• Publications
  • Influence
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
  • J. Wang, Y. Yagi
  • Computer Science, Medicine
  • IEEE Transactions on Image Processing
  • 1 February 2008
We extend the standard mean-shift tracking algorithm to an adaptive tracker by selecting reliable features from color and shape-texture cues according to their descriptive ability. The target modelExpand
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Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control
Variations in clothing alter an individual's appearance, making the problem of gait identification much more difficult. If the type of clothing differs between the gallery and a probe, certain partsExpand
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Coarse-to-fine vision-based localization by indexing scale-Invariant features
This paper presents a novel coarse-to-fine global localization approach inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by scale-invariantExpand
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Combining interest points and edges for content-based image retrieval
This paper presents a novel approach using combined features to retrieve images containing specific objects, scenes or buildings. The content of an image is characterized by two kinds of features:Expand
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Guided Feature Selection for Deep Visual Odometry
We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks. Different from current monocular visual odometryExpand
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Efficient Topological Localization Using Orientation Adjacency Coherence Histograms
This paper describes an efficient vision-based global topological localization approach that uses a coarse-to-fine strategy. Orientation adjacency coherence histogram (OACH), a novel image feature,Expand
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Local Supports Global: Deep Camera Relocalization With Sequence Enhancement
We propose to leverage the local information in a image sequence to support global camera relocalization. In contrast to previous methods that regress global poses from single images, we exploit theExpand
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Vision-based Global Localization Using a Visual Vocabulary
This paper presents a novel coarse-to-fine global localization approach that is inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by SIFTExpand
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Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry
Most previous learning-based visual odometry (VO) methods take VO as a pure tracking problem. In contrast, we present a VO framework by incorporating two additional components called Memory andExpand
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Many-to-Many Superpixel Matching for Robust Tracking
  • J. Wang, Y. Yagi
  • Mathematics, Computer Science
  • IEEE Transactions on Cybernetics
  • 12 May 2014
We present a robust tracking method based on many-to-many image superpixel matching (MMM). Our MMM tracker represents a target and its background using two sets of superpixels. Multiple hypothesesExpand
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