scholarly journals Registration and Fusion of the Autofluorescent and Infrared Retinal Images

2008 ◽  
Vol 2008 ◽  
pp. 1-11 ◽  
Author(s):  
Radim Kolar ◽  
Libor Kubecka ◽  
Jiri Jan
Keyword(s):  
Laser Scanning ◽  
Retinal Images ◽  
Infrared Images ◽  
Scanning Device ◽  
Fusion Methods ◽  
Level Fusion ◽  
Heidelberg Retina Angiograph

This article deals with registration and fusion of multimodal opththalmologic images obtained by means of a laser scanning device (Heidelberg retina angiograph). The registration framework has been designed and tested for combination of autofluorescent and infrared images. This process is a necessary step for consecutive pixel level fusion and analysis utilizing information from both modalities. Two fusion methods are presented and compared.

Display unit using laser scanning device

2018 ◽  
Vol 26 (11) ◽  
pp. 631-640 ◽  
Author(s):  
Takahiro Matsuda ◽  
Shinsuke Onoe ◽  
Yoshiho Seo ◽  
Satoshi Ouchi
Keyword(s):  
Laser Scanning ◽  
Scanning Device ◽  
Display Unit

Rank Level Fusion

2013 ◽  
pp. 69-79
Keyword(s):  
Information Aggregation ◽  
Regression Method ◽  
Borda Count ◽  
Fusion Method ◽  
Advantages And Disadvantages ◽  
Current State ◽  
Fusion Methods ◽  
Logistic Regression Method ◽  
Level Fusion ◽  
Voting Method

Rank level fusion is one of the after matching fusion methods used in multibiometric systems. The problem of rank information aggregation has been raised before in various fields. This chapter extensively discusses the rank level fusion methodology, starting with existing literature from the last decade in different application scenarios. Several approaches of existing biometric rank level fusion methods, such as plurality voting method, highest rank method, Borda count method, logistic regression method, and quality-based rank fusion method, are discussed along with their advantages and disadvantages in the context of the current state-of-the-art in the discipline.

A Hybrid Approach for Person Identification Using Palmprint and Face Biometrics

2015 ◽  
Vol 29 (06) ◽  
pp. 1556009 ◽  
Author(s):  
Mina Farmanbar ◽  
Önsen Toygar
Keyword(s):  
Hybrid Approach ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Extraction Methods ◽  
Score Level Fusion ◽  
Linear Discriminant ◽  
Hybrid Approaches ◽  
Fusion Methods ◽  
Level Fusion

This paper proposes hybrid approaches based on both feature level and score level fusion strategies to provide a robust recognition system against the distortions of individual modalities. In order to compare the proposed schemes, a virtual multimodal database is formed from FERET face and PolyU palmprint databases. The proposed hybrid systems concatenate features extracted by local and global feature extraction methods such as Local Binary Patterns, Log Gabor, Principal Component Analysis and Linear Discriminant Analysis. Match score level fusion is performed in order to show the effectiveness and accuracy of the proposed schemes. The experimental results based on these databases reported a significant improvement of the proposed schemes compared with unimodal systems and other multimodal face–palmprint fusion methods.

Audiovisual Facial Action Unit Recognition using Feature Level Fusion

2016 ◽  
Vol 7 (1) ◽  
pp. 60-76 ◽  
Author(s):  
Zibo Meng ◽  
Shizhong Han ◽  
Min Chen ◽  
Yan Tong
Keyword(s):  
Time Shift ◽  
Visual Features ◽  
Visual Channel ◽  
Facial Action ◽  
Audio Channel ◽  
Feature Level Fusion ◽  
Fusion Methods ◽  
The Difference ◽  
Facial Images ◽  
Level Fusion

Recognizing facial actions is challenging, especially when they are accompanied with speech. Instead of employing information solely from the visual channel, this work aims to exploit information from both visual and audio channels in recognizing speech-related facial action units (AUs). In this work, two feature-level fusion methods are proposed. The first method is based on a kind of human-crafted visual feature. The other method utilizes visual features learned by a deep convolutional neural network (CNN). For both methods, features are independently extracted from visual and audio channels and aligned to handle the difference in time scales and the time shift between the two signals. These temporally aligned features are integrated via feature-level fusion for AU recognition. Experimental results on a new audiovisual AU-coded dataset have demonstrated that both fusion methods outperform their visual counterparts in recognizing speech-related AUs. The improvement is more impressive with occlusions on the facial images, which would not affect the audio channel.

Comparing airborne laser scanning-imagery fusion methods based on geometric accuracy in forested areas

2011 ◽  
Vol 115 (8) ◽  
pp. 1942-1954 ◽  
Author(s):  
Rubén Valbuena ◽  
Francisco Mauro ◽  
Francisco José Arjonilla ◽  
José Antonio Manzanera
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Geometric Accuracy ◽  
Airborne Laser ◽  
Fusion Methods ◽  
Forested Areas
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