KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Ohnishi, Noboru

Search Results

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Vehicle detection and classification system based on virtual detection zone
    (2016-11-18)
    Seenouvong, Nilakorn
    ;
    Watchareeruetai, Ukrit
    ;
    Nuthong, Chaiwat
    ;
    Khongsomboon, Khamphong
    ;
    Ohnishi, Noboru
    This paper proposes a vehicle detection and classification system based on virtual detection zone (VDZ). The proposed system consists of four main steps: foreground extraction, vehicle detection, vehicle feature extraction and vehicle classification. A moving vehicle is firstly detected based on Gaussian mixture model (GMM). Then, several techniques including region of interest selection, adaptive morphological operation, and contour processing are applied to obtain correct foreground objects. Next, vehicle features are calculated when the centroid of a vehicle is on the VDZ. Finally, vehicles are classified by using k-nearest neighbor classifier. Experimental results show that the proposed method can accurately detect and classify vehicles with an accuracy of 98.53%.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A computer vision based vehicle detection and counting system
    (2016-03-23)
    Seenouvong, Nilakorn
    ;
    Watchareeruetai, Ukrit
    ;
    Nuthong, Chaiwat
    ;
    Khongsomboon, Khamphong
    ;
    Ohnishi, Noboru
    A vehicle detection and counting system plays an important role in an intelligent transportation system, especially for traffic management. This paper proposes a video-based method for vehicle detection and counting system based on computer vision technology. The proposed method uses background subtraction technique to find foreground objects in a video sequence. In order to detect moving vehicles more accurately, several computer vision techniques, including thresholding, hole filling and adaptive morphology operations, are then applied. Finally, vehicle counting is done by using a virtual detection zone. Experimental results show that the accuracy of the proposed vehicle counting system is around 96%.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Redundancies in linear GP, canonical transformation, and its exploitation: A demonstration on image feature synthesis
    (2011-03-01)
    Watchareeruetai, Ukrit
    ;
    Takeuchi, Yoshinori
    ;
    Matsumoto, Tetsuya
    ;
    Kudo, Hiroaki
    ;
    Ohnishi, Noboru
    This paper concerns redundancies in representation of linear genetic programming (GP). We identify the causes of redundancies in linear GP and propose a canonical transformation that converts original linear representations into a canonical form in which structural redundancies are removed. In canonical form, we can easily verify whether two representations represent an identical program. We then discuss exploitation of the proposed canonical transformation, and demonstrate a way to improve search performance of linear GP by avoiding redundant individuals. Experiments were conducted with an image feature synthesis problem. Firstly, we have verified that there are really a lot of redundancies in conventional linear GP. We then investigate the effect of avoiding redundant individuals. The results yield that linear GP with avoidance of redundant individuals obviously outperforms conventional linear GP. © 2010 Springer Science+Business Media, LLC.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Multi-objective genetic programming with redundancy-regulations for automatic construction of image feature extractors
    (2010-01-01)
    Watchareeruetai, Ukrit
    ;
    Matsumoto, Tetsuya
    ;
    Takeuchi, Yoshinori
    ;
    Kudo, Hiroaki
    ;
    Ohnishi, Noboru
    We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-TT. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-TT and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem. Copyright © 2010 The Institute of Electronics, Information and Communication Engineers.