List of Articles Region of interest

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - License Plate Detection and Recognition based on Neural Networks in Complex Environments
  .N. . Ameena Bibi Purru Supriya
  Now a days due to the rapid advancement of economy around the world the count of vehicles increases day by day. Increase in the number of vehicles causes violation detection, road congestion, accidents at different traffic situations, uneven illumination, lighting and w More

  Now a days due to the rapid advancement of economy around the world the count of vehicles increases day by day. Increase in the number of vehicles causes violation detection, road congestion, accidents at different traffic situations, uneven illumination, lighting and weather conditions. To overcome this issue license plate number is recognized but due to variations in license plate layout, font size of characters, tilted number plates, weather conditions, dirt plate and motion blur license plate recognition becomes difficult. License plate recognition has two main tasks, one is to detect the license plate and the other is to identify the license plate characters. By using region of interest license plate is detected. For recognition first tilted images are corrected using affine transformation and to improve the quality of a low-resolution image super resolution CNN is employed and connected component analysis, horizontal and vertical projection profile area used for separating each individuals characters. Each individual character image is fed to the Convolutional Neural Network (CNN) for character extraction and for classification and the license plate is recognized using convolutional neural networks. The main aim of this paper is to recognize different plate layout with different conditions with minimum data set and less processing time with maximum efficiency. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Fast Intra and Inter Prediction Mode Decision of H.264/AVC for Medical Image Compression Based on Region of Interest
  Mehdi Jafari Homayoun Mahdavi-Nasab Shohreh Kasaei
  This paper aims at applying H.264 in medical video compression applications and improving the H.264 Compression performance with better perceptual quality and low coding complexity. In order to achieve higher compression of medical video, while maintaining high image qu More

  This paper aims at applying H.264 in medical video compression applications and improving the H.264 Compression performance with better perceptual quality and low coding complexity. In order to achieve higher compression of medical video, while maintaining high image quality in the region of interest, with low coding complexity, here we propose a new model using H.264/AVC that uses lossless compression in the region of interest, and very high rate, lossy compression in other regions. This paper proposes a new method to achieve fast intra and inter prediction mode decision that is based on coarse macroblocks for intra and inter prediction mode decision of the background region and finer macroblocks for region of interest. Also the macroblocks of the background region are encoded with the maximum quantization parameter allowed by H.264/AVC in order to maximize the number of null coefficients. Experimental results show that the proposed algorithm achieves a higher compression rate on medical videos with a higher quality of region of interest with low coding complexity when compared to our previous algorithm and other standard algorithms reported in the literature. Manuscript profile