6663815
Mesh segmentation schemes for error resilient coding of 3-D graphic models
Most existing coding techniques for three-dimensional (3-D) graphic models focus on coding efficiency. Due to irregular structure of the 3-D mesh and the use of variable length entropy codes, channel errors often propagate in the coded bitstream and severely distort the decoded model. By segmenting a 3-D graphic model (or its connected components) into small pieces, the impact of channel errors is confined to directly corrupted pieces rather than the whole mesh. The mesh segmentation schemes should be compatible with the underlying encoding techniques to achieve the low computational and coding overhead. In this research, we examine four mesh segmentation schemes, i.e., multiseed traversal, threshold traversal, morphing-based volume splitting, and content-based segmentation, and apply them to the context of error resilient mesh coding based on the constructive traversal coding technique. The advantages and shortcomings of each segmentation method are discussed. These schemes segment a mesh into pieces according to the target piece size that is determined according to the channel error rate.
14903141
Error-resilient coding of 3-D graphic models via adaptive mesh segmentation
Current coding techniques for 3-D graphic models mainly focus on coding efficiency, which makes them extremely sensitive to channel errors due to the irregular mesh structure. We introduce a new approach for error-resilient coding of arbitrary 3-D graphic models by extending the error-free constructive traversal compression scheme proposed by Li and Kuo (see MPEG-4 Tokyo Meeting, 1998, Contribution Doc. M3324, and Proc. IEEE, vol.86, p.1052-63, 1998). A 3-D mesh of an arbitrary structure is partitioned into pieces of a smaller uniform size with joint boundaries. The size of a piece is determined adaptively based on the channel error rate. The topology and geometry information of each joint boundary and each piece of a connected component is coded independently. The coded topology and first several important bit-planes of the joint-boundary data are protected against channel errors by using the Bose-Chaudhuri-Hocquenghem error-correcting code. At the decoder, each piece is decoded and checked for channel errors. The decoded joint-boundary information is used to perform data recovery and error concealment on the corrupted piece data. All decoded pieces are combined together according to their configuration to reconstruct all connected components of the complete 3-D model. Our experiments demonstrate that the proposed approach has excellent error resiliency at a reasonable bit-rate overhead. The techniques is also capable of incrementally rendering one connected component of the 3-D model at a time.
34159357
A P1500 compliant programmable BistShell for embedded memories
We describe the design and implementation of an IEEE P1500 compliant programmable BIST for embedded memories. The proposed design can be embedded in other cores or systems with minimum test generation or test application overhead. The programmability of our BIST is useful when the algorithm is being refined while the memory architecture is under production. A variety of test algorithms can be implemented with the programmability provided in our design with no change to the BIST hardware. As an example we demonstrate the implementation of an algorithm to detect open decoder faults. This example is shown for its didactic content as it brings out the programmable axis of our design. Our design also offers means to perform dedicated delay tests as well as scan tests for diagnosis. We show by synthesis experiments that the extra area cost for the BIST hardware is relatively small for medium to large memories.
6958332
A hierarchical field framework for unified context-based classification
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
25064321
TimeTree: a public knowledge-base of divergence times among organisms
UNLABELLED
Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature.


AVAILABILITY
TimeTree is available at http://www.timetree.net
207251944
Discriminative Random Fields
In this research we address the problem of classification and labeling of regions given a single static natural image. Natural images exhibit strong spatial dependencies, and modeling these dependencies in a principled manner is crucial to achieve good classification accuracy. In this work, we present Discriminative Random Fields (DRFs) to model spatial interactions in images in a discriminative framework based on the concept of Conditional Random Fields proposed by lafferty et al.(2001). The DRFs classify image regions by incorporating neighborhood spatial interactions in the labels as well as the observed data. The DRF framework offers several advantages over the conventional Markov Random Field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in computer vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used for modeling observations in the MRF framework. Third, the interaction in labels in DRFs is based on the idea of pairwise discrimination of the observed data making it data-adaptive instead of being fixed a priori as in MRFs. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where the likelihood parameters are usually learned separately from the field parameters. We present preliminary experiments with man-made structure detection and binary image restoration tasks, and compare the DRF results with the MRF results.
17681919
Repairing CAD models
We describe an algorithm for repairing polyhedral CAD models that have errors in their B-REP. Errors like cracks, degeneracies, duplication, holes and overlaps are usually introduced in solid models due to imprecise arithmetic, model transformations, designer errors, programming bugs, etc. Such errors often hamper further processing such as finite element analysis, radiosity computation and rapid prototyping. Our fault-repair algorithm converts an unordered collection of polygons to a shared-vertex representation to help eliminate errors. This is done by choosing, for each polygon edge, the most appropriate edge to unify it with. The two edges are then geometrically merged into one, by moving vertices. At the end of this process, each polygon edge is either coincident with another or is a boundary edge for a polygonal hole or a dangling wall and may be appropriately repaired. Finally, in order to allow user-inspection of the automatic corrections, we produce a visualization of the repair and let the user mark the corrections that conflict with the original design intent. A second iteration of the correction algorithm then produces a repair that is commensurate with the intent. This, by involving the users in a feedback loop, we are able to refine the correction to their satisfaction.
195347321
Fast Paths in Concurrent Programs