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Dynamic Scene Understanding for Mobile Robot Navigation
Mikšík, Ondřej ; Horák, Karel (oponent) ; Žalud, Luděk (vedoucí práce)
The thesis deals with dynamic scene understanding for mobile robot navigation. In the first part, we propose a novel approach to self-supervised learning - a fusion of frequency based vanishing point estimation and probabilistically based color segmentation. Detection of a vanishing point is based on the estimation of a texture flow produced by a bank of Gabor wavelets and a voting function. Next, the vanishing point defines the training area which is used for self-supervised learning of color models. Finally, road patches are selected by measuring roadness score. A few rules deal with dark cast shadows, overexposed hightlights and adaptivity speed. In addition to that, the whole vanishing point estimation is refined - Gabor filters are approximated by Haar-like box functions, which enables efficient filtering via integral image trick. The tightest bottleneck, a voting scheme, is modified to coarse-to-fine, which provides a significant speed-up (more than 40x), while we loose only 3-5% in precision. The second part proposes a smoothing filter for spatio-temporal consistency of structured predictions, that are useful for more mature systems. The key part of the proposed smoothing filter is a new similarity metric, which is more discriminative than the standard Euclidean distance and can be used for various computer vision tasks. The smoothing filter first estimates optical flow to define a local neighborhood. This neighborhood is used for recursive averaging based on the similarity metric. The total accuracy of proposed method measured on pixels with inconsistent labels between the raw and smooth predictions is almost 18% higher than original predictions. Although we have used SHIM, the algorithm can be combined with any other system for structured predictions (MRF/CRF,...). The proposed smoothing filter represents a first step towards full inference.
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Fast feature matching for simultaneous localization and mapping
Mikšík, Ondřej ; Richter, Miloslav (oponent) ; Mikolajczyk,, Krystian (vedoucí práce)
The thesis deals with the fast feature matching for simultaneous localization and mapping. A brief description of local features invariant to scale, rotation, translation and affine transformations, their detectors and descriptors are included. In general, real–time response for matching is crucial for various computer vision applications (SLAM, object retrieval, wide–robust baseline stereo, tracking, . . . ). We solve the problem of sub–linear search complexity by multiple randomised KD–trees. In addition, we propose a novel way of splitting dataset into the multiple trees. Moreover, a new evaluation package for general use (KD–trees, BBD–trees, k–means trees) was developed.
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Fast feature matching for simultaneous localization and mapping
Mikšík, Ondřej ; Richter, Miloslav (oponent) ; Mikolajczyk,, Krystian (vedoucí práce)
The thesis deals with the fast feature matching for simultaneous localization and mapping. A brief description of local features invariant to scale, rotation, translation and affine transformations, their detectors and descriptors are included. In general, real–time response for matching is crucial for various computer vision applications (SLAM, object retrieval, wide–robust baseline stereo, tracking, . . . ). We solve the problem of sub–linear search complexity by multiple randomised KD–trees. In addition, we propose a novel way of splitting dataset into the multiple trees. Moreover, a new evaluation package for general use (KD–trees, BBD–trees, k–means trees) was developed.
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Dynamic Scene Understanding for Mobile Robot Navigation
Mikšík, Ondřej ; Horák, Karel (oponent) ; Žalud, Luděk (vedoucí práce)
The thesis deals with dynamic scene understanding for mobile robot navigation. In the first part, we propose a novel approach to self-supervised learning - a fusion of frequency based vanishing point estimation and probabilistically based color segmentation. Detection of a vanishing point is based on the estimation of a texture flow produced by a bank of Gabor wavelets and a voting function. Next, the vanishing point defines the training area which is used for self-supervised learning of color models. Finally, road patches are selected by measuring roadness score. A few rules deal with dark cast shadows, overexposed hightlights and adaptivity speed. In addition to that, the whole vanishing point estimation is refined - Gabor filters are approximated by Haar-like box functions, which enables efficient filtering via integral image trick. The tightest bottleneck, a voting scheme, is modified to coarse-to-fine, which provides a significant speed-up (more than 40x), while we loose only 3-5% in precision. The second part proposes a smoothing filter for spatio-temporal consistency of structured predictions, that are useful for more mature systems. The key part of the proposed smoothing filter is a new similarity metric, which is more discriminative than the standard Euclidean distance and can be used for various computer vision tasks. The smoothing filter first estimates optical flow to define a local neighborhood. This neighborhood is used for recursive averaging based on the similarity metric. The total accuracy of proposed method measured on pixels with inconsistent labels between the raw and smooth predictions is almost 18% higher than original predictions. Although we have used SHIM, the algorithm can be combined with any other system for structured predictions (MRF/CRF,...). The proposed smoothing filter represents a first step towards full inference.
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