In order to face the various needs of users, user-driven segmentation methods are expected to provide more relevant results than fully automatic approaches. Within Mathematical Morphology, several user-driven approaches have been proposed, mostly relying on the watershed transform. Nevertheless, Soille (IEEE TPAMI, 2008) has recently suggested another solution by gathering puzzle pieces compute...

Temporal synchronization is a problem that cannot be ignored in most video watermarking application. In this paper, a video watermarking scheme is proposed to resist temporal desynchronization by using shot segmentation and two-dimensional principal component analysis (2DPCA). In this scheme, the to-be-watermarked video is first segmented into different shots. And each shot is analyzed by 2DPCA...

This paper presents a new multi-step, skeleton-based approach for the temporal segmentation of human activities from video sequences. Several signals are first extracted from a skeleton sequence. These signals are then segmented individually to localize their cyclic segments. Finally, all individual segmentations are merged with respect to the global set of signals. Our approach requires no pri...

Semi-supervised video object segmentation is a task of propagating instance masks given in the first frame to entire video. It challenging since it usually suffers from heavy occlusions, large deformation, and variations objects. To alleviate these problems, many existing works apply time-consuming techniques such as fine-tuning, post-processing, or extracting optical flow, which makes them int...

Text characters embedded in images and video sequences represents a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values and complex backgrounds. This thesis investigates methods for building an efficient application system for detecting and recogn...

We propose an original approach for the characterization of video dynamic content with a view to supplying new functionalities for motion-based video indexing and retrieval with query by example. We have designed a statistical framework for motion content description without any prior motion segmentation, and for motion-based video classi cation and retrieval. Contrary to other proposed methods...

In this fast paced digital age, a vast amount of videos are produced every day, such as movies, TV programs, personal home videos, surveillance video, etc. This places a high demand for effective video data analysis and management techniques. In this dissertation, we have developed new techniques for segmentation, linking and understanding of video scenes. Firstly, we have developed a video sce...

The dominant paradigm for video-based action segmentation is composed of two steps: first, for each frame, compute low-level features using Dense Trajectories or a Convolutional Neural Network that encode spatiotemporal information locally, and second, input these features into a classifier that captures high-level temporal relationships, such as a Recurrent Neural Network (RNN). While often ef...

Slow Feature Analysis (SFA) is a subspace learning method inspired by the human visual system, however, it is seldom seen in computer vision. Motivated by its application for unsupervised activity analysis, we develop SFA’s first implementation of online temporal video segmentation to detect episodes of motion changes. We utilize a domainspecific indefinite kernel which takes the data represent...

A scene boundary detection method is presented, which analyzes both aural and visual information sources and accounts for their inter-relations and coincidence to semantically identify video scenes. Audio analysis focuses on the segmentation of the audio source into three types of semantic primitives, i.e. silence, speech and music. Further processing on speech segments aims at locating speaker...