Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An Effective and Fast Soccer Ball Detection and Tracking Method
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Trajectory-Based Ball Detection and Tracking in Broadcast Soccer Video
IEEE Transactions on Multimedia
Soccer ball detection by comparing different feature extraction methodologies
Advances in Artificial Intelligence
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In this paper a new method for ball recognition in soccer images is proposed. It combines Circular Hough Transform and Scale Invariant Feature Transform to recognize the ball in each acquired frame. The method is invariant to image scale, rotation, affine distortion, noise and changes in illumination. Compared with classical supervised approaches, it is not necessary to build different positive training sets to properly manage the great variance in ball appearances. Moreover, it does not require the construction of negative training sets that, in a context as soccer matches where many no-ball examples can be found, it can be a tedious and long work. The proposed approach has been tested on a number of image sequences acquired during real matches of the Italian Soccer "Serie A" championship. Experimental results demonstrate a satisfactory capability of the proposed approach to recognize the ball.