Singular point detection of discontinuous magnetization processes

1985 ◽  
Vol 58 (5) ◽  
pp. 1924-1934 ◽  
Author(s):  
G. Asti ◽  
F. Bolzoni
Keyword(s):  
Singular Point ◽  
Point Detection ◽  
Magnetization Processes

Magnetic anisotropy of RE2(Fe, Co)17 detected by singular point detection technique

Physica B+C ◽  
1977 ◽  
Vol 86-88 ◽  
pp. 210-212 ◽  
Author(s):  
R. Gröβinger ◽  
W. Steiner ◽  
F. Culetto ◽  
H. Kirchmayr
Keyword(s):  
Singular Point ◽  
Magnetic Anisotropy ◽  
Detection Technique ◽  
Point Detection

scholarly journals Core point pixel-level localization by fingerprint features in spatial domain

2022 ◽  
Vol 19 (1) ◽  
pp. 707-737
Author(s):  
Xueyi Ye ◽  
◽  
Yuzhong Shen ◽  
Maosheng Zeng ◽  
Yirui Liu ◽  
...  
Keyword(s):  
Singular Point ◽  
Reference Point ◽  
False Positive ◽  
Spatial Domain ◽  
Fingerprint Recognition ◽  
Localization Accuracy ◽  
Localization Method ◽  
Core Point ◽  
Point Detection ◽  
Fingerprint Alignment

<abstract> <p>Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples.</p> </abstract>

scholarly journals A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection

2020 ◽  
Vol 10 (11) ◽  
pp. 3868
Author(s):  
Jiong Chen ◽  
Heng Zhao ◽  
Zhicheng Cao ◽  
Fei Guo ◽  
Liaojun Pang
Keyword(s):  
Singular Point ◽  
Semantic Segmentation ◽  
Singular Points ◽  
Detection Methods ◽  
Orientation Field ◽  
Global Features ◽  
Fingerprint Image ◽  
Detection Rates ◽  
Fingerprint Classification ◽  
Point Detection

As one of the most important and obvious global features for fingerprints, the singular point plays an essential role in fingerprint registration and fingerprint classification. To date, the singular point detection methods in the literature can be generally divided into two categories: methods based on traditional digital image processing and those on deep learning. Generally speaking, the former requires a high-precision fingerprint orientation field for singular point detection, while the latter just needs the original fingerprint image without preprocessing. Unfortunately, detection rates of these existing methods, either of the two categories above, are still unsatisfactory, especially for the low-quality fingerprint. Therefore, regarding singular point detection as a semantic segmentation of the small singular point area completely and directly, we propose a new customized convolutional neural network called SinNet for segmenting the accurate singular point area, followed by a simple and fast post-processing to locate the singular points quickly. The performance evaluation conducted on the publicly Singular Points Detection Competition 2010 (SPD2010) dataset confirms that the proposed method works best from the perspective of overall indexes. Especially, compared with the state-of-art algorithms, our proposal achieves an increase of 10% in the percentage of correctly detected fingerprints and more than 16% in the core detection rate.

Singular point detection in multidomain samples

1993 ◽  
Vol 73 (1) ◽  
pp. 323-333 ◽  
Author(s):  
G. Asti ◽  
F. Bolzoni ◽  
R. Cabassi
Keyword(s):  
Singular Point ◽  
Point Detection
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