121937998
3D human pose from silhouettes by relevance vector regression
We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body pans in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouette segmentation errors, silhouette shape is encoded by histogram-of-shape-contexts descriptors. For the main regression, we evaluate both regularized least squares and relevance vector machine (RVM) regressors over both linear and kernel bases. The RVM's provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. For realism and good generalization with respect to viewpoints, we train the regressors on images resynthesized from real human motion capture data, and test it both quantitatively on similar independent test data, and qualitatively on a real image sequence. Mean angular errors of 6-7 degrees are obtained - a factor of 3 better than the current state of the art for the much simpler upper body problem.
958128
The Panum Proxy Algorithm for Dense Stereo Matching over a Volume of Interest
Stereo matching algorithms conventionally match over a range of disparities sufficient to encompass all visible 3D scene points. Human vision however does not do this. It works over a narrow band of disparities - Panum’s fusional band - whose typical range may be as little as 1/20 of the full range of disparities for visible points. Points inside the band are fused visually and the remainder of points are seen as "diplopic" - that is with double vision. The Panum band restriction is important also in machine vision, both with active (pan/tilt) cameras, and with high resolution cameras and digital pan/tilt.
2887541
Recovering 3D human pose from monocular images
We describe a learning-based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labeling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouette segmentation errors, silhouette shape is encoded by histogram-of-shape-contexts descriptors. We evaluate several different regression methods: ridge regression, relevance vector machine (RVM) regression, and support vector machine (SVM) regression over both linear and kernel bases. The RVMs provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. The loss of depth and limb labeling information often makes the recovery of 3D pose from single silhouettes ambiguous. To handle this, the method is embedded in a novel regressive tracking framework, using dynamics from the previous state estimate together with a learned regression value to disambiguate the pose. We show that the resulting system tracks long sequences stably. For realism and good generalization over a wide range of viewpoints, we train the regressors on images resynthesized from real human motion capture data. The method is demonstrated for several representations of full body pose, both quantitatively on independent but similar test data and qualitatively on real image sequences. Mean angular errors of 4-6/spl deg/ are obtained for a variety of walking motions.
9880007
A Local Basis Representation for Estimating Human Pose from Cluttered Images
Recovering the pose of a person from single images is a challenging problem. This paper discusses a bottom-up approach that uses local image features to estimate human upper body pose from single images in cluttered backgrounds. The method takes the image window with a dense grid of local gradient orientation histograms, followed by non negative matrix factorization to learn a set of bases that correspond to local features on the human body, enabling selective encoding of human-like features in the presence of background clutter. Pose is then recovered by direct regression. This approach allows us to key on gradient patterns such as shoulder contours and bent elbows that are characteristic of humans and carry important pose information, unlike current regressive methods that either use weak limb detectors or require prior segmentation to work. The system is trained on a database of images with labelled poses. We show that it estimates pose with similar performance levels to current example-based methods, but unlike them it works in the presence of natural backgrounds, without any prior segmentation.
34027055
Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach
Abstract We propose an improvement-heuristic approach for the general flow-shop problem (n/m/Cmax) based on the idea of adaptive learning. The approach employs a one-pass heuristic to give a good starting solution in the search space and uses a weight parameter to perturb the data of the original problem to obtain improved solutions. The weights are then adjusted employing a learning strategy which involves reinforcement and backtracking. The learning is similar to that in neural networks. The random perturbation allows a non-deterministic local search. We apply the improvement-heuristic approach in conjunction with three well-known heuristics in the literature, namely, Palmer’s Slope Index, CDS and NEH. We test our approach on several benchmark problem sets including Taillard’s, Carlier’s, Heller’s and Reeves’. We compare our results to the best-known upper-bound solutions and find that for many problems we match the best-known upper bound. For one problem we discover a new upper bound.
34654890
An efficient recovery procedure for fault tolerance in distributed systems
Abstract In this article, the problem of fault tolerance in real-time distributed systems is addressed by providing an efficient algorithmic procedure for recovery in such systems. Our recovery procedure does not require the application of an intrusive checkpointing procedure, but uses contextual information exchanged between the distributed system components during normal system progress. On detection of a failure, each process, through an efficient propagation mechanism, will have locally computed a recovery point that is mutually consistent with the recovery points computed by other processes. During recovery, each process considers the contextual information recorded after its established recovery point; a minimal amount of computations are redone. Also, a minimal number of processes are required to roll back, and the time needed to revert to normal operation is found to be minimal, thus contributing to the real-time requirements for such systems. Furthermore, the stable storage requirement needed to implement our recovery procedure is bounded and minimal. Proofs of correctness of our procedure are provided, in particular, the absence of orphan messages, message losses, and duplications is shown. An example illustrating our recovery procedure is also provided.
21945943
Heuristics and augmented neural networks for task scheduling with non-identical machines
Abstract We propose new heuristics along with an augmented-neural-network (AugNN) formulation for solving the makespan minimization task-scheduling problem for the non-identical machine environment. We explore four task and three machine-priority rules, resulting in 12 combinations of single-pass heuristics. The task-priority rules are Highest-Level-First (HLF), Highest-Total-Remaining-Processing-Time-First (HTRPTF), Smallest-Latest-Finish-Time-First (SLFTF) and Minimum-Slack-First (MSF). For machine priority, we propose a greedy rule called Fastest-Available-Machine-First (FAMF), and two non-greedy rules: (1) Fastest-Available-Machine-First-With-Conditional-Wait-1 (FAMF-CW-1), and (2) Fastest-Available-Machine-First-With-Conditional-Wait-2 (FMF-CW-2). The AugNN approach integrates neural-network learning with domain and problem specific knowledge through heuristics, to produce good results. A single-pass heuristic is embedded in a neural network designed specifically for each problem. We give the AugNN formulation for each of the 12 heuristics and show computational results on 100 randomly generated problems of sizes ranging from 20 to 70 tasks and 2 to 5 machines. Results demonstrate that AugNN provides significant improvement over single-pass heuristics. The reduction in the gap between the obtained solution and the lower-bound due to AugNN over single-pass heuristics ranged from 24.4% to 50%. As far as heuristics, the non-greedy machine-priority rules performed significantly better than the greedy rule. The average gaps for the non-greedy rules ranged from 16.1% to 23.5% compared to 33.7% to 40.4% for greedy. For AugNN, for non-greedy rules, the gap ranged from 11.5% to 15.5% compared to 18.4% to 25.0% for greedy. The HLF and HTRPTF task priority rules performed better than the other two. The HTRPTF/FAMF-CW-1 combination gave the best results, closely followed by HLF/FAMF-CW-2 and HTRPTF/FAMF-CW-2 combinations.
27266792
A unified approach to fault-tolerance in communication protocols based on recovery procedures
Discusses fault tolerance in computer communication protocols, modeled by communicating finite state machines, by providing an efficient algorithmic procedure for recovery in such systems. Even when the communication network is reliable and maintains the order of messages, any kind of transient error that may not be detected immediately could contaminate the system, resulting in protocol failure. To achieve fault-tolerance, the protocol must be able to detect the error, and then it must recover from that error and eventually reach a legal (or consistent) state, and resume its normal execution. A protocol that possesses the latter feature of recovering and continuing its execution starting from a legal state is also called a self-stabilizing protocol. Our recovery procedure does not require the application of an intrusive checkpointing procedure. The stable storage requirement for each process is less than that required for other proposed recovery procedures. The recovery procedure provides us with a legal protocol state, which is the global state before reaching any illegal state and before the effects of the error make other states illegal. Only a minimal number of processes affected by error propagation are required to rollback. Our recovery procedure can be used to recover from any number of transient errors in the system. Our recovery procedure has also been modeled in PROMELA, a language to describe validation models, which shows the syntactic correctness of our recovery protocol design. Finally, our procedure is compared with the existing approaches of handing the errors, and an illustrative example is provided.
6169933
Learning Parameters in Entity Relationship Graphs from Ranking Preferences
Semi-structured entity-relation (ER) data graphs have diverse node and edge types representing entities (paper, person, company) and relations (wrote, works for). In addition, nodes contain text snippets. Extending from vector-space information retrieval, we wish to automatically learn ranking function for searching such typed graphs. User input is in the form of a partial preference order between pairs of nodes, associated with a query. We present a unified model for ranking in ER graphs, and propose an algorithm to learn the parameters of the model. Experiments with carefully-controlled synthetic data as well as real data (garnered using CiteSeer, DBLP and Google Scholar) show that our algorithm can satisfy training preferences and generalize to test preferences, and estimate meaningful model parameters that represent the relative importance of ER types.