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.
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.
39910433
Augmented neural networks for task scheduling
Abstract We propose a new approach, called Augmented Neural Networks (AugNN) for solving the task-scheduling problem. This approach is a hybrid of the heuristic and the neural networks approaches. While retaining all the advantages of the heuristic approach, AugNN incorporates learning, to find improved solutions iteratively. This new framework maps the problem structure to a neural network and utilizes domain specific knowledge for finding solutions. The problem we address is that of minimizing the makespan in scheduling n tasks on m machines where the tasks follow a precedence relation and task pre-emption is not allowed. Solutions obtained from AugNN using various learning rules are compared with six different commonly used heuristics. AugNN approach provides significant improvements over heuristic results. In just a few iterations, the gap between the lower bound and the obtained solution is reduced by as much as 58% for some heuristics, without any increase in computational complexity. While the heuristics found solutions in the range of 5.8–26.9% of the lower bound, on average, AugNN found solutions in the range of 3.3–11.1%, a significant improvement.
117080125
Logarithmic Regret Algorithms for Online Convex Optimization
In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., chooses a sequence of points in Euclidean space, from a fixed feasible set. After each point is chosen, it encounters a sequence of (possibly unrelated) convex cost functions. Zinkevich [Zin03] introduced this framework, which models many natural repeated decision-making problems and generalizes many existing problems such as Prediction from Expert Advice and Cover’s Universal Portfolios. Zinkevich showed that a simple online gradient descent algorithm achieves additive regret$O({\sqrt{T}})$, for an arbitrary sequence of T convex cost functions (of bounded gradients), with respect to the best single decision in hindsight. 
 
In this paper, we give algorithms that achieve regret O(log(T)) for an arbitrary sequence of strictly convex functions (with bounded first and second derivatives). This mirrors what has been done for the special cases of prediction from expert advice by Kivinen and Warmuth [KW99], and Universal Portfolios by Cover [Cov91]. We propose several algorithms achieving logarithmic regret, which besides being more general are also much more efficient to implement. 
 
The main new ideas give rise to an efficient algorithm based on the Newton method for optimization, a new tool in the field. Our analysis shows a surprising connection to follow-the-leader method, and builds on the recent work of Agarwal and Hazan [AH05]. We also analyze other algorithms, which tie together several different previous approaches including follow-the-leader, exponential weighting, Cover’s algorithm and gradient descent.
6368187
Algorithms for derivation of structurally stable Hamiltonian signed graphs
A graph Sp,q,n refers to a signed graph with p nodes and q edges with n being the number of negative edges. We introduce two theorems to facilitate identification of the complete set of balanced signed graph configurations for any p-node Hamiltonian signed graph in terms of p, q and n. This allows for the development of computational procedures to efficiently determine the structural stability of a signed graph. This is potentially useful for the planning and analysis of complex situations or scenarios which can be depicted as signed graphs. Through the application of the theorems, the state of balance of a signed graph structure or its affinity towards balance can be determined in a more time-efficient manner compared to any explicit enumeration algorithm. E-mail: pg0212208t@ntu.edu.sg E-mail: fnh@crl.nmsu.edu E-mail: dwunsch@ece.umr.edu
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.
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.