16347620
Towards detection of human motion
Detecting humans in images is a useful application of computer vision. Loose and textured clothing, occlusion and scene clutter make it a difficult problem because bottom-up segmentation and grouping do not always work. We address the problem of detecting humans from their motion pattern in monocular image sequences; extraneous motions and occlusion may be present. We assume that we may not rely on segmentation or grouping and that the vision front-end is limited to observing the motion of key points and textured patches in between pairs of frames. We do not assume that we are able to track features for more than two frames. Our method is based on learning an approximate probabilistic model of the joint position and velocity of different body features. Detection is performed by hypothesis testing on the maximum a posteriori estimate of the pose and motion of the body. Our experiments on a dozen of walking sequences indicate that our algorithm is accurate and efficient.
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.
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.
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.
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.
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.
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
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
28755708
Automatically generating metadata for digital photographs with geographic coordinates
Given location information on digital photographs, we can automatically generate an abundance of photo-related metadata using off-the-shelf and web-based data sources. These metadata can serve as additional memory cues and filters when browsing a personal or global collection of photos.
1688557
Counting All Possible Ancestral Configurations of Sample Sequences in Population Genetics
Given a set D of input sequences, a genealogy for D can be constructed backward in time using such evolutionary events as mutation, coalescent, and recombination. An ancestral configuration (AC) can be regarded as the multiset of all sequences present at a particular point in time in a possible genealogy for D. The complexity of computing the likelihood of observing D depends heavily on the total number of distinct ACs of D and, therefore, it is of interest to estimate that number. For D consisting of binary sequences of finite length, we consider the problem of enumerating exactly all distinct ACs. We assume that the root sequence type is known and that the mutation process is governed by the infinite-sites model. When there is no recombination, we construct a general method of obtaining closed-form formulas for the total number of ACs. The enumeration problem becomes much more complicated when recombination is involved. In that case, we devise a method of enumeration based on counting contingency tables and construct a dynamic programming algorithm for the approach. Last, we describe a method of counting the number of ACs that can appear in genealogies with less than or equal to a given number R of recombinations. Of particular interest is the case in which R is close to the minimum number of recombinations for D
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.