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17th IEEE Winter Conference on Applications of computer vision, WACV 2017

作者： Bailo, Oleksandr Lee, Seokju Rameau, Francois Yoon, Jae Shin Kweon, In So KAIST Robotics and Computer Vision Laboratory United States

ISBN: (纸本)9781509048229

This paper presents a robust approach for road marking detection and recognition from images captured by an embedded camera mounted on a car. Our method is designed to cope with illumination changes, shadows, and harsh meteorological conditions. Furthermore, the algorithm can effectively group complex multi-symbol shapes into an individual road marking. For this purpose, the proposed technique relies on MSER features to obtain candidate regions which are further merged using density-based clustering. Finally, these regions of interest are recognized using machine learning approaches. Worth noting, the algorithm is versatile since it does not utilize any prior information about lane position or road space. The proposed method compares favorably to other existing works through a large number of experiments on an extensive road marking dataset. © 2017 IEEE.

关键词： Road and street markings

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Cost-aware depth map estimation for Lytro camera

Cost-aware depth map estimation for Lytro camera

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作者： Kim, Min-Jung Oh, Tae-Hyun Kweon, In So Robotics and Computer Vision Laboratory KAIST Korea Republic of

ISBN: (纸本)9781479957514

Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera. © 2014 IEEE.

关键词： Cameras

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Deep representation of industrial components using simulated images

Deep representation of industrial components using simulated...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

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

ISBN: (纸本)9781509046331

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. © 2017 IEEE.

关键词： Monte Carlo methods

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Cost-aware depth map estimation for Lytro camera

Cost-aware depth map estimation for Lytro camera

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IEEE International Conference on Image Processing

作者： Min-Jung Kim Tae-Hyun Oh In So Kweon Robotics and Computer Vision Laboratory KAIST

ISBN: (纸本)9781479957521

关键词： Cameras Estimation Reliability Optimization Three-dimensional displays computer vision Conferences

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LINE MEETS AS-PROJECTIVE-AS-POSSIBLE IMAGE STITCHING WITH MOVING DLT

LINE MEETS AS-PROJECTIVE-AS-POSSIBLE IMAGE STITCHING WITH MO...

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IEEE International Conference on Image Processing

作者： Kyungdon Joo Namil Kim Tae-Hyun Oh In So Kweon Robotics and Computer Vision Laboratory KAIST

ISBN: (纸本)9781479983407

We propose a spatially varying stitching method with line correspondences. We are motivated by the observation that point features could be spatially biased or not matched in practice, e.g., repeated textures or homogeneous regions of man-made structures. In this scenario, line matches can provide strong correspondences as well as supplement cues, such as the structure preserving property. With these advantages, we adopt a feature fusion method that combines point and line correspondences into a unified framework for spatially varying stitching. We then estimate the balancing parameter between the point and line terms using geometric error. Our experiments show accurate alignment for challenging but common cases.

关键词： Spatially varying stitching Image alignment Line correspondence Moving direct linear transformation image registration fusion method sewing isostructure geometrical error images matching line

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Fast Landmark Tracking and Localization Algorithm for the Mobile Robot Self-Localization

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IFAC Proceedings Volumes 2001年第4期34卷 185-190页

作者： Kuk-Jin Yoon Gi-Jeong Jang Sung-Ho Kim In-So Kweon Robotics and Computer Vision Laboratory Dept. of Electrical Engineering and Computer Science KAIST Korea

We present a simple artificial landmark model and a robust landmark tracking alg orithm for mobile robot localization. The landmark model, consisting of symmetric and repetitive color patches, produces color histograms that are invariant under the geometric and photometric distortions. A stochastic approach based on the CONDENSATION tracks the landmark model robustly even under the varying illumination conditions. After the landmark detection, relative position of the mobile robot to the landmark is calculated. Experimental results show that the proposed landmark model is effective and can be detected and tracked in a cluttered scenerobustly. The tracked single landmark is enough to detennine the position of the robot.

关键词： tracking applications robot vision

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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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2005 IEEE computer Society Conference on computer vision and Pattern Recognition, CVPR 2005

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

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： Visual communication

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REAL-TIME MOTION DETECTION BASED ON DISCRETE COSINE TRANSFORM

REAL-TIME MOTION DETECTION BASED ON DISCRETE COSINE TRANSFOR...

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IEEE International Conference on Image Processing

作者： Tae-Hyun Oh Joon-Young Lee In So Kweon Robotics and Computer Vision Laboratory KAIST South Korea

We present a motion detection algorithm by a change detection filter matrix derived from Discrete Cosine Transform. Recently, a Fourier reconstruction scheme shows good results for motion detection. However, its computational cost is a major drawback. We revisit the problem and achieve two orders of magnitude faster than the previous algorithm with better performance. The proposed algorithm runs at about 800 frames per second for VGA resolution images on a consumer hardware by using only integer matrix multiplication and the symmetric property of the change detection filter matrix. In addition, our algorithm is fundamentally robust to sudden illumination changes because it works based on edge information. We verify our algorithm with challenging datasets that contain strong and sudden illumination changes.

关键词： Discrete cosine transforms Motion detection Lighting Vectors Change detection algorithms Robustness Algorithm design and analysis

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Crosswalk and traffic light detection via integral framework

Crosswalk and traffic light detection via integral framework

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Korea-Japan Joint Workshop on Frontiers of computer vision

作者： Jongwon Choi Byung Tae Ahn In So Kweon Robotics and Computer Vision Laboratory KAIST Daejeon South Korea

This research is conducted to detect crosswalks and traffic lights with small false positive and negative errors. We propose an integral framework of the algorithms. Crosswalk and traffic light detection based on vision are challenging problem because brightness and hue value of scene can be easily changed, and it causes a number of false positive and negative errors. In order to solve it, we integrate those two algorithms with an assumption that traffic light and crosswalk exist together frequently. The proposed algorithm is tested with movies captured by cameras located on a vehicle and results are compared with ground truth which was chosen manually. The proposed algorithms run in real-time and can be easily installed on vehicle.

关键词： Cameras Detection algorithms Vehicles Conferences computer vision Algorithm design and analysis Roads

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Category-level metric scale object shape and pose estimation

arXiv

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arXiv 2021年

作者： Lee, Taeyeop Lee, Byeong-Uk Kim, Myungchul Kweon, In So Robotics and Computer Vision Laboratory KAIST Daejeon Korea Republic of

Advances in deep learning recognition have led to accurate object detection with 2D images. However, these 2D perception methods are insufficient for complete 3D world information. Concurrently, advanced 3D shape estimation approaches focus on the shape itself, without considering metric scale. These methods cannot determine the accurate location and orientation of objects. To tackle this problem, we propose a framework that jointly estimates a metric scale shape and pose from a single RGB image. Our framework has two branches: the Metric Scale Object Shape branch (MSOS) and the Normalized Object Coordinate Space branch (NOCS). The MSOS branch estimates the metric scale shape observed in the camera coordinates. The NOCS branch predicts the normalized object coordinate space (NOCS) map and performs similarity transformation with the rendered depth map from a predicted metric scale mesh to obtain 6d pose and size. Additionally, we introduce the Normalized Object Center Estimation (NOCE) to estimate the geometrically aligned distance from the camera to the object center. We validated our method on both synthetic and real-world datasets to evaluate category-level object pose and shape. Copyright © 2021, The Authors. All rights reserved.

关键词： Cameras

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