23,351 research outputs found
Interaction between high-level and low-level image analysis for semantic video object extraction
Authors of articles published in EURASIP Journal on Advances in Signal Processing are the copyright holders of their articles and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate the article, according to the SpringerOpen copyright and license agreement (http://www.springeropen.com/authors/license)
A video object generation tool allowing friendly user interaction
In this paper we describe an interactive video object segmentation tool developed in the framework of the ACTS-AC098 MOMUSYS project. The Video Object Generator with User Environment (VOGUE) combines three different sets of automatic and semi-automatic-tool (spatial segmentation, object tracking and temporal segmentation) with general purpose tools for user interaction. The result is an integrated environment allowing the user-assisted segmentation of any sort of video sequences in a friendly and efficient manner.Peer ReviewedPostprint (published version
Relative Facial Action Unit Detection
This paper presents a subject-independent facial action unit (AU) detection
method by introducing the concept of relative AU detection, for scenarios where
the neutral face is not provided. We propose a new classification objective
function which analyzes the temporal neighborhood of the current frame to
decide if the expression recently increased, decreased or showed no change.
This approach is a significant change from the conventional absolute method
which decides about AU classification using the current frame, without an
explicit comparison with its neighboring frames. Our proposed method improves
robustness to individual differences such as face scale and shape, age-related
wrinkles, and transitions among expressions (e.g., lower intensity of
expressions). Our experiments on three publicly available datasets (Extended
Cohn-Kanade (CK+), Bosphorus, and DISFA databases) show significant improvement
of our approach over conventional absolute techniques. Keywords: facial action
coding system (FACS); relative facial action unit detection; temporal
information;Comment: Accepted at IEEE Winter Conference on Applications of Computer
Vision, Steamboat Springs Colorado, USA, 201
A dynamic texture based approach to recognition of facial actions and their temporal models
In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set
Machine Analysis of Facial Expressions
No abstract
- …