Decision tree classification of land cover from remotely sensed data

A survey of shape analysis techniques

This article presents a review of the methods used in recognition and analysis of the human gait from three different approaches: image processing, floor sensors and sensors placed on the body. Progress in new technologies has led the development of a series of devices and techniques which allow for objective evaluation, making measurements more efficient and effective and providing specialists with reliable information. Firstly, an introduction of the key gait parameters and semi-subjective methods is presented. Secondly, technologies and studies on the different objective methods are reviewed. Finally, based on the latest research, the characteristics of each method are discussed. 40% of the reviewed articles published in late 2012 and 2013 were related to non-wearable systems, 37.5% presented inertial sensor-based systems, and the remaining 22.5% corresponded to other wearable systems. An increasing number of research works demonstrate that various parameters such as precision, conformability, usability or transportability have indicated that the portable systems based on body sensors are promising methods for gait analysis.

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Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications.

Rough set methods in feature selection and recognition

Techniques from the image and signal processing domain can be successfully applied to designing, modifying, and adapting animated motion. For this purpose, we introduce multiresolution motion filtering, multitarget motion interpolation with dynamic timewarping, waveshaping and motion displacement mapping. The techniques are well-suited for reuse and adaptation of existing motion data such as joint angles, joint coordinates or higher level motion parameters of articulated figures with many degrees of freedom. Existing motions can be modified and combined interactively and at a higher level of abstraction than conventional systems support. This general approach is thus complementary to keyframing, motion capture, and procedural animation.

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Motion signal processing

Principles of Computer Vision. Three-Dimensional Shape Representation. Three-Dimensional Shape Recovery from Line Drawings. Recovery of 3D Shape of Curved Objects. Surface Reflection Mechanism. Extracting Shape from Shading. Range Image Analysis. Stereo Vision. Machine Learning of Computer Vision Algorithms. Image Sequence Analysis for 3D Perception of Dynamic Scenes. Nonrigid Motion Analysis. Analysis and Synthesis of Human Movement. Relational Matching. Three-Dimensional Object Recognition. Fundamental Principles of Robot Vision. Chapter References.

Tzay Y. Young

Papers

Handbook of pattern recognition and image processing