National Repository of Grey Literature 4 records found  Search took 0.00 seconds. 
Traffic Signs Detection and Recognition
Číp, Pavel ; Honec, Peter (referee) ; Horák, Karel (advisor)
The thesis deals with traffic sign detection and recongnition in the urban environment and outside the town. A precondition for implementation of the system is built-in camera, usually in a car rear-view mirror. The camera scans the scene before the vehicle. The image data are transfered to the connected PC, where the data are transformation to information and evalutations. If the sign was detected the system is visually warned the driver. For a successful goal is divided into four separate blocks. The first part is the preparing of the image data. There are color segmentation with knowledge of color combination traffic signs in Czech Republic. Second part is deals with shape detection in segmentation image. Part number three is deals with recognition of inner pictogram and its finding in the image database. The final part is the visual output of displaying founded traffic signs. The thesis has been prepader so as to ensure detection of all relevant traffic signs in three basic color combinations according to existing by Decree of Ministry of Transport of Czech Republic. The result is the source code for the program MATLAB. .
Traffic Signs Recognition by Means of Machine Learning Approach
Zakarovský, Matúš ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
This thesis researches methods of traffic sign recognition using various approaches. Technique based on machine learning utilizing convolutional neural networks was selected forfurther implementation. Influence of number of convolutional layers on neural network’s performance is studied. The resulting network is tested on German Traffic Sign Recognition Benchmark and author’s dataset.
Traffic Signs Recognition by Means of Machine Learning Approach
Zakarovský, Matúš ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
This thesis researches methods of traffic sign recognition using various approaches. Technique based on machine learning utilizing convolutional neural networks was selected forfurther implementation. Influence of number of convolutional layers on neural network’s performance is studied. The resulting network is tested on German Traffic Sign Recognition Benchmark and author’s dataset.
Traffic Signs Detection and Recognition
Číp, Pavel ; Honec, Peter (referee) ; Horák, Karel (advisor)
The thesis deals with traffic sign detection and recongnition in the urban environment and outside the town. A precondition for implementation of the system is built-in camera, usually in a car rear-view mirror. The camera scans the scene before the vehicle. The image data are transfered to the connected PC, where the data are transformation to information and evalutations. If the sign was detected the system is visually warned the driver. For a successful goal is divided into four separate blocks. The first part is the preparing of the image data. There are color segmentation with knowledge of color combination traffic signs in Czech Republic. Second part is deals with shape detection in segmentation image. Part number three is deals with recognition of inner pictogram and its finding in the image database. The final part is the visual output of displaying founded traffic signs. The thesis has been prepader so as to ensure detection of all relevant traffic signs in three basic color combinations according to existing by Decree of Ministry of Transport of Czech Republic. The result is the source code for the program MATLAB. .

Interested in being notified about new results for this query?
Subscribe to the RSS feed.