Accurate and Robust Feature Description and Dense Point-wise Matching based on Feature Fusion for Endoscopic Images

This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray difference (ORB-TPLGD). ORB takes a breakthrough in real-time aspect. However, subtle changes of the image may greatly affect its final binary description. In this paper, the feature description generation is focused. On one hand, instead of pixel patch pairs comparison method used in present ORB algorithm, a three-pixel patch group comparison method is adopted to generate the binary string. In each group, the gray value of the main patch is compared with that of the other two companion patches to determine the corresponding bit of the binary description. On the other hand, the present ORB algorithm simply uses the gray size comparison between pixel patch pairs, while ignoring the information of the gray difference value. In this paper, another binary string based on the gray difference information mentioned above is generated. Finally, the feature fusion method is adopted to combine the binary strings generated in the above two steps to generate a new feature description. Experiment results indicate that our improved ORB algorithm can achieve greater performance than ORB and some other related algorithms.

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Novel Local Coding Algorithm for Finger Multimodal Feature Description and Recognition

Sensors ◽

10.3390/s19092213 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2213 ◽

Cited By ~ 1

Author(s):

Shuyi Li ◽

Haigang Zhang ◽

Yihua Shi ◽

Jinfeng Yang

Keyword(s):

Feature Fusion ◽

Recognition Performance ◽

Recognition System ◽

Personal Identification ◽

Orientation Relationships ◽

User Friendliness ◽

Feature Description ◽

Pose Variation ◽

Local Coding ◽

Feature Expression

Recently, finger-based biometrics, including fingerprint (FP), finger-vein (FV) and finger-knuckle-print (FKP) with high convenience and user friendliness, have attracted much attention for personal identification. The features expression which is insensitive to illumination and pose variation are beneficial for finger trimodal recognition performance improvement. Therefore, exploring suitable method of reliable feature description is of great significance for developing finger-based biometric recognition system. In this paper, we first propose a correction approach for dealing with the pose inconsistency among the finger trimodal images, and then introduce a novel local coding-based feature expression method to further implement feature fusion of FP, FV, and FKP traits. First, for the coding scheme a bank of oriented Gabor filters is used for direction feature enhancement in finger images. Then, a generalized symmetric local graph structure (GSLGS) is developed to fully express the position and orientation relationships among neighborhood pixels. Experimental results on our own-built finger trimodal database show that the proposed coding-based approach achieves excellent performance in improving the matching accuracy and recognition efficiency.

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EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE USING DEEP LEARNING FOR DETECTING GASTRIC CANCER IN ENDOSCOPIC IMAGES

10.1055/s-0038-1637183 ◽

2018 ◽

Author(s):

T Hirasawa ◽

K Aoyama ◽

J Fujisaki ◽

T Tada

Keyword(s):

Artificial Intelligence ◽

Gastric Cancer ◽

Deep Learning ◽

Endoscopic Images

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Multi-Feature Fusion Identification of Important Nodes in Traffic Network

International Conference on Transportation and Development 2020 ◽

10.1061/9780784483152.015 ◽

2020 ◽

Author(s):

Yuxin Xiao ◽

Jianming Hu ◽

Zuo Zhang ◽

Yi Zhang

Keyword(s):

Feature Fusion ◽

Traffic Network ◽

Important Nodes

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A Study on Utilization of Three-Dimensional Sensor Lip Image for Developing a Pronunciation Recognition System

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2019.63.5.050402 ◽

2019 ◽

Vol 63 (5) ◽

pp. 50402-1-50402-9 ◽

Cited By ~ 1

Author(s):

Ing-Jr Ding ◽

Chong-Min Ruan

Keyword(s):

Principal Component Analysis ◽

Automatic Speech Recognition ◽

Feature Fusion ◽

Three Dimensional ◽

Principal Component ◽

Recognition System ◽

Geometrical Characteristics ◽

3D Geometry ◽

Different Types ◽

The Disabled

Abstract The acoustic-based automatic speech recognition (ASR) technique has been a matured technique and widely seen to be used in numerous applications. However, acoustic-based ASR will not maintain a standard performance for the disabled group with an abnormal face, that is atypical eye or mouth geometrical characteristics. For governing this problem, this article develops a three-dimensional (3D) sensor lip image based pronunciation recognition system where the 3D sensor is efficiently used to acquire the action variations of the lip shapes of the pronunciation action from a speaker. In this work, two different types of 3D lip features for pronunciation recognition are presented, 3D-(x, y, z) coordinate lip feature and 3D geometry lip feature parameters. For the 3D-(x, y, z) coordinate lip feature design, 18 location points, each of which has 3D-sized coordinates, around the outer and inner lips are properly defined. In the design of 3D geometry lip features, eight types of features considering the geometrical space characteristics of the inner lip are developed. In addition, feature fusion to combine both 3D-(x, y, z) coordinate and 3D geometry lip features is further considered. The presented 3D sensor lip image based feature evaluated the performance and effectiveness using the principal component analysis based classification calculation approach. Experimental results on pronunciation recognition of two different datasets, Mandarin syllables and Mandarin phrases, demonstrate the competitive performance of the presented 3D sensor lip image based pronunciation recognition system.

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