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20 Papers

Slag Removal Path Estimation by Slag Distribution and Deep Learning

Topics: Machine Learning Technologies for Vision; Features Extraction; Vision for Robotics; Deep Learning for Visual Understanding

Authors: Junesuk Lee 1 , Geon-Tae Ahn 2 , Byoung-Ju Yun 1 and Soon-Yong Park 1

Affiliations: 1 School of Electronics Engineering, Kyungpook National University, Daegu, South Korea , 2 Research Institute of Industrial Science and Technology, Pohang, South Korea

Digital Object Identifier: 10.5220/0008944602460252

Proceedings of VISAPP 2020, ISBN: 978-989-758-402-2

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In the steel manufacturing process, de-slagging machine is used to remove slag floating on molten metal in a ladle. In general, temperature of floating slag on the surface of the molten metal is above 1,500℃. The process of removing such slag at high temperatures is dangerous and is only performed by trained human operators. In this paper, we propose a deep learning method for estimating the slag removal path to automate slag removal task. We propose an idea of developing a slag distribution image structure(SDIS); combined with a deep learning model to estimate the removal path in an environment in which the flow of molten metal cannot be controlled. The SDIS is given as the input into to the proposed deep learning model, which we train by imitating the removal task of experienced operators. We use both quantitative and qualitative analyses to evaluate the accuracy of the proposed method with the experienced operators.

Calibration of Two 3D Sensors with Perpendicular Scanning Directions by using a Piece of Paper

Topics: Image-Based Modeling and 3D Reconstruction; Device Calibration, Characterization and Modeling; Image Enhancement and Restoration

Authors: Ju-Hwan Lee and Soon-Yong Park

Affiliation: School of Computer Science & Engineering, Kyungpook National University, Daegu and South Korea

Digital Object Identifier: 10.5220/0007383301620168

Proceedings of VISAPP 2019, ISBN: 978-989-758-354-4

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It is difficult to find the 3D transformation relationship between two 3D sensors when the scanning directions of the two sensors has very large angle, for example 90 degrees or more. In this paper, we propose a very simple and efficient calibration method to get 3D transformation between two 3D sensors using a piece of white-colored paper. A piece of white-colored paper is folded in a quadrangular pyramid shape. The paper calibration object is placed on the transparent acryl board to get the shape of the object from two 3D sensors whose scanning direction is about 90 degree. The performance of the proposed calibration method is verified through 3D model reconstruction experiments. The calibration error between two sensors is less than 0.5 mm.
Collapse Button Related Ontology Subjects/Areas/Topics (9)
Applications

Geometry and Modeling

Image-Based Modeling

Computer Vision, Visualization and Computer Graphics

Image Enhancement and Restoration

Pattern Recognition

Device Calibration, Characterization and Modeling

Image Formation and Preprocessing

Software Engineering

3D Pose Estimation of Bin Picking Object using Deep Learning and 3D Matching

Topics: Image Processing; Vision, Recognition and Reconstruction

Authors: Junesuk Lee 1 , Sangseung Kang 2 and Soon-Yong Park 1

Affiliations: 1 School of Computer Science and Engineering, Kyungpook National University, Daegu and South Korea , 2 Intelligent Robotics Research Division, Electronics and Telecommunications Research Institute, Daejeon and South Korea

Digital Object Identifier: 10.5220/0006858203180324

Proceedings of ICINCO 2018, ISBN: 978-989-758-321-6

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In this paper, we propose a method to estimate 3D pose information of an object in a randomly piled-up environment by using image data obtained from an RGB-D camera. The proposed method consists of two modules: object detection by deep learning, and pose estimation by Iterative Closest Point (ICP) algorithm. In the first module, we propose an image encoding method to generate three channel images by integrating depth and infrared images captured by the camera. We use these encoded images as both the input data and training data set in a deep learning-based object detection step. Also, we propose a depth-based filtering method to improve the precision of object detection and to reduce the number of false positives by preprocessing input data. ICP-based 3D pose estimation is done in the second module, where we applied a plane-fitting method to increase the accuracy of the estimated pose.
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Image Processing

Robotics and Automation

Vision, Recognition and Reconstruction
Informatics in Control, Automation and Robotics

Accurate Pose Estimation of a Hand-held RGBD Camera based on Sub-volume Matching for 3D Modeling

Topics: Real-Time Systems Control; Engineering Applications on Intelligent Control Systems and Optimization

Authors: Eung-su Kim and Soon-Yong Park

Affiliation: School of Computer Science and Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu and South Korea

Digital Object Identifier: 10.5220/0006857903320335

Proceedings of ICINCO 2018, ISBN: 978-989-758-321-6

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The smoothness of the result of full body 3D reconstruction, also known as 360â—¦ reconstruction using a single hand-held sensor depends on the accuracy of the pose estimation. In this paper, we present a new idea for accurate pose estimation of such a single hand-held RGBD sensor based on subvolumetric reconstruction. In our method, we first estimate initial pose of both RGB and depth sensors through 3D coarse registration. Thereafter, in the precision matching step, we select only the keyframes for matching and estimate relative pose between them based on registration refinement. If there is a large pose estimation error between the keyframes, a subvolume is constructed using data of adjacent frames of each keyframe, and refine the final relative pose between keyframes using subvolume estimations. A series of 3D reconstruction experiments are preformed to evaluate the accuracy of the estimated pose.
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Informatics in Control, Automation and Robotics

Real-Time Systems Control

Signal Processing, Sensors, Systems Modeling and Control

Calibration of a Different Field-of-view Stereo Camera System using an Embedded Checkerboard Pattern

Topics: Stereo Vision and Structure from Motion; Image Formation, Acquisition Devices and Sensors; Device Calibration, Characterization and Modeling; Color and Texture Analyses

Authors: Pathum Rathnayaka , Seung-Hae Baek and Soon-Yong Park

Affiliation: Kyungpook National University, Korea, Republic of

Digital Object Identifier: 10.5220/0006267802940300

Proceedings of VISAPP 2017/VISIGRAPP, ISBN: 978-989-758-225-7

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Knowing the correct relative pose between cameras is considered as the first and foremost important step in a stereo camera system. It has been of the interest in many computer vision related experiments. Much work has been introduced for stereo systems with relatively common field-of-views; where a few number of advanced feature points-based methods have been presented for partially overlapping field-of-view systems. In this paper, we propose a new, yet simplified, method to calibrate a partially overlapping field-of-view heterogeneous stereo camera system using a specially designed embedded planar checkerboard pattern. The embedded pattern is a combination of two differently colored planar patterns with different checker sizes. The heterogeneous camera system comprises a lower focal length wide-angle camera and a higher focal length conventional narrow-angle camera. Relative pose between the cameras is calculated by multiplying transformation matrices. Our proposed method becomes a decent alternative to many advanced feature-based techniques. We show the robustness of our method through re-projection error and comparing point difference values in ’Y’ axis in image rectification results.
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Color and Texture Analyses

Image and Video Analysis

Motion, Tracking and Stereo Vision

Computer Vision, Visualization and Computer Graphics

Image Formation and Preprocessing

Stereo Vision and Structure from Motion
Device Calibration, Characterization and Modeling

Image Formation, Acquisition Devices and Sensors

Lane-level Positioning based on 3D Tracking Path of Traffic Signs

Topics: Stereo Vision and Structure from Motion; Object Detection and Localization; Tracking and Visual Navigation

Authors: Sung-ju Kim and Soon-Yong Park

Affiliation: Kyungpook National University, Korea, Republic of

Digital Object Identifier: 10.5220/0005721106420648

Proceedings of VISAPP 2016/VISIGRAPP, ISBN: 978-989-758-175-5

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Lane-level vehicle positioning is an important task for enhancing the accuracy of in-vehicle navigation systems and the safety of autonomous vehicles. GPS (Global Positioning System) or DGPS (Differential GPS) techniques are generally used in lane-level poisoning systems, which only provide an accuracy level up to 2-3 m. In this paper, we introduce a vision based lane-level positioning technique that provides more accurate prediction results. The proposed method predicts the current driving lane of the vehicle by tracking the 3D location of the traffic signs that are in the side-way of the road using a stereo camera. Several experiments are conducted to analyse the feasibility of the proposed method in driving lane level prediction. According to the experimental results, the proposed method could achieve 90.9% accuracy.
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Computer Vision, Visualization and Computer Graphics

Stereo Vision and Structure from Motion

Tracking and Visual Navigation
Motion, Tracking and Stereo Vision

A New 2-Point Absolute Pose Estimation Algorithm under Plannar Motion

Topics: Image-Based Modeling and 3D Reconstruction; Stereo Vision and Structure from Motion; Tracking and Visual Navigation; Vision for Robotics

Authors: Sung-In Choi and Soon-Yong Park

Affiliation: Kyungpook National University, Korea, Republic of

Digital Object Identifier: 10.5220/0005360406430647

Proceedings of VISAPP 2015/VISIGRAPP, ISBN: 978-989-758-091-8

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Several motion estimation algorithms, such as n-point and perspective n-point (PnP) have been introduced over the last few decades to solve relative and absolute pose estimation problems. Since the n-point algorithms cannot decide the real scale of robot motion, the PnP algorithms are often addressed to find the absolute scale of motion. This paper introduces a new PnP algorithm which uses only two 3D-2D correspondences by considering only planar motion. Experiment results prove that the proposed algorithm solves the absolute motion in real scale with high accuracy and less computational time compared to previous algorithms.
Collapse Button Related Ontology Subjects/Areas/Topics (10)
Applications

Motion, Tracking and Stereo Vision

Software Engineering

Computer Vision, Visualization and Computer Graphics

Pattern Recognition

Stereo Vision and Structure from Motion

Geometry and Modeling

Robotics

Tracking and Visual Navigation
Image-Based Modeling

A Novel Stereo-radiation Detection Device Calibration Method using Planar Homography

Topics: Image Formation, Acquisition Devices and Sensors; Device Calibration, Characterization and Modeling

Authors: Pathum Rathnayaka , Seung-Hae Baek and Soon-Yong Park

Affiliation: Kyungpook National University, Korea, Republic of

Digital Object Identifier: 10.5220/0005305401120116

Proceedings of VISAPP 2015/VISIGRAPP, ISBN: 978-989-758-089-5

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A radiation detection device, also known as a particle detector, is a device used to detect, track and identify the presence of radiation sources within a given area or environment. In general, a stereo-radiation detection device consists with two radiation detection devices and used to estimate 3D distances to radiation sources accurately. In computer vision, device calibration is more important and to obtain accurate results using such devices, they have to be calibrated first. Many stereo camera calibration methods have been introduced throughout the last few decades but a proper stereo radiation device calibration method has not yet been introduced. In this work, we propose a new stereo-radiation detector calibration method using planar Homography. The calibrated devices are used to estimate 3D distances to radiation sources and we obtained very accurate results with an error of less than 6%.
Collapse Button Related Ontology Subjects/Areas/Topics (4)
Computer Vision, Visualization and Computer Graphics

Image Formation and Preprocessing

Image Formation, Acquisition Devices and Sensors
Device Calibration, Characterization and Modeling

High Voltage Integrated Chip Power Recovering Topology for Implantable Wireless Biomedical Devices

Topics: Microelectronics; Low-Power Design; Biocomputing and Biochips; Implantable Electronics; Wireless Systems

Authors: Vijith Vijayakumaran Nair , Jinhwan Youn and Jun Rim Choi

Affiliation: Kyungpook National University, Korea, Republic of

Digital Object Identifier: 10.5220/0005194000130022

Proceedings of BIODEVICES 2015/BIOSTEC, ISBN: 978-989-758-071-0

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In near field wireless power links for biomedical implants, inductive voltage at receiver end (Rx) largely exceeds the compliance of low voltage integrated power recovery circuits. To limit the magnitude of induced signal, most of the low voltage (LV) integrated power recovery schemes employ methods like voltage clipping and shunt regulation. These methods are proved to be power inefficient. Therefore, to overcome the voltage limitation and to improve the power efficiency, we propose an on-chip high-voltage (HV) power recovery scheme based on step-down approach, which allows supply voltage as high as 30V. The proposed design comprises of enhanced semi-active HV bridge rectifier, reference voltage generator and HV series voltage regulator. In addition, a battery management circuit that ensures safe and reliable charging of the implant battery is proposed and implemented. The proposed design is fabricated with 0.35μm HV BCD technology based on LOCOS 0.35μm CMOS process. Rectifier and regulator power efficacy are analyzed and compared through simulation and measurement results.
Collapse Button Related Ontology Subjects/Areas/Topics (7)
Biocomputing and Biochips

Implantable Electronics

Microelectronics

Biomedical Engineering

Low-Power Design

Wireless Systems
Biomedical Instruments and Devices

Multi-spectral Flash Imaging under Low-light Condition using Optimization with Weight Map

Topic: Image Enhancement and Restoration

Authors: Bong-Seok Choi , Dae-Chul Kim , Wang-Jun Kyung and Yeong-Ho Ha

Affiliation: Kyungpook National University, Korea, Republic of

Digital Object Identifier: 10.5220/0004657500330039

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-003-1

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Long exposure shot and flash lights are generally used to acquire images under low-light environments. However, flash lights often induce color distortion, red-eye effect, and they can disturb the subject. The other hand, long-exposure shots are prone to motion-blur due to camera shake or subject-motion. Recently, multi-spectral flash imaging has been introduced to overcome the limitations of traditional low-light photography. Multi-spectral flash imaging is performed by combining the invisible and visible spectrum information. However, common multi spectral flash approaches induce color distortion due to the lower accuracy of the invisible spectrum image. In this paper, we propose a multi-spectral flash imaging algorithm using optimization with weight map in order to improve color accuracy and brightness of image. The UV/IR and visible spectrum images are firstly captured, respectively. Then, to compensate luminance value under low light condition, tone reproduction is performed by using adaptive curve due to image features that is obtained by Naka-Rushton formula. Next, to discriminate uniform regions from detail regions, weight map is generated by using Canny operator. Finally, the optimization object function takes into account the output likelihood with respect to the visible light image, the sparsity of image gradients as well as the spectral constraints for the IR-red channels and UV-blue channels. The performance of the proposed method has been subjectively evaluated using z-score, and we also show that output images have improved color accuracy and lower noise with respect to other methods.
Collapse Button Related Ontology Subjects/Areas/Topics (3)
Computer Vision, Visualization and Computer Graphics

Image Enhancement and Restoration

Image Formation and Preprocessing

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