Detection and Tracking of Multiple,Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors

Detection and tracking of humans in video streams is important for many applications. We present an approach to automatically detect and track multiple, possibly partially occluded humans in a walking or standing pose from a single camera, which may be stationary or moving. A human body is represented as an assembly of body parts. Part detectors are learned by boosting a number of weak classifiers which are based on edgelet features. Responses of part detectors are combined to form a joint likelihood model that includes an analysis of possible occlusions. The combined detection responses and the part detection responses provide the observations used for tracking. Trajectory initialization and termination are both automatic and rely on the confidences computed from the detection responses. An object is tracked by data association and meanshift methods. Our system can track humans with both inter-object and scene occlusions with static or non-static backgrounds. Evaluation results on a number of images and videos and comparisons with some previous methods are given. Electronic Supplementary Material Supplementary material is available in the online version of this article at     

Automatic Lighting Design using a Perceptual Quality Metric

Lighting has a crucial impact on the appearance of 3D objects and on the ability of an image to communicate information about a 3D scene to a human observer. This paper presents a new automatic lighting design approach for comprehensible rendering of 3D objects. Given a geometric model of a 3D object or scene, the material properties of the surfaces in the model, and the desired viewing parameters, our approach automatically determines the values of various lighting parameters by optimizing a perception-based image quality objective function. This objective function is designed to quantify the extent to which an image of a 3D scene succeeds in communicating scene information, such as the 3D shapes of the objects, fine geometric details, and the spatial relationships between the objects.
Our results demonstrate that the proposed approach is an effective lighting design tool, suitable for users without expertise or knowledge in visual perception or in lighting design.     

Indexing,Elaboration and Refinement: Incremental Learning of Explanatory Cases

This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner's memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good lessons to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices. We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program.     

A study of the oxidation kinetics of CuFeO2 in the Cu-Fe-O system

The oxidation kinetics of CuFeO₂ in the Cu-Fe-O system have been studied between 500 and 900° C in an atmosphere containing 1 vol% oxygen in a nitrogen stream using thermogravimetric analysis (TGA). It was found that addition of Fe₂O₃ to the CuFeO₂ caused a decrease in the oxidation rate while addition of CuO caused an increase. On increasing the concentration of Fe₂O₃ the activation energy was found to increase from ~ 18 kcal mol^–1 to ~ 45 kcal mol^–1 and the exponent n in Avrami's equationf=1-exp (–kt ⁿ) was also observed to increase, from 1.3 to 2.3. On adding CuO to the CuFeO₂ in the Cu-Fe-O system the activation energy decreased from ~ 18 kcal mol^–1 to ~ 8 kcal mol^–1. The variation in both values indicates changes in the oxidation mechanisms. The microstructural changes associated with oxidation have been studied using optical microscopy. A model has been proposed to explain the results.     

F-MPJ: scalable Java message-passing communications on parallel systems

This paper presents F-MPJ (Fast MPJ), a scalable and efficient Message-Passing in Java (MPJ) communication middleware for parallel computing. The increasing interest in Java as the programming language of the multi-core era demands scalable performance on hybrid architectures (with both shared and distributed memory spaces). However, current Java communication middleware lacks efficient communication support. F-MPJ boosts this situation by: (1) providing efficient non-blocking communication, which allows communication overlapping and thus scalable performance; (2) taking advantage of shared memory systems and high-performance networks through the use of our high-performance Java sockets implementation (named JFS, Java Fast Sockets); (3) avoiding the use of communication buffers; and (4) optimizing MPJ collective primitives. Thus, F-MPJ significantly improves the scalability of current MPJ implementations. A performance evaluation on an InfiniBand multi-core cluster has shown that F-MPJ communication primitives outperform representative MPJ libraries up to 60 times. Furthermore, the use of F-MPJ in communication-intensive MPJ codes has increased their performance up to seven times.     

Multiobjective hierarchical control architecture for greenhouse crop growth

The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.     

A Haar wavelet quasilinearization approach for numerical simulation of Burgers’ equation

In this paper, an efficient numerical scheme based on uniform Haar wavelets and the quasilinearization process is proposed for the numerical simulation of time dependent nonlinear Burgers’ equation. The equation has great importance in many physical problems such as fluid dynamics, turbulence, sound waves in a viscous medium etc. The Haar wavelet basis permits to enlarge the class of functions used so far in the collocation framework. More accurate solutions are obtained by wavelet decomposition in the form of a multi-resolution analysis of the function which represents a solution of boundary value problems. The accuracy of the proposed method is demonstrated by three test problems. The numerical results are compared with existing numerical solutions found in the literature. The use of the uniform Haar wavelet is found to be accurate, simple, fast, flexible, convenient and has small computation costs.     

Support and precursor effects on the preparation of new heterogenized Pt/Sn catalysts for the selective hydroformylation of 1-pentene

New heterogenized Pt/Sn catalysts selective for the hydroformylation of 1-pentene have been synthesized. The complex cis-[PtCl₂(PPh₃)₂] and the SnCl₂.2H₂O or SnC₂O₄ precursors have been anchored on silica-, magnesia- and alumina-carriers. X-ray photoelectron spectroscopy was used to determine the surface composition and the nature of the anchored species. The hydroformylation activity was found to depend on the type of support and tin precursor used. Only the silica supported catalysts were active in the hydroformylation reaction. Samples prepared from SnCl₂-2H₂O were 200-fold more active than those prepared from SnC₂O₄. Selectivity ton-hexanal of the silica-supported catalyst prepared from SnCl₂-2H₂O was as high as 94.4% at 39.2% conversion of 1-pentene.     

Network Discovery and Verification

Due to its fast, dynamic, and distributed growth process, it is hard to obtain an accurate map of the Internet. In many cases, such a map-representing the structure of the Internet as a graph with nodes and links-is a prerequisite when investigating properties of the Internet. A common way to obtain such maps is to make certain local measurements at a small subset of the nodes, and then to combine these in order to "discover" (an approximation of) the actual graph. Each of these measurements is potentially quite costly. It is thus a natural objective to minimize the number of measurements which still discover the whole graph. We formalize this problem as a combinatorial optimization problem and consider it for two different models characterized by different types of measurements. We give several upper and lower bounds on the competitive ratio (for the online network discovery problem) and the approximation ratio (for the offline network verification problem) in both models. Furthermore, for one of the two models, we compare four simple greedy strategies in an experimental analysis     

Orthographic Structure Versus Morphological Structure: Principles of Lexical Organization in a Given Language.

Most models of visual word recognition in alphabetic orthographies assume that words are lexically organized according to orthographic similarity. Support for this is provided by form-priming experiments that demonstrate robust facilitation when primes and targets share similar sequences of letters. The authors examined form-orthographic priming effects in Hebrew, Arabic, and English. Hebrew and Arabic have an alphabetic writing system but a Semitic morphological structure. Hebrew morphemic units are composed of noncontiguous phonemic (and letter) sequences in a given word. Results demonstrate that form-priming effects in Hebrew or Arabic are unreliable, whereas morphological priming effects with minimal letter overlap are robust. Hebrew bilingual subjects, by contrast, showed robust form-priming effects with English material, suggesting that Semitic words are lexically organized by morphological rather than orthographic principles. The authors conclude that morphology can constrain lexical organization even in alphabetic orthographies and that visual processing of words is first determined by morphological characteristics. (PsycINFO Database Record (c) 2010 APA, all rights reserved)