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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

共 2178 条记录，以下是301-310 订阅

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Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation 30

Multi-Scale Continuous CRFs as Sequential Deep Networks for ...

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

作者： Xu, Dan Ricci, Elisa Ouyang, Wanli Wang, Xiaogang Sebe, Nicu Univ Trento Trento Italy Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Univ Sydney Sydney NSW Australia Fdn Bruno Kessler Trento Italy Univ Perugia Perugia Italy

ISBN: (纸本)9781538604571

this paper addresses the problem of depth estimation from a single still image. Inspired by recent works on multi-scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. through extensive experimental evaluation we demonstrate the effectiveness of the proposed approach and establish new state of the art results on publicly available datasets.

关键词： Estimation Training Neural networks Predictive models Semantics Fuses

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SRN: Side-output Residual Network for Object Symmetry Detection in the Wild 30

SRN: Side-output Residual Network for Object Symmetry Detect...

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

作者： Ke, Wei Chen, Jie Jiao, Jianbin Zhao, Guoying Ye, Qixiang Univ Chinese Acad Sci Beijing Peoples R China Univ Oulu CMVS Oulu Finland

ISBN: (纸本)9781538604571

In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. the new benchmark, named Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond those in existing datasets. the proposed symmetry detection approach, named Side-output Residual Network (SRN), leverages output Residual Units (RUs) to fit the errors between the object symmetry ground-truth and the outputs of RUs. By stacking RUs in a deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales. Experimental results validate both the benchmark and its challenging aspects related to real-world images, and the state-of-the-art performance of our symmetry detection approach. the benchmark and the code for SRN are publicly available at https://***/KevinKecc/SRN.

关键词： Benchmark testing Skeleton Image segmentation Stacking Training Color Semantics

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HSfM: Hybrid Structure-from-Motion 30

HSfM: Hybrid Structure-from-Motion

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

作者： Cui, Hainan Gao, Xiang Shen, Shuhan Hu, Zhanyi Chinese Acad Sci Inst Automat NLPR Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9781538604571

Structure-from-Motion (SfM) methods can be broadly categorized as incremental or global according to their ways to estimate initial camera poses. While incremental system has advanced in robustness and accuracy, the efficiency remains its key challenge. To solve this problem, global reconstruction system simultaneously estimates all camera poses from the epipolar geometry graph, but it is usually sensitive to outliers. In this work, we propose a new hybrid SfM method to tackle the issues of efficiency, accuracy and robustness in a unified framework. More specifically, we propose an adaptive community-based rotation averaging method first to estimate camera rotations in a global manner. then, based on these estimated camera rotations, camera centers are computed in an incremental way. Extensive experiments show that our hybrid method performs similarly or better than many of the state-of-theart global SfM approaches, in terms of computational efficiency, while achieves similar reconstruction accuracy and robustness with two other state-of-the-art incremental SfM approaches.

关键词： Incremental Cameras Robustness HYBRID Hybrid electric vehicles Computational efficiency outliers Posing crop rotation Rotations

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Cartesian k-means

Cartesian k-means

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Norouzi, Mohammad Fleet, David J. Univ Toronto Dept Comp Sci Toronto ON M5S 1A1 Canada

ISBN: (纸本)9780769549897

A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity increases super-linearly in the number of parameters. this allows one to effectively quantize data using billions or trillions of centers. We formulate two such models, Orthogonal k-means and Cartesian k-means. they are closely related to one another, to k-means, to methods for binary hash function optimization like ITQ [5], and to Product Quantization for vector quantization [7]. the models are tested on large-scale ANN retrieval tasks (1M GIST, 1B SIFT features), and on codebook learning for object recognition (CIFAR-10).

关键词： feature extraction image representation learning (artificial intelligence) neural nets object recognition pattern clustering transforms vector quantisation

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Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos 30

Spatio-Temporal Self-Organizing Map Deep Network for Dynamic...

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

作者： Du, Yang Yuan, Chunfeng Li, Bing Hu, Weiming Maybank, Stephen Univ Chinese Acad Sci Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Tech Natl Lab Pattern RecognitInst Automat Beijing Peoples R China Birkbeck Coll London England

ISBN: (纸本)9781538604571

In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background. this paper proposes a new Spatio-Temporal Self-Organizing Map (STSOM) deep network to detect dynamic objects in complex scenarios. the proposed approach has several contributions: First, a novel STSOM shared by all pixels in a video frame is presented to efficiently model complex background. We exploit the fact that the motions of complex background have the global variation in the space and the local variation in the time, to train STSOM using the whole frames and the sequence of a pixel over time to tackle the variance of complex background. Second, a Bayesian parameter estimation based method is presented to learn thresholds automatically for all pixels to filter out the background. Last, in order to model the complex background more accurately, we extend the single-layer STSOM to the deep network. then the background is filtered out layer by layer. Experimental results on CDnet 2014 dataset demonstrate that the proposed STSOM deep network outperforms numerous recently proposed methods in the overall performance and in most categories of scenarios.

关键词： Self-organizing feature maps Object detection Videos Dynamics Bayes methods Kernel computer vision

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Instance-aware Image and Sentence Matching with Selective Multimodal LSTM 30

Instance-aware Image and Sentence Matching with Selective Mu...

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

作者： Huang, Yan Wang, Wei Wang, Liang Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Ctr Excellence Brain Sci & Intelligence Tech Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

Effective image and sentence matching depends on how to well measure their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal Long Short-Term Memory network (sm-LSTM) for instance-aware image and sentence matching. the sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence, by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregates them with hidden states to obtain a final matching score as the desired global similarity. Extensive experiments show that our model can well match image and sentence with complex content, and achieve the state-of-the-art results on two public benchmark datasets.

关键词： Dogs Roads Image color analysis Aggregates Detectors pattern recognition

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Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction 27

Fast and Accurate Image Matching with Cascade Hashing for 3D...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Cheng, Jian Leng, Cong Wu, Jiaxiang Cui, Hainan Lu, Hanqing Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Image matching is one of the most challenging stages in 3D reconstruction, which usually occupies half of computational cost and inaccurate matching may lead to failure of reconstruction. therefore, fast and accurate image matching is very crucial for 3D reconstruction. In this paper, we proposed a Cascade Hashing strategy to speed up the image matching. In order to accelerate the image matching, the proposed Cascade Hashing method is designed to be three-layer structure: hashing lookup, hashing remapping, and hashing ranking. Each layer adopts different measures and filtering strategies, which is demonstrated to be less sensitive to noise. Extensive experiments show that image matching can be accelerated by our approach in hundreds times than brute force matching, even achieves ten times or more than Kd-tree based matching while retaining comparable accuracy.

关键词： Image matching

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Automated detection of cetaceans in their natural habitats via R-CNN and computer vision techniques 7

Automated detection of cetaceans in their natural habitats v...

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7th Iberian Robotics conference

作者： Roman Ruiz, Marta Dominguez, Sergio Rossi, Claudio Univ Politecn Madrid Ctr Automat & Robot UPM CSIC Madrid Spain

ISBN: (纸本)9798350376371;9798350376364

Passive acoustic monitoring is essential for monitoring cetaceans in their natural habitats. In this paper, we consider the monitoring of Fin Whales (Balaenoptera physalus). the deployment of automated tools for this purpose is essential to efficiently handle and analyse the vast amounts of data generated by hundreds of hours of recordings, something humans cannot do. In this paper, we present two methods for automated detection systems based on a convolutional neural network (CNN) classifier and a circle detection technique. Both of them use a spectrogram of the recordings as input which converts sounds into images. the first method consists of a two-stage R-CNN classifier with 26 layers. the second method is an image-based technique that uses classical computer vision algorithms based on the morphology of pulses. Both approaches demonstrate good performance, with circle detection showing better results, even as it is a simpler method. the results obtained on a large dataset demonstrate that the proposed approach is highly effective in detecting and characterising animals in their habitats, thus offering valuable information to identify seasonal patterns.

关键词： Fin Whales passive acoustic monitoring bioacoustics Deep Neural Networks pattern recognition computer vision

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A Non-Local Low-Rank Framework for Ultrasound Speckle Reduction 30

A Non-Local Low-Rank Framework for Ultrasound Speckle Reduct...

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

作者： Zhu, Lei Fu, Chi-Wing Brown, Michael S. Heng, Pheng-Ann Chinese Univ Hong Kong Sha Tin Hong Kong Peoples R China York Univ N York ON Canada Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Virtual Real & Human Interact Te Beijing Peoples R China

ISBN: (纸本)9781538604571

'Speckle' refers to the granular patterns that occur in ultrasound images due to wave interference. Speckle removal can greatly improve the visibility of the underlying structures in an ultrasound image and enhance subsequent post-processing. We present a novel framework for speckle removal based on low-rank non-local filtering. Our approach works by first computing a guidance image that assists in the selection of candidate patches for non-local filtering in the face of significant speckles. the candidate patches are further refined using a low-rank minimization estimated using a truncated weighted nuclear norm (TWNN) and structured sparsity. We show that the proposed filtering framework produces results that outperform state-of-the-art methods both qualitatively and quantitatively. this framework also provides better segmentation results when used for pre-processing ultrasound images.

关键词： Speckle Ultrasonic imaging Noise measurement Image segmentation Minimization

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computer vision and pattern recognition Technology on Account of Deep Neural Network 4th

Computer Vision and Pattern Recognition Technology on Accoun...

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4th International conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND)

作者： Ren, Yiming Han, Jiake Chen, Chongyu Xu, Yang Bao, Tingxuan Shenzhen MSU BIT Univ Shenzhen 518172 Guangdong Peoples R China Shanghai Univ Shanghai 201900 Peoples R China Tianjin Univ Sci & Technol 1038 Dagu South Rd Tianjin Hexi Tianjin 300222 Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the rapid development of computer, computer vision technology is also making rapid progress. In this paper, deep neural network algorithm is used to improve the technology of computer vision, improve the visual effect, and at the same time, innovative algorithm structure, improve the identification of pattern recognition. computer vision and pattern recognition is a very cutting edge technology that has given people very advanced tools for vision and recognition. At present, the technology can control more accurate image resolution and improve the ability of pattern recognition. this article mainly explains the process of using deep neural network algorithm to improve computer vision and pattern recognition from the internal mechanism, and reveals the working principle and internal mechanism of computer vision and pattern recognition technology application. Data analysis proves that the pattern recognition application established by deep neural network algorithm performs very well in the field of vision and pattern recognition.

关键词： Deep neural network algorithm computer vision pattern recognition Technical application

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