A morphological approach for segmentation and tracking of human faces

A new technique for segmenting and tracking human faces in video sequences is presented. The technique relies on morphological tools such as using connected operators to extract the connected component that more likely belongs to a face, and partition projection to track this component through the sequence. A binary partition tree (BPT) is used to implement the connected operator. The BPT is constructed based on the chrominance criteria and its nodes are analyzed so that the selected node maximizes an estimation of the likelihood of being part of a face. The tracking is performed using a partition projection approach. Images are divided into face and non-face parts, which are tracked through the sequence. The technique has been successfully assessed using several test sequences from the MPEG-4 (raw format) and the MPEG-7 databases (MPEG-1 format).Peer ReviewedPostprint (published version

Region-based representations of image and video: segmentation tools for multimedia services

This paper discusses region-based representations of image and video that are useful for multimedia services such as those supported by the MPEG-4 and MPEG-7 standards. Classical tools related to the generation of the region-based representations are discussed. After a description of the main processing steps and the corresponding choices in terms of feature spaces, decision spaces, and decision algorithms, the state of the art in segmentation is reviewed. Mainly tools useful in the context of the MPEG-4 and MPEG-7 standards are discussed. The review is structured around the strategies used by the algorithms (transition based or homogeneity based) and the decision spaces (spatial, spatio-temporal, and temporal). The second part of this paper proposes a partition tree representation of images and introduces a processing strategy that involves a similarity estimation step followed by a partition creation step. This strategy tries to find a compromise between what can be done in a systematic and universal way and what has to be application dependent. It is shown in particular how a single partition tree created with an extremely simple similarity feature can support a large number of segmentation applications: spatial segmentation, motion estimation, region-based coding, semantic object extraction, and region-based retrieval.Peer ReviewedPostprint (published version

Semi-automatic video object segmentation for multimedia applications

A semi-automatic video object segmentation tool is presented for segmenting both still pictures and image sequences. The approach comprises both automatic segmentation algorithms and manual user interaction. The still image segmentation component is comprised of a conventional spatial segmentation algorithm (Recursive Shortest Spanning Tree (RSST)), a hierarchical segmentation representation method (Binary Partition Tree (BPT)), and user interaction. An initial segmentation partition of homogeneous regions is created using RSST. The BPT technique is then used to merge these regions and hierarchically represent the segmentation in a binary tree. The semantic objects are then manually built by selectively clicking on image regions. A video object-tracking component enables image sequence segmentation, and this subsystem is based on motion estimation, spatial segmentation, object projection, region classification, and user interaction. The motion between the previous frame and the current frame is estimated, and the previous object is then projected onto the current partition. A region classification technique is used to determine which regions in the current partition belong to the projected object. User interaction is allowed for object re-initialisation when the segmentation results become inaccurate. The combination of all these components enables offline video sequence segmentation. The results presented on standard test sequences illustrate the potential use of this system for object-based coding and representation of multimedia

A review on intelligent monitoring and activity interpretation

This survey paper provides a tour of the various monitoring and activity interpretation frameworks found in the literature. The needs of monitoring and interpretation systems are presented in relation to the area where they have been developed or applied. Their evolution is studied to better understand the characteristics of current systems. After this, the main features of monitoring and activity interpretation systems are defined.Este trabajo presenta una revisión de los marcos de trabajo para monitorización e interpretación de actividades presentes en la literatura. Dependiendo del área donde dichos marcos se han desarrollado o aplicado, se han identificado diferentes necesidades. Además, para comprender mejor las particularidades de los marcos de trabajo, esta revisión realiza un recorrido por su evolución histórica. Posteriormente, se definirían las principales características de los sistemas de monitorización e interpretación de actividades.This work was partially supported by Spanish Ministerio de Economía y Competitividad / FEDER under DPI2016-80894-R grant

Video object tracking : contributions to object description and performance assessment

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 201

Adaptive video delivery using semantics

The diffusion of network appliances such as cellular phones, personal digital assistants and hand-held computers has created the need to personalize the way media content is delivered to the end user. Moreover, recent devices, such as digital radio receivers with graphics displays, and new applications, such as intelligent visual surveillance, require novel forms of video analysis for content adaptation and summarization. To cope with these challenges, we propose an automatic method for the extraction of semantics from video, and we present a framework that exploits these semantics in order to provide adaptive video delivery. First, an algorithm that relies on motion information to extract multiple semantic video objects is proposed. The algorithm operates in two stages. In the first stage, a statistical change detector produces the segmentation of moving objects from the background. This process is robust with regard to camera noise and does not need manual tuning along a sequence or for different sequences. In the second stage, feedbacks between an object partition and a region partition are used to track individual objects along the frames. These interactions allow us to cope with multiple, deformable objects, occlusions, splitting, appearance and disappearance of objects, and complex motion. Subsequently, semantics are used to prioritize visual data in order to improve the performance of adaptive video delivery. The idea behind this approach is to organize the content so that a particular network or device does not inhibit the main content message. Specifically, we propose two new video adaptation strategies. The first strategy combines semantic analysis with a traditional frame-based video encoder. Background simplifications resulting from this approach do not penalize overall quality at low bitrates. The second strategy uses metadata to efficiently encode the main content message. The metadata-based representation of object's shape and motion suffices to convey the meaning and action of a scene when the objects are familiar. The impact of different video adaptation strategies is then quantified with subjective experiments. We ask a panel of human observers to rate the quality of adapted video sequences on a normalized scale. From these results, we further derive an objective quality metric, the semantic peak signal-to-noise ratio (SPSNR), that accounts for different image areas and for their relevance to the observer in order to reflect the focus of attention of the human visual system. At last, we determine the adaptation strategy that provides maximum value for the end user by maximizing the SPSNR for given client resources at the time of delivery. By combining semantic video analysis and adaptive delivery, the solution presented in this dissertation permits the distribution of video in complex media environments and supports a large variety of content-based applications

Video object segmentation.

Wei Wei.Thesis submitted in: December 2005.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 112-122).Abstracts in English and Chinese.Abstract --- p.IIList of Abbreviations --- p.IVChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview of Content-based Video Standard --- p.1Chapter 1.2 --- Video Object Segmentation --- p.4Chapter 1.2.1 --- Video Object Plane (VOP) --- p.4Chapter 1.2.2 --- Object Segmentation --- p.5Chapter 1.3 --- Problems of Video Object Segmentation --- p.6Chapter 1.4 --- Objective of the research work --- p.7Chapter 1.5 --- Organization of This Thesis --- p.8Chapter 1.6 --- Notes on Publication --- p.8Chapter Chapter 2 --- Literature Review --- p.10Chapter 2.1 --- What is segmentation? --- p.10Chapter 2.1.1 --- Manual Segmentation --- p.10Chapter 2.1.2 --- Automatic Segmentation --- p.11Chapter 2.1.3 --- Semi-automatic segmentation --- p.12Chapter 2.2 --- Segmentation Strategy --- p.14Chapter 2.3 --- Segmentation of Moving Objects --- p.17Chapter 2.3.1 --- Motion --- p.18Chapter 2.3.2 --- Motion Field Representation --- p.19Chapter 2.3.3 --- Video Object Segmentation --- p.25Chapter 2.4 --- Summary --- p.35Chapter Chapter 3 --- Automatic Video Object Segmentation Algorithm --- p.37Chapter 3.1 --- Spatial Segmentation --- p.38Chapter 3.1.1 --- k:-Medians Clustering Algorithm --- p.39Chapter 3.1.2 --- Cluster Number Estimation --- p.41Chapter 3.1.2 --- Region Merging --- p.46Chapter 3.2 --- Foreground Detection --- p.48Chapter 3.2.1 --- Global Motion Estimation --- p.49Chapter 3.2.2 --- Detection of Moving Objects --- p.50Chapter 3.3 --- Object Tracking and Extracting --- p.50Chapter 3.3.1 --- Binary Model Tracking --- p.51Chapter 3.3.1.2 --- Initial Model Extraction --- p.53Chapter 3.3.2 --- Region Descriptor Tracking --- p.59Chapter 3.4 --- Results and Discussions --- p.65Chapter 3.4.1 --- Objective Evaluation --- p.65Chapter 3.4.2 --- Subjective Evaluation --- p.66Chapter 3.5 --- Conclusion --- p.74Chapter Chapter 4 --- Disparity Estimation and its Application in Video Object Segmentation --- p.76Chapter 4.1 --- Disparity Estimation --- p.79Chapter 4.1.1. --- Seed Selection --- p.80Chapter 4.1.2. --- Edge-based Matching by Propagation --- p.82Chapter 4.2 --- Remedy Matching Sparseness by Interpolation --- p.84Chapter 4.2 --- Disparity Applications in Video Conference Segmentation --- p.92Chapter 4.3 --- Conclusion --- p.106Chapter Chapter 5 --- Conclusion and Future Work --- p.108Chapter 5.1 --- Conclusion and Contribution --- p.108Chapter 5.2 --- Future work --- p.109Reference --- p.11