AUTOMATIC RECOGNITION OF TRAFFIC SIGNS USING FANN AND OPEN CV

Automation Recognition of Traffic Signs is integrated and automation software for Traffic Symbol Recognition. The proposed system detects candidate regions as Maximally Stable Extremely Region (MSERs), which offers robustness to variations in lighting conditions. Recognition is based on Artificial Neural Network (ANN) classifiers. The training data are generated from real footage road signs which will be fetched using camera board and by applying threshold values we get proper training data for each frame. By applying thinning mechanism like erode and corrode and segmentation we can recognize proper shape and symbol. The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 10 frames per second, and recognizes all classes of ideogram-based (non-text) traffic symbols from real footage road signs. Comprehensive comparative results to illustrate the performance of the system are presented. https://journalnx.com/journal-article/2015023

AUTOMATIC RECOGNITION OF TRAFFIC SIGNS USING FANN AND OPENCV

Automation Recognition of Traffic Signs is integrated and automation software for Traffic Symbol Recognition. The proposed system detects candidate regions as Maximally Stable Extremely Region (MSERs), which offers robustness to variations in lighting conditions. Recognition is based on Artificial Neural Network (ANN) classifiers. The training data are generated from real footage road signs which will be fetched using camera board and by applying threshold values we get proper training data for each frame. By applying thinning mechanism like erode and corrode and segmentation we can recognize proper shape and symbol. The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 10 frames per second, and recognizes all classes of ideogram-based (non-text) traffic symbols from real footage road signs. Comprehensive comparative results to illustrate the performance of the system are presented

Automatic Recognition Of Traffic Signs Using Fann And Opencv

Automation Recognition of Traffic Signs is integrated and automation software for Traffic Symbol Recognition. The proposed system detects candidate regions as Maximally Stable Extremely Region (MSERs), which offers robustness to variations in lighting conditions. Recognition is based on Artificial Neural Network (ANN) classifiers. The training data are generated from real footage road signs which will be fetched using camera board and by applying threshold values we get proper training data for each frame. By applying thinning mechanism like erode and corrode and segmentation we can recognize proper shape and symbol. The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 10 frames per second, and recognizes all classes of ideogram-based (non-text) traffic symbols from real footage road signs. Comprehensive comparative results to illustrate the performance of the system are presented

Infant Feeding Practices in A Well Baby Clinic of A Tertiary Hospital in North Kerala

ABSTRACT Aim and objective: To study the infant feeding practices among mothers accompanying infants in a wel