Graph based K-nearest neighbor minutiae clustering for fingerprint recognition

Pawar, Vaishali; Zaveri, Mukesh A.

doi:10.1109/icnc.2014.6975917

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unsupervised classification techniques offer numerous benefits over supervised ones in terms of addressing classification challenges. Numerous clustering techniques, such as K-means, spectral clustering, mean shift [ 38 ], and graph theory [ 39 ], are the most often used algorithms. Unsupervised techniques are highly effective yet have poor accuracy since they do not require any prior training or understanding.…”

Section: Road Generationmentioning

confidence: 99%

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

Zhu¹,

Wang²,

Meng³

et al. 2022

Sensors

View full text Add to dashboard Cite

The advancement of autonomous driving technology has had a significant impact on both transportation networks and people’s lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field test, which has large costs, lengthy durations, and few diverse test scenarios, cannot satisfy the autonomous driving system’s need for reliable and safe testing. Functional testing is the emphasis of the test since features such as frontal collision and traffic sign warning influence driving safety. As a result, simulation testing will undoubtedly emerge as a new technique for unmanned vehicle testing. A crucial aspect of simulation testing is the creation of test scenarios. With an emphasis on the map generating method and the dynamic scenario production method in the test scenarios, this article explains many scenarios and scenario construction techniques utilized in the process of self-driving car testing. A thorough analysis of the state of relevant research is conducted, and approaches for creating common scenarios as well as brand-new methods based on machine learning are emphasized.

show abstract

Section: Road Generationmentioning

confidence: 99%

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

Zhu¹,

Wang²,

Meng³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The unsupervised road extraction does not need training samples, but instead usually uses clustering algorithms to extract the road networks. The clustering algorithms used in road extraction include K-means, spectral clustering, mean shift [21,22], and graph theory [23,24]. Miao et al proposed a semi-automatic approach using the mean shift to detect roads [22].…”

Section: Road Extractionmentioning

confidence: 99%

Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline

Chen

et al. 2019

IJGI

View full text Add to dashboard Cite

Road networks play a significant role in modern city management. It is necessary to continually extract current road structure, as it changes rapidly with the development of the city. Due to the success of semantic segmentation based on deep learning in the application of computer vision, extracting road networks from VHR (Very High Resolution) imagery becomes a method of updating geographic databases. The major shortcoming of deep learning methods for road networks extraction is that they need a massive amount of high quality pixel-wise training datasets, which is hard to obtain. Meanwhile, a large amount of different types of VGI (volunteer geographic information) data including road centerline has been accumulated in the past few decades. However, most road centerlines in VGI data lack precise width information and, therefore, cannot be directly applied to conventional supervised deep learning models. In this paper, we propose a novel weakly supervised method to extract road networks from VHR images using only the OSM (OpenStreetMap) road centerline as training data instead of high quality pixel-wise road width label. Large amounts of paired Google Earth images and OSM data are used to validate the approach. The results show that the proposed method can extract road networks from the VHR images both accurately and effectively without using pixel-wise road training data. scribble annotation for road extraction. The ground truth of the pixel-level annotation in Figure 1b should label every pixel, which is difficult to generate. Figure 1c represents the scribble annotation, which can be easily obtained from OSM. Because the full annotation dataset is expensive to obtain and the scribble annotation is easy to generate, the study of road networks extraction using scribble labels is of great importance.Recently, weakly supervised learning was popular in image segmentation [13][14][15][16]. In these methods, scribbles [17,18], bounding boxes [19,20], clicks [19], and image-level tags [18] are used as supervision for image segmentation. In this work, the OSM centerline is used as a typical scribbles supervision for road extraction.In order to improve annotation efficiency and road extraction performance for automated VHR (Very High Resolution, spatial resolution 0∼2 m/pixel) images interpretation, this paper proposes a weakly supervised method to extract the road network supervised only by the scribble annotation OSM centerline. In this method, graph cut theory and a deep learning technique named Multi-Dilated-ResUNet (MD-ResUNet) are used to make efficient roads extraction.The main contributions of this paper are as follows:1. A novel deep learning approach based on revised ResUNet with hybrid loss is proposed for road extraction, which can only be supervised by weakly labeled OSM centerline instead of carefully notated pixel-wise road width information. 2. In order to improve the performance of the proposed model furtherly, a novel multi-dilation network with learnable parameters is added to conventional ResUnet. Th...

show abstract

“…Classifier K nearest neighbor was used to classify Curvelet co-efficient matrix for reorganization of the specific fingerprint. Vaishali Pawar and Mukesh Zaveri [23] implemented a graph clustering and matching algorithms based fingerprint recognition system. Structural features of the minutiae from the fingerprint were extracted.…”

Section: Short Review On Fingerprint Recognition Techniquesmentioning

confidence: 99%

Wavelet Based Fingerprint Authentication System: A Review

Verma¹

2016

ELELIJ

View full text Add to dashboard Cite

Biometric recognition refers to the use of biological characteristics for identification and verification of individuals. Use of biometric is increasing nowadays because biometric characteristics are difficult to replicate and lifelong. Applications which require only the authorized persons to access the resources are information systems, National id systems, voter and driver registrations, documentations, military area and ATMs. Among all biometrics, fingerprints is the most widely used and accepted by the user as the acquisition of fingerprint image is minimally invasive and require little hardware. Traditional fingerprint identification algorithm based on Minutiae Extraction, Discrete cosine transform(DCT) and fast Fourier transform require so much computation. Wavelet based algorithm do not require any preprocessing and post processing steps and hence is the key for low cost fingerprint identification system. This paper presents the overview of different fingerprint recognition system. In comparison to older fingerprint recognition system that is based on minutiae and FFT, the wavelet based system has high recognition rates.

show abstract

Graph based K-nearest neighbor minutiae clustering for fingerprint recognition

Cited by 5 publications

References 16 publications

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline

Wavelet Based Fingerprint Authentication System: A Review

Contact Info

Product

Resources

About