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32
How Multirobot Systems Research Will Accelerate Our Understanding of Social Animal Behavior
, 2006
"... Researchers are tracking movements of ants and monkeys using robotics algorithms; they hope to automatically recognize animal behavior and to simulate it using robots. ..."
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Cited by 8 (2 self)
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Researchers are tracking movements of ants and monkeys using robotics algorithms; they hope to automatically recognize animal behavior and to simulate it using robots.
Point Probe Decision Trees for Geometric Concept Classes
, 1993
"... A fundamental problem in modelbased computer vision is that of identifying to which of a given set of concept classes of geometric models an observed model belongs. Considering a "probe" to be an oracle that tells whether or not the observed model is present at a given point in an image, ..."
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Cited by 7 (5 self)
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A fundamental problem in modelbased computer vision is that of identifying to which of a given set of concept classes of geometric models an observed model belongs. Considering a "probe" to be an oracle that tells whether or not the observed model is present at a given point in an image, we study the problem of computing efficient strategies ("decision trees") for probing an image, with the goal to minimize the number of probes necessary (in the worst case) to determine in which class the observed model belongs. We prove a hardness result and give strategies that obtain decision trees whose height is within a log factor of optimal. These results grew out of discussions that began in a series of workshops on Geometric Probing in Computer Vision, sponsored by the Center for Night Vision and ElectroOptics, Fort Belvoir, Virginia, and monitored by the U.S. Army Research Office. The views, opinions, and/or findings contained in this report are those of the authors and should not be con...
The Complexity of Sensing by Point Sampling
"... this paper we consider the problem of finding the minimum number of sensing points required to distinguish between a finite set of polygonal shapes. For instance, we might imagine embedding a series of point light detectors in a feeder tray. Then we would be interested in the question "What is ..."
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Cited by 7 (1 self)
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this paper we consider the problem of finding the minimum number of sensing points required to distinguish between a finite set of polygonal shapes. For instance, we might imagine embedding a series of point light detectors in a feeder tray. Then we would be interested in the question "What is the minimum number of light detectors that can fully distinguish between all the possible shapes?" Or we might imagine a set of mechanical probes that touches the feeder at a finite number of predetermined points. Then we would ask "What are the minimum number of probing points and where should the probes be located in order to distinguish all the possible shapes?" We address these questions in this paper. Intuitively, each sensing point can be regarded as a binary bit that has two values `contained' and `not contained '. So the robot senses a shape by reading out the binary representation of the shape, that is, by checking which points are contained in the shape and which are not. The formalized sensing problem: Given n polygons with a total of m edges in the plane, locate the fewest points such that each polygon contains a distinct subset of points in its interior. We show that this problem is equivalent to an NPcomplete settheoretic problem introduced as Discriminating Set. By a reduction to Hitting Set (and hence to Set Covering), an O(n
Shape Recognition and Twenty Questions
 IN PROC. RECONNAISSANCE DES FORMES ET INTELLIGENCE ARTIFICIELLE (RFIA
, 1993
"... We formulate shape recognition as a coding problem. There is a finite list of possible "hypotheses"  shape classes and/or spatial positionings  and we wish to determine which one is true based on the results of various "tests," which are local image features. We use a decision ..."
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Cited by 6 (2 self)
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We formulate shape recognition as a coding problem. There is a finite list of possible "hypotheses"  shape classes and/or spatial positionings  and we wish to determine which one is true based on the results of various "tests," which are local image features. We use a decision tree: each interior node is assigned one of the tests and each terminal node is assigned one of the hypotheses. The assignment of tests, or "strategy," is recursive: along each branch choose the next test to remove as much uncertainty as possible (as measured by entropy) about the true hypothesis. In contrast to the standard approach of "hypothesize and test," there is no repeated elicitation of hypotheses; instead, the "indexing" is dynamic and stochastic. We gradually formulate specific conjectures as the evolving distribution on hypotheses becomes increasingly peaked. We apply this "twenty questions" approach to the recognition of two types of linear, deformable structures: handwritten numerals and roads i...
Recognizing Polygonal Parts from Width Measurements
, 1995
"... Automatic recognition of parts is an important problem in many industrial applications. One model of the problem is: Given a finite set of polygonal parts, use a set of "width" measurements taken by a paralleljaw gripper to determine which part is present. We study the problem of compu ..."
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Cited by 5 (0 self)
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Automatic recognition of parts is an important problem in many industrial applications. One model of the problem is: Given a finite set of polygonal parts, use a set of "width" measurements taken by a paralleljaw gripper to determine which part is present. We study the problem of computing efficient strategies ("grasp plans"), with the goal to minimize the number of measurements necessary in the worst case. We show that finding a minimum length grasp plan is Af7hard, and give a polynomial time approximation algorithm that is simple and produces a solution that is within a log factor from optimal.
Randomized Inquiries About Shape; an Application to Handwritten Digit Recognition
, 1994
"... We describe an approach to shape recognition based on asking relational questions about the arrangement of landmarks, basically localized and oriented boundary segments. The questions are grouped into highly structured inquiries in the form of a tree. There are, in fact, many trees, each constructed ..."
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Cited by 4 (1 self)
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We describe an approach to shape recognition based on asking relational questions about the arrangement of landmarks, basically localized and oriented boundary segments. The questions are grouped into highly structured inquiries in the form of a tree. There are, in fact, many trees, each constructed from training data based on entropy reduction. The outcome of each tree is not a classification but rather a distribution over shape classes. The final classification is based on an aggregate distribution. The framework is nonEuclidean and there is no feature vector in the standard sense. Instead, the representation of the image data is graphical and each question is associated with a labeled subgraph. The ordering of the questions is highly constrained in order to maintain computational feasibility, and dependence among the trees is reduced by randomly subsampling from the available pool of questions. Experiments are reported on the recognition of handwritten digits. Although the amount ...
Localizing an Object With Finger Probes
"... We consider the problem of identifying one of a set of polygonal models in the plane using point probes and finger probes. In particular, we give strategies for using a minimum number of finger probes to determine a finite number of possible locations of an unknown interior point p in one of the mod ..."
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Cited by 2 (0 self)
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We consider the problem of identifying one of a set of polygonal models in the plane using point probes and finger probes. In particular, we give strategies for using a minimum number of finger probes to determine a finite number of possible locations of an unknown interior point p in one of the models. A finger probe takes as input an interior point p of a polygon P and a direction`, and it outputs the first point of intersection of a ray emanating from p in direction ` with the boundary of P . We show that without a priori knowledge of what the models look like, no finite number of finger probes will suffice to localize the point p. When the models are given in advance, we give both batch and dynamic probing strategies for solving the problem. We consider both the case where the models are aligned rectilinear polygons and the case where the models are simple polygons.
Probe Trees for Touching Character Recognition
 In Proc. International Conference on Imaging Science, Systems and Technology, (CISST
, 1998
"... The problem of touching characters is very important for the recognition of low quality text. A solution is presented here for the problem of touching character recognition for fixed font, using a decision tree classifier paradigm. The method is based on the concept of probe trees, a fast recognitio ..."
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Cited by 2 (2 self)
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The problem of touching characters is very important for the recognition of low quality text. A solution is presented here for the problem of touching character recognition for fixed font, using a decision tree classifier paradigm. The method is based on the concept of probe trees, a fast recognition method that uses character probes to acquire knowledge about the input samples. Touching characters are recognized without segmentation, so errors common in segmentationbased methods are avoided. Speed is achieved by constructing a decision tree for a specific font offline, before any samples are seen. A deformation model is used to generate probes that withstand certain image distortions. Experimental results are presented in support of the method. Keywords: Touching Characters, Probe Trees, Optical Character Recognition, Document Image Understanding. sazaklis@cs.sunysb.edu. Supported in part by a grant from Syngen Corp. and by the Strategic Partnership for Industrial Resurgence, Coll...
Decision Tree Construction in Fixed Dimensions: Being Global is Hard but Local Greed is Good
, 1995
"... We study the problem of finding optimal linear decision trees for classifying a set of points in R^d partitioned into concept classes, where d is a fixed, but arbitrary, constant. We show that optimal decision tree construction is NPcomplete, even for 3dimensional point sets. Nevertheless, we can ..."
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Cited by 1 (1 self)
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We study the problem of finding optimal linear decision trees for classifying a set of points in R^d partitioned into concept classes, where d is a fixed, but arbitrary, constant. We show that optimal decision tree construction is NPcomplete, even for 3dimensional point sets. Nevertheless, we can prove a number of interesting approximation bounds on the use of random sampling for finding optimal splitting hyperplanes in greedy decision tree constructions. We give experimental evidence that, while providing asymptotic guarantees on split quality, this random sampling approach behaves as good in practice as uniform randomization strategies that do not provide such guarantees. Finally, we provide experimental justification for coupling this random sampling strategy with locallygreedy "hill climbing" methods.
Microsoft Reserach New England
"... We study poolbased active learning of halfspaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and exper ..."
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We study poolbased active learning of halfspaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it can be preferable to mellow approaches. Our efficient aggressive active learner of halfspaces has formal approximation guarantees that hold when the pool is separable with a margin. While our analysis is focused on the realizable setting, we show that a simple heuristic allows using the same algorithm successfully for pools with low error as well. We further compare the aggressive approach to the mellow approach, and prove that there are cases in which the aggressive approach results in significantly better label complexity compared to the mellow approach. Experiments demonstrate that substantial improvements in label complexity can be achieved using the aggressive approach, in realizable and lowerror settings. 1.