Matching three-dimensional objects using silhouettes

A method for matching three-dimensional objects against a library of models from an observed sequence of silhouettes is presented in this correspondence. Based upon the observed silhouettes, the three-dimensional structure of the object is constructed and refined. The principal moments and three primary silhouettes are computed for the constructed three-dimensional objects to represent the aggregate and detailed structure parameters. The adaptive matching technique requires that sufficient silhouettes be added to modify the structure of the unknown object until consistent and steady matching results are obtained. The library for matching is based on three primary silhouettes of the model objects. Experiments conducted show a fast convergence to a consistent result may be achieved provided that a reasonable choice of silhouettes is made.     

Detection of edges using range information

Range data provide an important source of 3-D shape information. This information can be used to extract jump boundaries which correspond to occluding boundaries of objects in a scene and ``edges' which correspond to points lying between significantly different regions on the surface of objects. We are mainly interested in range data obtained from sensors such as lasers. The main problem with this type of range finder is the fact that the accuracy of the measurements depends on the power of the signal that reaches the receiver. This study describes how a range edge detection procedure can be designed that has low sensitivity to noise and imbeds all the knowledge available on the range measurement accuracy.     

Exploring context and content links in social media: a latent space method

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.     

Computer tracking of objects moving in space

A method is developed to represent movement of convex blocks in three-dimensional space from a sequence of two-dimensional camera images. The goals are to determine the objects' movement toward or away from the camera as well as left/right and up/down movement in the image plane and to build models of the blocks. The movement information is used as part of a hierarchical matching process that determines the correspondence of blocks between scenes.     

An interactive method for extracting grammar from programs

The grammar of the language in which some given code is written is essential for developing automated tools for maintenance, reengineering, and program analysis. Frequently grammar is available for a language but not for its variants that are implemented by various vendors and in which the given code may be written. In this work we address the problem of obtaining the grammar from source code, which can then be used for generating tools for the programs. We propose an incremental method for obtaining grammar for a particular language variant, from a set of programs written in the language variant and an approximate grammar (presumably of the standard language) with some user interaction. We also present the design of a tool for implementing this approach and our experience in working with grammars of C, C++ and COBOL. Copyright © 2004 John Wiley & Sons, Ltd.     

Soft information set for multicriteria decision making

An information source value is perceived differently by different agents. In this paper, we present a new knowledge representation structure, termed as soft information set (SIS), to provide a parameterized representation of the information values, as perceived by an agent. The properties of SIS are investigated and the notion of relations in SIS is devised. The significance of the proposed SIS in aiding multi criteria decision making is illustrated through real case-studies.     

Leading One Detection Hyperbolic CORDIC with Enhanced Range of Convergence

This paper focuses on developing an area efficient hyperbolic Coordinate Rotation Digital Computer (CORDIC) algorithm with performance improvement. The algorithm eliminates the need of scale factor calculation in the Range of Convergence (ROC). At the same time the range of convergence offered is higher than the conventional CORDIC ROC in the hyperbolic rotation mode. Being the only kind of algorithm in hyperbolic rotation with sign sequence μ?=?1 always, one complete operation requires just 5 iterations. Thus the pipelined implementation has 5 stages which provides a 50% increase in throughput in comparison to conventional CORDIC. As far as the area improvement is considered, 16-bit processor can be realized using 56% less number of full adders required by Flat-CORDIC. The x and y datapath are based on series expansion of hyperbolic functions. The complete algorithm design along with pipelined architecture implementation is detailed.     

Home Security System Using Wireless Sensors Network

Wireless Personal Communications - In this paper, we propose a smart and robust home security system. This is for intrusion detection along with a proprietary Android application. Intruder-Spi...     

Image coding based on maximum entropy partitioning for identifying improbable intensities related to facial expressions

In this paper we investigate information-theoretic image coding techniques that assign longer codes to improbable, imprecise and non-distinct intensities in the image. The variable length coding techniques when applied to cropped facial images of subjects with different facial expressions, highlight the set of low probability intensities that characterize the facial expression such as the creases in the forehead, the widening of the eyes and the opening and closing of the mouth. A new coding scheme based on maximum entropy partitioning is proposed in our work, particularly to identify the improbable intensities related to different emotions. The improbable intensities when used as a mask decode the facial expression correctly, providing an effective platform for future emotion categorization experiments.