Wisnu Widiarto
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KEYFRAME SELECTION OF FRAME SIMILARITY TO GENERATE SCENE SEGMENTATION BASED ON POINT OPERATION Widiarto, Wisnu; Hariadi, Mochamad; Yuniarno, Eko Mulyanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (246.835 KB) | DOI: 10.11591/ijece.v8i5.pp2839-2846

Abstract

Video segmentation has been done by grouping similar frames according to the threshold. Two-frame similarity calculations have been performed based on several operations on the frame: point operation, spatial operation, geometric operation and arithmatic operation. In this research, similarity calculations have been applied using point operation: frame difference, gamma correction and peak signal to noise ratio. Three-point operation has been performed in accordance with the intensity and pixel frame values. Frame differences have been operated based on the pixel value level. Gamma correction has analyzed pixel values and lighting values. The peak signal to noise ratio (PSNR) has been related to the difference value (noise) between the original frame and the next frame. If the distance difference between the two frames was smaller then the two frames were more similar. If two frames had a higher gamma correction factor, then the correction factor would have an increasingly similar effect on the two frames. If the value of PSNR was greater then the comparison of two frames would be more similar. The combination of the three point operation methods would be able to determine several similar frames incorporated in the same segment
Object Classfification in Computer Vision with Discriminant Analysis Hamzahan, Amir; Santosa, Gatot; Widiarto, Wisnu
Makara Journal of Technology Vol 6, No 1 (2002)
Publisher : Directorate of Research and Community Services, Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (98.459 KB) | DOI: 10.7454/mst.v6i1.40

Abstract

A robotic sensor system is always supported by a computer system called computer vision. The important concept of computer vision is object classfifi cation. In this study two algorithms for object classifi cation in this system will be compared. Firstly, A simple method that do not need complex computation and that considered as an informal method is called binary tree decision structure. This method is based on modest caracteristic decriptors of an object such as vertical line, horizontal line or ellipse line. Unfortunately this method has weakness in recognize an image that contaminated by a noise. Secondly, a more formal method with high variability descriptors. In this contect a multivariate statistical approach named discriminant analysis is proposed as an alternative for object classifi cation. This method is operated by computation of a function called Fisher discriminant function that can be used for separating an object. From the data simulation and analysis for calssifi cation of two object i.e. screw and bolt and three objects i.e. alphabet T,O and S it can be shown that discriminant analysis approach can classify an object better than binary decision algorithm. The superority of discriminant method is especially seen when this method is applied for classifi cation of a noisy image of object.