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The next best underwater view

The next best underwater view

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Sheinin, Mark Schechner, Yoav Y. Viterbi Faculty of Electrical Engineering Technion - Israel Inst. of Technology Haifa32000 Israel

ISBN: (纸本)9781467388511

To image in high resolution large and occlusion-prone scenes, a camera must move above and around. Degradation of visibility due to geometric occlusions and distances is exacerbated by scattering, when the scene is in a participating medium. Moreover, underwater and in other media, artificial lighting is needed. Overall, data quality depends on the observed surface, medium and the timevarying poses of the camera and light source (C&L). This work proposes to optimize C&L poses as they move, so that the surface is scanned efficiently and the descattered recovery has the highest quality. The work generalizes the next best view concept of robot vision to scattering media and cooperative movable lighting. It also extends descattering to platforms that move optimally. The optimization criterion is information gain, taken from information theory. We exploit the existence of a prior rough 3D model, since underwater such a model is routinely obtained using sonar. We demonstrate this principle in a scaled-down setup.

关键词： computer vision

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Regularity-driven building facade matching between aerial and street views

Regularity-driven building facade matching between aerial an...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wolff, Mark Collins, Robert T. Liu, Yanxi School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA16802 United States

ISBN: (纸本)9781467388511

We present an approach for detecting and matching building facades between aerial view and street-view images. We exploit the regularity of urban scene facades as captured by their lattice structures and deduced from median-tiles' shape context, color, texture and spatial similarities. Our experimental results demonstrate effective matching of oblique and partially-occluded facades between aerial and ground views. Quantitative comparisons for automated urban scene facade matching from three cities show superior performance of our method over baseline SIFT, Root-SIFT and the more sophisticated Scale-Selective Self-Similarity and Binary Coherent Edge descriptors. We also illustrate regularity-based applications of occlusion removal from street views and higher-resolution texture-replacement in aerial views.

关键词： Antennas

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What value do explicit high level concepts have in vision to language problems?

What value do explicit high level concepts have in vision to...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wu, Qi Shen, Chunhua Liu, Lingqiao Dick, Anthony Van Den Hengel, Anton School of Computer Science University of Adelaide Australia

ISBN: (纸本)9781467388511

Much recent progress in vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we investigate whether this direct approach succeeds due to, or despite, the fact that it avoids the explicit representation of high-level information. We propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering. We also show that the same mechanism can be used to introduce external semantic information and that doing so further improves performance. We achieve the best reported results on both image captioning and VQA on several benchmark datasets, and provide an analysis of the value of explicit high-level concepts in V2L problems.

关键词： High level languages

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Prior-less compressible structure from Motion

Prior-less compressible structure from Motion

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Kong, Chen Lucey, Simon Carnegie Mellon University 5000 Forbes Ave. PittsburghPA United States

ISBN: (纸本)9781467388511

Many non-rigid 3D structures are not modelled well through a low-rank subspace assumption. This is problematic when it comes to their reconstruction through Structure from Motion (SfM). We argue in this paper that a more expressive and general assumption can be made around compressible 3D structures. The vision community, however, has hitherto struggled to formulate effective strategies for recovering such structures after projection without the aid of additional priors (e.g. temporal ordering, rigid substructures, etc.). In this paper we present a "prior-less" approach to solve compressible SfM. Specifically, we demonstrate how the problem of SfM - assuming compressible 3D structures - can be theoretically characterized as a block sparse dictionary learning problem. We validate our approach experimentally by demonstrating reconstructions of 3D structures that are intractable using current state-of-theart low-rank SfM approaches.

关键词： computer vision

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Instance-level segmentation for autonomous driving with deep densely connected MRFs

Instance-level segmentation for autonomous driving with deep...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Ziyu Fidler, Sanja Urtasun, Raquel Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781467388511

Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving. We build on recent work [32] that trained a convolutional neural net to predict instance labeling in local image patches, extracted exhaustively in a stride from an image. A simple Markov random field model using several heuristics was then proposed in [32] to derive a globally consistent instance labeling of the image. In this paper, we formulate the global labeling problem with a novel densely connected Markov random field and show how to encode various intuitive potentials in a way that is amenable to efficient mean field inference [15]. Our potentials encode the compatibility between the global labeling and the patch-level predictions, contrast-sensitive smoothness as well as the fact that separate regions form different instances. Our experiments on the challenging KITTI benchmark [8] demonstrate that our method achieves a significant performance boost over the baseline [32].

关键词： Markov processes

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HD maps: Fine-grained road segmentation by parsing ground and aerial images

HD maps: Fine-grained road segmentation by parsing ground an...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Máttyus, Gellért Wang, Shenlong Fidler, Sanja Urtasun, Raquel Remote Sensing Technology Institute German Aerospace Center Germany Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781467388511

In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes. Towards this goal, we propose an efficient approach that is able to estimate these fine grained categories by doing joint inference over both, monocular aerial imagery, as well as ground images taken from a stereo camera pair mounted on top of a car. Important to this is reasoning about the alignment between the two types of imagery, as even when the measurements are taken with sophisticated GPS+IMU systems, this alignment is not sufficiently accurate. We demonstrate the effectiveness of our approach on a new dataset which enhances KITTI [8] with aerial images taken with a camera mounted on an airplane and flying around the city of Karlsruhe, Germany.

关键词： Stereo image processing

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Camera calibration from dynamic silhouettes using motion barcodes

Camera calibration from dynamic silhouettes using motion bar...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Ben-Artzi, Gil Kasten, Yoni Peleg, Shmuel Werman, Michael School of Computer Science and Engineering Hebrew University of Jerusalem Israel

ISBN: (纸本)9781467388511

Computing the epipolar geometry between cameras with very different viewpoints is often problematic as matching points are hard to find. In these cases, it has been proposed to use information from dynamic objects in the scene for suggesting point and line correspondences. We propose a speed up of about two orders of magnitude, as well as an increase in robustness and accuracy, to methods computing epipolar geometry from dynamic silhouettes. This improvement is based on a new temporal signature: motion barcode for lines. Motion barcode is a binary temporal sequence for lines, indicating for each frame the existence of at least one foreground pixel on that line. The motion barcodes of two corresponding epipolar lines are very similar, so the search for corresponding epipolar lines can be limited only to lines having similar barcodes. The use of motion barcodes leads to increased speed, accuracy, and robustness in computing the epipolar geometry.

关键词： Bar codes

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Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers

Exploit all the layers: Fast and accurate CNN object detecto...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Yang, Fan Choi, Wongun Lin, Yuanqing Department of Computer Science University of Maryland College Park United States NEC Laboratories America United States

ISBN: (纸本)9781467388511

In this paper, we investigate two new strategies to detect objects accurately and efficiently using deep convolutional neural network: 1) scale-dependent pooling and 2) layerwise cascaded rejection classifiers. The scale-dependent pooling (SDP) improves detection accuracy by exploiting appropriate convolutional features depending on the scale of candidate object proposals. The cascaded rejection classifiers (CRC) effectively utilize convolutional features and eliminate negative object proposals in a cascaded manner, which greatly speeds up the detection while maintaining high accuracy. In combination of the two, our method achieves significantly better accuracy compared to other state-of-the-arts in three challenging datasets, PASCAL object detection challenge, KITTI object detection benchmark and newly collected Inner-city dataset, while being more efficient.

关键词： Object detection

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Backtracking ScSPM image classifier forweakly supervised top-down saliency

Backtracking ScSPM image classifier forweakly supervised top...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Cholakkal, Hisham Johnson, Jubin Rajan, Deepu Multimedia Lab. School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781467388511

Top-down saliency models produce a probability map that peaks at target locations specified by a task/goal such as object detection. They are usually trained in a supervised setting involving annotations of objects. We propose a weakly supervised top-down saliency framework using only binary labels that indicate the presence/absence of an object in an image. First, the probabilistic contribution of each image patch to the confidence of an ScSPM-based classifier produces a Reverse-ScSPM (R-ScSPM) saliency map. Neighborhood information is then incorporated through a contextual saliency map which is estimated using logistic regression learnt on patches having high R-ScSPM saliency. Both the saliency maps are combined to obtain the final saliency map. We evaluate the performance of the proposed weakly supervised top-down saliency and achieves comparable performance with fully supervised approaches. Experiments are carried out on 5 challenging datasets across 3 different applications.

关键词： computer vision

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Densecap: Fully Convolutional localization networks for dense captioning

Densecap: Fully Convolutional localization networks for dens...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Johnson, Justin Karpathy, Andrej Fei-Fei, Li Department of Computer Science Stanford University United States

ISBN: (纸本)9781467388511

We introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning task generalizes object detection when the descriptions consist of a single word, and Image Captioning when one predicted region covers the full image. To address the localization and description task jointly we propose a Fully Convolutional Localization Network (FCLN) architecture that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with a single round of optimization. The architecture is composed of a Convolutional Network, a novel dense localization layer, and Recurrent Neural Network language model that generates the label sequences. We evaluate our network on the Visual Genome dataset, which comprises 94,000 images and 4,100,000 region-grounded captions. We observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings.

关键词： Network architecture

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