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A survey of 2D shape representation: Methods, evaluations, and future research directions
Kurnianggoro, Laksono,Wahyono, Laksono,Jo, Kang-Hyun Elsevier 2018 Neurocomputing Vol.300 No.-
<P>In the past few years, the research studies in image-based shape representation have been proliferating due to its usefulness and importance for various application. This field has been evolved, from simple descriptor-based instance retrieval to utilization of machine learning approaches. Thus, this papers aims to provide a comprehensive survey to summarize the overall view of this research topic. It covers several concepts including the traditional shape descriptors, boundary and region partitioning strategies, and more advanced techniques which commonly exist in the recent studies. This manuscript discusses the advantages and drawbacks of these methods by providing comparisons of evaluation results on well-known public datasets under the various types of similarity metrics and assessment procedures. To complete the survey, it also suggests diverse possibilities of future research directions. (C) 2018 Published by Elsevier B.V.</P>
Dense Optical Flow in Stabilized Scenes for Moving Object Detection from a Moving Camera
Laksono Kurnianggoro,Ajmal Shahbaz,Kang-Hyun Jo 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10
This paper proposes a method for detecting moving objects appeared in video captured by a moving camera. The proposed method relies on dense optical flow to differentiate moving objects from static background. Whenever video taken from a static camera is used, the dense optical flow itself is sufficient to determine the moving object in the scenes. However, in a non-static camera, all pixels are moving making which lead to incapability of optical flow to differentiate the moving objects from the static background. In order to solve this problem, a stabilization method is incorporated by the mean of global motion extraction, which can be done by analyzing the homography transformation between two consequtive frames. Finally, by applying a threshold on the dense optical flow, the region of moving object is acquired. The proposed method has been evaluated in the experiments and produce satisfying results with 98% accuracy.
Framework of Real-time Car Detection using Calibrated Camera and LRF
Laksono Kurnianggoro,Kang-Hyun Jo 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
This paper proposes a framework for a real-time car detection method using calibrated system of camera and Laser Range Finder (LRF). Car candidates are extracted from the LRF data using a gridding method. The points sensed by LRF are grouped into 2D grid. Two adjacent occupied grid elements are marked with same label, forming an object. The objects formed by the labeling method are filtered out based on their size. A region of interest (ROI) in camera image is generated for each object located in 2D grid using the property of the calibrated camera and LRF system. From each ROI, Histogram of oriented gradient (HOG) features are extracted. In order to achieve a faster computation time, the dimension of the HOG feature is reduced using genetic algorithm approach, with a machine learning approach as the validation method. Experiments result shows that the proposed framework achieves around 68 fps of processing speed.
YU YANG,Laksono Kurnianggoro,조강현 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.7
A fast and effective moving object detection method for a moving camera is proposed in this paper. The global motion is estimated through tracking the grid-based key points using optical flow. After the motioncompensation, the background model, candidate background model and candidate age are used for the background modelling. Then the local pixel difference and the consistency of local changes between the current frame and thebackground model are used for the background subtraction. The lighting influence threshold and the local pixeldifference between the current frame and two previous aligned frames are used to reduce the lighting influences. Finally, Gaussian filter, connected-components analysis, erosion and dilation are used to refine the results. Theperformance evaluation shows that this proposed method works very fast in real time and has competitive resultscompared with others in the public dataset.
A Comparative Study of Foreground Detection using Gaussian Mixture Models- Novice to Novel
Ajmal Shahbaz,Laksono Kurnianggoro,Kang-Hyun Jo 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10
Foreground detection is the classical computer vision task of segmenting out motion information from a particular scene. Foreground detection using Gaussian Mixture Models (GMM) is the famous choice. Since first time proposed, many researchers tried to improve GMM. This paper focuses on the comparative evaluation of three most famous improvements in the algorithm. The improved methods are compared both qualitatively and quantitatively using standard datasets available online.