayesian adaptive thresholding and Monte-Carlo sampling. The algorithm approximates the posterior distribution of segmentation thresholds of text pixels in an ...

Video streams are ubiquitous in applications such as surveillance, games, and live broadcast. Processing and analyzing these data is challenging because algorithms have to be efficient in order to process the data on the fly. From a theoretical standpoint, video streams have their own specificities – they mix spatial and temporal dimensions, and compared to standard video sequences, half of the...

As content-based multimedia applications become increasingly important, demand for technologies on semantic video object segmentation is growing, where the segmented objects are expected to be in line with human visual perception. Existing research is limited to semi-automatic approach, in which human intervene is often required. These include manual selection of seeds for region growing or man...

In video sequence coding, a combination of temporal and spatial coding technique is used in order to remove the predictable or redundant image content and encode only the unpredictable information. The objective of video compression technique is to increase the coding efficiency and to increase the data rate savings. A segmentation-based compression method is proposed to achieve this goal. In t...

Video content understanding for surveillance and security applications such as smart CCTV cameras has become a research hotspot in the last decade. This paper presents applications of volume construction and volume-based cluster segmentation approaches to video event detection. It starts with a description of the translation between original video frames and 3D volume structures denoted by spat...

In this paper we propose a general framework for videobased object segmentation using a new spatio-temporal Markov Random Field (MRF) model. Video-based object segmentation has the potential to improve the performance of static image segmentation by fusing information over the temporal scale. Built upon a spatio-temporal MRF model, our method offers three advantages in a unified framework. Firs...

Despite advances in the archiving of digital video, we are still unable to efficiently search and retrieve the portions that interest us. Video indexing by shot segmentation has been a proposed solution and several research efforts are seen in the literature. Shot segmentation alone cannot solve the problem of content based access to video. Recognition of text in video has been proposed as an a...