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Person Tracking Control of Mobile Robots Using a Lightweight Object Detection and Tracking System
2024 8th International Conference on Robotics and Automation Sciences (ICRAS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
This paper aims to develop a lightweight person tracking control system to facilitate real-time pedestrian tracking tasks for mobile robots. The proposed system utilizes deep learning technology and lightweight network architecture to perform pedestrian detection tasks to achieve powerful pedestrian detection capabilities. Subsequently, the integration of multi-object tracking technology enables t...Show More
Enhancing Ease-of-Disassembly Tools for Electronic Products: Insights from Assessing Computer Mice
Yi-Hsieh Wang;Chi-Yi Tsai;Xiaoling Liu;Zhaoyang Hank Han;Ingrid de Pauw;Bas Flipsen
2024 Electronics Goes Green 2024+ (EGG)
Year: 2024 | Conference Paper |
To make electronic products fit for circular economy strategies such as life extension, refurbishment, and recycling, ease of disassembly is a key design quality. Several tools are available to assess the ease of disassembly of products during the design process, such as the ease of Disassembly Metric (eDiM) and Hotspot Mapping (HSM). The eDiM method uses the time-to-dismantle as a unit for calcul...Show More
A Real-time Affordance-based Object Pose Estimation Approach for Robotic Grasp Pose Estimation
2023 International Conference on System Science and Engineering (ICSSE)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
This paper proposes a pose estimation system for robot grasping based on a novel Object Affordance Detection and Segmentation (OADS) network. The proposed system consists of four modules: (1) OADS network; (2) point cloud extraction; (3) object pose estimation; (4) grasp pose estimation. Based on the OADS network, the proposed system achieves affordance-based object pose estimation results. The pr...Show More
Generic Development of Bin Pick-and-Place System Based on Robot Operating System
Ching-Chang Wong;Chi-Yi Tsai;Ren-Jie Chen;Shao-Yu Chien;Yi-He Yang;Shang-Wen Wong;Chun-An Yeh
Bin pick-and-place is an important topic in factory automation and warehouse automation. In this paper, a bin pick-and-place system based on robot operating system (ROS) is implemented to make a six-degree-of-freedom (6-DOF) robot manipulator to complete multiple pick-and-place tasks. The proposed system uses ROS to integrate an object perception module and a pick-and-place module, where the forme...Show More
Design of Superior Parameterized Quantum Circuits for Quantum Image Classification
2022 14th International Conference on Computer and Automation Engineering (ICCAE)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
In this paper, we present a novel quantum neural network (QNN) algorithm enhanced with transfer learning to perform multi-class image classification. The proposed QNN extracts quantum image encoding measurements through the quantum state tomography framework and passes the sampled features through the classical neural network architecture to the proposed learnable parameterized quantum circuit (PQ...Show More
The Ninth Visual Object Tracking VOT2021 Challenge Results
Matej Kristan;Jiří Matas;Aleš Leonardis;Michael Felsberg;Roman Pflugfelder;Joni-Kristian Kämäräinen;Hyung Jin Chang;Martin Danelljan;Luka Čehovin Zajc;Alan Lukežič;Ondrej Drbohlav;Jani Käpylä;Gustav Häger;Song Yan;Jinyu Yang;Zhongqun Zhang;Gustavo Fernández;Mohamed Abdelpakey;Goutam Bhat;Llukman Cerkezi;Hakan Cevikalp;Shengyong Chen;Xin Chen;Miao Cheng;Ziyi Cheng;Yu-Chen Chiu;Ozgun Cirakman;Yutao Cui;Kenan Dai;Mohana Murali Dasari;Qili Deng;Xingping Dong;Daniel K. Du;Matteo Dunnhofer;Zhen-Hua Feng;Zhiyong Feng;Zhihong Fu;Shiming Ge;Rama Krishna Gorthi;Yuzhang Gu;Bilge Gunsel;Qing Guo;Filiz Gurkan;Wencheng Han;Yanyan Huang;Felix Järemo Lawin;Shang-Jhih Jhang;Rongrong Ji;Cheng Jiang;Yingjie Jiang;Felix Juefei-Xu;Yin Jun;Xiao Ke;Fahad Shahbaz Khan;Byeong Hak Kim;Josef Kittler;Xiangyuan Lan;Jun Ha Lee;Bastian Leibe;Hui Li;Jianhua Li;Xianxian Li;Yuezhou Li;Bo Liu;Chang Liu;Jingen Liu;Li Liu;Qingjie Liu;Huchuan Lu;Wei Lu;Jonathon Luiten;Jie Ma;Ziang Ma;Niki Martinel;Christoph Mayer;Alireza Memarmoghadam;Christian Micheloni;Yuzhen Niu;Danda Paudel;Houwen Peng;Shoumeng Qiu;Aravindh Rajiv;Muhammad Rana;Andreas Robinson;Hasan Saribas;Ling Shao;Mohamed Shehata;Furao Shen;Jianbing Shen;Kristian Simonato;Xiaoning Song;Zhangyong Tang;Radu Timofte;Philip Torr;Chi-Yi Tsai;Bedirhan Uzun;Luc Van Gool;Paul Voigtlaender;Dong Wang;Guangting Wang;Liangliang Wang;Lijun Wang;Limin Wang;Linyuan Wang;Yong Wang;Yunhong Wang;Chenyan Wu;Gangshan Wu;Xiao-Jun Wu;Fei Xie;Tianyang Xu;Xiang Xu;Wanli Xue;Bin Yan;Wankou Yang;Xiaoyun Yang;Yu Ye;Jun Yin;Chengwei Zhang;Chunhui Zhang;Haitao Zhang;Kaihua Zhang;Kangkai Zhang;Xiaohan Zhang;Xiaolin Zhang;Xinyu Zhang;Zhibin Zhang;Shaochuan Zhao;Ming Zhen;Bineng Zhong;Jiawen Zhu;Xue-Feng Zhu
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Year: 2021 | Conference Paper |
Cited by: Papers (63)
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challen...Show More
The Ninth Visual Object Tracking VOT2021 Challenge Results
Matej Kristan;Jiří Matas;Aleš Leonardis;Michael Felsberg;Roman Pflugfelder;Joni-Kristian Kämäräinen;Hyung Jin Chang;Martin Danelljan;Luka Čehovin Zajc;Alan Lukežič;Ondrej Drbohlav;Jani Käpylä;Gustav Häger;Song Yan;Jinyu Yang;Zhongqun Zhang;Gustavo Fernández;Mohamed Abdelpakey;Goutam Bhat;Llukman Cerkezi;Hakan Cevikalp;Shengyong Chen;Xin Chen;Miao Cheng;Ziyi Cheng;Yu-Chen Chiu;Ozgun Cirakman;Yutao Cui;Kenan Dai;Mohana Murali Dasari;Qili Deng;Xingping Dong;Daniel K. Du;Matteo Dunnhofer;Zhen-Hua Feng;Zhiyong Feng;Zhihong Fu;Shiming Ge;Rama Krishna Gorthi;Yuzhang Gu;Bilge Gunsel;Qing Guo;Filiz Gurkan;Wencheng Han;Yanyan Huang;Felix Järemo Lawin;Shang-Jhih Jhang;Rongrong Ji;Cheng Jiang;Yingjie Jiang;Felix Juefei-Xu;Yin Jun;Xiao Ke;Fahad Shahbaz Khan;Byeong Hak Kim;Josef Kittler;Xiangyuan Lan;Jun Ha Lee;Bastian Leibe;Hui Li;Jianhua Li;Xianxian Li;Yuezhou Li;Bo Liu;Chang Liu;Jingen Liu;Li Liu;Qingjie Liu;Huchuan Lu;Wei Lu;Jonathon Luiten;Jie Ma;Ziang Ma;Niki Martinel;Christoph Mayer;Alireza Memarmoghadam;Christian Micheloni;Yuzhen Niu;Danda Paudel;Houwen Peng;Shoumeng Qiu;Aravindh Rajiv;Muhammad Rana;Andreas Robinson;Hasan Saribas;Ling Shao;Mohamed Shehata;Furao Shen;Jianbing Shen;Kristian Simonato;Xiaoning Song;Zhangyong Tang;Radu Timofte;Philip Torr;Chi-Yi Tsai;Bedirhan Uzun;Luc Van Gool;Paul Voigtlaender;Dong Wang;Guangting Wang;Liangliang Wang;Lijun Wang;Limin Wang;Linyuan Wang;Yong Wang;Yunhong Wang;Chenyan Wu;Gangshan Wu;Xiao-Jun Wu;Fei Xie;Tianyang Xu;Xiang Xu;Wanli Xue;Bin Yan;Wankou Yang;Xiaoyun Yang;Yu Ye;Jun Yin;Chengwei Zhang;Chunhui Zhang;Haitao Zhang;Kaihua Zhang;Kangkai Zhang;Xiaohan Zhang;Xiaolin Zhang;Xinyu Zhang;Zhibin Zhang;Shaochuan Zhao;Ming Zhen;Bineng Zhong;Jiawen Zhu;Xue-Feng Zhu
Year: 2021 | Conference Paper |
An ESP-Based Lightweight Model for Joint Object Detection and Affordance Segmentation
2021 6th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Affordance segmentation is one of the most challenging and popular research topics in the field of robotic vision in recent years. Although the existing affordance segmentation methods can achieve accurate segmentation results, the model parameters and network architecture are quite large, and most of these methods cannot achieve real-time performance. In order to reduce the number of model parame...Show More
This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method fo...Show More
Mobilenet-SSDv2: An Improved Object Detection Model for Embedded Systems
Yu-Chen Chiu;Chi-Yi Tsai;Mind-Da Ruan;Guan-Yu Shen;Tsu-Tian Lee
2020 International Conference on System Science and Engineering (ICSSE)
Year: 2020 | Conference Paper |
Cited by: Papers (103)
Object detection plays an important role in the field of computer vision. Many superior object detection algorithms have been proposed in literature; however, most of them are designed to improve the detection accuracy. As a result, the requirement of reducing computational complexity is usually ignored. To achieve real-time performance, these superior object detectors need to operate with a high-...Show More
Visually Guided Picking Control of an Omnidirectional Mobile Manipulator Based on End-to-End Multi-Task Imitation Learning
Chi-Yi Tsai;Yung-Shan Chou;Ching-Chang Wong;Yu-Cheng Lai;Chien-Che Huang
In this paper, a novel deep convolutional neural network (CNN) based high-level multi-task control architecture is proposed to address the visual guide-and-pick control problem of an omnidirectional mobile manipulator platform based on deep learning technology. The proposed mobile manipulator control system only uses a stereo camera as a sensing device to accomplish the visual guide-and-pick contr...Show More
Planar Tracking based on Deep Learning
2019 8th International Conference on Innovation, Communication and Engineering (ICICE)
Year: 2019 | Conference Paper |
Planar tracking is an essential function in computer vision to address the problem of tracking a planar target affected by illumination, deformation and blurring with real-time performance. To achieve this goal, the tracking algorithm has to train a model of the planar target and uses this model to estimate the change of target pose. However, when the target is changed, the algorithm needs to retr...Show More
The Seventh Visual Object Tracking VOT2019 Challenge Results
Matej Kristan;Jirí Matas;Aleš Leonardis;Michael Felsberg;Roman Pflugfelder;Joni-Kristian Kämäräinen;Luka Cehovin Zajc;Ondrej Drbohlav;Alan Lukezic;Amanda Berg;Abdelrahman Eldesokey;Jani Käpylä;Gustavo Fernández;Abel Gonzalez-Garcia;Alireza Memarmoghadam;Andong Lu;Anfeng He;Anton Varfolomieiev;Antoni Chan;Ardhendu Shekhar Tripathi;Arnold Smeulders;Bala Suraj Pedasingu;Bao Xin Chen;Baopeng Zhang;Baoyuan Wu;Bi Li;Bin He;Bin Yan;Bing Bai;Bing Li;Bo Li;Byeong Hak Kim;Chao Ma;Chen Fang;Chen Qian;Cheng Chen;Chenglong Li;Chengquan Zhang;Chi-Yi Tsai;Chong Luo;Christian Micheloni;Chunhui Zhang;Dacheng Tao;Deepak Gupta;Dejia Song;Dong Wang;Efstratios Gavves;Eunu Yi;Fahad Shahbaz Khan;Fangyi Zhang;Fei Wang;Fei Zhao;George De Ath;Goutam Bhat;Guangqi Chen;Guangting Wang;Guoxuan Li;Hakan Cevikalp;Hao Du;Haojie Zhao;Hasan Saribas;Ho Min Jung;Hongliang Bai;Hongyuan Yu;Houwen Peng;Huchuan Lu;Hui Li;Jiakun Li;Jianhua Li;Jianlong Fu;Jie Chen;Jie Gao;Jie Zhao;Jin Tang;Jing Li;Jingjing Wu;Jingtuo Liu;Jinqiao Wang;Jinqing Qi;Jinyue Zhang;John K. Tsotsos;Jong Hyuk Lee;Joost van de Weijer;Josef Kittler;Jun Ha Lee;Junfei Zhuang;Kangkai Zhang;Kangkang Wang;Kenan Dai;Lei Chen;Lei Liu;Leida Guo;Li Zhang;Liang Wang;Liangliang Wang;Lichao Zhang;Lijun Wang;Lijun Zhou;Linyu Zheng;Litu Rout;Luc Van Gool;Luca Bertinetto;Martin Danelljan;Matteo Dunnhofer;Meng Ni;Min Young Kim;Ming Tang;Ming-Hsuan Yang;Naveen Paluru;Niki Martinel;Pengfei Xu;Pengfei Zhang;Pengkun Zheng;Pengyu Zhang;Philip H.S. Torr;Qi Zhang Qiang Wang;Qing Guo;Radu Timofte;Rama Krishna Gorthi;Richard Everson;Ruize Han;Ruohan Zhang;Shan You;Shao-Chuan Zhao;Shengwei Zhao;Shihu Li;Shikun Li;Shiming Ge;Shuai Bai;Shuosen Guan;Tengfei Xing;Tianyang Xu;Tianyu Yang;Ting Zhang;Tomás Vojir;Wei Feng;Weiming Hu;Weizhao Wang;Wenjie Tang;Wenjun Zeng;Wenyu Liu;Xi Chen;Xi Qiu;Xiang Bai;Xiao-Jun Wu;Xiaoyun Yang;Xier Chen;Xin Li;Xing Sun;Xingyu Chen;Xinmei Tian;Xu Tang;Xue-Feng Zhu;Yan Huang;Yanan Chen;Yanchao Lian;Yang Gu;Yang Liu;Yanjie Chen;Yi Zhang;Yinda Xu;Yingming Wang;Yingping Li;Yu Zhou;Yuan Dong;Yufei Xu;Yunhua Zhang;Yunkun Li;Zeyu Wang Zhao Luo;Zhaoliang Zhang;Zhen-Hua Feng;Zhenyu He;Zhichao Song;Zhihao Chen;Zhipeng Zhang;Zhirong Wu;Zhiwei Xiong;Zhongjian Huang;Zhu Teng;Zihan Ni
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (257)
The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well ...Show More
The Seventh Visual Object Tracking VOT2019 Challenge Results
Matej Kristan;Jirí Matas;Aleš Leonardis;Michael Felsberg;Roman Pflugfelder;Joni-Kristian Kämäräinen;Luka Cehovin Zajc;Ondrej Drbohlav;Alan Lukezic;Amanda Berg;Abdelrahman Eldesokey;Jani Käpylä;Gustavo Fernández;Abel Gonzalez-Garcia;Alireza Memarmoghadam;Andong Lu;Anfeng He;Anton Varfolomieiev;Antoni Chan;Ardhendu Shekhar Tripathi;Arnold Smeulders;Bala Suraj Pedasingu;Bao Xin Chen;Baopeng Zhang;Baoyuan Wu;Bi Li;Bin He;Bin Yan;Bing Bai;Bing Li;Bo Li;Byeong Hak Kim;Chao Ma;Chen Fang;Chen Qian;Cheng Chen;Chenglong Li;Chengquan Zhang;Chi-Yi Tsai;Chong Luo;Christian Micheloni;Chunhui Zhang;Dacheng Tao;Deepak Gupta;Dejia Song;Dong Wang;Efstratios Gavves;Eunu Yi;Fahad Shahbaz Khan;Fangyi Zhang;Fei Wang;Fei Zhao;George De Ath;Goutam Bhat;Guangqi Chen;Guangting Wang;Guoxuan Li;Hakan Cevikalp;Hao Du;Haojie Zhao;Hasan Saribas;Ho Min Jung;Hongliang Bai;Hongyuan Yu;Houwen Peng;Huchuan Lu;Hui Li;Jiakun Li;Jianhua Li;Jianlong Fu;Jie Chen;Jie Gao;Jie Zhao;Jin Tang;Jing Li;Jingjing Wu;Jingtuo Liu;Jinqiao Wang;Jinqing Qi;Jinyue Zhang;John K. Tsotsos;Jong Hyuk Lee;Joost van de Weijer;Josef Kittler;Jun Ha Lee;Junfei Zhuang;Kangkai Zhang;Kangkang Wang;Kenan Dai;Lei Chen;Lei Liu;Leida Guo;Li Zhang;Liang Wang;Liangliang Wang;Lichao Zhang;Lijun Wang;Lijun Zhou;Linyu Zheng;Litu Rout;Luc Van Gool;Luca Bertinetto;Martin Danelljan;Matteo Dunnhofer;Meng Ni;Min Young Kim;Ming Tang;Ming-Hsuan Yang;Naveen Paluru;Niki Martinel;Pengfei Xu;Pengfei Zhang;Pengkun Zheng;Pengyu Zhang;Philip H.S. Torr;Qi Zhang Qiang Wang;Qing Guo;Radu Timofte;Rama Krishna Gorthi;Richard Everson;Ruize Han;Ruohan Zhang;Shan You;Shao-Chuan Zhao;Shengwei Zhao;Shihu Li;Shikun Li;Shiming Ge;Shuai Bai;Shuosen Guan;Tengfei Xing;Tianyang Xu;Tianyu Yang;Ting Zhang;Tomás Vojir;Wei Feng;Weiming Hu;Weizhao Wang;Wenjie Tang;Wenjun Zeng;Wenyu Liu;Xi Chen;Xi Qiu;Xiang Bai;Xiao-Jun Wu;Xiaoyun Yang;Xier Chen;Xin Li;Xing Sun;Xingyu Chen;Xinmei Tian;Xu Tang;Xue-Feng Zhu;Yan Huang;Yanan Chen;Yanchao Lian;Yang Gu;Yang Liu;Yanjie Chen;Yi Zhang;Yinda Xu;Yingming Wang;Yingping Li;Yu Zhou;Yuan Dong;Yufei Xu;Yunhua Zhang;Yunkun Li;Zeyu Wang Zhao Luo;Zhaoliang Zhang;Zhen-Hua Feng;Zhenyu He;Zhichao Song;Zhihao Chen;Zhipeng Zhang;Zhirong Wu;Zhiwei Xiong;Zhongjian Huang;Zhu Teng;Zihan Ni
Year: 2019 | Conference Paper |
Reptile Meta-Tracking
2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
Generic object tracking (GOT) is one of the main topics in computer vision for many years. The goal of GOT is to recognize and locate a specific object in the form of bounding box throughout a sequence of images. Moreover, GOT also requires algorithms to locate objects down to instances level. These requirements produce some unique challenges especially for deep learning based GOT algorithms that ...Show More
Data-Driven Visual Picking Control of a 6-DoF Manipulator Using End-to-End Imitation Learning
2018 International Automatic Control Conference (CACS)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
This paper presents a novel convolutional neural network (CNN) based high-level control architecture that uses deep learning technique to realize autonomous picking control of a six-degree-of-freedom (6-DoF) manipulator using the visual information only. The proposed manipulator control system uses a stereo camera as a measurement device to capture a stereo image of the scene in front of the robot...Show More
Visual Object Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network
Chien-Ming Lin;Chi-Yi Tsai;Yu-Cheng Lai;Shin-An Li;Ching-Chang Wong
In recent years, deep learning-based object recognition algorithms become emerging in robotic vision applications. This paper addresses the design of a novel deep learning-based visual object recognition and pose estimation system for a robot manipulator to handle random object picking tasks. The proposed visual control system consists of a visual perception module, an object pose estimation modul...Show More
Object recognition and pose estimation are essential functions in applications of computer vision, and they also are fundamental modules in robotic vision systems. In recent years, RGB-D cameras become more and more popular, and the 3D object recognition technology has got more and more attention. In this paper, a novel design of simultaneous 3D object recognition and pose estimation algorithm is ...Show More
An efficient automatic traffic sign detection and recognition method for smartphones
2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Year: 2017 | Conference Paper |
Cited by: Papers (6)
The usage of automatic driving assistance systems (ADAS) has become more and more popular in recent years. In the design of ADAS, traffic sign detection (TSD) and traffic sign recognition (TSR) are two important functions and have been widely studied in the literature. This paper addresses the design of a vision-based TSD and recognition (TSDR) system, which is computationally efficient and can ru...Show More
An efficient RGB-D camera based point cloud registration algorithm
2017 International Conference on Applied System Innovation (ICASI)
Year: 2017 | Conference Paper |
This paper proposes an indoor scene three-dimensional (3D) reconstruction system using pan-tilt platform and RGB-D camera. The proposed system can automatically reconstruct 3D indoor scenes on a fixed position. An efficient point cloud registration algorithm is proposed to align point clouds based on extrinsic parameters of the RGB-D camera from every presetted pan-tilt control points. Then, a loc...Show More
A novel translation, rotation, and scale-invariant shape description method for real-time speed-limit sign recognition
2016 International Conference on Advanced Materials for Science and Engineering (ICAMSE)
Year: 2016 | Conference Paper |
Speed-limit sign (SLS) recognition is an important function to realize automatic driving assistance systems (ADAS). This paper presents a novel design of an image-based SLS recognition algorithm, which can efficiently detect and recognize SLS in real-time. To improve the robustness of the proposed SLS algorithm, this paper also proposes a new shape description method to describe the detected SLS u...Show More
A Hybrid Switched Reactive-Based Visual Servo Control of 5-DOF Robot Manipulators for Pick-and-Place Tasks
Chi-Yi Tsai;Ching-Chang Wong;Chia-Jun Yu;Chih-Cheng Liu;Tsung-Yen Liu
This paper addresses the problem of visual servo control for a five-degree-of-freedom robot manipulator to perform pick-and-place tasks. A new hybrid switched reactive-based visual servo control structure, inspired from a sensing-and-reaction behavior without the inverse interaction matrix computing, is proposed to handle this problem efficiently. The proposed structure is similar to the logic-bas...Show More
Multilevel image thresholding based on an extended within-class variance criterion
2014 19th International Conference on Digital Signal Processing
Year: 2014 | Conference Paper |
Cited by: Papers (1)
This paper addresses the issue of multilevel thresholding design for gray image segmentation. Most of the current multilevel image thresholding techniques require employing a criterion function to determine N-1 optimal thresholds for separating an image into N classes. In this paper, a new variance-based criterion function is proposed. Unlike the existing criterion functions, the proposed one is a...Show More
Design and implementation of a RANSAC RGB-D mapping algorithm for multi-view point cloud registration
2013 CACS International Automatic Control Conference (CACS)
Year: 2013 | Conference Paper |
Cited by: Papers (5)
This paper addresses multi-view point cloud registration, which is one of the major techniques to develop a robust visual tracking system. The proposed algorithm first uses Kinect camera to capture point clouds of an object-of-interest or a complex environment with different view points. Then, a novel RANSAC RGB-D mapping algorithm is developed to efficiently and accurately align the multiple poin...Show More
A novel image-based object orientation estimation algorithm for robotic manipulator applications
2012 International Symposium on Intelligent Signal Processing and Communications Systems
Year: 2012 | Conference Paper |
Cited by: Papers (1)
In many robotic manipulator applications, the robot manipulator usually requires knowing the orientation of an object-of-interest to perform grasp planning. This paper presents a novel image-based object orientation estimation algorithm that efficiently and accurately estimates the orientation of a geometric object from vision information only. First, a boundary-point set is defined from the bound...Show More
A fast dynamic range compression with local contrast preservation algorithm for low dynamic range image enhancement
2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2012)
Year: 2012 | Conference Paper |
This paper presents a new fast dynamic range compression format with a local-contrast-preservation (FDRCLCP) algorithm to efficiently resolve low dynamic range (LDR) image enhancement problem for natural color images. The proposed FDRCLCP algorithm can combine with any continuously differentiable intensity transfer function to achieve LDR image enhancement. In combination with the FDRCLCP algorith...Show More
A Fast Dynamic Range Compression With Local Contrast Preservation Algorithm and Its Application to Real-Time Video Enhancement
This study addresses low dynamic range (LDR) image/video enhancement for digital video cameras. A new fast dynamic range compression format with a local-contrast-preservation (FDRCLCP) algorithm resolves this problem efficiently. The proposed FDRCLCP algorithm can combine with any continuously differentiable intensity transfer function to achieve LDR image enhancement. In combination with the FDRC...Show More
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