This paper outlines methods to detect key anchor points in 3D face scanner data. These anchor points can be used to estimate the pose and then match the test image to a 3D face model. We present two algorithms for det...
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This paper outlines methods to detect key anchor points in 3D face scanner data. These anchor points can be used to estimate the pose and then match the test image to a 3D face model. We present two algorithms for detecting face anchor points in the context of face verification; One for frontal images and one for arbitrary pose. We achieve 99% success in finding anchor points in frontal images and 86% success in scans with large variations in pose and changes in expression. These results demonstrate the challenges in 3D face recognition under arbitrary pose and expression. We are currently working on robust ?tting algorithms to localize more precisely the anchor points for arbitrary pose images.
We describe a method to align ASL video subtitles with a closed-caption transcript. Our alignments are partial, based on spotting words within the video sequence, which consists of joined (rather than isolated) signs ...
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We describe a method to align ASL video subtitles with a closed-caption transcript. Our alignments are partial, based on spotting words within the video sequence, which consists of joined (rather than isolated) signs with unknown word boundaries. We start with windows known to contain an example of a word, but not limited to it. We estimate the start and end of the word in these examples using a voting method. This provides a small number of training examples (typically three per word). Since there is no shared structure, we use a discriminative rather than a generative word model. While our word spotters are not perfect, they are sufficient to establish an alignment. We demonstrate that quite small numbers of good word spotters results in an alignment good enough to produce simple English-ASL translations, both by phrase matching and using word substitution.
Digital library access is driven by features, but the relevance of a feature for a query is not always obvious. This paper describes an approach for integrating a large number of context-dependent features into a semi...
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Digital library access is driven by features, but the relevance of a feature for a query is not always obvious. This paper describes an approach for integrating a large number of context-dependent features into a semi-automated tool. Instead of requiring universal similarity measures or manual selection of relevant features, the approach provides a learning algorithm for selecting and combining groupings of the data, where groupings can be induced by highly specialized features. The selection process is guided by positive and negative examples from the user. The inherent combinatorics of using multiple features is reduced by a multistage grouping generation, weighting, and collection process. The stages closest to the user are trained fastest and slowly propagate their adaptations back to earlier stages. The weighting stage adapts the collection stage's search space across uses, so that, in later interactions, good groupings are found given few examples from the user.
Efficient algorithms are provided for computing the Hausdorff distance between a binary image and all possible relative positions (translations) of a model, or a portion of that model. The computation is in many ways ...
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Efficient algorithms are provided for computing the Hausdorff distance between a binary image and all possible relative positions (translations) of a model, or a portion of that model. The computation is in many ways similar to binary correlation. However, it is more tolerant of perturbations in the locations of points because it measures proximity rather than exact superposition.< >
Near regular textures are pervasive in man-made and natural world. Their global regularity and local randomness pose new difficulties to the state of the art texture analysis and synthesis algorithms. We carry out a s...
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Near regular textures are pervasive in man-made and natural world. Their global regularity and local randomness pose new difficulties to the state of the art texture analysis and synthesis algorithms. We carry out a systematic comparison study on the performance of four texture synthesis algorithms on near-regular textures. Our results confirm that faithful near-regular texture synthesis remains a challenging problem for the state of the art general purpose texture synthesis algorithms. In addition, we provide comparison of human perception with computer evaluations on the quality of the texture synthesis results.
An approach for autonomous localization of ground vehicles on natural terrain is proposed. The localization problem is solved using measurements including attitude, heading, and distances to specific environmental poi...
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An approach for autonomous localization of ground vehicles on natural terrain is proposed. The localization problem is solved using measurements including attitude, heading, and distances to specific environmental points. The algorithm utilizes random acquisition of distance measurements to prune the possible location(s) of the viewer. The approach is also applicable to airborne localization.< >
We develop and demonstrate an object recognition system capable of accurately detecting, localizing, and recovering the kinematic configuration of textured animals in real images. We build a deformation model of shape...
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We develop and demonstrate an object recognition system capable of accurately detecting, localizing, and recovering the kinematic configuration of textured animals in real images. We build a deformation model of shape automatically from videos of animals and an appearance model of texture from a labeled collection of animal images, and combine the two models automatically. We develop a simple texture descriptor that outperforms the state of the art. We test our animal models on two datasets; images taken by professional photographers from the Corel collection, and assorted images from the Web returned by Google. We demonstrate quite good performance on both datasets. Comparing our results with simple baselines, we show that for the Google set, we can recognize objects from a collection demonstrably hard for object recognition.
Brain-computer Interfaces based on non-invasive electroencephalographic (EEG) signals were recently made practical through sophisticated algorithms and clever systems, in such a way that the dream of effortlessly tran...
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Brain-computer Interfaces based on non-invasive electroencephalographic (EEG) signals were recently made practical through sophisticated algorithms and clever systems, in such a way that the dream of effortlessly translating volition into action is coming true, albeit in a limited way. However, a low signal-to-noise ratio and the presence of frequent artefacts, such as eye blinks, contaminate the recordings and make the recognition of the underlying mental processes difficult. In this study, a novel waveletbased signal processing technique, ContinuousWavelet Regression, has been applied to refine EEG data in a wellknown setting. The recordings of spontaneous (i.e., asynchronous) signals of subjects performing highly different cognitive tasks have been processed by our algorithm, and then analyzed and classified, obtaining very promising results as compared with those obtained by previous studies.
In this paper we study the role of dynamics in dimensionality reduction problems applied to sequences. We propose a new family of marginal auto-regressive (MAR) models that describe the space of all stable auto-regres...
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In this paper we study the role of dynamics in dimensionality reduction problems applied to sequences. We propose a new family of marginal auto-regressive (MAR) models that describe the space of all stable auto-regressive sequences, regardless of their specific dynamics. We apply the MAR class of models as sequence priors in probabilistic sequence subspace embedding problems. In particular, we consider a Gaussian process latent variable approach to dimensionality reduction and show that the use of MAR priors may lead to better estimates of sequence subspaces than the ones obtained by traditional non-sequential priors. We then propose a learning method for estimating nonlinear dynamic system (NDS) models that utilizes the new MAR priors. The utility of the proposed methods is demonstrated on several synthetic datasets as well as on the task of tracking 3D articulated figures in monocular image sequences.
This paper addresses the subspace properties and the recovery of articulated motion. We point out that the global motion subspace of an articulated object is a combination of a number of intersecting rigid motion subs...
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This paper addresses the subspace properties and the recovery of articulated motion. We point out that the global motion subspace of an articulated object is a combination of a number of intersecting rigid motion subspaces of the parts. Depending on whether a link of two parts is a joint or an axis, the global motion subspace loses one or two in rank for each link. The rank loss results from the intersection between the rigid motion subspaces of linked parts. Furthermore, the intersection is, in fact, the motion subspace of the link. From these observations, we describe the rank constraint of the global motion subspace of an articulated object; we give an algorithm to recover the image motion of a link, either a joint or an axis; and we propose a novel but simple approach, which is based on subspace clustering, to recover articulated shape and motion from a single-view image sequence.
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