The criminal’s fingerprints often refer to those fingerprints that are extracted from crime scene and have played an important role in police’ investigation and cracking the cases, but these fingerprints have features such as blur, incompleteness and low-contrast of ridges. Traditional fingerprint enhancement and identification methods have some limitations and the current automated fingerprint identification system (AFIS) hasn’t not been applied extensively in police’ investigation. Since the Gabor filter has drawbacks such as poor efficiency, low preciseness of the extracted ridge’s orientation parameters, the enhancements of low-contrast fingerprint images can’t achieve the desired effects. Therefore, an improved Gabor enhancement for low-quality fingerprint is proposed in this paper. Firstly, orientation image templates with different scales were used to distinguish the orientation images in the fingerprint area, and then orientation parameters of ridge were calculated. Secondly, mean frequencies of ridge were extracted based on local window of ridge’s orientation and mean frequency parameters of ridges were calculated. Thirdly, the size and orientation of Gabor filter were self-adjusted according to local ridge’s orientation and mean frequency. Finally, the poor-quality fingerprint images were enhanced. In the experiment, the improved Gabor filter has better performance for low-quality fingerprint images when compared with the traditional filtering methods.
Infrared small target detection is a crucial and yet still is a difficult issue in aeronautic and astronautic applications. Sparse representation is an important mathematic tool and has been used extensively in image processing in recent years. Joint sparse representation is applied in dual-band infrared dim target detection in this paper. Firstly, according to the characters of dim targets in dual-band infrared images, 2-dimension Gaussian intensity model was used to construct target dictionary, then the dictionary was classified into different sub-classes according to different positions of Gaussian function’s center point in image block; The fact that dual-band small targets detection can use the same dictionary and the sparsity doesn’t lie in atom-level but in sub-class level was utilized, hence the detection of targets in dual-band infrared images was converted to be a joint dynamic sparse representation problem. And the dynamic active sets were used to describe the sparse constraint of coefficients. Two modified sparsity concentration index (SCI) criteria was proposed to evaluate whether targets exist in the images. In experiments, it shows that the proposed algorithm can achieve better detecting performance and dual-band detection is much more robust to noise compared with single-band detection. Moreover, the proposed method can be expanded to multi-spectrum small target detection.
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