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Ganesan, R.
- Implementation of Image Processing in Real-Time Road Traffic Control
Authors
1 Bharathiyar College of Engineering & Technology, Karaikal, U.T. of Puducherry, IN
Source
Digital Image Processing, Vol 4, No 15 (2012), Pagination: 828-833Abstract
Existing traffic control system is using sensors. As the sensor which collects traffic flow information, mainly Ultrasonic Vehicle Detector has been used. This detector detects vehicle presence by the time difference of the reflection of ultrasonic wave fired from above the road surface to just under it. But especially queue and delay length are measured indirectly by the number of passed vehicles in a unit time. So a sensor which can collect more precise traffic flow information is needed. Also each Ultrasonic Vehicle Detector has to be installed above the road surface per a measurement lane and so there is a fear of spoiling the beauty of the city. On the other hand, the Digital Image processes an image received from the CCTV camera installed aside and above the approach lane at the traffic signal intersection. In this, queue length will be detected. To detect and measure queue parameters, two different algorithms have been used. The first algorithm is motion detection and the second is a vehicle detection operation. In this process, we detect moving vehicle grouping and delay vehicle grouping from the results of Image Preprocess, and calculate the delay range. Also, we make a stable output which is strong in the noise by smoothing the calculated delay range by referring to the output at the time of back and forth. After the queue length detection, depend upon the vehicles in four sides the preference will give to the vehicles. This saves the time and reduces the error in the existing system.Keywords
Ultrasonic Vehicle Detector, Motion Detection, Vehicle Detection, Queue Length Parameters.
- Texture Analysis in CT Brain Images Using a Reduced Run-Length Method
Authors
1 Department of EIE, RVS College of Engineering, Dindigul, TamilNadu, IN
2 Department of CSE, Kalasalingam University, Srivilliputhur, TamilNadu, IN
Source
Digital Image Processing, Vol 1, No 3 (2009), Pagination: 88-92Abstract
In this paper a new method for texture classification of CT scan brain images based on Gray Level Run Length Method (GLRLM) is proposed. Other two conventional methods, Spatial Gray Level Dependency Method (SGLDM), and standard Gray Level Run-Length Method (GLRLM) are used to compare the performance of the proposed method. The feature vector consists of 14 Haralick features. The proposed algorithm applied to real time CT scan images. We achieved the classification rate 88% in the distinction between normal and abnormal images. Based on our experiments, the Reduced Gray Level Run Length Method (RGLRLM) is more appropriate than other methods for texture classification as it leads to higher classification accuracy.