Conference Paper

Permanent URI for this collectionhttps://hdl.handle.net/10361/7306

Browse

Recent Submissions

Now showing 1 - 2 of 2
  • Thumbnail Image
    listelement.badge.dso-type Item ,
    Detection and classification of speed limit traffic signs
    (© 2014 Institute of Electrical and Electronics Engineers Inc., 2014-10) Biswas, Rubel; Fleyeh, Hasan; Mostakim, Moin; Department of Computer Science and Engineering
    This paper presents a novel traffic sign recognition system which can aid in the development of Intelligent Speed Adaptation. This system is based on extracting the speed limit sign from the traffic scene by Circular Hough Transform (CHT) with the aid of colour and non-colour information of the traffic sign. The digits of the speed limit sign are then extracted and classified using SVM classifier which is trained for this purpose. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 270 images which were collected in different light conditions. To check the robustness of this system, it was tested against 210 images which contain 213 speed limit traffic sign and 288 Non- Speed limit signs. It was found that the accuracy of recognition was 98% which indicates clearly the high robustness targeted by this system.
  • Thumbnail Image
    listelement.badge.dso-type Item ,
    Automatic slice growing method based 3D reconstruction of liver with its vessels
    (© 2014 Institute of Electrical and Electronics Engineers Inc., 2014) Alom, Md. Zahangir; Mostakim, Moin; Biswas, Rubel; Chakrabarty, Amitabha; Department of Computer Science and Engineering
    In the recent years, reconstructing 3D liver and its vessels from abdominal CT volume images becomes an inevitable and necessary research field. In this paper, a method of 3D reconstruction of liver with its vessels has been implemented, which involves volume preprocessing, de-noising, segmentation, contouring, and combination of different modalities. An advanced liver segmentation algorithms have been proposed: The first one is a 2.5D method that utilizes automatic Slice Growing Method (SGM) to segment liver part of each slice of a data set. It takes advantage of curvature control of level set segmentation method to distinguish liver and adjacent organs. It is proved that the result of this proposed method is much better than simple 3D level set method in liver segmentation. In the case of liver vessel segmentation, we have proposed an improved smoothing method dedicate to 3D vascular volume which results from region growing segmentation method. The cooperation of region growing method and proposed smoothing method has been demonstrated the possibility of efficient vessel segmentation with very accurate results. And the results indicate that our method is suitable for anatomical studying and surgical planning.