6362823
Assigning textual names to sets of geographic coordinates
NameSet is a system that translates a set of geographic coordinates into a textual name based on the geographic regions where the coordinates occur. One possible application of NameSet is to concisely present the geographical scope of a set of geo-referenced observations to a human user. Another application is to generate text to depict a set of coordinates that appear on a web site -- text that could later be used for information retrieval applications. NameSet's computation is based on a simple algorithm, using off-the-shelf and web-based data sources. The system was proven effective in an application that automatically organizes and names sets of geo-referenced digital photographs.
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.
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.
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
1233583
Minimum Recombination Histories by Branch and Bound
Recombination plays an important role in creating genetic diversity within species, and inferring past recombination events is central to many problems in genetics. Given a set M of sampled sequences, finding an evolutionary history for M with the minimum number of recombination events is a computationally very challenging problem. In this paper, we present a novel branch and bound algorithm for tackling that problem. Our method is shown to be far more efficient than the only preexisting exact method, described in [1]. Our software implementing the algorithm discussed in this paper is publicly available.
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.
2647804
Extended DBP for (m, k)-Firm Based QoS
In this paper, an extended DBP (E_DBP) scheme is studied for (m,k)-firm constraint. The basic idea of the proposed algorithm takes into account the distance to exit a failure state, which is a symmetrical notion of distance to fall into a failure state in DBP. Quality of Service (QoS) in terms of dynamic failure and delay is evaluated. Simulation results reveal the effectiveness of E_DBP to provide better QoS.