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Group MAD competition - A new methodology to compare objective image quality models

Group MAD competition - A new methodology to compare objecti...

引用

2016 ieee conference on computer Vision and pattern recognition, CVPR 2016

作者： Ma, Kede Wu, Qingbo Wang, Zhou Duanmu, Zhengfang Yong, Hongwei Li, Hongliang Zhang, Lei University of Waterloo Canada University of Electronic Science and Technology of China China Xi'an Jiaotong University China Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781467388511

Objective image quality assessment (IQA) models aim to automatically predict human visual perception of image quality and are of fundamental importance in the field of image processing.and computer vision. With an increasing number of IQA models proposed, how to fairly compare their performance becomes a major challenge due to the enormous size of image space and the limited resource for subjective testing. The standard approach in literature is to compute several correlation metrics between subjective mean opinion scores (MOSs) and objective model predictions on several well-known subject-rated databases that contain distorted images generated from a few dozens of source images, which however provide an extremely limited representation of real-world images. Moreover, most IQA models developed on these databases often involve machine learning and/or manual parameter tuning steps to boost their performance, and thus their generalization capabilities are questionable. Here we propose a novel methodology to compare IQA models. We first build a database that contains 4,744 source natural images, together with 94,880 distorted images created from them. We then propose a new mechanism, namely group MAximum Differentiation (gMAD) competition, which automatically selects subsets of image pairs from the database that provide the strongest test to let the IQA models compete with each other. Subjective testing on the selected subsets reveals the relative performance of the IQA models and provides useful insights on potential ways to improve them. We report the gMAD competition results between 16 well-known IQA models, but the framework is extendable, allowing future IQA models to be added into the competition.

关键词： image quality

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Semantic object parsing with Local-Global Long Short-Term Memory

Semantic object parsing with Local-Global Long Short-Term Me...

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2016 ieee conference on computer Vision and pattern recognition, CVPR 2016

作者： Liang, Xiaodan Shen, Xiaohui Xiang, Donglai Feng, Jiashi Lin, Liang Yan, Shuicheng Sun Yat-sen University NUS Singapore 360 AI Institute China National University of Singapore Singapore Adobe Research United States

ISBN: (纸本)9781467388511

Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical for achieving such fine-grained pixel-level recognition. Prior methods often leverage the contextual information through post-processing.predicted confidence maps. In this work, we propose a novel deep Local-Global Long Short-Term Memory (LG-LSTM) architecture to seamlessly incorporate short-distance and long-distance spatial dependencies into the feature learning over all pixel positions. In each LG-LSTM layer, local guidance from neighboring positions and global guidance from the whole image are imposed on each position to better exploit complex local and global contextual information. Individual LSTMs for distinct spatial dimensions are also utilized to intrinsically capture various spatial layouts of semantic parts in the images, yielding distinct hidden and memory cells of each position for each dimension. In our parsing approach, several LG-LSTM layers are stacked and appended to the intermediate convolutional layers to directly enhance visual features, allowing network parameters to be learned in an end-to-end way. The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions. Comprehensive evaluations on three public datasets well demonstrate the significant superiority of our LG-LSTM over other state-of-the-art methods.

关键词： pattern recognition

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Piecewise-planar 3D approximation from wide-baseline stereo

Piecewise-planar 3D approximation from wide-baseline stereo

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2016 ieee conference on computer Vision and pattern recognition, CVPR 2016

作者： Verleysen, C. De Vleeschouwer, C. Louvain-la-Neuve Belgium

ISBN: (纸本)9781467388511

This paper approximates the 3D geometry of a scene by a small number of 3D planes. The method is especially suited to man-made scenes, and only requires two calibrated wide-baseline views as inputs. It relies on the computation of a dense but noisy 3D point cloud, as for example obtained by matching DAISY descriptors [35] between the views. It then segments one of the two reference images, and adopts a multi-model fitting process to assign a 3D plane to each region, when the region is not detected as occluded. A pool of 3D plane hypotheses is first derived from the 3D point cloud, to include planes that reasonably approximate the part of the 3D point cloud observed from each reference view between randomly selected triplets of 3D points. The hypothesis-to-region assignment problem is then formulated as an energy-minimization problem, which simultaneously optimizes an original data-fidelity term, the assignment smoothness over neighboring regions, and the number of assigned planar proxies. The synthesis of intermediate viewpoints demonstrates the effectiveness of our 3D reconstruction, and thereby the relevance of our proposed data fidelity-metric.

关键词： Stereo image processing

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Comprehensive Automated 3D Urban Environment Modelling Using Terrestrial Laser Scanning Point Cloud

Comprehensive Automated 3D Urban Environment Modelling Using...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Pouria Babahajiani Lixin Fan Joni-Kristian Kämäräinen Moncef Gabbouj Nokia Technologies Tampere Finland Department of Signal Processing Tampere University of Technology Tampere Finland

In this paper we present a novel street scene modelling framework, which takes advantage of 3D point cloud captured by a high definition LiDAR laser scanner. We propose an automatic and robust approach to detect, segment and classify urban objects from point clouds hence reconstructing a comprehensive 3D urban environment model. Our system first automatically segments grounds point cloud. Then building facades will be detected by using binary range image processing. Remained point cloud will be grouped into voxels and subsequently transformed into super voxels. Local 3D features are extracted from super voxels and classified by trained boosted decision trees with semantic classes e.g. tree, pedestrian, and car. Given labeled point cloud the proposed algorithm reconstructs the realistic model in two phases. Firstly building facades will be rendered by ShadVis algorithm. In the second step we apply a novel and fast method for fitting the solid predefined template mesh models to non-building labeled point cloud. The proposed method is evaluated both quantitatively and qualitatively on a challenging TLS NAVTEQ True databases.

关键词： Three-dimensional displays Buildings Solid modeling Feature extraction Laser radar Urban areas Fitting

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Unsupervised HEp-2 mitosis recognition in Indirect Immunofluorescence Imaging 37

Unsupervised HEp-2 mitosis recognition in Indirect Immunoflu...

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37th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-society (EMBC)

作者： Tonti, Simone Di Cataldo, Santa Macii, Enrico Ficarra, Elisa Politecn Torino Dept Comp & Control Engn Cso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9781424492701

Automated HEp-2 mitotic cell recognition in IIF images is an important and yet scarcely explored step in the computer-aided diagnosis of autoimmune disorders. Such step is necessary to assess the goodness of the HEp-2 samples and helps the early diagnosis of the most difficult or ambiguous cases. In this work, we propose a completely unsupervised approach for HEp-2 mitotic cell recognition that overcomes the problem of mitotic/non-mitotic class imbalance due to the limited number of mitotic cells. Our technique automatically selects a limited set of candidate cells from the HEp-2 slide and then applies a clustering algorithm to identify the mitotic ones based on their texture. Finally, a second stage of clustering discriminates between positive and negative mitoses. Experiments on public IIF images demonstrate the performance of our technique compared to previous approaches.

关键词： biomedical optical imaging cellular biophysics fluorescence image texture medical disorders medical image processing HEp-2 slide autoimmune disorders automated HEp-2 mitotic cell recognition clustering algorithm computer-aided diagnosis texture unsupervised HEp-2 mitosis recognition Accuracy Clustering algorithms image analysis image recognition image segmentation Imaging pattern recognition

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0TV: A new method for image restoration in the presence of impulse noise

0TV: A new method for image restoration in the presence of i...

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ieee conference on computer Vision and pattern recognition, CVPR 2015

作者： Yuan, Ganzhao Ghanem, Bernard China Saudi Arabia

ISBN: (纸本)9781467369640

Total Variation (TV) is an effective and popular prior model in the field of regularization-based image processing. This paper focuses on TV for image restoration in the presence of impulse noise. This type of noise frequently arises in data acquisition and transmission due to many reasons, e.g. a faulty sensor or analog-to-digital converter errors. Removing this noise is an important task in image restoration. State-of-the-art methods such as Adaptive Outlier Pursuit(AOP) [42], which is based on TV with 02-norm data fidelity, only give sub-optimal performance. In this paper, we propose a new method, called 0TV -PADMM, which solves the TV-based restoration problem with 0-norm data fidelity. To effectively deal with the resulting non-convex non-smooth optimization problem, we first reformulate it as an equivalent MPEC (Mathematical Program with Equilibrium Constraints), and then solve it using a proximal Alternating Direction Method of Multipliers (PADMM). Our 0TV -PADMM method finds a desirable solution to the original 0-norm optimization problem and is proven to be convergent under mild conditions. We apply 0TV -PADMM to the problems of image denoising and deblurring in the presence of impulse noise. Our extensive experiments demonstrate that 0TV -PADMM outperforms state-of-the-art image restoration methods. © 2015 ieee.

关键词： image reconstruction

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Dominant SIFT: A novel compact descriptor 40

Dominant SIFT: A novel compact descriptor

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40th ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2015

作者： Tra, Anh T. Lin, Weisi Kot, Alex IGS Nanyang Technological University Singapore School of CE Nanyang Technological University Singapore School of EEE Nanyang Technological University Singapore ROSE Lab Nanyang Technological University Singapore

ISBN: (纸本)9781467369978

Definition and extraction of local features play a very important role in image retrieval (IR), pattern recognition and computer vision. Fast growth of technology today calls for local features to be as compact as possible toward real-time and limited bandwidth applications. In this paper, we study the problem of representing images in a compact way to achieve low bit-rate transmission while maintaining good performance. To be more specific, we propose a novel compact descriptor, dominant SIFT, which only uses 48 bits to describe local features. Importantly, our descriptor is training-free, vocabulary-free and suitable for real-time and mobile applications. We show the effectiveness of the proposed compact descriptor in image retrieval. © 2015 ieee.

关键词： descriptor image retrieval Local feature

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13th International Work-conference on Artificial Neural Networks, IWANN 2015 13

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13th International Work-conference on Artificial Neural Networks, IWANN 2015

ISBN: (纸本)9783319192574

The proceedings contain 57 papers. The special focus in this conference is on Computing Languages with Bio-Inspired Devices, Brain-computer Interfaces, Multi-Robot Systems, Video and image processing. The topics include: Grammatical inference model for measuring language complexity;a proposal for contextual grammatical inference;training in realistic virtual environments;real-time monitoring of biomedical signals to improve road safety;authentication of brain-computer interface users in network applications;a first step toward a possibilistic swarm multi-robot task allocation;a bottom-up robot architecture based on learnt behaviors driven design;from human eye fixation to human-like autonomous artificial vision;a mini robot for scientific applications;visualization of complex datasets with the self-organizing spanning tree;a detection system for vertical slot fishways using laser technology and computer vision techniques;interactive relevance visual learning for image retrieval;scene classification based on local binary pattern and improved bag of visual words;a novel framework for hyperemia grading based on artificial neural networks;finding the texture features characterizing the most homogeneous texture segment in the image;bio-inspired motion estimation with event-driven sensors;domain generalization based on transfer component analysis;deep transfer learning ensemble for classification;transfer learning for the recognition of immunogold particles in TEM imaging;improved retrieval for challenging scenarios in clique-based neural networks;deep neural networks for wind energy prediction and convolutional neural networks for detecting and mapping crowds in first person vision applications.

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A Piggyback Representation for Action recognition

A Piggyback Representation for Action Recognition

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27th ieee conference on computer Vision and pattern recognition (CVPR)

作者： Wolf, Lior Hanani, Yair Hassner, Tal Tel Aviv Univ Balvatnik Sch Comp Sci IL-69978 Tel Aviv Israel Open Univ Israel Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781479943098

In video understanding, the spatial patterns formed by local space-time interest points hold discriminative information. We encode these spatial regularities using a word2vec neural network, a recently proposed tool in the field of text processing. Then, building upon recent accumulator based image representation solutions, input videos are represented in a hybrid manner: the appearance of local space time interest points is used to collect and associate the learned descriptors, which capture the spatial patterns. Promising results are shown on recent action recognition benchmarks, using well established methods as the underlying appearance descriptors.

关键词： Text processing

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A Piggyback Representation for Action recognition

A Piggyback Representation for Action Recognition

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Lior Wolf Yair Hanani Tal Hassner The Balvatnik School of Computer Science Tel Aviv University Dept. of Mathematics and Computer Science The Open University of Israel

关键词： Vectors Dictionaries Benchmark testing Training Encoding Trajectory Visualization

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