Neuro-inspired edge feature fusion using Choquet integrals

Online defect detection system is a necessary technical measure and important means for large-scale industrial printing production. It is effective to reduce artificial detection fatigue and improve the accuracy and stability of industry printing line. However, the existing defect detection algorithms are mainly developed based on high-quality database and it is difficult to detect the defects on low-quality printing images. In this paper, we propose a new multi-edge feature fusion algorithm which is effective in solving this problem. Firstly, according to the characteristics of sheet-fed printing system, a new printing image database is established; compared with the existing databases, it has larger translation, deformation, and uneven illumination variation. These interferences make defect detection become more challenging. Then, SIFT feature is employed to register the database. In order to reduce the number of false detections which are caused by the position, deformation, and brightness deviation between the detected image and reference image, multi-edge feature fusion algorithm is proposed to overcome the effects of these disturbances. Lastly, the experimental results of mAP (92.65%) and recall (96.29%) verify the effectiveness of the proposed method which can effectively detect defects in low-quality printing database. The proposed research results can improve the adaptability of visual inspection system on a variety of different printing platforms. It is better to control the printing process and further reduce the number of operators.

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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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