A critical issue in the development of an automated fingerprint identification system (AFIS) is to detect the reference point accurately and reliably. Most existing techniques are based on the singular points of the fingerprint which is sensitive to fingerprint artifacts and is not well defined for arch type fingerprints. In this study, we present a topological interpretation of fingerprint reference point, where the reference points are posed as topological features of fingerprint structure and can be detected seamlessly from either arch type or non-arch type fingerprints. Extensive experiments demonstrate the advantages of the proposed method over the existing ones.
In this paper, we propose an algorithm to recognize the time of video clock in broadcast soccer videos. A naive OCR approach to the problem will be confounded by standard image segmentation issues such as merged digits and low-resolution. In contrast, our methods achieve robustness by exploiting the domain knowledge governing clock digit transitions. The time periodicity information enables the reliable recognition of overlaid digit video clock and also facilitates self-consistency checks. A robust time recognition algorithm was presented in our previous papers. However, that algorithm requires the precise ROI (Region Of Interest) as its input. Besides inheriting the existing techniques this paper proposes two new ones to overcome the impreciseness of ROIs. The first one is the extended-ROI digit transit periodicity detection. It can correctly detect the second transit even though the known ROI is not precise. The second one is a Hough-like search for precise second ROI in digit-sequence similarity measure. Experimental results on 70 soccer video clips confirm that our methods can achieve the accurate results.
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