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International Conference on Pattern Recognition

作者： Kuk-Jin Yoon In-So Kweon Department of Electric Engineering and Computer Science KAIST Robotics and Computer Vision Laboratory South Korea

ISBN: (纸本)0769521282

We present a new algorithm for color image quantization based on human color perception properties. We construct two kinds of map by analyzing the spatial color distributions to take account of the human visual system: homogeneity map (H-map) and distinctiveness map (D-map). Then, we assign weight value to all color vectors by combining these maps to consider two factors at the same time. To extract representative colors, we define a new cost function and use the LKMA (local k-means algorithm) with weighted color vectors. In this stage, we utilize an incremental splitting scheme with a penalty term to determine optimal number of clusters adaptively. The experimental results show that the proposed algorithm reproduces an image preserving significant local features while removing unimportant details of an original image from the viewpoint of human.

关键词： Humans Quantization Clustering algorithms Image color analysis Visual system Cost function Image segmentation Distortion measurement Robot vision systems computer vision

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Camera Exposure Control for Robust Robot vision with Noise-Aware Image Quality Assessment

Camera Exposure Control for Robust Robot Vision with Noise-A...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ukcheol Shin Jinsun Park Gyumin Shim Francois Rameau In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea

In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture best-exposed images, which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric that captures complementary quality attributes and ensures light-weight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows the preservation of sharp edges and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a real-time and fully automatic exposure and gain control technique based on the Nelder-Mead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates the higher qualitative and quantitative performance compared with conventional approaches.

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Deep representation of industrial components using simulated images

Deep representation of industrial components using simulated...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Seong-Heum Kim Gyeongmin Choe Byungtae Ahn In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea

In this paper, we present a visual learning framework to retrieve a 3D model and estimate its pose from a single image. To increase the quantity and quality of training data, we define our simulation space in the near infrared (NIR) band, and utilize the quasi-Monte Carlo (MC) method for scalable photorealistic rendering of manufactured components. Two types of convolutional neural network (CNN) architectures are trained over these synthetic data and a relatively small amount of real data. The first CNN model seeks the most discriminative information and uses it to classify industrial components with fine-grained shape attributes. Once a 3D model is identified, one of the category-specific CNNs is tested for pose regression in the second phase. The mixed data for learning object categories is useful in domain adaptation and attention mechanism in our system. We validate our data-driven method with 88 component models, and the experimental results are qualitatively demonstrated. Also, the CNNs trained with various conditions of mixed data are quantitatively analyzed.

关键词： Three-dimensional displays Rendering (computer graphics) Computational modeling Solid modeling Shape Cameras Adaptation models

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Locally adaptive support-weight approach for visual correspondence search

Locally adaptive support-weight approach for visual correspo...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Kuk-Jin Yoon In-So Kweon Robotics and Computer Vision Laboratory Department of Electrical Engineering and Computer Science KAIST Daejeon South Korea

ISBN: (纸本)0769523722

In this paper, we present a new area-based method for visual correspondence search that focuses on the dissimilarity computation. Local and area-based matching methods generally measure the similarity (or dissimilarity) between the image pixels using local support windows. In this approach, an appropriate support window should be selected adaptively for each pixel to make the measure reliable and certain. Finding the optimal support window with an arbitrary shape and size is, however, very difficult and generally known as an NP-hard problem. For this reason, unlike the existing methods that try to find an optimal support window, we adjusted the support-weight of each pixel in a given support window. The adaptive support-weight of a pixel is computed based on the photometric and geometric relationship with the pixel under consideration. Dissimilarity is then computed using the raw matching costs and support-weights of both support windows, and the correspondence is finally selected by the WTA (winner-takes-all) method. The experimental results for the rectified real images show that the proposed method successfully produces piecewise smooth disparity maps while preserving sharp depth discontinuities accurately.

关键词： Shape Pixel Costs computer vision NP-hard problem Robot vision systems Laboratories computer science Area measurement Photometry

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Stereo Matching with Symmetric Cost Functions

Stereo Matching with Symmetric Cost Functions

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Kuk-Jin Yoon In So Kweon Robotics and Computer Vision Laboratory Department Electrical Engineering and Computer Science KAIST Daejeon South Korea

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.

关键词： Cost function Stereo vision Pixel computer vision Markov random fields Optimization methods Image segmentation Belief propagation Robot vision systems computer science

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Camera exposure control for robust robot vision with noise-aware image quality assessment

arXiv

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arXiv 2019年

作者： Shin, Ukcheol Park, Jinsun Shim, Gyumin Rameau, Francois Kweon, In So Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon34141 Korea Republic of

In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture the best-exposed image which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric which captures complementary quality attributes and ensures light-weight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows preserving sharp edge and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a real-time and fully automatic exposure and gain control technique based on the Nelder-Mead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates higher qualitative and quantitative performances when compared with conventional approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： computer vision

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Auto-adjusting Camera Exposure for Outdoor robotics using Gradient Information

Auto-adjusting Camera Exposure for Outdoor Robotics using Gr...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Inwook Shim Joon-Young Lee In So Kweon Robotics and Computer Vision Laboratory Division of Future Vehicle KAIST Daejeon 305-701 Korea Robotics and Computer Vision Laboratory Department of Electrical Engineering KAIST Daejeon 305-701 Korea Department of Electrical Engineering KAIST Daejeon 307-701 Korea

ISBN: (纸本)9781479969357

We present a new method to auto-adjust camera exposure for outdoor robotics. In outdoor environments, scene dynamic range may be wider than the dynamic range of the cameras due to sunlight and skylight. This can results in failures of vision-based algorithms because important image features are missing due to under-/over-saturation. To solve the problem, we adjust camera exposure to maximize image features in the gradient domain. By exploiting the gradient domain, our method naturally determines the proper exposure needed to capture important image features in a manner that is robust against illumination conditions. The proposed method is implemented using an off-the-shelf machine vision camera and is evaluated using outdoor robotics applications. Experimental results demonstrate the effectiveness of our method, which improves the performance of robot vision algorithms.

关键词： Cameras Lighting Robot vision systems Manuals Dynamic range Heuristic algorithms

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Fast Separation of Reflection Components using a Specularity-Invariant Image Representation

Fast Separation of Reflection Components using a Specularity...

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IEEE International Conference on Image Processing

作者： Kuk-jin Yoon Yoojin Choi In So Kweon Department Electrical Engineering and Computer Science KAIST Robotics and Computer Vision Laboratory South Korea Dept. Electrical Engineering and Computer Science Robotics and Computer Vision Lab. Korea

In this paper, we propose a fast method for separating reflection components using a single color image. We first propose a specular-free two-band image that is a specularity-invariant color image representation. Reflection components separation is achieved by comparing local ratios at each pixel and making those ratios equal in an iterative framework. The proposed method is very fast and shows reasonable results for textured indoor/outdoor images.

关键词： Reflection Image representation Layout Color Polarization computer vision Image segmentation Filters Lighting Argon

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Correlate-and-excite: Real-time stereo matching via guided cost volume excitation

arXiv

引用

arXiv 2021年

作者： Bangunharcana, Antyanta Cho, Jae Won Lee, Seokju Kweon, In So Kim, Kyung-Soo Kim, Soohyun Mechatronics Systems and Control Laboratory KAIST Daejeon34141 Korea Republic of Robotics and Computer Vision Laboratory KAIST Daejeon34141 Korea Republic of

Volumetric deep learning approach towards stereo matching aggregates a cost volume computed from input left and right images using 3D convolutions. Recent works showed that utilization of extracted image features and a spatially varying cost volume aggregation complements 3D convolutions. However, existing methods with spatially varying operations are complex, cost considerable computation time, and cause memory consumption to increase. In this work, we construct Guided Cost volume Excitation (GCE) and show that simple channel excitation of cost volume guided by image can improve performance considerably. Moreover, we propose a novel method of using top-k selection prior to soft-argmin disparity regression for computing the final disparity estimate. Combining our novel contributions, we present an end-to-end network that we call Correlate-and-Excite (CoEx). Extensive experiments of our model on the SceneFlow, KITTI 2012, and KITTI 2015 datasets demonstrate the effectiveness and efficiency of our model and show that our model outperforms other speed-based algorithms while also being competitive to other state-of-the-art algorithms. © 2021, CC BY.

关键词： Convolution

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A new camera calibration method for robotic applications

A new camera calibration method for robotic applications

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： Jun-Sik Kim In-So Kweon Department of Electrical Engineering and Computer Science Robotics and Computer Vision Laboratory KAIST South Korea Department of Electrical Engineering and Computer Science KAIST South Korea

In this paper, we present a camera calibration method using two views of three pairs of concentric circles with known sizes. A new invariant property for circle is introduced to determine the position of the center of the projected circle. Given two image ellipses and their corresponding centers, a cross-ratio based method estimates the center of projected circles using the new invariant property. The accurate center of projected concentric circles provides correct correspondences between ellipses in the image plane and circles in 3D plane. We also demonstrate that two views of three pairs of coplanar concentric circles are enough to determine the intrinsic camera parameters, such as the focal length, the aspect ratio, and the principal point. We validate the performance of the method using both synthetic and real images. Our method shows a comparable performance with respect to similar calibration methods using planes. The use of concentric circles, however, provides correct correspondences between 3D target points and their image points, and greatly simplifies the calibration problem.

关键词： Robot vision systems Cameras Calibration Mobile robots Application software computer vision Laboratories computer science Layout Robot kinematics

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