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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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Object segmentation of indoor scenes using perceptual organization on RGB-D images

Object segmentation of indoor scenes using perceptual organi...

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international conference on Wireless Communications and Signal processing (WCSP)

作者： Chaonan Wang Yanbing Xue Hua Zhang Guangping Xu Zan Gao Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (纸本)9781509028610

In this paper, we address the problem of object segmentation, which is important for further scene analysis and scene understanding. To improve the accuracy of object segmentation from the images with indoor scenes, we propose a new algorithm which combined perceptual organization method with color information and depth information. In the proposed algorithm, firstly, the gPb-ucm method is used for initial segmentation, and then, color information and depth information are both used for perceptual organization. Color information and depth information are combined to achieve a complementary effect, and on the basis of image foremost segmentation, the segmentation result is modified by perceptual organization. Experimental results demonstrate the proposed method can effectively improve the accuracy of object segmentation.

关键词： image color analysis image segmentation Object segmentation Organizations Transmission line matrix methods computer vision image analysis

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Enhancing computer vision to detect face spoofing attack utilizing a single frame from a replay video attack using deep learning

Enhancing computer vision to detect face spoofing attack uti...

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international conference on Optoelectronics and image processing (ICOIP)

作者： Aziz Alotaibi Ausif Mahmood University of Bridgeport CT USA

ISBN: (纸本)9781509008810

Recently, automatic face recognition has been applied in many web and mobile applications. Developers integrate and implement face recognition as an access control into these applications. However, face recognition authentication is vulnerable to several attacks especially when an attacker presents a 2-D printed image or recorded video frames in front of the face sensor system to gain access as a legitimate user. This paper introduces a non-intrusive method to detect face spoofing attacks that utilize a single frame of sequenced frames. We propose a specialized deep convolution neural network to extract complex and high features of the input diffused frame. We tested our method on the Replay Attack dataset which consists of 1200 short videos of both real-access and spoofing attacks. An extensive experimental analysis was conducted that demonstrated better results when compared to previous static algorithms results.

关键词： Face recognition computers Mobile communication Performance evaluation Face

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Abnormal behavior detection using Conditional Random Fields

Abnormal behavior detection using Conditional Random Fields

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international conference on Audio, Language and image processing, ICALIP

作者： Ben-Syuan Huang Shih-Chung Hsu Chung-Lin Huang Electrical Engineering Department National Tsing-Hua University Hsin-Chu Taiwan Dept. of M-Commerce and Multimedia Applications Asia University Taichung Taiwan

ISBN: (纸本)9781509006557

This paper proposes a real-time abnormal behavior detection using Conditional Random Fields(CRFs). A normal behavior can be characterized by the spatial and temporal features obtained from the video of human activities. The difficult of abnormal behavior detection is that human behavior varies in both motion and appearance. It is a continuous action stream, interspersed with transitional activities between abnormal and normal events. Here, we propose Bag of Words (BoWs) to describe the motion information as the observations. Then, we apply the CRFs and adaptive thresholding to identify the abnormal behaviors. Different from previous methods, our method can identify the undefined and unknown abnormal activities.

关键词： Hidden Markov models Feature extraction image motion analysis computer vision Vocabulary Training Integrated optics

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Colour-texture image segmentation based on graph cut using student's t distribution

Colour-texture image segmentation based on graph cut using s...

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international conference on Inventive Computation Technologies (ICICT)

作者： Sonali Warudkar S. R. Kinge M. T. Kolte Department of ENTC Maeer's Mitcoe Kothrud Pune MH India

ISBN: (纸本)9781509012862

image segmentation for analysis is a major aspect of perception and till date it is challenging issue for machine perception. Many years of study in computer vision prove that segmenting an image into meaningful regions for subsequent processing (e.g., pattern recognition) is just as hard problem as invariant pattern recognition. In this paper, the proposed work uses the different type of procedures has been followed to carry out colour-texture image segmentation. Segmentation methods are designed to integrate more feature information, with high accuracy and satisfactory visual entirety. The segmentation process is based on MSST and student's t-distribution method.

关键词： image segmentation image color analysis Signal processing algorithms Nonhomogeneous media Tensile stress Algorithm design and analysis Filtering

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SSSC-AM: A UNIFIED FRAMEWORK FOR VIDEO CO-SEGMENTATION BY STRUCTURED SPARSE SUBSPACE CLUSTERING WITH APPEARANCE AND MOTION FEATURES

SSSC-AM: A UNIFIED FRAMEWORK FOR VIDEO CO-SEGMENTATION BY ST...

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IEEE international conference on image processing

作者： Junlin Yao Frank Nielsen Ecole Polytechnique Department of Computer Science

ISBN: (纸本)9781467399623

Video co-segmentation typically refers to the task to jointly segment common objects existing in a given group of videos. In practice, high-dimensional data such as videos are often conceptually thought of being drawn from a union of subspaces corresponding to multiple categories. Therefore, segmenting data into respective subspaces, known as subspace clustering, has widespread applications in computer vision, including co-segmentation. State-of-the-art methods via subspace clustering seek to solve the problem in two steps: learning an affinity matrix, followed by applying spectral clustering to the affinity matrix. However, it is insufficient to obtain an optimal solution since it does not take into account the interdependence of the affinity matrix and the segmentation. In this paper, we present a new unified video co-segmentation framework inspired by Structured Sparse Subspace Clustering (S~3C), which yields more consistent segmentation results. In order to improve the detectability of motion features with missing trajectories, we add an extra signature to motion trajectories. Moreover, we reformulate the S~3C algorithm by adding the affine subspace constraint in order to make it more suitable to segment rigid motions lying in affine subspaces of dimension at most 3. Experiments on MOViCS dataset demonstrate the effectiveness of our approaches and robustness with heavy noise.

关键词： Object segmentation Subspace clustering Video Videotapes Subspace Object segmentation Cluster Analysis images computer vision Static synchronous series compensator

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Object Depth Estimation from a Single image Using Fully Convolutional Neural Network

Object Depth Estimation from a Single Image Using Fully Conv...

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Proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： Ahmed J. Afifi Olaf Hellwich Computer Vision & Remote Sensing Technische Universität Berlin Berlin Germany

Convolutional Neural Network (CNN) has been used successfully in solving different computer vision tasks such as classification, detection, and segmentation. This paper addresses the problem of estimating object depth from a single RGB image. While stereo depth estimation is a straightforward task, predicting depth map of an object from a single RGB image is a more challenging task due to the lack of information from various image cues. Prior work focuses on exploiting geometric information or hand-crafted features. Also, they proposed the L2 norm to perform the optimization of a CNN during the training process. Using the L2 norm in regression tasks for optimization will bias the model. To address this problem, we propose a regression model with a fully convolutional neural network. To achieve robustness to outliers, we optimize the model using Tukey's biweight loss function, which is an M-estimator that is robust against outliers. The predictions are given by a single fully CNN without any post-processing techniques. In our experiments, we show that the quantitative and the qualitative results of using Tukey's biweight loss for optimization are better than of using L2 norm.

关键词： Estimation Training computer vision image segmentation Neural networks Robustness computer architecture

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Analysis of error resiliency of belief propagation in computer vision

Analysis of error resiliency of belief propagation in comput...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jungwook Choi Ameya D. Patil Rob A. Rutenbar Naresh R. Shanbhag IBM T. J. Watson Research Center Yorktown Heights NY University of Illinois College of Law Champaign IL US Department of Computer Science University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign

ISBN: (纸本)9781479999897

Probabilistic inference is a versatile tool to solve a large variety of pixel-labeling problems in computer vision such as stereo matching and image denoising. Belief Propagation (BP) is an effective method for such inference tasks, and has also shown attractive error-resilience properties-the ability to converge to usable solutions in the presence of low-level hardware errors. This is of increasing interest, as the looming end of Moore's Law scaling brings with it a vast increase in the statistical variability of nanoscale circuit fabrics. In this work we seek to understand why certain combinations of BP and error-resilience mechanisms work so well in practice. We focus on Algorithmic Noise Tolerance (ANT) techniques for the resilience mechanisms, and Max-Product BP for inference. We analyze the error characteristics of BP in this hardware context, derive novel asymptotic error bounds, and provide theoretical reasoning to explain why ANT works well in this BP context. Experimental results from detailed resilient-BP simulations for various stereo matching tasks offer empirical support for this analysis.

关键词： Probabilistic logic Dynamic range computer vision Hardware Inference algorithms Belief propagation Error analysis

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ROBUST ONLINE MULTIPLE OBJECT TRACKING BASED ON THE CONFIDENCE-BASED RELATIVE MOTION NETWORK AND CORRELATION FILTER

ROBUST ONLINE MULTIPLE OBJECT TRACKING BASED ON THE CONFIDEN...

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IEEE international conference on image processing

作者： Se-Hoon Park Kyuewang Lee Kuk-Jin Yoon Computer Vision Laboratory School of Information and Communications GIST Republic of Korea

ISBN: (纸本)9781467399623

When tracking multiple objects in an image sequence, various difficulties such as occlusion, mis-detection, false detection, and abrupt camera motion often occur together. Nevertheless, previous methods on multi-object tracking generally focus on only one or two of them. For that reason, the previous methods could not handle various problematic situations, where multiple difficulties occur simultaneously. To overcome this limitation, we propose a unified framework that can handle such difficulties concurrently, where we effectively combine the confidence-based two-step data association and relative motion network with correlation filtering. We show that the proposed unified framework yields noticeable performance enhancement under various difficulties.

关键词： Correlation Target tracking Cameras Object tracking Detectors Object detection

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Deeper Depth Prediction with Fully Convolutional Residual Networks

Deeper Depth Prediction with Fully Convolutional Residual Ne...

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international conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Iro Laina Christian Rupprecht Vasileios Belagiannis Federico Tombari Nassir Navab Technische Universität München Munich Germany Johns Hopkins University Baltimore MD USA University of Oxford Oxford United Kingdom University of Bologna Bologna Italy

ISBN: (纸本)9781509054084

This paper addresses the problem of estimating the depth map of a scene given a single RGB image. We propose a fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps. In order to improve the output resolution, we present a novel way to efficiently learn feature map up-sampling within the network. For optimization, we introduce the reverse Huber loss that is particularly suited for the task at hand and driven by the value distributions commonly present in depth maps. Our model is composed of a single architecture that is trained end-to-end and does not rely on post-processing techniques, such as CRFs or other additional refinement steps. As a result, it runs in real-time on images or videos. In the evaluation, we show that the proposed model contains fewer parameters and requires fewer training data than the current state of the art, while outperforming all approaches on depth estimation. Code and models are publicly available.

关键词： Estimation image resolution Three-dimensional displays computer architecture computer vision Semantics Optimization

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LINEAR CANONICAL CORRELATION ANALYSIS BASED RANKING APPROACH FOR FACIAL AGE ESTIMATION

LINEAR CANONICAL CORRELATION ANALYSIS BASED RANKING APPROACH...

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IEEE international conference on image processing

作者： Hong Liu Xiaohu Sun Key Laboratory of Machine Perception Shenzhen Graduate School Peking University

ISBN: (纸本)9781467399623

Facial age estimation is an important and challenging problem in computer vision and pattern recognition. Linear canonical correlation analysis (CCA) has been widely applied owing to low complexity, small and fixed amount of model parameters and good scalability. However, linear CCA based regression gets lower accuracy than its kernel version on the age estimation problem. The inexactness of metric distance information carried by age labels increases the complexity of using regression-based methods to estimate age. Hence, we propose a linear 2-norm regularized LS-CCA based ranking approach only exploiting the ordinal information carried by age labels. It gains the advantages of both linear 2-norm regularized LS-CCA and the ranking approach. Our method achieves competitive accuracy with the state-of-the-arts with sharply lower time cost and less amount of model parameters, which makes it appealing for real-time, large-scale applications or embedded systems. Additionally, experimental results on multi-source cross-population age estimation problem demonstrate that it is more robust against race and gender variations than the state-of-the-arts.

关键词： Facial Age Estimation Rank Canonical Correlation Analysis Linearity Accelerated aging Rank of data Linearity images computer vision model parameters Correlation analysis Arts

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