A fast fingerprint image enhancement algorithm using a parabolic mask

Çavuşoğlu, Abdullah; Görgünoğlu, Salih

doi:10.1016/j.compeleceng.2006.11.006

Cited by 18 publications

(4 citation statements)

References 19 publications

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“…2 is applied to fingerprint image taking the direction of ridge into consideration. By means of this process, tiny disconnections are repaired and also the ridge is smoothed [6]. ( .sin .cos ),…”

Section: Enhancement Algorithmsmentioning

confidence: 99%

Fingerprint Pre-processing on ARM and DSP Platforms

Gok¹,

Görgünoğlu²,

Orak³

2014

ElAEE

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In minutiae based fingerprint analysis, fingerprint image is pre-processed before extracting features. The pre-processing is carried out to obtain more accurate minutiae points. Implementing fingerprint programs on embedded systems can be considered as important especially for real time standalone applications. Reducing the preprocessing time is important for identification and verification in real time embedded systems. In this study, pre-processing of minutiae based fingerprint system is implemented on two different platforms: Texas Instruments Sitara AM3359 which is a single board computer and OMAP-L138 which is a development kit. OMAP-L138 is a low power application processor based on ARM9 and C674x DSP cores. AM3359 is microprocessor unit based on ARM Cortex-A8 core. Fingerprint pre-processing algorithms are implemented using C/C++ compiler and tested on three different cores: ARM9, DSP and ARM Cortex-A8. The execution times are compared with each other. The results show that using DSP core, execution time is substantially improved.

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Section: Enhancement Algorithmsmentioning

confidence: 99%

Fingerprint Pre-processing on ARM and DSP Platforms

Gok¹,

Görgünoğlu²,

Orak³

2014

ElAEE

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show abstract

“…2. is applied to fingerprint image taking the direction of ridge into consideration, by this process, tiny disconnections are repaired and also the ridge is smoothed [5].…”

Section: Enhancement Algorithmsmentioning

confidence: 99%

Untitled

2014

AECE

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In analysis of minutiae based fingerprint systems, fingerprints needs to be pre-processed. The preprocessing is carried out to enhance the quality of the fingerprint and to obtain more accurate minutiae points. Reducing the pre-processing time is important for identification and verification in real time systems and especially for databases holding large fingerprints information. Parallel processing and parallel CPU computing can be considered as distribution of processes over multi core processor. This is done by using parallel programming techniques. Reducing the execution time is the main objective in parallel processing. In this study, pre-processing of minutiae based fingerprint system is implemented by parallel processing on multi core computers using OpenMP and on graphics processor using CUDA to improve execution time. The execution times and speedup ratios are compared with the one that of single core processor. The results show that by using parallel processing, execution time is substantially improved. The improvement ratios obtained for different pre-processing algorithms allowed us to make suggestions on the more suitable approaches for parallelization.

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“…The neighborhoods of the pixels and operators of the Sobel filter are given in Figure 2. Sobel operators are also used to determine the direction of a pixel or the local direction of a block in an image [17]. The pixel's gradient values of the x and y directions are calculated as given in Eqs.…”

Section: Preprocessing Operationsmentioning

confidence: 99%

Motion clustering on video sequences using a competitive learning network

Görgünoğlu¹,

Altay²

2014

Turk J Elec Eng & Comp Sci

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Abstract:It is necessary to track human movements in crowded places and environments such as stations, subways, metros, and schoolyards, where security is of great importance. As a result, undesired injuries, accidents, and unusual movements can be determined and various precautionary measures can be taken against them. In this study, real-time or existing video sequences are used within the system. These video sequences are obtained from objects such as humans or vehicles, moving actively in various environments. At first, some preprocesses are made respectively, such as converting gray scale, finding the edges of the objects existing in the images, and thresholding the images. Next, motion vectors are generated by utilizing a full search algorithm. Afterwards, k-means, nearest neighbor, image subdivision, and a competitive learning network are used as clustering methods to determine dense active regions on the video sequence using these motion vectors, and then their performances are compared. It is seen that the competitive learning network significantly determines the classification of dense active regions, including motion. Moreover, the algorithms are analyzed in terms of their time performances.

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A fast fingerprint image enhancement algorithm using a parabolic mask

Cited by 18 publications

References 19 publications

Fingerprint Pre-processing on ARM and DSP Platforms

Fingerprint Pre-processing on ARM and DSP Platforms

Untitled

Motion clustering on video sequences using a competitive learning network

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