An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation

An appearance-based framework for 3D hand shape classification and simultaneous camera viewpoint estimation is presented. Given an input image of a segmented hand, the most similar matches from a large database of synthetic hand images are retrieved. The ground truth labels of those matches, containing hand shape and camera viewpoint information, are returned by the system as estimates for the input image. Database retrieval is done hierarchically, by first quickly rejecting the vast majority of all database views, and then ranking the remaining candidates in order of similarity to the input. Four different similarity measures are employed, based on edge location, edge orientation, finger location and geometric moments.

Treatment of HIV/AIDS at South Africa's Largest Employers: Myth and Reality

Background: In the past three years, many large employers in South Africa have announced publicly their intention of making antiretroviral treatment (ART) available to employees. Reports of the scope and success of these programs have been mostly anecdotal. This study surveyed the largest private sector employers in South Africa to determine the proportion of employees with access to ART through employer-sponsored HIV/AIDS treatment programs. Methods: All 64 private sector and parastatal employers in South Africa with more than 6,000 employees were identified and contacted. Those that agreed to participate were interviewed by telephone using a structured questionnaire. Results: 52 companies agreed to participate. Among these companies, 63% of employees had access to employer-sponsored care and treatment for HIV/AIDS. Access varied widely by sector, however. Approximately 27% of suspected HIV-positive employees were enrolled in HIV/AIDS disease management programs, or 4.4% of the workforce overall. Fewer than 4,000 employees in the entire sample were receiving antiretroviral therapy. In-house (employer) disease management programs and independent disease management programs achieved higher uptake of services than did medical aid schemes. Conclusions: Publicity by large employers about their treatment programs should be interpreted cautiously. While there is a high level of access to treatment, uptake of services is low and only a small fraction of employees medically eligible for antiretroviral therapy are receiving it.

B Cell Activation in Insulin Resistance and Obesity

Our group has demonstrated that inflammatory diseases such as type 2 diabetes (DM), inflammatory bowel disease (IBD), and periodontal disease (PD) are associated with altered B cell function that may contribute to disease pathogenesis. B cells were found to be highly activated with characteristics of inflammatory cells. Obesity is a pre-disease state for cardiovascular disease and type 2 diabetes and is considered a state of chronic inflammation. Therefore, we sought to better characterize B cell function and phenotype in obese patients. We demonstrate that (Toll-like receptor) TLR4 and CD36 expression by B cells is elevated in obese subjects, suggesting increased sensing of lipopolysaccharide (LPS) and other TLR ligands. These ligands may be of microbial, from translocation from a leaky gut, or host origin. To better assess microbial ligand burden and host response in the bloodstream, we measured LPS binding protein (LBP), bacterial/permeability increasing protein (BPI), and high mobility group box 1 (HMGB1). Thus far, our data demonstrate an increase in LBP in DM and obesity indicating increased responses to TLR ligands in the blood. Interestingly, B cells responded to certain types of LPS by phosphorylating extracellular-signal-regulated kinases (ERK) 1/2. A better understanding of the immunological state of obesity and the microbial and endogenous TLR ligands that may be activating B cells will help identify novel therapeutics to reduce the risk of more dangerous conditions, such as cardiovascular disease.

Automatic 2D Hand Tracking in Video Sequences

In gesture and sign language video sequences, hand motion tends to be rapid, and hands frequently appear in front of each other or in front of the face. Thus, hand location is often ambiguous, and naive color-based hand tracking is insufficient. To improve tracking accuracy, some methods employ a prediction-update framework, but such methods require careful initialization of model parameters, and tend to drift and lose track in extended sequences. In this paper, a temporal filtering framework for hand tracking is proposed that can initialize and reset itself without human intervention. In each frame, simple features like color and motion residue are exploited to identify multiple candidate hand locations. The temporal filter then uses the Viterbi algorithm to select among the candidates from frame to frame. The resulting tracking system can automatically identify video trajectories of unambiguous hand motion, and detect frames where tracking becomes ambiguous because of occlusions or overlaps. Experiments on video sequences of several hundred frames in duration demonstrate the system's ability to track hands robustly, to detect and handle tracking ambiguities, and to extract the trajectories of unambiguous hand motion.

Modal Matching for Correspondence and Recognition

Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition and categorization, where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects. In general, modes provide a global-to-local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison. In contrast to previous techniques, which required correspondence to be computed with an initial or prototype shape, modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information. This improved formulation provides greater generality and accuracy, and is applicable to data of any dimensionality. Correspondence results with 2-D contour and point feature data are shown, and recognition experiments with 2-D images of hand tools and airplanes are described.

3D Hand Pose Estimation by Finding Appearance-Based Matches in a Large Database of Training Views

Ongoing work towards appearance-based 3D hand pose estimation from a single image is presented. A large database of synthetic hand views is generated using a 3D hand model and computer graphics. The views display different hand shapes as seen from arbitrary viewpoints. Each synthetic view is automatically labeled with parameters describing its hand shape and viewing parameters. Given an input image, the system retrieves the most similar database views, and uses the shape and viewing parameters of those views as candidate estimates for the parameters of the input image. Preliminary results are presented, in which appearance-based similarity is defined in terms of the chamfer distance between edge images.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.