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检索条件"任意字段=30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017"

共 212 条记录，以下是31-40 订阅

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Anti-Glare: Tightly Constrained Optimization for Eyeglass Reflection Removal 30

Anti-Glare: Tightly Constrained Optimization for Eyeglass Re...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sandhan, Tushar Choi, Jin Young Seoul Natl Univ Dept ECE ASRI Seoul South Korea

ISBN: (纸本)9781538604571

Absence of a clear eye visibility not only degrades the aesthetic value of an entire face image but also creates difficulties in many computer vision tasks. Even mild reflections produce the undesired superpositions of visual information, whose decomposition into the background and reflection layers using a single image is a highly ill-posed problem. In this work, we enforce the tight constraints derived by thoroughly analysing the properties of an eyeglass reflection. In addition, our strategy regularizes gradients of the reflection layer to be highly sparse and proposes the facial symmetry prior via formulating a non-convex optimization scheme, which removes the reflections within a few iterations. Experiments on frontal face image inputs demonstrate the high quality reflection removal results and improvement of the iris detection rate.

关键词： Reflection Eyeglasses reflecting layer images visual information computer vision Constrained optimization aesthetic value Imagery (Psychotherapy) Image decomposition

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An Exact Penalty Method for Locally Convergent Maximum Consensus 30

An Exact Penalty Method for Locally Convergent Maximum Conse...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Le, Huu Chin, Tat-Jun Suter, David Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9781538604571

Maximum consensus estimation plays a critically important role in computer vision. Currently, the most prevalent approach draws from the class of non-deterministic hypothesize-and-verify algorithms, which are cheap but do not guarantee solution quality. On the other extreme, there are global algorithms which are exhaustive search in nature and can be costly for practical-sized inputs. this paper aims to fill the gap between the two extremes by proposing a locally convergent maximum consensus algorithm. Our method is based on a formulating the problem with linear complementarity constraints, then defining a penalized version which is provably equivalent to the original problem. Based on the penalty problem, we develop a Frank-Wolfe algorithm that can deterministically solve the maximum consensus problem. Compared to the randomized techniques, our method is deterministic and locally convergent;relative to the global algorithms, our method is much more practical on realistic input sizes. Further, our approach is naturally applicable to problems with geometric residuals(1).

关键词： Robustness computer vision Estimation Computational modeling Pollution measurement Minimization

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SCC: Semantic Context Cascade for Efficient Action Detection 30

SCC: Semantic Context Cascade for Efficient Action Detection

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Heilbron, Fabian Caba Barrios, Wayner Escorcia, Victor Ghanem, Bernard KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781538604571

Despite the recent advances in large-scale video analysis, action detection remains as one of the most challenging unsolved problems in computer vision. this snag is in part due to the large volume of data that needs to be analyzed to detect actions in videos. Existing approaches have mitigated the computational cost, but still, these methods lack rich high-level semantics that helps them to localize the actions quickly. In this paper, we introduce a Semantic Cascade Context (SCC) model that aims to detect action in long video sequences. By embracing semantic priors associated with human activities, SCC produces high-quality class-specific action proposals and prune unrelated activities in a cascade fashion. Experimental results in ActivityNet unveils that SCC achieves state-of-the-art performance for action detection while operating at real time.

关键词： Stress corrosion cracking Semantics Action large volume data cascades large volume images computer vision Videotapes

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Product Split Trees 30

Product Split Trees

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Babenko, Artem Lempitsky, Victor Yandex Moscow Russia Natl Res Univ Higher Sch Econ Moscow Russia Skolkovo Inst Sci & Technol Skoltech Moscow Russia

ISBN: (纸本)9781538604571

In this work, we introduce a new kind of spatial partition trees for efficient nearest-neighbor search. Our approach first identifies a set of useful data splitting directions, and then learns a codebook that can be used to encode such directions. We use the product-quantization idea in order to make the effective codebook large, the evaluation of scalar products between the query and the encoded splitting direction very fast, and the encoding itself compact. As a result, the proposed data srtucture (Product Split tree) achieves compact clustering of data points, while keeping the traversal very efficient. In the nearest-neighbor search experiments on high-dimensional data, product split trees achieved state-of-the-art performance, demonstrating better speed-accuracy tradeoff than other spatial partition trees.

关键词： Vegetation Nearest neighbor searches Databases Memory management computer vision Partitioning algorithms Reactive power Nearest neighbor query Partitioning algorithms Store management Reactive power images computer vision Database vegetation codebook Trees trees

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Re-Sign: Re-Aligned End-to-End Sequence Modelling with Deep Recurrent CNN-HMMs 30

Re-Sign: Re-Aligned End-to-End Sequence Modelling with Deep ...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Koller, Oscar Zargaran, Sepehr Ney, Hermann Rhein Westfal TH Aachen Human Language Technol & Pattern Recognit Grp Aachen Germany

ISBN: (纸本)9781538604571

this work presents an iterative re-alignment approach applicable to visual sequence labelling tasks such as gesture recognition, activity recognition and continuous sign language recognition. Previous methods dealing with video data usually rely on given frame labels to train their classifiers. Looking at recent data sets, these labels often tend to be noisy which is commonly overseen. We propose an algorithm that treats the provided training labels as weak labels and refines the label-to-image alignment on-the-fly in a weakly supervised fashion. Given a series of frames and sequence-level labels, a deep recurrent CNN-BLSTM network is trained end-to-end. Embedded into an HMM, the resulting deep model corrects the frame labels and continuously improves its performance in several re-alignments. We evaluate on two challenging publicly available sign recognition benchmark data sets featuring over 1000 classes. We outperform the state-of-the-art by up to 10% absolute and 30% relative.

关键词： Hidden Markov models Gesture recognition Assistive technology Training Speech recognition Mathematical model Gesture recognition Hidden Markov models Self-Help Devices Speech recognition Mathematical Model Functional training end-to-end handicapped aids Training video data FRAMES Frame

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the Impact of Typicality for Informative Representative Selection 30

The Impact of Typicality for Informative Representative Sele...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bappy, Jawadul H. Paul, Sujoy Tuncel, Ertem Roy-Chowdhury, Amit K. Univ Calif Riverside Dept ECE Riverside CA 92521 USA

ISBN: (纸本)9781538604571

In computer vision, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem. Furthermore, it is also useful to reduce the annotation cost, as it is time consuming to annotate unlabeled samples. In this paper, motivated by the theories in data compression, we propose a novel sample selection strategy which exploits the concept of typicality from the domain of information theory. Typicality is a simple and powerful technique which can be applied to compress the training data to learn a good classification model. In this work, typicality is used to identify a subset of the most informative samples for labeling, which is then used to update the model using active learning. the proposed model can take advantage of the inter-relationships between data samples. Our approach leads to a significant reduction of manual labeling cost while achieving similar or better recognition performance compared to a model trained with entire training set. this is demonstrated through rigorous experimentation on five datasets.

关键词： Computational modeling computer vision Data models Entropy Context modeling Activity recognition

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Deep Affordance-grounded Sensorimotor Object recognition 30

Deep Affordance-grounded Sensorimotor Object Recognition

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： thermos, Spyridon Papadopoulos, Georgios th. Daras, Petros Potamianos, Gerasimos Ctr Res & Technol Hellas Informat Technol Inst Thermi Greece Univ Thessaly Dept Elect & Comp Engn Volos Greece

ISBN: (纸本)9781538604571

It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them. this fact has recently motivated the "sensorimotor" approach to the challenging task of automatic object recognition, where both information sources are fused to improve robustness. In this work, the aforementioned paradigm is adopted, surpassing current limitations of sensorimotor object recognition research. Specifically, the deep learning paradigm is introduced to the problem for the first time, developing a number of novel neuro-biologically and neuro-physiologically inspired architectures that utilize state-of-the-art neural networks for fusing the available information sources in multiple ways. the proposed methods are evaluated using a large RGB-D corpus, which is specifically collected for the task of sensorimotor object recognition and is made publicly available. Experimental results demonstrate the utility of affordance information to object recognition, achieving an up to 29% relative error reduction by its inclusion.

关键词： Object recognition Robot sensing systems Visualization three-dimensional displays computer vision Biological neural networks object recognition three-dimensional displays Biological neural networks Manipulator sensing systems images computer vision Visualization Information source

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Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures 30

Efficient Global Point Cloud Alignment using Bayesian Nonpar...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Straub, Julian Campbell, Trevor How, Jonathan P. Fisher, John W., III MIT Cambridge MA 02139 USA

ISBN: (纸本)9781538604571

Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and branch and bound (BB) optimization to recover the relative transformation. BB uses a novel, refinable, near-uniform tessellation of rotation space using 4D tetrahedra, leading to more efficient optimization compared to the common axis-angle tessellation. We provide objective function bounds for pruning given the proposed tessellation, and prove that BB converges to the optimum of the cost function along with providing its computational complexity. Finally, we empirically demonstrate the efficiency of the proposed approach as well as its robustness to real-world conditions such as missing data and partial overlap.

关键词： images computer vision Tessellation Cost functions CLOUDS Clouds object recognition missing data Bayesian inference robotics Objective function branches Alignment tree surgery

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End-to-End Instance Segmentation with Recurrent Attention 30

End-to-End Instance Segmentation with Recurrent Attention

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ren, Mengye Zemel, Richard S. Univ Toronto Toronto ON Canada Canadian Inst Adv Res Toronto ON Canada

ISBN: (纸本)9781538604571

While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine large graphical models with low-level vision have been proposed to address this problem;however, we propose an end-to-end recurrent neural network (RNN) architecture with an attention mechanism to model a human-like counting process, and produce detailed instance segmentations. the network is jointly trained to sequentially produce regions of interest as well as a dominant object segmentation within each region. the proposed model achieves competitive results on the CVPPP [27], KITTI [12], and Cityscapes [8] datasets.

关键词： Image segmentation Indexes Computational modeling Training Convolution computer vision

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Why You Should Forget Luminance Conversion and Do Something Better 30

Why You Should Forget Luminance Conversion and Do Something ...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Nguyen, Rang M. H. Brown, Michael S. Natl Univ Singapore Singapore Singapore York Univ N York ON Canada

ISBN: (纸本)9781538604571

One of the most frequently applied low-level operations in computer vision is the conversion of an RGB camera image into its luminance representation. this is also one of the most incorrectly applied operations. Even our most trusted softwares, Matlab and OpenCV, do not perform luminance conversion correctly. In this paper, we examine the main factors that make proper RGB to luminance conversion difficult, in particular: 1) incorrect white-balance, 2) incorrect gamma/tone-curve correction, and 3) incorrect equations. Our analysis shows errors up to 50% for various colors are not uncommon. As a result, we argue that for most computer vision problems there is no need to attempt luminance conversion;instead, there are better alternatives depending on the task.

关键词： Image color analysis Cameras Standards computer vision Pipelines Observers Power distribution images computer vision image colour analysis Cameras pipeline luminance Observers Electricity Distribution Systems piping

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