25329703
Monocular Perception of Biological Motion in Johansson Displays
Computer perception of biological motion is key to developing convenient and powerful human?computer interfaces. Algorithms have been developed for tracking the body; however, initialization is done by hand. We propose a method for detecting a moving human body and for labeling its parts automatically in scenes that include extraneous motions and occlusion. We assume a Johansson display, i.e., that a number of moving features, some representing the unoccluded body joints and some belonging to the background, are supplied in the scene. Our method is based on maximizing the joint probability density function (PDF) of the position and velocity of the body parts. The PDF is estimated from training data. Dynamic programming is used for calculating efficiently the best global labeling on an approximation of the PDF. Detection is performed by hypothesis testing on the best labeling found. The computational cost is on the order of N4 where N is the number of features detected. We explore the performance of our method with experiments carried on a variety of periodic and nonperiodic body motions viewed monocularly for a total of approximately 30,000 frames. The algorithm is demonstrated to be accurate and efficient.
2185149
Monocular perception of biological motion-detection and labeling
Computer perception of biological motion is key to developing convenient and powerful human-computer interfaces. Successful body tracking algorithms have been developed; however initialization is done by hand. We propose a method for detecting a moving human body and for labeling its parts automatically. It is based on maximizing the joint probability density function (PDF) of the position and velocity of the body parts. The PDF is estimated from training data. Dynamic programming is used for calculating efficiently the best global labeling on an approximation of the PDF. The computational cost is on the order of N/sup 4/ where N is the number of features detected. We explore the performance of our method with experiments carried on a variety of periodic and non-periodic body motions viewed monocularly for a total of approximately 30,000 frames. Point-markers were strapped to the joints of the subject for facilitating image analysis. We find an average of 2.3% labeling error; the experiments also suggest a high degree of viewpoint-invariance.
1562361
Learning probabilistic structure for human motion detection
Decomposable triangulated graphs have been shown to be efficient and effective for modeling the probabilistic spatio-temporal structure of brief stretches of human motion. In previous work such model structure was handcrafted by expert human observers and labeled data were needed for parameter learning. We present a method to build automatically the structure of the decomposable triangulated graph from unlabeled data. It is based on maximum-likelihood. Taking the labeling of the data as hidden variables, a variant of the EM algorithm can be applied. A greedy algorithm is developed to search for the optimal structure of the decomposable model based on the (conditional) differential entropy of variables. Our algorithm is demonstrated by learning models of human motion completely automatically from unlabeled real image sequences with clutter and occlusion. Experiments on both motion captured data and grayscale image sequences show that the resulting models perform better than the hand-constructed models.
6822921
Unsupervised Learning of Human Motion
An unsupervised learning algorithm that can obtain a probabilistic model of an object composed of a collection of parts (a moving human body in our examples) automatically from unlabeled training data is presented. The training data include both useful "foreground" features as well as features that arise from irrelevant background clutter - the correspondence between parts and detected features is unknown. The joint probability density function of the parts is represented by a mixture of decomposable triangulated graphs which allow for fast detection. To learn the model structure as well as model parameters, an EM-like algorithm is developed where the labeling of the data (part assignments) is treated as hidden variables. The unsupervised learning technique is not limited to decomposable triangulated graphs. The efficiency and effectiveness of our algorithm is demonstrated by applying it to generate models of human motion automatically from unlabeled image sequences, and testing the learned models on a variety of sequences.
1116497
Unsupervised Learning of Human Motion Models
This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatically from unlabeled data. The distinguished part of this work is that it is based on unlabeled data, i.e., the training features include both useful foreground parts and background clutter and the correspondence between the parts and detected features are unknown. We use decomposable triangulated graphs to depict the probabilistic independence of parts, but the unsupervised technique is not limited to this type of graph. In the new approach, labeling of the data (part assignments) is taken as hidden variables and the EM algorithm is applied. A greedy algorithm is developed to select parts and to search for the optimal structure based on the differential entropy of these variables. The success of our algorithm is demonstrated by applying it to generate models of human motion automatically from unlabeled real image sequences.
14072276
An Efficient Algorithm for Statistical Multiple Alignment on Arbitrary Phylogenetic Trees
We present an efficient algorithm for statistical multiple alignment based on the TKF91 model of Thorne, Kishino, and Felsenstein (1991) on an arbitrary k-leaved phylogenetic tree. The existing algorithms use a hidden Markov model approach, which requires at least O( radical 5(k)) states and leads to a time complexity of O(5(k)L(k)), where L is the geometric mean sequence length. Using a combinatorial technique reminiscent of inclusion/exclusion, we are able to sum away the states, thus improving the time complexity to O(2(k)L(k)) and considerably reducing memory requirements. This makes statistical multiple alignment under the TKF91 model a definite practical possibility in the case of a phylogenetic tree with a modest number of leaves.
2088404
A dispatching mechanism providing REMPLI applications with QoS
For supporting applications with different timing constraints, a PLC (power line communication) system must provide differentiated QoS (quality of service). In this paper a traffic dispatching policy is specified and implemented which guarantees both required periodic data update and short end-to-end delay of aperiodic data request services. This policy is compared with the classic dual-priority one showing its good performance
23745644
Parsimonious Reconstruction of Sequence Evolution and Haplotype Blocks
Under the infinite-sites model of mutation, we consider the problem of finding the minimum number of recombination events which must have occurred in the evolutionary history of sampled DNA sequences. Our approach is deterministic and is based on the combinatorics of leaf-labelled rooted trees. In contrast to previously known approaches, which only yield estimated lower bounds, our approach always gives the exact minimum number of recombination events. Furthermore, our method can be used to reconstruct explicitly evolutionary histories with the minimum number of recombination events. As an additional application, we discuss how our work can be used to define haplotype blocks.
10098796
Constructing Minimal Ancestral Recombination Graphs
By viewing the ancestral recombination graph as defining a sequence of trees, we show how possible evolutionary histories consistent with given data can be constructed using the minimum number of recombination events. In contrast to previously known methods, which yield only estimated lower bounds, our method of detecting recombination always gives the minimum number of recombination events if the right kind of rooted trees are used in our algorithm. A new lower bound can be defined if rooted trees with fewer constraints are used. As well as studying how often it actually is equal to the minimum, we test how this new lower bound performs in comparison to some other lower bounds. Our study indicates that the new lower bound is an improvement on earlier bounds. Also, using simulated data, we investigate how well our method can recover the actual site-specific evolutionary relationships. In the presence of recombination, using a single tree to describe the evolution of the entire locus clearly leads to lower average recovery percentages than does our method. Our study shows that recovering the actual local tree topologies can be done more accurately than estimating the actual number of recombination events.
15862894
Quantitative evaluation of the safety of X-by-Wire architecture subject to EMI perturbations
The X-by-Wire systems in cars can only be accepted if they provide at least the same dependability than the traditional ones. In this paper we propose a new approach to evaluate the impact of the EMI perturbations on the dependability of an X-by-Wire architecture. The considered X-by-Wire architecture is distributed around a TDMA-like communication protocol. So a perturbation causes the loss of a communication cycle with a certain probability. The vehicle level failure is then defined as the consecutive loss of a certain number of communication cycles. Its reliability is modeled as that of the well-known consecutive-k-out-of-n:F systems. A case study, together with the EMI perturbations collected on the roads in France, is used to illustrate our approach