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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>
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.
A Low Power, Low Supply Voltage, and Wide dB-Linear Range Pseudo-Exponential Function Generator
Laksono Widyo Isworo,Cosy Muto,Hiroshi Ochi 대한전자공학회 2009 ITC-CSCC :International Technical Conference on Ci Vol.2009 No.7
A new topology of pseudo-exponential function generator, which features low supply voltage (1.2V), low power (0.3㎽), and wide ㏈-linear range (120 ㏈), is proposed. Its rail-to-rail topology results in an improved implementation of the pseudo-exponential function to achieve wider ㏈-linear range. These features make the pseudo-exponential function generator suitable for realizing very wide gain range variable gain amplifier (VGA). The pseudo-exponential function generator is simulated in 90㎚ CMOS technology and verified by ADS simulation. The comparison between the proposed topology and the reference topology is also presented.
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.
Characteristics of Magnetic Sengon Wood Impregnated with Nano Fe3O4 and Furfuryl Alcohol
Gilang Dwi LAKSONO,Istie Sekartining RAHAYU,Lina KARLINASARI,Wayan Darmawan,Esti PRIHATINI 한국목재공학회 2023 목재공학 Vol.51 No.1
Sengon (Falcataria moluccana Miq.) tree offers a wood of low quality and durability owing to its low density and thin cell walls. This study aimed to improve the properties of sengon wood by making the wood magnetic, producing new functions, and characterizing magnetic sengon wood. Each wood sample was treated using one of the following impregnation solutions: Untreated, 7.5% nano magnetite-furfuryl alcohol (Fe3O4-FA), 10% nano Fe3O4-FA, and 12.5% nano Fe3O4-FA. The impregnation process began with vacuum treatment at 0.5 bar for 2 h, followed by applying a pressure of 1 bar for 2 h. The samples were then tested for dimensional stability and density and characterized using scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM–EDX), Fourier transform infrared spectrometry (FTIR), X-ray diffraction (XRD) analysis, and vibrating sample magnetometry (VSM) analysis. The results showed that the Fe3O4-FA impregnation treatment considerable affected the dimensional stability, measured in terms of weight percent gain, anti-swelling efficiency, water uptake, and bulking effect, as well as the density of sengon wood. Changes in wood morphology were detected by the presence of Fe deposits in the cell walls and cell cavities of the wood using SEM–EDX analysis. XRD and FTIR analyses showed the appearance of magnetite peaks in the diffractogram and Fe-O functional groups. Based on the VSM analysis, treated sengon wood is classified as a superparamagnetic material with soft magnetic properties. Overall, 10% Fe3O4-FA treatment led to the highest increase in dimensional stability and density of sengon wood.