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 frame the problem of local representation of imaging data as the computation of minimal sufficient statistics that are invariant to nuisance variability induced by viewpoint and illumination. We show that, under ve...
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ISBN:
(纸本)9781467369640
We frame the problem of local representation of imaging data as the computation of minimal sufficient statistics that are invariant to nuisance variability induced by viewpoint and illumination. We show that, under very stringent conditions, these are related to "feature descriptors" commonly used in computervision. Such conditions can be relaxed if multiple views of the same scene are available. We propose a sampling-based and a point-estimate based approximation of such a representation, compared empirically on image-to-(multiple) image matching, for which we introduce a multi-view wide-baseline matching benchmark, consisting of a mixture of real and synthetic objects with ground truth camera motion and dense three-dimensional geometry.
We propose a new computervision task we call " distractor prediction." Distractors are the regions of an image that draw attention away from the main subjects and reduce the overall image quality. Removing ...
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ISBN:
(纸本)9781467369640
We propose a new computervision task we call " distractor prediction." Distractors are the regions of an image that draw attention away from the main subjects and reduce the overall image quality. Removing distractors-for example, using in-painting -can improve the composition of an image. In this work we created two datasets of images with user annotations to identify the characteristics of distractors. We use these datasets to train an algorithm to predict distractor maps. Finally, we use our predictor to automatically enhance images.
In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance ...
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ISBN:
(纸本)9781467369640
In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computervision problems. To encode such a function, we opt for a CNN-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this task. We show that such an approach can significantly outperform the state-ofthe-art on several problems and benchmark datasets.
Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computervision community will need to face in order to realize its goal of recognizing all object categories. Current stat...
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ISBN:
(纸本)9781467369640
Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computervision community will need to face in order to realize its goal of recognizing all object categories. Current state-of-the-art techniques rely heavily upon the use of keypoint or part annotations, but scaling up to hundreds or thousands of domains renders this annotation cost-prohibitive for all but the most important categories. In this work we propose a method for fine-grained recognition that uses no part annotations. Our method is based on generating parts using co-segmentation and alignment, which we combine in a discriminative mixture. Experimental results show its efficacy, demonstrating state-of-the-art results even when compared to methods that use part annotations during training.
We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module shoul...
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ISBN:
(纸本)9781467369640
We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major contribution is to propose a valve linkage function (VLF) for back-propagation chaining and form our deep localization, alignment and classification (LAC) system. The VLF can adaptively compromise the errors of classification and alignment when training the LAC model. It in turn helps update localization. The performance on fine-grained object data bears out the effectiveness of our LAC system.
Three different statistical models of colour data for use in segmentation or tracking algorithms are proposed. Results of a performance comparison of a tracking algorithm, applied to two separate applications, using e...
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ISBN:
(纸本)0780342364
Three different statistical models of colour data for use in segmentation or tracking algorithms are proposed. Results of a performance comparison of a tracking algorithm, applied to two separate applications, using each of the three different types of underlying model of the data are presented. From these a comparison of the performance of the statistical colour models themselves is obtained.
Domain adaptation (DA) has gained a lot of success in the recent years in computervision to deal with situations where the learning process has to transfer knowledge from a source to a target domain. In this paper, w...
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ISBN:
(纸本)9781467369640
Domain adaptation (DA) has gained a lot of success in the recent years in computervision to deal with situations where the learning process has to transfer knowledge from a source to a target domain. In this paper, we introduce a novel unsupervised DA approach based on both subspace alignment and selection of landmarks similarly distributed between the two domains. Those landmarks are selected so as to reduce the discrepancy between the domains and then are used to non linearly project the data in the same space where an efficient subspace alignment (in closed-form) is performed. We carry out a large experimental comparison in visual domain adaptation showing that our new method outperforms the most recent unsupervised DA approaches.
We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multi-window scheme using left-right consistency to compute disparity an...
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ISBN:
(纸本)0780342364
We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multi-window scheme using left-right consistency to compute disparity and its associated uncertainty. We demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve an those of closely related techniques for both robustness and efficiency.
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