A review on image segmentation techniques

http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shots matching and credit card verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges. Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different sets of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exists no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a detailed overview of move than 20 years of research done in the engineering community. Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. Global transform and feature based methods using statistical, structural and neural classifiers are summarized. >

Human and machine recognition of faces: a survey

There are now a wide Abstract There are now a wide variety of image segmentation techniques, some considered general purpose and some designed for specific classes of images. These techniques can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid linkage region growing schemes, centroid linkage region growing schemes, spatial clustering schemes, and split-and-merge schemes. In this paper, we define each of the major classes of image segmentation techniques and describe several specific examples of each class of algorithm. We illustrate some of the techniques with examples of segmentations performed on real images.

Image Segmentation Techniques

http://vision.ucsd.edu/~sagarwal/exam/black_anandan_1996.pdf

The Robust Estimation of Multiple Motions

The detection of moving objects is important in many tasks. This paper examines moving object detection based primarily on optical flow. We conclude that in realistic situations, detection using visual information alone is quite difficult, particularly when the camera may also be moving. The availability of additional information about camera motion and/or scene structure greatly simplifies the problem. Two general classes of techniques are examined. The first is based upon the motion epipolar constraint—translational motion produces a flow field radially expanding from a “focus of expansion” (FOE). Epipolar methods depend on knowing at least partial information about camera translation and/or rotation. The second class of methods is based on comparison of observed optical flow with other information about depth, for example from stereo vision. Examples of several of these techniques are presented.

Detecting moving objects

http://www.inf.u-szeged.hu/~kato/papers/ivc2001.pdf

A markov random field image segmentation model for color textured images

This paper presents new approaches in characterizing and segmenting the content of video. These approaches are developed based upon the pattern analysis of spatio-temporal slices. While traditional approaches to motion sequence analysis tend to formulate computational methodologies on two or three adjacent frames, spatio-temporal slices provide rich visual patterns along a larger temporal scale. We first describe a motion computation method based on a structure tensor formulation. This method encodes visual patterns of spatio-temporal slices in a tensor histogram, on one hand, characterizing the temporal changes of motion over time, on the other hand, describing the motion trajectories of different moving objects. By analyzing the tensor histogram of an image sequence, we can temporally segment the sequence into several motion coherent subunits, in addition, spatially segment the sequence into various motion layers. The temporal segmentation of image sequences expeditiously facilitates the motion annotation and content representation of a video, while the spatial decomposition of image sequences leads to a prominent way of reconstructing background panoramic images and computing foreground objects.

/pdf/motion-analysis-and-segmentation-through-spatio-temporal-2i5lkyonuo.pdf

Motion analysis and segmentation through spatio-temporal slices processing

Based on the analysis of temporal slices, we propose novel approaches for clustering and retrieval of video shots. Temporal slices are a set of two-dimensional (2-D) images extracted along the time dimension of an image volume. They encode rich set of visual patterns for similarity measure. In this paper, we first demonstrate that tensor histogram features extracted from temporal slices are suitable for motion retrieval. Subsequently, we integrate both tensor and color histograms for constructing a two-level hierarchical clustering structure. Each cluster in the top level contains shots with similar color while each cluster in bottom level consists of shots with similar motion. The constructed structure is then used for the cluster-based retrieval. The proposed approaches are found to be useful particularly for sports games, where motion and color are important visual cues when searching and browsing the desired video shots.

/pdf/on-clustering-and-retrieval-of-video-shots-through-temporal-3y9wbguti0.pdf

On clustering and retrieval of video shots through temporal slices analysis

We present a novel approach for video partitioning by detecting three essential types of camera breaks, namely cuts, wipes, and dissolves. The approach is based on the analysis of temporal slices which are extracted from the video by slicing through the sequence of video frames and collecting temporal signatures. Each of these slices contains both spatial and temporal information from which coherent regions are indicative of uninterrupted video partitions separated by camera breaks. Properties could further be extracted from the slice for both the detection and classification of camera breaks. For example, cut and wipes are detected by color-texture properties, while dissolves are detected by statistical characteristics. The approach has been tested by extensive experiments.

/pdf/video-partitioning-by-temporal-slice-coherency-hkt7xtiiyg.pdf

Ting-Chuen Pong

Papers

Detecting moving objects

A markov random field image segmentation model for color textured images

Motion analysis and segmentation through spatio-temporal slices processing

On clustering and retrieval of video shots through temporal slices analysis

Video partitioning by temporal slice coherency