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.
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.
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.
11873336
A concise necessary and sufficient condition for the existence of a galled-tree
Galled-trees are a special class of graphical representation of evolutionary history that has proven amenable to efficient, polynomial-time algorithms. The goal of this paper is to construct a concise necessary and sufficient condition for the existence of a galled-tree for M, a set of binary sequences that purportedly have evolved in the presence of recombination. Both root-known and root-unknown cases are considered here
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
7172331
Enhanced Strict Multilevel Successive Elimination Algorithm for Fast Motion Estimation
This paper presents an enhanced strict multilevel successive elimination algorithm (EMSEA) for fast block-matching motion estimation, which is based on the previous strict multilevel successive elimination algorithm (SMSEA) (Song et al., 2006). Different to the SMSEA algorithm with fixed parameters, in EMSEA algorithm, the whole search area is divided into two regions and each region has its own parameters. Therefore, the computation complexity of SMSEA algorithm can be further decreased. Experiments show that the proposed EMSEA algorithm can reduce 16.2% of the SMSEA computation and maintain almost the same image quality, which is better than the TSS and DS algorithms.
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
16612931
Efficient computation of close lower and upper bounds on the minimum number of recombinations in biological sequence evolution
MOTIVATION
We are interested in studying the evolution of DNA single nucleotide polymorphism sequences which have undergone (meiotic) recombination. For a given set of sequences, computing the minimum number of recombinations needed to explain the sequences (with one mutation per site) is a standard question of interest, but it has been shown to be NP-hard, and previous algorithms that compute it exactly work either only on very small datasets or on problems with special structure.


RESULTS
In this paper, we present efficient, practical methods for computing both upper and lower bounds on the minimum number of needed recombinations, and for constructing evolutionary histories that explain the input sequences. We study in detail the efficiency and accuracy of these algorithms on both simulated and real data sets. The algorithms produce very close upper and lower bounds, which match exactly in a surprisingly wide range of data. Thus, with the use of new, very effective lower bounding methods and an efficient algorithm for computing upper bounds, this approach allows the efficient, exact computation of the minimum number of needed recombinations, with high frequency in a large range of data. When upper and lower bounds match, evolutionary histories found by our algorithm correspond to the most parsimonious histories.


AVAILABILITY
HapBound and SHRUB, programs implementing the new algorithms discussed in this paper, are available at http://wwwcsif.cs.ucdavis.edu/~gusfield/lu.html