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International Conference on Physiological Computing Systems, PhyCS 2014

作者： Dang, Thi-Hai-Ha Tapus, Adriana Computer Vision and Robotics Lab. ENSTA-Paris Palaiseau France

ISBN: (纸本)9789897580062

Driver's cognitive load has always been associated with the driver's heart rate activity and his/her skin conductance activity. However, what aspects of cognitive load that these signals relate to have never been clearly studied. This paper presents our preliminary results about the relationship between the different physiological signals (heart rate and skin conductance) and the driver's cognitive load. Via one experiment with simulated car driving environment and one experiment in real flying environment, our data suggests that subjects' heart rate relates to the number of events to be processed by the human driver while the skin conductance relates to the novelty of the driving task. Given the small population involved in these experiments, tests on more subjects are planned and reported in the future. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.

关键词： Automobile drivers

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Pushing the envelope of modern methods for bundle adjustment

Pushing the envelope of modern methods for bundle adjustment

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Yekeun Jeong David Nister Drew Steedly Richard Szeliski In-So Kweon Robotics and Computer Vision Lab. KAIST Microsoft Research South Korea Robotics and Computer Vision Lab. KAIST Microsoft Research Robotics and Computer Vision Lab. KAIST Microsoft Research USA

In this paper, we present results and experiments with several methods for bundle adjustment, producing the fastest bundle adjuster ever published. The fastest methods work with the well known reduced camera system and handle the block-sparse pattern arising in the reduced camera system in a natural way. Adapting to the naturally arising block-sparsity allows the use of BLAS3, efficient memory handling, fast variable ordering, and customized sparse solving all at the same time. We present two methods, one using exact minimum degree ordering and block-based LDL solving, and one using block-based preconditioned conjugate gradient, both on the reduced camera system. We show experimentally that the adaptation to the natural block sparsity allows both these methods to perform better than previous ones. Further speed improvements are achieved by the novel use of embedded point iterations. The embedded point iterations take place inside each camera update step, yielding a higher cost decrease from each camera update step. This is especially true for points projecting far out on the flatter region of the robustifier.

关键词： Cameras Robot vision systems Large-scale systems computer vision Costs Convergence Character generation Robustness Frequency conversion Humans

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Accurate Camera Calibration Robust to Defocus using a Smartphone

Accurate Camera Calibration Robust to Defocus using a Smartp...

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IEEE International Conference on computer vision

作者： Hyowon Ha Yunsu Bok Kyungdon Joo Jiyoung Jung In So Kweon Robotics and Computer Vision Lab. KAIST

ISBN: (纸本)9781467383929

We propose a novel camera calibration method for defocused images using a smartphone under the assumption that the defocus blur is modeled as a convolution of a sharp image with a Gaussian point spread function (PSF). In contrast to existing calibration approaches which require well-focused images, the proposed method achieves accurate camera calibration with severely defocused images. This robustness to defocus is due to the proposed set of unidirectional binary patterns, which simplifies 2D Gaussian deconvolution to a 1D Gaussian deconvolution problem with multiple observations. By capturing the set of patterns consecutively displayed on a smartphone, we formulate the feature extraction as a deconvolution problem to estimate feature point locations in sub-pixel accuracy and the blur kernel in each location. We also compensate the error in camera parameters due to refraction of the glass panel of the display device. We evaluate the performance of the proposed method on synthetic and real data. Even under severe defocus, our method shows accurate camera calibration result.

关键词： defocusing camera calibration Smartphone GLASS PLATES display devices Feature extraction deconvolution

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Globally Optimal Manhattan Frame Estimation in Real-time

Globally Optimal Manhattan Frame Estimation in Real-time

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IEEE Conference on computer vision and Pattern Recognition

作者： Kyungdon Joo Tae-Hyun Oh Junsik Kim In So Kweon Robotics and Computer Vision Lab. KAIST

ISBN: (纸本)9781467388528

Given a set of surface normals, we pose a Manhattan Frame (MF) estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. We solve this problem through a branch-and-bound framework, which mathematically guarantees a globally optimal solution. However, the computational time of conventional branch-and-bound algorithms are intractable for real-time performance. In this paper, we propose a novel bound computation method within an efficient measurement domain for MF estimation, i.e., the extended Gaussian image (EGI). By relaxing the original problem, we can compute the bounds in real-time, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for synthetic and real-world data. We also show the versatility of our approach through two applications: extension to multiple MF estimation and video stabilization.

关键词： Manhattan surface normal inliers Real time computational methods Frame EGI gene SYNTHETICS

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Artificial landmark tracking based on the color histogram

Artificial landmark tracking based on the color histogram

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2001 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Yoon, Kuk-Jin Kweon, In-So Robotics and Computer Vision Lab. Dept. of Elec. Eng. and Comp. Sci. KAIST Taejon Korea Republic of

For the fast and accurate self-localization of mobile robots, landmarks can be used very efficiently in the complex workspace. In this paper, we propose a simple color landmark model for self-localization and a fast landmark detection and tracking algorithm based on the proposed landmark model. We develop a color landmark with a symmetric and repetitive structure, which shows invariant color histogram characteristics under some geometric distortions. Detection and tracking of the model are accomplished by a factored sampling technique in which color similarity is estimated by the color histogram intersection. We also use the color similarity to update the color histogram model of the landmark model for robust tracking under illumination change. We demonstrate the feasibility of the proposed technique through experiments in cluttered indoor environments.

关键词： Mobile robots

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Radiometric calibration by transform invariant low-rank structure

Radiometric calibration by transform invariant low-rank stru...

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作者： Lee, Joon-Young Shi, Boxin Matsushita, Yasuyuki Kweon, In-So Ikeuchi, Katsushi Robotics and Computer Vision Lab KAIST Korea Republic of University of Tokyo Japan Microsoft Research Asia China

ISBN: (纸本)9781457703942

We present a robust radiometric calibration method that capitalizes on the transform invariant low-rank structure of sensor irradiances recorded from a static scene with different exposure times. We formulate the radiometric calibration problem as a rank minimization problem. Unlike previous approaches, our method naturally avoids over-fitting problem;therefore, it is robust against biased distribution of the input data, which is common in practice. When the exposure times are completely unknown, the proposed method can robustly estimate the response function up to an exponential ambiguity. The method is evaluated using both simulation and real-world datasets and shows a superior performance than previous approaches. © 2011 IEEE.

关键词： Radiometry

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Stereo matching with symmetric cost functions

Stereo matching with symmetric cost functions

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2006 IEEE computer Society Conference on computer vision and Pattern Recognition, CVPR 2006

作者： Yoon, Kuk-Jin Kweon, In So Robotics and Computer Vision Lab. Dept. Electrical Engineering and Computer Science KAIST 373-1 Guseong-dong Yuseong-gu Daejeon 305-701 Korea Republic of

ISBN: (纸本)0769525970

Recently, many global stereo methods have achieved good results by modeling a disparity surface as a Markov random field (MRF) and by solving an optimization problem with various techniques. However, most global methods mainly focus on how to minimize conventional cost functions efficiently, although it is more important to define cost functions well to improve performance. In this paper, we propose new symmetric cost functions for global stereo methods. We first present a symmetric data cost function for the likelihood and then propose a symmetric discontinuity cost function for the prior in the MRF model for stereo. In defining cost function, both the reference image and the target image are taken into account to improve performance without modeling half-occluded pixels explicitly and without using color segmentation. The performance improvement of stereo matching due to the proposed symmetric cost functions is verified by applying the proposed symmetric cost functions to the belief propagation (BP) based stereo method. Experimental results for standard testbed images show that the performance of the BP based stereo method is greatly improved by the proposed symmetric cost functions. © 2006 IEEE.

关键词： Pattern matching

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A unified approach of multi-scale deep and hand-crafted features for defocus estimation

arXiv

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arXiv 2017年

作者： Park, Jinsun Tai, Yu-Wing Cho, Donghyeon Kweon, In So Robotics and Computer Vision Lab. KAIST Tencent YouTu Lab.

In this paper, we introduce robust and synergetic handcrafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation. This paper systematically analyzes the effectiveness of different features, and shows how each feature can compensate for the weaknesses of other features when they are concatenated. For a full defocus map estimation, we extract image patches on strong edges sparsely, after which we use them for deep and hand-crafted feature extraction. In order to reduce the degree of patch-scale dependency, we also propose a multi-scale patch extraction strategy. A sparse defocus map is generated using a neural network classifier followed by a probability-joint bilateral filter. The final defocus map is obtained from the sparse defocus map with guidance from an edge-preserving filtered input image. Experimental results show that our algorithm is superior to state-of-the-art algorithms in terms of defocus estimation. Our work can be used for applications such as segmentation, blur magnification, all-in-focus image generation, and 3-D estimation. Copyright © 2017, The Authors. All rights reserved.

关键词： Extraction

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Stereo Matching with Color and Monochrome Cameras in Low-light Conditions

Stereo Matching with Color and Monochrome Cameras in Low-lig...

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IEEE Conference on computer vision and Pattern Recognition

作者： Hae-Gon Jeon Joon-Young Lee Sunghoon Im Hyowon Ha In So Kweon Robotics and Computer Vision Lab. KAIST Adobe Research

ISBN: (纸本)9781467388528

Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability. However, those systems usually suffer from low imaging quality and inaccurate depth acquisition under low-light conditions. To address the problem, we present a new stereo matching method with a color and monochrome camera pair. We focus on the fundamental trade-off that monochrome cameras have much better light-efficiency than color-filtered cameras. Our key ideas involve compensating for the radiometric difference between two cross-spectral images and taking full advantage of complementary data. Consequently, our method produces both an accurate depth map and high-quality images, which are applicable for various depth-aware image processing. Our method is evaluated using various datasets and the performance of our depth estimation consistently outperforms state-of-the-art methods.

关键词： Cameras Image quality Stereo cameras Color Dataset Image processing

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Fast correspondence-based system for 2-D shape classification

Fast correspondence-based system for 2-D shape classificatio...

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Proceedings of the International Conference on Imaging Science, Systems and Technology, CISST'03

作者： Super, Boaz J. Computer Vision and Robotics Lab. Computer Science Department University of Illinois at Chicago Chicago IL United States

In earlier work, we developed a fast method for 2-D shape retrieval based on point correspondences of silhouette contours. That method did not assume that the database shapes had class lab.ls. If class lab.l information is added, then the shape retrieval system can be used for 2-D shape classification. This paper explores this application, by extending our prior shape retrieval method to make use of class lab.ls, and then evaluating its classification accuracy on a large test database containing 70 object classes. The results show that the method is both highly accurate (96.5-99.6%) and fast (< 1 second).

关键词： Image analysis

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