Rehg, James M.

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Now showing 1 - 10 of 30
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    ITR/SY: a distributed programming infrastructure for integrating smart sensors
    (Georgia Institute of Technology, 2009-11-30) Ramachandran, Umakishore ; DeWeerth, Stephen P. ; Mackenzie, Kenneth M. ; Starner, Thad ; Hutto, Phil ; Wolenetz, Matt ; Rehg, James M.
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    Visual Place Categorization: Problem, Dataset, and Algorithm
    (Georgia Institute of Technology, 2009-10) Wu, Jianxin ; Rehg, James M. ; Christensen, Henrik I.
    In this paper we describe the problem of Visual Place Categorization (VPC) for mobile robotics, which involves predicting the semantic category of a place from image measurements acquired from an autonomous platform. For example, a robot in an unfamiliar home environment should be able to recognize the functionality of the rooms it visits, such as kitchen, living room, etc. We describe an approach to VPC based on sequential processing of images acquired with a conventional video camera.We identify two key challenges: Dealing with non-characteristic views and integrating restricted-FOV imagery into a holistic prediction. We present a solution to VPC based upon a recently-developed visual feature known as CENTRIST (CENsus TRansform hISTogram). We describe a new dataset for VPC which we have recently collected and are making publicly available. We believe this is the first significant, realistic dataset for the VPC problem. It contains the interiors of six different homes with ground truth labels. We use this dataset to validate our solution approach, achieving promising results.
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    CENTRIST: A Visual Descriptor for Scene Categorization
    (Georgia Institute of Technology, 2009-07-23) Wu, Jianxin ; Rehg, James M.
    CENTRIST (CENsus TRansform hISTogram), a new visual descriptor for recognizing topological places or scene categories, is introduced in this paper. We show that place and scene recognition, especially for indoor environments, require its visual descriptor to possess properties that are different from other vision domains (e.g. object recognition). CENTRIST satisfy these properties and suits the place and scene recognition task. It is a holistic representation and has strong generalizability for category recognition. CENTRIST mainly encodes the structural properties within an image and suppresses detailed textural information. Our experiments demonstrate that CENTRIST outperforms the current state-of-the art in several place and scene recognition datasets, compared with other descriptors such as SIFT and Gist. Besides, it is easy to implement. It has nearly no parameter to tune, and evaluates extremely fast.
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    HDCCSR: software self-awareness using dynamic analysis and Markov models
    (Georgia Institute of Technology, 2008-12-20) Harrold, Mary Jean ; Rugaber, Spencer ; Rehg, James M.
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    Learning and Inferring Motion Patterns Using Parametric Segmental Switching Linear Dynamic Systems
    (Georgia Institute of Technology, 2008) Oh, Sang Min ; Rehg, James M. ; Balch, Tucker ; Dellaert, Frank
    Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS provides the possibility to describe complex temporal patterns more concisely and accurately than an HMM by using continuous hidden states. However, the use of SLDS models in practical applications is challenging for several reasons. First, exact inference in SLDS models is computationally intractable. Second, the geometric duration model induced in standard SLDSs limits their representational power. Third, standard SLDSs do not provide a systematic way to robustly interpret systematic variations governed by higher order parameters. The contributions in this paper address all three challenges above. First, we present a data-driven MCMC sampling method for SLDSs as a robust and efficient approximate inference method. Second, we present segmental switching linear dynamic systems (S-SLDS), where the geometric distributions are replaced with arbitrary duration models. Third, we extend the standard model with a parametric model that can capture systematic temporal and spatial variations. The resulting parametric SLDS model (P-SLDS) uses EM to robustly interpret parametrized motions by incorporating additional global parameters that underly systematic variations of the overall motion. The overall development of the proposed inference methods and extensions for SLDSs provide a robust framework to interpret complex motions. The framework is applied to the honey bee dance interpretation task in the context of the on-going BioTracking project at Georgia Institute of Technology. The experimental results suggest that the enhanced models provide an effective framework for a wide range of motion analysis applications.
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    CAREER: motion capture from movies: video-based tracking and modeling of human motion
    (Georgia Institute of Technology, 2007-05-31) Rehg, James M.
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    Parameterized Duration Modeling for Switching Linear Dynamic Systems
    (Georgia Institute of Technology, 2006-06) Oh, Sang Min ; Rehg, James M. ; Dellaert, Frank
    We introduce an extension of switching linear dynamic systems (SLDS) with parameterized duration modeling capabilities. The proposed model allows arbitrary duration models and overcomes the limitation of a geometric distribution induced in standard SLDSs. By incorporating a duration model which reflects the data more closely, the resulting model provides reliable inference results which are robust against observation noise. Moreover, existing inference algorithms for SLDSs can be adopted with only modest additional effort in most cases where an SLDS model can be applied. In addition, we observe the fact that the duration models would vary across data sequences in certain domains, which complicates learning and inference tasks. Such variability in duration is overcome by introducing parameterized duration models. The experimental results on honeybee dance decoding tasks demonstrate the robust inference capabilities of the proposed model.
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    On-line Learning of the Traversability of Unstructured Terrain for Outdoor Robot Navigation
    (Georgia Institute of Technology, 2006) Oh, Sang Min ; Rehg, James M. ; Dellaert, Frank
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    Learning from Examples in Unstructured, Outdoor Environments
    (Georgia Institute of Technology, 2006) Sun, J. ; Mehta, Tejas R. ; Wooden, David ; Powers, Matthew ; Rehg, James M. ; Balch, Tucker ; Egerstedt, Magnus B.
    In this paper, we present a multi-pronged approach to the "Learning from Example" problem. In particular, we present a framework for integrating learning into a standard, hybrid navigation strategy, composed of both plan-based and reactive controllers. Based on the classification of colors and textures as either good or bad, a global map is populated with estimates of preferability in conjunction with the standard obstacle information. Moreover, individual feedback mappings from learned features to learned control actions are introduced as additional behaviors in the behavioral suite. A number of real-world experiments are discussed that illustrate the viability of the proposed method.
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    Shadow Elimination and Blinding Light Suppression for Interactive Projected Displays
    (Georgia Institute of Technology, 2006) Summet, Jay W. ; Flagg, Matthew ; Cham, Tat-Jen ; Rehg, James M. ; Sukthankar, Rahul
    A major problem with interactive displays based on front-projection is that users cast undesirable shadows on the display surface. This situation is only partially-addressed by mounting a single projector at an extreme angle and warping the projected image to undo keystoning distortions. This paper demonstrates that shadows can be muted by redundantly-illuminating the display surface using multiple projectors, all mounted at different locations. However, this technique alone does not eliminate shadows: multiple projectors create multiple dark regions on the surface (penumbral occlusions) and cast undesirable light onto the users. These problems can be solved by eliminating shadows and suppressing the light that falls on occluding users by actively modifying the projected output. This paper categorizes various methods that can be used to achieve redundant illumination, shadow elimination, and blinding light suppression, and evaluates their performance.