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Results 1 - 10
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20,607
Multi-Cue Pedestrian Classification With Partial Occlusion Handling
"... This paper presents a novel mixture-of-experts framework for pedestrian classification with partial occlusion handling. The framework involves a set of component-based expert classifiers trained on features derived from intensity, depth and motion. To handle partial occlusion, we compute expert weig ..."
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Cited by 55 (7 self)
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This paper presents a novel mixture-of-experts framework for pedestrian classification with partial occlusion handling. The framework involves a set of component-based expert classifiers trained on features derived from intensity, depth and motion. To handle partial occlusion, we compute expert
A MULTI-LEVEL MIXTURE-OF-EXPERTS FRAMEWORK FOR PEDESTRIAN CLASSIFICATION
, 2011
"... Notwithstanding many years of progress, pedestrian recognition is still a difficult but important problem. We present a novel multi-level Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification. On pose-level, shape ..."
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Cited by 16 (3 self)
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Notwithstanding many years of progress, pedestrian recognition is still a difficult but important problem. We present a novel multi-level Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification. On pose-level, shape
LLVM: A compilation framework for lifelong program analysis & transformation
, 2004
"... ... a compiler framework designed to support transparent, lifelong program analysis and transformation for arbitrary programs, by providing high-level information to compiler transformations at compile-time, link-time, run-time, and in idle time between runs. LLVM defines a common, low-level code re ..."
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Cited by 852 (20 self)
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... a compiler framework designed to support transparent, lifelong program analysis and transformation for arbitrary programs, by providing high-level information to compiler transformations at compile-time, link-time, run-time, and in idle time between runs. LLVM defines a common, low-level code
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning al ..."
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Cited by 578 (16 self)
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graph-based approaches) we obtain a natural out-of-sample extension to novel examples and so are able to handle both transductive and truly semi-supervised settings. We present experimental evidence suggesting that our semi-supervised algorithms are able to use unlabeled data effectively. Finally we
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of ..."
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Cited by 699 (15 self)
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We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of
Cognitive networks
- IN PROC. OF IEEE DYSPAN 2005
, 2005
"... This paper presents a definition and framework for a novel type of adaptive data network: the cognitive network. In a cognitive network, the collection of elements that make up the network observes network conditions and then, using prior knowledge gained from previous interactions with the network ..."
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Cited by 1106 (7 self)
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This paper presents a definition and framework for a novel type of adaptive data network: the cognitive network. In a cognitive network, the collection of elements that make up the network observes network conditions and then, using prior knowledge gained from previous interactions
Shape Matching and Object Recognition Using Shape Contexts
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv- ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning transform ..."
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Cited by 1809 (21 self)
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We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv- ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning
Establishing Pairwise Keys in Distributed Sensor Networks
, 2003
"... Pairwise key establishment is a fundamental security service in sensor networks; it enables sensor nodes to communicate securely with each other using cryptographic techniques. However, due to the resource constraints on sensors, it is infeasible to use traditional key management techniques such as ..."
Abstract
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Cited by 543 (29 self)
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such as public key cryptography and key distribution center (KDC). To facilitate the study of novel pairwise key predistribution techniques, this paper presents a general framework for establishing pairwise keys between sensors on the basis of a polynomial-based key predistribution protocol [2]. This paper
Pictorial Structures for Object Recognition
- IJCV
, 2003
"... In this paper we present a statistical framework for modeling the appearance of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance ..."
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Cited by 816 (15 self)
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In this paper we present a statistical framework for modeling the appearance of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to model an object by a collection of parts arranged in a deformable configuration
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
- STATISTICS AND COMPUTING
, 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is develop ..."
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Cited by 1051 (76 self)
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In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework
Results 1 - 10
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20,607
