Browsing by Author "Ajanthan, T"

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    item: Conference-Full-text
    Automatic Glaucoma Detection by Using Funduscopic Images
    Atheesan, S; Shanmugarajah, Y; Ajanthan, T; Ranathunga, L
    This paper describes an automatic system to identify glaucoma disease from funduscopic images by using digital image processing. Glaucoma caused by increase of pressure in eye and damages in optic nerve. Glaucoma tends to be grown and may not show until final stage. Through this system, doctors can easily identify patient’s condition quickly and do treatment. Rural people also will get advantage through this system. Glaucoma is identified through cup to disc ratio (CDR) calculation and orientation of the blood vessels in this system. For that Optical disk’s inner circle (cup) and outer circle (disc) is extracted. From that radius is calculated. The outer and inner circles are extracted by using average and maximum grey level pixels respectively with the use of histogram. Then find contours and draw circle which is best fitting the contours. The radius of cup and disc are found. After calculating CDR, the abnormal image can be found if CDR exceeds a particular threshold value. Otherwise it is normal image. The system extracts the blood vessels and through the orientation of the blood vessel glaucoma is identified.
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    item: Conference-Abstract
    Automatic number plate recognition in low quality videos
    (2014-06-20) Ajanthan, T; Kamalaruban, P; Rodrigo, BKRP
    Typical Automatic Number Plate Recognition (ANPR) system uses high resolution cameras to acquire good quality images of the vehicles passing through. In these images, license plates are localized, characters are segmented, and recognized to determine the identity of the vehicles. However, the steps in this workflow will fail to produce expected results in low resolution images and in a less constrained environment. Thus in this work, several improvements are made to this ANPR workflow by incorporating intelligent heuristics, image processing techniques and domain knowledge to build an ANPR system that is capable of identifying vehicles even in low resolution video frames. Main advantages of our system are that it is able to operate in real-time, does not rely on special hardware, and not constrained by environmental conditions. Low quality surveillance video data acquired from a toll system is used to evaluate the performance of our system. We were able to obtain more than 90% plate level recognition accuracy. The experiments with this dataset have shown that the system is robust to variations in illumination, view point, and scale.
  • Thumbnail Image
    item: Conference-Abstract
    Automatic number plate recognition in low quality videos
    Ajanthan, T; Kamalaruban, P; Rodrigo, R
    Typical Automatic Number Plate Recognition (ANPR) system uses high resolution cameras to acquire good quality images of the vehicles passing through. In these images, license plates are localized, characters are segmented, and recognized to determine the identity of the vehicles. However, the steps in this workflow will fail to produce expected results in low resolution images and in a less constrained environment. Thus in this work, several improvements are made to this ANPR workflow by incorporating intelligent heuristics, image processing techniques and domain knowledge to build an ANPR system that is capable of identifying vehicles even in low resolution video frames. Main advantages of our system are that it is able to operate in real-time, does not rely on special hardware, and not constrained by environmental conditions. Low quality surveillance video data acquired from a toll system is used to evaluate the performance of our system. We were able to obtain more than 90% plate level recognition accuracy. The experiments with this dataset have shown that the system is robust to variations in illumination, view point, and scale.
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    item: Conference-Abstract
    Automatic number plate recognition in low quality videos
    (2014-06-19) Ajanthan, T; Kamalaruban, P; Rodrigo, R
    Typical Automatic Number Plate Recognition (ANPR) system uses high resolution cameras to acquire good quality images of the vehicles passing through. In these images, license plates are localized, characters are segmented, and recognized to determine the identity of the vehicles. However, the steps in this workflow will fail to produce expected results in low resolution images and in a less constrained environment. Thus in this work, several improvements are made to this ANPR workflow by incorporating intelligent heuristics, image processing techniques and domain knowledge to build an ANPR system that is capable of identifying vehicles even in low resolution video frames. Main advantages of our system are that it is able to operate in real-time, does not rely on special hardware, and not constrained by environmental conditions. Low quality surveillance video data acquired from a toll system is used to evaluate the performance of our system. We were able to obtain more than 90% plate level recognition accuracy. The experiments with this dataset have shown that the system is robust to variations in illumination, view point, and scale.