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A Global Hypothesis Verification Framework for 3D Object recognition in Clutter

ieee TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2016年第7期38卷 1383-1396页

作者： Aldoma, Aitor Tombari, Federico Di Stefano, Luigi Vincze, Markus Vienna Univ Technol Grp ACIN Vision4Robot A-1060 Vienna Austria Univ Bologna CVLAB Grp DISI Bologna Italy

Pipelines to recognize 3D objects despite clutter and occlusions usually end up with a final verification stage whereby recognition hypotheses are validated or dismissed based on how well they explain sensor measurements. Unlike previous work, we propose a Global Hypothesis Verification (GHV) approach which regards all hypotheses jointly so as to account for mutual interactions. GHV provides a principled framework to tackle the complexity of our visual world by leveraging on a plurality of recognition paradigms and cues. Accordingly, we present a 3D object recognition pipeline deploying both global and local 3D features as well as shape and color. thereby, and facilitated by the robustness of the verification process, diverse object hypotheses can be gathered and weak hypotheses need not be suppressed too early to trade sensitivity for specificity. Experiments demonstrate the effectiveness of our proposal, which significantly improves over the state-of-art and attains ideal performance (no false negatives, no false positives) on three out of the six most relevant and challenging benchmark datasets.

关键词： 3D object recognition hypothesis verification correspondence grouping scene understanding

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Distributed Multi-Target Tracking and Data Association in vision Networks

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ieee TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2016年第7期38卷 1397-1397页

作者： Kamal, Ahmed T. Bappy, Jawadul H. Farrell, Jay A. Roy-Chowdhury, Amit K. Univ Calif Riverside Riverside CA 92521 USA

Distributed algorithms have recently gained immense popularity. With regards to computer vision applications, distributed multi-target tracking in a camera network is a fundamental problem. the goal is for all cameras to have accurate state estimates for all targets. Distributed estimation algorithms work by exchanging information between sensors that are communication neighbors. vision-based distributed multi-target state estimation has at least two characteristics that distinguishes it from other applications. First, cameras are directional sensors and often neighboring sensors may not be sensing the same targets, i.e., they are naive with respect to that target. Second, in the presence of clutter and multiple targets, each camera must solve a data association problem. this paper presents an information-weighted, consensus-based, distributed multi-target tracking algorithm referred to as the Multi-target Information Consensus (MTIC) algorithm that is designed to address both the naivety and the data association problems. It converges to the centralized minimum mean square error estimate. the proposed MTIC algorithm and its extensions to non-linear camera models, termed as the Extended MTIC (EMTIC), are robust to false measurements and limited resources like power, bandwidth and the realtime operational requirements. Simulation and experimental analysis are provided to support the theoretical results.

关键词： Consensus distributed tracking data association camera networks

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Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art

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MACHINE vision AND APPLICATIONS 2016年第8期27卷 1275-1288页

作者： Fuhl, Wolfgang Tonsen, Marc Bulling, Andreas Kasneci, Enkelejda Univ Tubingen Percept Engn Grp Tubingen Germany Max Planck Inst Informat Perceptual User Interfaces Grp Saarbrucken Germany

Robust and accurate detection of the pupil position is a key building block for head-mounted eye tracking and prerequisite for applications on top, such as gaze-based human-computer interaction or attention analysis. Despite a large body of work, detecting the pupil in images recorded under real-world conditions is challenging given significant variability in the eye appearance (e.g., illumination, reflections, occlusions, etc.), individual differences in eye physiology, as well as other sources of noise, such as contact lenses or make-up. In this paper we review six state-of-the-art pupil detection methods, namely ElSe (Fuhl et al. in Proceedings of the ninth biennial ACM symposium on eye tracking research&applications, ACM. New York, NY, USA, pp 123130, 2016), ExCuSe (Fuhl et al. in computer analysis of images and patterns. Springer, New York, pp 39-51, 2015), Pupil Labs (Kassner et al. in Adjunct proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (UbiComp), pp 1151-1160, 2014. doi: 10.1145/2638728.2641695), SET (Javadi et al. in Front Neuroeng 8, 2015), Starburst (Li et al. in computer vision and pattern recognition-workshops, 2005. ieee computer society conference on cvpr workshops. ieee, pp 79-79, 2005), and Swirski (Swirski et al. in Proceedings of the symposium on eye tracking research and applications (ETRA). ACM, pp 173-176, 2012. doi: 10.1145/2168556.2168585). We compare their performance on a large-scale data set consisting of 225,569 annotated eye images taken from four publicly available data sets. Our experimental results show that the algorithm ElSe (Fuhl et al. 2016) outperforms other pupil detection methods by a large margin, offering thus robust and accurate pupil positions on challenging everyday eye images.

关键词： Pupil detection Head-mounted eye tracking Data set computer vision Image processing

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Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Inside-Outside Net: Detecting Objects in Context with Skip P...

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ieee conference on computer vision and pattern recognition

作者： Sean Bell C. Lawrence Zitnick Kavita Bala Ross Girshick Cornell University Microsoft Research

ISBN: (纸本)9781467388528

It is well known that contextual and multi-scale representations are important for accurate visual recognition. In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest. Contextual information outside the region of interest is integrated using spatial recurrent neural networks. Inside, we use skip pooling to extract information at multiple scales and levels of abstraction. through extensive experiments we evaluate the design space and provide readers with an overview of what tricks of the trade are important. ION improves state-of-the-art on PASCAL VOC 2012 object detection from 73.9% to 77.9% mAP. On the new and more challenging MS COCO dataset, we improve state-of-the-art from 19.7% to 33.1% mAP. In the 2015 MS COCO Detection Challenge, our ION model won "Best Student Entry" and finished 3rd place overall. As intuition suggests, our detection results provide strong evidence that context and multi-scale representations improve small object detection.

关键词： Context Ions Recurrent neural networks Feature extraction Convolution computer architecture Detectors

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Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation

Learning Cross-Domain Landmarks for Heterogeneous Domain Ada...

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ieee conference on computer vision and pattern recognition

作者： Yao-Hung Hubert Tsai Yi-Ren Yeh Yu-Chiang Frank Wang Res. Center for IT Innovation Taipei Taiwan Dept. of Math. Nat. Kaohsiung Normal Univ. Kaohsiung Taiwan

ISBN: (纸本)9781467388528

While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. this is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. the effectiveness of our proposed method can be successfully verified.

关键词： Correlation pattern recognition Training Target recognition Adaptation models conferences computer vision

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Constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection

Constrained joint cascade regression framework for simultane...

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

作者： Wu, Yue Ji, Qiang ECSE Department Rensselaer Polytechnic Institute 110 8th street TroyNY United States

ISBN: (纸本)9781467388511

Cascade regression framework has been shown to be effective for facial landmark detection. It starts from an initial face shape and gradually predicts the face shape update from the local appearance features to generate the facial landmark locations in the next iteration until convergence. In this paper, we improve upon the cascade regression framework and propose the Constrained Joint Cascade Regression Framework (CJCRF) for simultaneous facial action unit recognition and facial landmark detection, which are two related face analysis tasks, but are seldomly exploited together. In particular, we first learn the relationships among facial action units and face shapes as a constraint. then, in the proposed constrained joint cascade regression framework, with the help from the constraint, we iteratively update the facial landmark locations and the action unit activation probabilities until convergence. Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection. the experimental results also demonstrate the effectiveness of the proposed method comparing to the state-of-the-art works.

关键词： Face recognition

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Uncertain LDA: Including Observation Uncertainties in Discriminative Transforms

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ieee TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2016年第7期38卷 1479-88页

作者： Saeidi, Rahim Astudillo, Ramon Fernandez Kolossa, Dorothea Aalto Univ Dept Signal Proc & Acoust Espoo Uusimaa Finland INESC ID Spoken Language Syst Lab Lisbon Portugal Ruhr Univ Bochum Inst Commun Acoust Univ Str 150 Bochum Nrw Germany

Linear discriminant analysis (LDA) is a powerful technique in pattern recognition to reduce the dimensionality of data vectors. It maximizes discriminability by retaining only those directions that minimize the ratio of within-class and between-class variance. In this paper, using the same principles as for conventional LDA, we propose to employ uncertainties of the noisy or distorted input data in order to estimate maximally discriminant directions. We demonstrate the efficiency of the proposed uncertain LDA on two applications using state-of-the-art techniques. First, we experiment with an automatic speech recognition task, in which the uncertainty of observations is imposed by real-world additive noise. Next, we examine a full-scale speaker recognition system, considering the utterance duration as the source of uncertainty in authenticating a speaker. the experimental results show that when employing an appropriate uncertainty estimation algorithm, uncertain LDA outperforms its conventional LDA counterpart.

关键词： Uncertainty linear discriminant analysis LDA speaker recognition speech recognition

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Plasmonic crystal gas sensor incorporating graphene oxide for detection of volatile organic compounds

Plasmonic crystal gas sensor incorporating graphene oxide fo...

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ieee International conference on Micro Electro Mechanical Systems

作者： Shawana Tabassum Qiugu Wang Wentai Wang Seval Oren Md. Azahar Ali Ratnesh Kumar Liang Dong Department of Electrical and Computer Engineering Iowa State University Ames Iowa USA

this paper reports a gas sensor consisting of a thin layer of graphene oxide (GO) assembled on the top surface of mushroom plasmonic nanostructures. the plasmonic crystal structure is formed by an array of polymeric nanoposts with gold disks at the top and perforated nanoholes in a gold film at the bottom. the optical response of the plasmonic nanostructure is altered due to absorbing different concentrations of gas in presence of GO coating. the refractive index change of the GO layer with different thicknesses allows for selective detection of different gas species with the help of the principal component analysis-based pattern recognition algorithm. the present plasmonic nanostructure offers a promising approach to detect various volatile organic compounds.

关键词： Plasmons Gas detectors Graphene Optical surface waves Crystals Sensor arrays

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Partial Optimality by Pruning for MAP-Inference with General Graphical Models

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ieee TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2016年第7期38卷 1370-82页

作者： Swoboda, Paul Shekhovtsov, Alexander Kappes, Joerg Hendrik Schnoerr, Christoph Savchynskyy, Bogdan Heidelberg Univ Image & Pattern Anal Grp IPA Speyerer Str 6 D-69115 Heidelberg Germany Graz Univ Technol Inst Comp Graph & Vis ICG Inffeldgasse 16 A-8010 Graz Austria Heidelberg Univ Heidelberg Collaboratory Image Proc HCI Speyerer Str 6 D-69115 Heidelberg Germany

We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. the method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method's runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.

关键词： MAP-inference Markov random fields energy minimization persistency partial optimality local polytope

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Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback

Simultaneous Active Learning of Classifiers & Attributes via...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Biswas, Arijit Parikh, Devi Univ Maryland College Pk MD 20742 USA Virginia Tech Blacksburg VA USA

ISBN: (纸本)9780769549897

Active learning provides useful tools to reduce annotation costs without compromising classifier performance. However it traditionally views the supervisor simply as a labeling machine. Recently a new interactive learning paradigm was introduced that allows the supervisor to additionally convey useful domain knowledge using attributes. the learner first conveys its belief about an actively chosen image e. g. "I think this is a forest, what do you think?". If the learner is wrong, the supervisor provides an explanation e. g. "No, this is too open to be a forest". With access to a pre-trained set of relative attribute predictors, the learner fetches all unlabeled images more open than the query image, and uses them as negative examples of forests to update its classifier this rich human-machine communication leads to better classification performance. In this work, we propose three improvements over this set-up. First, we incorporate a weighting scheme that instead of making a hard decision reasons about the likelihood of an image being a negative example. Second, we do away with pre-trained attributes and instead learn the attribute models on the fly, alleviating overhead and restrictions of a pre-determined attribute vocabulary. Finally, we propose an active learning framework that accounts for not just the label-but also the attributes-based feedback while selecting the next query image. We demonstrate significant improvement in classification accuracy on faces and shoes. We also collect and make available the largest relative attributes dataset containing 29 attributes of faces from 60 categories.

关键词： image retrieval interactive systems learning (artificial intelligence) man-machine systems pattern classification

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