Wavelet directional histograms for classification of human gestures represented by spatio-temporal templates
This paper evaluates the efficacy of directional information of wavelet multi-resolution decomposition for histogram-based classification of human gestures represented by spatio-temporal templates. This template collapses temporal component into gesture representation in a way that no explicit sequence matching or temporal analysis is needed, and characterizes the motion from a very high dimensional space to a low dimensional space. These templates are modified to be invariant to translation, rotation and scale. Two dimensional, 3 level dyadic wavelet transform applied on these templates results in one low pass subimage and nine highpass directional subimages. Histograms of wavelet coefficients at different scales are compared to establish significance of available information for classification. The preliminary experiments show that while the statistical properties of the template provide high level of classification accuracy, the available information in high pass or low pass decompositions by itself is not sufficient to provide significant efficiency of accuracy.

title: Recognition of human actions using moment based features and artificial neural networks
abstract: This paper presents performance of view-based approach in automated recognition of predefined hand and gross body actions using artificial neural network. This approach represents motion by a static grey scale image template computed by collapsing the temporal components into the cumulative image-difference of frames. The seven invariant Hu moments are used as the feature vectors. The performance of the system is tested in real time using feed forward multilayer perceptron (MLP) based on back propagation.

title: Evaluating synchronization on shared address space multiprocessors: methodology and performance
abstract: Synchronization is an area that exhibits rich hardware-software interactions in multiprocessors. It was studied extensively using microbenchmarks a decade ago. However, its performance implications are not well understood on modern systems or on real applications. We study the impact of synchronization primitives and algorithms on a modern, 64processor, hardware-coherent shared address space multiprocessor: the SGI Origin 2000. In addition to the actual results on a modern system, we examine the key methodological issues in studying synchronization, for both microbenchmarks and applications. We find that although the efficient hardware support (Fetch&Op) for synchronization provided on our machine usually helps lock and barrier microbenchmarks, it does not help in improving application performance when compared to good software algorithms that use the processor-provided LL-SC instructions. This is true even in applications that spend a significant amount of time in synchronization operations. More elaborate hardware support is unlikely to have a significant benefit either. From the applications’ perspective, it is usually the waiting time due to load imbalance or serialization that dominates synchronization time, not the overhead of the synchronization operations themselves, even in apparently balanced cases where the overhead may be expected to be substantial.

title: An observation-constrained generative approach for probabilistic classification of image regions
abstract: In this paper, we propose a probabilistic region classification scheme for natural scene images. In conventional generative methods, a generative model is learnt for each class using all the available training data belonging to that class. However, if an input image has been generated from only a subset of the model support, use of the full model to assign generative probabilities can produce serious artifacts in the probability assignments. This problem arises mainly when the different classes have multimodal distributions with considerable overlap in the feature space. We propose an approach to constrain the class generative probability of a set of newly observed data by exploiting the distribution of the new data itself and using linear weighted mixing. A Kullback–Leibler Divergence-based fast model selection procedure is also proposed for learning mixture models in a low dimensional feature space. The preliminary results on the natural scene images support the effectiveness of the proposed approach.

title: A combinatorial approach for studying LOCC transformations of multipartite states
abstract: We develop graph theoretic methods for analysing maximally entangled pure states distributed between a number of different parties. We introduce a technique called {\it bicolored merging}, based on the monotonicity feature of entanglement measures, for determining combinatorial conditions that must be satisfied for any two distinct multiparticle states to be comparable under local operations and classical communication (LOCC). We present several results based on the possibility or impossibility of comparability of pure multipartite states. We show that there are exponentially many such entangled multipartite states among $n$ agents. Further, we discuss a new graph theoretic metric on a class of multi-partite states, and its implications.

title: Using model checking to debug device firmware
abstract: Device firmware is a piece of concurrent software that achieves high performance at the cost of software complexity. They contain subtle race conditions that make them difficult to debug using traditional debugging techniques. The problem is further compounded by the lack of debugging support on the devices. This is a serious problem because the device firmware is trusted by the operating system.Model checkers are designed to systematically verify properties of concurrent systems. Therefore, model checking is a promising approach to debugging device firmware. However, model checking involves an exponential search. Consequently, the models have to be small to allow effective model checking.This paper describes the abstraction techniques used by the ESP compiler to extract abstract models from device firmware written in ESP. The abstract models are small because they discard some of the details in the firmware that is irrelevant to the particular property being verified. The programmer is required to specify the abstractions to be performed. The ESP compiler uses the abstraction specification to extract models conservatively. Therefore, every bug in the original program will be present in the extracted model.This paper also presents our experience with using Spin model checker to develop and debug VMMC firmware for the Myrinet network interfaces.

title: A hierarchical field framework for unified context-based classification
abstract: We present a two-layer hierarchical formulation to exploit different levels of contextual information in images for robust classification. Each layer is modeled as a conditional field that allows one to capture arbitrary observation-dependent label interactions. The proposed framework has two main advantages. First, it encodes both the short-range interactions (e.g., pixelwise label smoothing) as well as the long-range interactions (e.g., relative configurations of objects or regions) in a tractable manner. Second, the formulation is general enough to be applied to different domains ranging from pixelwise image labeling to contextual object detection. The parameters of the model are learned using a sequential maximum-likelihood approximation. The benefits of the proposed framework are demonstrated on four different datasets and comparison results are presented