In the depth from defocus (DFD) method two defocused images of a scene are obtained by capturing the scene with different sets of camera parameters. An arbitrary selection of the camera settings can result in observed...
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ISBN:
(纸本)0780342364
In the depth from defocus (DFD) method two defocused images of a scene are obtained by capturing the scene with different sets of camera parameters. An arbitrary selection of the camera settings can result in observed images whose relative blurring is insufficient to yield a good estimate of the depth. In this paper, we study the effect of the degree of relative blurring on the accuracy of the estimate of the depth by addressing the DFD problem in a maximum likelihood-based framework. We propose a criterion for optimal selection of camera parameters to obtain an improved estimate of the depth. The optimality criterion is based on the Cramer-Rao bound of the variance of the error in the estimate of blur. Simulations as well as experimental results on real images are presented for validation.
In this paper, we present a new approach to extract characters on a license plate of a moving vehicle given a sequence of perspective distortion corrected license plate images. We model the extraction of characters as...
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ISBN:
(纸本)0780342364
In this paper, we present a new approach to extract characters on a license plate of a moving vehicle given a sequence of perspective distortion corrected license plate images. We model the extraction of characters as a Markov random field (MRF), With the MRF modeling, the extraction of characters is formulated as the problem of maximizing the a posteriori probability based on given prior and observations. A genetic algorithm with local greedy mutation operator is employed do optimize the objective function. Experiments and comparison study were conducted. It is shown that our approach provides better performance than other single frame methods.
This paper introduces a unified approach to the problem of verifying Alignment hypotheses in the presence of substantial amounts of uncertainty in the predicted locations of projected model features. Our approach is i...
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ISBN:
(纸本)0780342364
This paper introduces a unified approach to the problem of verifying Alignment hypotheses in the presence of substantial amounts of uncertainty in the predicted locations of projected model features. Our approach is independent of whether the uncertainty is distributed or bounded, and, moreover, incorporates information about the domain in a formally correct manner. Information which can be incorporated includes the error model, the distribution of background features, and the positions of the data features near each predicted model feature. Experiments are described that demonstrate the improvement over previously used methods. Furthermore, our method is efficient in that the number of operations is on the order of the number of image features that lie nearby the predicted model features.
We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data de...
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ISBN:
(纸本)9781424469840
We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the latest published algorithms. Its simplicity allows the algorithm to detect texts in many fonts and languages.
Given a stereo pair it is possible to recover a depth map and use that depth to render a synthetically defocused image. Though stereo algorithms are well-studied, rarely are those algorithms considered solely in the c...
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ISBN:
(纸本)9781467369640
Given a stereo pair it is possible to recover a depth map and use that depth to render a synthetically defocused image. Though stereo algorithms are well-studied, rarely are those algorithms considered solely in the context of producing these defocused renderings. In this paper we present a technique for efficiently producing disparity maps using a novel optimization framework in which inference is performed in "bilateral-space". Our approach produces higher-quality "defocus" results than other stereo algorithms while also being 10-100 x faster than comparable techniques.
We present a new approach for resolving occlusions in augmented reality. The main interest is that it does not require 3D reconstruction of the considered scene. Our idea is to use a contour based approach and to labe...
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ISBN:
(纸本)0780342364
We present a new approach for resolving occlusions in augmented reality. The main interest is that it does not require 3D reconstruction of the considered scene. Our idea is to use a contour based approach and to label each contour point as being ''behind'' or ''in front of'', depending on whether it is in front of or behind the virtual object. This labeling step only requires that the contours can be tracked from frame to frame. A proximity graph is then built in order to group the contours that belong to the same occluding object. Finally, we use some kind of active contours to accurately recover the mask of the occluding object.
We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensional convolutional network for correspon...
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ISBN:
(纸本)9781728171685
We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensional convolutional network for correspondence confidence prediction, a differentiable Weighted Procrustes algorithm for closed-form pose estimation, and a robust gradient-based SE(3) optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art methods, both learning-based and classical, on real-world data.
This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features. This representation leads to a compact and discriminative encoding of...
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ISBN:
(纸本)9781479951178
This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features. This representation leads to a compact and discriminative encoding of images that can be used for image classification, object detection or object recognition. The method relies on (i) multiple random projections of the input space followed by local binarization of projected histograms encoded as sets of items, and (ii) the representation of images as Histograms of pattern Sets (HoPS). The approach is validated on four publicly available datasets (Daimler Pedestrian, Oxford Flowers, KTH Texture and PASCAL VOC2007), allowing comparisons with many recent approaches. The proposed image representation reaches state-of-the-art performance on each one of these datasets.
In visual recognition tasks, the design of low level image feature representation is fundamental. The advent of local patch features from pixel attributes such as SIFT and LBP, has precipitated dramatic progresses. Re...
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ISBN:
(纸本)9780769549897
In visual recognition tasks, the design of low level image feature representation is fundamental. The advent of local patch features from pixel attributes such as SIFT and LBP, has precipitated dramatic progresses. Recently, a kernel view of these features, called kernel descriptors (KDES) [1], generalizes the feature design in an unsupervised fashion and yields impressive results. In this paper, we present a supervised framework to embed the image level label information into the design of patch level kernel descriptors, which we call supervised kernel descriptors (SKDES). Specifically, we adopt the broadly applied bag-of-words (BOW) image classification pipeline and a large margin criterion to learn the lowlevel patch representation, which makes the patch features much more compact and achieve better discriminative ability than KDES. With this method, we achieve competitive results over several public datasets comparing with state-of-the-art methods.
Recently active learning has attracted a lot of attention in computervision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning appr...
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ISBN:
(纸本)9780769549897
Recently active learning has attracted a lot of attention in computervision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning approaches employed in computervision adopt most uncertainty measures as instance selection criteria. Although most uncertainty query selection strategies are very effective in many circumstances, they fail to take information in the large amount of unlabeled instances into account and are prone to querying outliers. In this paper we present a novel adaptive active learning approach that combines an information density measure and a most uncertainty measure together to select critical instances to label for image classifications. Our experiments on two essential tasks of computervision, object recognition and scene recognition, demonstrate the efficacy of the proposed approach.
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