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Hierarchical morphological segmentation for image sequence coding

This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of the coding approach.

Morphological operators for image and video compression

This paper deals with the use of some morphological tools for image and video coding. Mathematical morphology can be considered as a shape-oriented approach to signal processing, and some of its features make it very useful for compression. Rather than describing a coding algorithm, the purpose of this paper is to describe some morphological tools that have proved attractive for compression. Four sets of morphological transformations are presented: connected operators, the region-growing version of the watershed, the geodesic skeleton, and a morphological interpolation technique. The authors discuss their implementation, and show how they can be used for image and video segmentation, contour coding, and texture coding.

Segmentation-based video coding system allowing the manipulation of objects

This paper presents a generic video coding algorithm allowing the content-based manipulation of objects. This manipulation is possible thanks to the definition of a spatiotemporal segmentation of the sequences. The coding strategy relies on a joint optimization in the rate-distortion sense of the partition definition and of the coding techniques to be used within each region. This optimization creates the link between the analysis and synthesis parts of the coder. The analysis defines the time evolution of the partition, as well as the elimination or the appearance of regions that are homogeneous either spatially or in motion. The coding of the texture as well as of the partition relies on region-based motion compensation techniques. The algorithm offers a good compromise between the ability to track and manipulate objects and the coding efficiency.

Facial animation parameters extraction and expression recognition using Hidden Markov Models

Abstract The video analysis system described in this paper aims at facial expression recognition consistent with the MPEG4 standardized parameters for facial animation, FAP. For this reason, two levels of analysis are necessary: low-level analysis to extract the MPEG4 compliant parameters and high-level analysis to estimate the expression of the sequence using these low-level parameters. The low-level analysis is based on an improved active contour algorithm that uses high level information based on principal component analysis to locate the most significant contours of the face (eyebrows and mouth), and on motion estimation to track them. The high-level analysis takes as input the FAP produced by the low-level analysis tool and, by means of a Hidden Markov Model classifier, detects the expression of the sequence.

3D morphological segmentation and motion estimation for image sequences

Abstract In this paper a method for segmenting image sequnces and its application for motion estimation are presented. This method is based on a three-dimensional (3D) morphological segmentation. A 3D (i.e. two spatial dimensions plus time) approach has advantages over a 2D one, as it produces a coherent segmentation along the time dimension. Mathematical morphology is a very attractive tool for segmentation purposes because it deals with geometric features, such as size, shape, contrast or connectivity, which can be considered as object-oriented, and therefore segmentation-oriented features. The method proposed follows a purely top-down procedure, i.e. first produces a coarse segmentation in a first level and refines it in the following levels. The original image sequences are considered as functions defined on a 3D space. As a result, it will directly segment 3D regions. Furthermore, a time-recursive approach is introduced in order to deal with interactive applications, thus avoiding the drawbacks of purely 3D methods. Sequence segmentation has many applications in image sequence processing. In this paper, its application for motion analysis is discussed. As the segmentation is performed in a three-dimensional space, the produced regions are connected components in this space which can be related with moving objects. This implies a complete knowledge about the position and shape of every segmented object of the scene in every time section. From this information, an affine transformation is used within each connected component in order to estimate the parameters of motion of every region.

Motion estimation based tracking of active contours

Abstract This paper addresses the application of active contours or snakes for tracking of contours in image sequences. We propose to use the dynamic programming implementation of the snakes in order to restrict the possible candidates for a given snaxel to those that have a high correlation with the corresponding snaxel in the previous frame. Besides, we claim that, in tracking applications, the motion compensation error has to be introduced in the external energy of the snake to be able to track generic contours.

TOF imaging in Smart room environments towards improved people tracking

In this paper we present the use of time-of-flight (TOF) cameras in smart-rooms and how this leads to improved results in segmenting the people in the room from the background and consequently better 3D reconstruction of the people. A calibrated rig of one Swissranger SR3100 time-of-flight range camera and a high resolution standard camera is set in a smart-room consisting of 5 other standard cameras. A probabilistic background model is used to segment each view and a shape from silhouette 3D volume is constructed. It is shown that the presence of the range camera gives ways of eliminating regional artifacts and therefore a more robust input for higher level applications such people tracking or human motion analysis.

Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models

The use of hand gestures offers an alternative to the commonly used human computer interfaces, providing a more intuitive way of navigating among menus and multimedia applications. This paper presents a system for hand gesture recognition devoted to control windows applications. Starting from the images captured by a time-of-flight camera (a camera that produces images with an intensity level inversely proportional to the depth of the objects observed) the system performs hand segmentation as well as a low-level extraction of potentially relevant features which are related to the morphological representation of the hand silhouette. Classification based on these features discriminates between a set of possible static hand postures which results, combined with the estimated motion pattern of the hand, in the recognition of dynamic hand gestures. The whole system works in real-time, allowing practical interaction between user and application.

Coding-Oriented Segmentation of Video sequences

The importance of developing coding-oriented spatial segmentation techniques is stated. The specific problems of image sequence segmentation for coding purposes are analyzed. In order to both overcome such problems and improve the performance of segmentation-based coding schemes, a general segmentation structure is defined. This structure has five main steps: Partition projection, Image modeling, Image simplification, Marker extraction and Decision. In order to validate it, two different implementations of this structure are presented. The first utilizes a compound random field as image sequence model whereas the second relies on morphological tools.

3d morphological segmentation for image sequence processing

The aim of this paper is to present a method for segmenting image sequences. This method is based on a three dimensional (3D) morphological segmentation. As morphological tools are very efficient in order to obtain a segmentation based on the real objects of the scene, the proposed scheme extends to three dimensions a morphological segmentation method for still images. This extension arises different problems due to the fact that we are not dealing with three dimensional objects but with a sequence of images taken at discrete instants on the time dimension. Two different solutions are proposed to deal with the 3D morphological segmentation: interpolation of the lattice of the image sequence and a modification of the basic morphological tool for segmentation: the watershed algorithm.