Simple Fingerprint Minutiae Extraction Algorithm Using Crossing Number On Valley Structure

Sudiro, Sunny Arief; Paindavoine, Michel; Kusuma, Tubagus Maulana

doi:10.1109/autoid.2007.380590

Cited by 26 publications

(15 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use the Crossing Number (CN ) method [11] to extract crease endings and bifurcations from the skeleton crease images by examining the local neighborhood of each crease point using a 3 × 3 window. The CN for a crease pixel is given by:…”

Section: Gabor Filteringmentioning

confidence: 99%

Characterization of Mountain Drainage Patterns for GPS-Denied UAS Navigation Augmentation

Wang

Çelik

Somani

2014

2014 22nd International Conference on Pattern Recognition

View full text Add to dashboard Cite

We present a novel approach to use mountain drainage patterns for GPS-Denied navigation of small unmanned aerial systems (UAS such as ScanEagle), utilizing a down-looking fixed focus monocular imager. We leverage the analogy between mountain drainage patterns, human arteriograms, and human fingerprints. We match local drainage patterns to GPU-rendered parallax occlusion maps of offline geo-registered radar returns (GRRR) in real-time. We represent a given mountain area with a set of spatially distributed minutiae of drainage patterns so that conventional minutiae-based fingerprint matching approaches can be used. We use medical arteriography processing techniques to extract these patterns. The minutiae-based representation of mountains is achieved by exposing mountain ridges/valleys with a series of filters and then extracting mountain minutiae from these ridges/valleys. Effectiveness of minutiae-based mountain representation method is experimentally validated with no human interaction and no human-made geographic objects. Our research was in part funded by Rockwell Collins.

show abstract

Section: Gabor Filteringmentioning

confidence: 99%

Characterization of Mountain Drainage Patterns for GPS-Denied UAS Navigation Augmentation

Wang

Çelik

Somani

2014

2014 22nd International Conference on Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…One of the key techniques of fingerprint identification is fingerprint feature detection in which it is much important to keep the fingerprint rotation and shift invariant [1][2][3][4][5][6][7][8][9][10][11][12][13]. A novel technique for fingerprint feature extraction using fixed size templates is proposed in reference [1],but the fingerprint recognition is limited to partial rotation invariant.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fingerprint Bifurcation Extraction Algorithm Based on Neural Network Template Matching

Leng

Luo³

2008

2008 International Symposium on Computer Science and Computational Technology

View full text Add to dashboard Cite

Fingerprint feature detection is one of the key techniques of automatic fingerprint identification. There are some problems of rotation and shift in the present fingerprint feature detection. This paper has given an algorithm of bifurcation detection based on neural network template matching. Correlative matching is to calculate the correlative value between template and target image according to a certain criteria when the template moves on the target image. The center of best matching is considered as fingerprint feature. The criteria of scaling the matching value is regarded as optimization function. Thus, matching recognition is converted into function optimization. The designed matcher based on neural network template can keep rotation invariant when the target image has the rotation of a certain angle. Finally, bifurcation can be extracted by multi-input and singleoutput there-layer feed forward neural network. The experimental results based on FVCX, have shown that the algorithm has rotation and shift invariant.

show abstract

“…There is a reliable method [4] to obtain the information which is capturing an image with the target characteristic information and processing the image. In general, the image-based biologic feature extraction method is mostly a single-band full-color image, which obtains target region shape [5][6][7][8][9] by calculating the shape of the image or filtering enhancement of the image. However, there are obvious defects in the traditional single-band full-color image.…”

Section: Introductionmentioning

confidence: 99%

Background Information Self-Learning Based Hyperspectral Target Detection

Tian

Yang

et al. 2018

Complexity

View full text Add to dashboard Cite

Hyperspectral imaging has been proved as an effective way to explore the useful information behind the land objects. And it can also be adopted for biologic information extraction, by which the origin information can be acquired from the image repeatedly without contamination. In this paper we proposed a target detection method based on background self-learning to extract the biologic information from the hyperspectral images. The conventional unstructured target detectors are very difficult to estimate the background statistics accurately in either a global or local way. Considering the spatial spectral information, its performance can be further improved by avoiding the above problem. It is especially designed to extract fingerprint and tumor region from hyperspectral biologic images. The experimental results show the validity and the superiority of our method on detecting the biologic information from hyperspectral images.

show abstract

Simple Fingerprint Minutiae Extraction Algorithm Using Crossing Number On Valley Structure

Cited by 26 publications

References 2 publications

Characterization of Mountain Drainage Patterns for GPS-Denied UAS Navigation Augmentation

Characterization of Mountain Drainage Patterns for GPS-Denied UAS Navigation Augmentation

A Novel Fingerprint Bifurcation Extraction Algorithm Based on Neural Network Template Matching

Background Information Self-Learning Based Hyperspectral Target Detection

Contact Info

Product

Resources

About