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

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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Efficient deep learning for stereo matching

Efficient deep learning for stereo matching

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

作者： Luo, Wenjie Schwing, Alexander G. Urtasun, Raquel Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781467388511

In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation. However, current architectures rely on siamese networks which exploit concatenation followed by further processing.layers, requiring a minute of GPU computation per image pair. In contrast, in this paper we propose a matching network which is able to produce very accurate results in less than a second of GPU computation. Towards this goal, we exploit a product layer which simply computes the inner product between the two representations of a siamese architecture. We train our network by treating the problem as multi-class classification, where the classes are all possible disparities. This allows us to get calibrated scores, which result in much better matching performance when compared to existing approaches.

关键词： Stereo image processing

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image style transfer using Convolutional Neural Networks

Image style transfer using Convolutional Neural Networks

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

作者： Gatys, Leon A. Ecker, Alexander S. Bethge, Matthias Centre for Integrative Neuroscience University of Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Germany Graduate School of Neural Information Processing University of Tübingen Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Baylor College of Medicine HoustonTX United States

ISBN: (纸本)9781467388511

Rendering the semantic content of an image in different styles is a difficult image processing.task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.

关键词： Convolution

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Stacked attention networks for image question answering

Stacked attention networks for image question answering

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

作者： Yang, Zichao He, Xiaodong Gao, Jianfeng Deng, Li Smola, Alex Carnegie Mellon University United States Microsoft Research RedmondWA98052 United States

ISBN: (纸本)9781467388511

This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. SANs use semantic representation of a question as query to search for the regions in an image that are related to the answer. We argue that image question answering (QA) often requires multiple steps of reasoning. Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively. Experiments conducted on four image QA data sets demonstrate that the proposed SANs significantly outperform previous state-of-the-art approaches. The visualization of the attention layers illustrates the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer.

关键词： Query processing

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Progressive prioritized multi-view stereo

Progressive prioritized multi-view stereo

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

作者： Locher, Alex Perdoch, Michal Van Gool, Luc Computer Vision Laboratory ETH Zurich Switzerland VISICS KU Leuven Belgium

ISBN: (纸本)9781467388511

This work proposes a progressive patch based multiview stereo algorithm able to deliver a dense point cloud at any time. This enables an immediate feedback on the reconstruction process in a user centric scenario. With increasing processing.time, the model is improved in terms of resolution and accuracy. The algorithm explicitly handles input images with varying effective scale and creates visually pleasing point clouds. A priority scheme assures that the limited computational power is invested in scene parts, where the user is most interested in or the overall error can be reduced the most. The architecture of the proposed pipeline allows fast processing.times in large scenes using a pure open-source CPU implementation. We show the performance of our algorithm on challenging standard datasets as well as on real-world scenes and compare it to the baseline.

关键词： Stereo image processing

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Automated 3D face reconstruction from multiple images using quality measures

Automated 3D face reconstruction from multiple images using ...

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

作者： Piotraschke, Marcel Blanz, Volker Institute for Vision and Graphics University of Siegen Germany

ISBN: (纸本)9781467388511

Automated 3D reconstruction of faces from images is challenging if the image material is difficult in terms of pose, lighting, occlusions and facial expressions, and if the initial 2D feature positions are inaccurate or unreliable. We propose a method that reconstructs individual 3D shapes from multiple single images of one person, judges their quality and then combines the best of all results. This is done separately for different regions of the face. The core element of this algorithm and the focus of our paper is a quality measure that judges a reconstruction without information about the true shape. We evaluate different quality measures, develop a method for combining results, and present a complete processing.pipeline for automated reconstruction.

关键词： image reconstruction

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Uncalibrated photometric stereo by stepwise optimization using principal components of isotropic BRDFs

Uncalibrated photometric stereo by stepwise optimization usi...

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

作者： Midorikawa, Keisuke Yamasaki, Toshihiko Aizawa, Kiyoharu University of Tokyo Japan

ISBN: (纸本)9781467388511

The uncalibrated photometric stereo problem for non-Lambertian surfaces is challenging because of the large number of unknowns and its ill-posed nature stemming from unknown reflectance functions. We propose a model that represents various isotropic reflectance functions by using the principal components of items in a dataset, and formulate the uncalibrated photometric stereo as a regression problem. We then solve it by stepwise optimization utilizing principal components in order of their eigenvalues. We have also developed two techniques that lead to convergence and highly accurate reconstruction, namely (1) a coarse-to-fine approach with normal grouping, and (2) a randomized multipoint search. Our experimental results with synthetic data showed that our method significantly outperformed previous methods. We also evaluated the algorithm in terms of real image data, where it gave good reconstruction results.

关键词： Stereo image processing

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Saliency guided dictionary learning forweakly-supervised image parsing

Saliency guided dictionary learning forweakly-supervised ima...

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

作者： Lai, Baisheng Gong, Xiaojin College of Information Science and Electronic Engineering Zhejiang University Hangzhou Zhejiang China

ISBN: (纸本)9781467388511

In this paper, we propose a novel method to perform weakly-supervised image parsing based on the dictionary learning framework. To deal with the challenges caused by the label ambiguities, we design a saliency guided weight assignment scheme to boost the discriminative dictionary learning. More specifically, with a collection of tagged images, the proposed method first conducts saliency detection and automatically infers the confidence for each semantic class to be foreground or background. These clues are then incorporated to learn the dictionaries, the weights, as well as the sparse representation coefficients in the meanwhile. Once obtained the coefficients of a superpixel, we use a sparse representation classifier to determine its semantic label. The approach is validated on the MSRC21, PAS-CAL VOC07, and VOC12 datasets. Experimental results demonstrate the encouraging performance of our approach in comparison with some state-of-the-arts.

关键词： image processing

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Real-time salient object detection with a minimum spanning tree

Real-time salient object detection with a minimum spanning t...

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

作者： Tu, Wei-Chih He, Shengfeng Yang, Qingxiong Chien, Shao-Yi Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Computer Science City University of Hong Kong Hong Kong School of Information Science and Technology University of Science and Technology of China China

ISBN: (纸本)9781467388511

In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connectivity efficiently is a challenging problem. Existing methods either rely on superpixel representation to reduce the processing.units or approximate the distance transform. Instead, we propose an exact and iteration free solution on a minimum spanning tree. The minimum spanning tree representation of an image inherently reveals the object geometry information in a scene. Meanwhile, it largely reduces the search space of shortest paths, resulting an efficient and high quality distance transform algorithm. We further introduce a boundary dissimilarity measure to compliment the shortage of distance transform for salient object detection. Extensive evaluations show that the proposed algorithm achieves the leading performance compared to the state-of-the-art methods in terms of efficiency and accuracy.

关键词： pattern recognition

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Comparative deep learning of hybrid representations for image recommendations

Comparative deep learning of hybrid representations for imag...

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

作者： Lei, Chenyi Liu, Dong Li, Weiping Zha, Zheng-Jun Li, Houqiang CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781467388511

In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two subnetworks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other stateof-the-art image recommendation solutions.

关键词： pattern recognition

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