Diala Jomaa

Segmentation of low quality fingerprint images

This paper presents a new algorithm to segment fingerprint images. The algorithm uses four features, the global mean, the local mean, variance and coherence of the image to achieve the fingerprint segmentation. Based on these features, a rule based system is built to segment the image.

Effectiveness of Trigger Speed of Vehicle-Activated Signs on Mean and Standard Deviation of Speed

ABSTRACT Excessive or inappropriate speeds are a key factor in traffic fatalities and crashes. Vehicle-activated signs (VASs) are therefore being extensively used to reduce speeding to increase traffic safety. A VAS is triggered by an individual vehicle when the driver exceeds a speed threshold, otherwise known as trigger speed (TS). The TS is usually set to a constant, normally proportional to the speed limit on the particular segment of road. Decisions concerning the TS largely depend on the local traffic authorities. The primary objective of this article is to help authorities determine the TS that gives an optimal effect on the Mean and Standard Deviation of speed. The data were systematically collected using radar technology whilst varying the TS. The results show that when the applied TS was set near the speed limit, the standard deviation was high. However, the Standard Deviation decreased substantially when the threshold was set to the 85th percentile. This decrease occurred without a significant increase in the mean speed. It is concluded that the optimal threshold speed should approximate the 85th percentile, though VASs should ideally be individually calibrated to the traffic conditions at each site.

Segmentation of fingerprint images based on bi-level processing using fuzzy rules

This paper presents a new approach to segment low quality fingerprint images which are collected by low quality fingerprint readers. Images collected using such readers are easy to collect but difficult to segment. The proposed approach is based on combining global and local processing to achieve segmentation of fingerprint images. On the global level, the fingerprint is located and extracted from the rest of the image by using a global thresholding followed by dilation and edge detection of the largest object in the image. On the local level, fingerprints foreground and its border image are treated using different fuzzy rules which the two images are segmented. These rules are based on the mean and variance of the block under consideration. The approach is implemented in three stages; preprocessing, segmentation, and post-processing. Segmentation of 100 images was performed and compared with manual examinations by human experts. The experiments showed that 96% of images under test are correctly segmented. The results from the quality of segmentation test revealed that the average error in block segmentation was 2.84% and the false positive and false negatives were approximately 1.4%. This indicates the high robustness of the proposed approach.

Segmentation of fingerprint images based on bi-level combination of global and local processing

Abstract. This paper presents a new approach to segment low quality fingerprint images which are collected by low quality fingerprint readers. Images collected using such readers are easy to collect but difficult to segment. The proposed approach is based on combining global and local processing to achieve segmentation of fingerprint images. On the global level, the fingerprint is located and extracted from the rest of the image by using a global thresholding followed by dilation and edge detection of the largest object in the image. On the local level, fingerprints foreground and its border image are treated using different fuzzy rules. These rules are based on the mean and variance of the block under consideration. The approach is implemented in three stages: pre-processing, segmentation, and post-processing. Segmentation of 100 images was performed and compared with manual examinations by human experts. The experiments showed that 96% of images under test are correctly segmented. The results from the quality of segmentation test revealed that the average error in block segmentation was 2.84% and the false positive and false negatives were approximately 1.4%. This indicates the high robustness of the proposed approach.