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Applied Sciences
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Innovative Technologies in Image Processing for Robot Vision
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Innovative Technologies in Image Processing for Robot Vision

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 8267

Special Issue Editors

Dr. Deng Chen

E-Mail Website
Guest Editor
Hubei Province Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430079, China
Interests: software security; program constraint mining and verification; intelligent technology of industrial robots; multimedia data processing
Dr. Hong Zheng

E-Mail Website
Guest Editor
School of Electronic Information Engineering, Wuhan University, Wuhan, China
Interests: machine vision; image processing; biological recognition; intelligent system

Special Issue Information

Dear Colleagues,

Robot vision is one of the primary approaches for robots to sense the environment. It has been widely used in various kinds of robots, including industrial robots, service robots, and specialized robots. Image processing is the fundamental technique of robot vision. The rapid development of artificial intelligence significantly benefits innovative technologies in image processing. Many new ideas and techniques in image processing have emerged, which may drive progress in robot vision technologies. Therefore, this Special Issue is intended for the presentation of new ideas and experimental results of image processing techniques in the application of robot vision.

Research work of robot vision based on RGB images, RGBD images, the point could data, x-ray images, infrared images, etc. are all topics of interest. The subtasks of robot vision include, but are not limited to, visual object detection, visual object tracking, image face recognition, facial expression recognition, human posture detection, hand gesture recognition, vision-based navigation, vision measurement, product defect detection, etc.

This Special Issue will publish high-quality, original research papers, in the overlapping fields of:

  • Image processing;
  • Machine vision;
  • Robot vision;
  • Deep learning;
  • Machine  learning;
  • Artificial intelligence;
  • Visual object detection;
  • Visual object tracking;
  • Image face recognition;
  • Facial expression recognition;
  • Human posture detection;
  • Hand gesture recognition;
  • Vision based navigation;
  • Vision measurement;
  • Product defect detection.

Dr. Deng Chen
Dr. Hong Zheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence, machine learning and deep learning
  • machine vision
  • robot vision
  • object detection
  • object tracking
  • face recognition
  • facial expression recognition
  • human posture recognition
  • vision based navigation
  • vision measurement
  • defect detection RGB-D image point could data X-ray images infrared images medical image processing remote sensing image processing

Published Papers (5 papers)

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Research

16 pages, 4291 KiB  
Article
U-Net-Embedded Gabor Kernel and Coaxial Correction Methods to Dorsal Hand Vein Image Projection System
by Liukui Chen, Monan Lv, Junfeng Cai, Zhongyuan Guo and Zuojin Li
Appl. Sci. 2023, 13(20), 11222; https://doi.org/10.3390/app132011222 - 12 Oct 2023
Viewed by 740
Abstract
Vein segmentation and projection correction constitute the core algorithms of an auxiliary venipuncture device, responding to accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting veins and a [...] Read more.
Vein segmentation and projection correction constitute the core algorithms of an auxiliary venipuncture device, responding to accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting veins and a coaxial correction for image alignment in the self-built vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by means of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combines coaxial correction and a homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, we used a hot mirror to adjust the light paths of both the projector and the camera to be coaxial, and then aligned the projected image with the dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection-over-union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in 0.09 s, and the largest distance error of vein projection onto the dorsal hand is 0.53 mm. The experiments show that the device has reached practical accuracy and has values of research and application. Full article
(This article belongs to the Special Issue Innovative Technologies in Image Processing for Robot Vision)
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16 pages, 11418 KiB  
Article
A Stereo-Vision-Based Spatial-Positioning and Postural-Estimation Method for Miniature Circuit Breaker Components
by Ziran Wu, Zhizhou Bao, Jingqin Wang, Juntao Yan and Haibo Xu
Appl. Sci. 2023, 13(14), 8432; https://doi.org/10.3390/app13148432 - 21 Jul 2023
Viewed by 859
Abstract
This paper proposes a stereo-vision-based method that detects and registers the positions and postures of muti-type, randomly placed miniature circuit breaker (MCB) components within scene point clouds acquired by a 3D stereo camera. The method is designed to be utilized in the flexible [...] Read more.
This paper proposes a stereo-vision-based method that detects and registers the positions and postures of muti-type, randomly placed miniature circuit breaker (MCB) components within scene point clouds acquired by a 3D stereo camera. The method is designed to be utilized in the flexible assembly of MCBs to improve the precision of gripping small-sized and complex-structured components. The proposed method contains the following stages: First, the 3D computer-aided design (CAD) models of the components are converted to surface point cloud models by voxel down-sampling to form matching templates. Second, the scene point cloud is filtered, clustered, and segmented to obtain candidate-matching regions. Third, point cloud features are extracted by Intrinsic Shape Signatures (ISSs) from the templates and the candidate-matching regions and described by Fast Point Feature Histogram (FPFH). We apply Sample Consensus Initial Alignment (SAC-IA) to the extracted features to obtain a rough matching. Fourth, fine registration is performed by employing Iterative Closest Points (ICPs) with a K-dimensional Tree (KD-tree) between the templates and the roughly matched targets. Meanwhile, Random Sample Consensus (RANSAC), which effectively solves the local optimal problem in the classic ICP algorithm, is employed to remove the incorrectly matching point pairs for further precision improvement. The experimental results show that the proposed method achieves spatial positioning errors smaller than 0.2 mm and postural estimation errors smaller than 0.5°. The precision and efficiency meet the requirements of the robotic flexible assembly for MCBs. Full article
(This article belongs to the Special Issue Innovative Technologies in Image Processing for Robot Vision)
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18 pages, 3435 KiB  
Article
Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
by Hong Zheng, Chengzhuo Zhou, Xi Li, Tianyu Wang and Changhui You
Appl. Sci. 2023, 13(14), 8101; https://doi.org/10.3390/app13148101 - 11 Jul 2023
Cited by 1 | Viewed by 2088
Abstract
At present, anti-counterfeiting schemes based on the combination of anti-counterfeiting patterns and two-dimensional codes is a research hotspot in digital anti-counterfeiting technology. However, many existing identification schemes rely on special equipment such as scanners and microscopes; there are few methods for authentication that [...] Read more.
At present, anti-counterfeiting schemes based on the combination of anti-counterfeiting patterns and two-dimensional codes is a research hotspot in digital anti-counterfeiting technology. However, many existing identification schemes rely on special equipment such as scanners and microscopes; there are few methods for authentication that use smartphones. In particular, the ability to classify blurry pattern images is weak, leading to a low recognition rate when using mobile terminals. In addition, the existing methods need a sufficient number of counterfeit patterns for model training, which is difficult to acquire in practical scenarios. Therefore, an authentication scheme for an anti-counterfeiting pattern captured by smartphones is proposed in this paper, featuring a single classifier consisting of two modules. A feature extraction module based on U-Net extracts the features of the input images; then, the extracted feature is input to a one-class classification module. The second stage features a boundary-optimized OCSVM classification method. The classifier only needs to learn positive samples to achieve effective identification. The experimental results show that the proposed approach has a better ability to distinguish the genuine and counterfeit anti-counterfeiting pattern images. The precision and recall rate of the approach reach 100%, and the recognition rate for the blurry images of the genuine anti-counterfeiting patterns is significantly improved. Full article
(This article belongs to the Special Issue Innovative Technologies in Image Processing for Robot Vision)
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15 pages, 28982 KiB  
Article
Fast Point Cloud Registration Method with Incorporation of RGB Image Information
by Haiyuan Cao, Deng Chen, Zhaohui Zheng, Yanduo Zhang, Huabing Zhou and Jianping Ju
Appl. Sci. 2023, 13(8), 5161; https://doi.org/10.3390/app13085161 - 20 Apr 2023
Cited by 1 | Viewed by 1626
Abstract
Point cloud registration has a wide range of applications in 3D reconstruction, pose estimation, intelligent driving, heritage conservation, and digital cities. The traditional iterative closest point (ICP) algorithm has strong dependence on the initial position, poor robustness, and low timeliness. To address the [...] Read more.
Point cloud registration has a wide range of applications in 3D reconstruction, pose estimation, intelligent driving, heritage conservation, and digital cities. The traditional iterative closest point (ICP) algorithm has strong dependence on the initial position, poor robustness, and low timeliness. To address the above issues, a fast point cloud registration method that incorporates RGB image information is proposed. The SIFT algorithm is used to detect feature points of point clouds corresponding to the RGB image, followed by feature point matching. The RANSAC algorithm is applied to remove erroneous point pairs in order to calculate the initial transformation matrix. After applying a pass-through filter for noise reduction and transiting down with a voxel grid, the point cloud is subjected to rotation and translation transformation for initial registration. On the basis of initial alignment, the FR-ICP algorithm is utilized for achieving precise registration. This method not only avoids the problem of ICP easily getting stuck in local optima, but also has higher registration accuracy and efficiency. Experimental studies were conducted based on point clouds of automotive parts collected in real scenes, and the results showed that the proposed method has a registration error of only 0.487 mm. Among the same group of experimental point clouds with comparable registration error, the proposed method showed a speed improvement of 69%/48% compared to ICP/FR-ICP with regard to registration speed. Full article
(This article belongs to the Special Issue Innovative Technologies in Image Processing for Robot Vision)
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18 pages, 1769 KiB  
Article
Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition
by Zongze Chen, Wenxia Yang and Xin Li
Appl. Sci. 2023, 13(3), 1750; https://doi.org/10.3390/app13031750 - 30 Jan 2023
Cited by 3 | Viewed by 1876
Abstract
Chinese characters carry a wealth of morphological and semantic information; therefore, zero-shot Chinese character recognition with the morphology of Chinese characters has drawn significant attention. The previous methods are mainly based on radical-level decomposition or stroke-level decomposition, which usually cannot capture adequately the [...] Read more.
Chinese characters carry a wealth of morphological and semantic information; therefore, zero-shot Chinese character recognition with the morphology of Chinese characters has drawn significant attention. The previous methods are mainly based on radical-level decomposition or stroke-level decomposition, which usually cannot capture adequately the structural and spatial information of Chinese characters. In this paper, we develop a stroke-based autoencoder (SAE), to model the sophisticated morphology of Chinese characters with a self-supervised method. Following its canonical writing order, we first represent a Chinese character as a series of stroke images with a fixed writing order, and then our SAE model is trained to reconstruct this stroke image sequence. This pre-trained SAE model can predict the stroke image series for unseen characters, as long as their strokes or radicals are in the training set. We have designed two contrasting SAE architectures on different forms of stroke images. One is fine-tuned on existing stroke-based method for zero-shot recognition of handwritten Chinese characters, and the other is applied to enrich the Chinese word embeddings from their morphological features. The experimental results validate that after pre-training, our SAE architecture outperforms other existing methods in zero-shot recognition and enhances the representation of Chinese characters with their abundant morphological and semantic information. Full article
(This article belongs to the Special Issue Innovative Technologies in Image Processing for Robot Vision)
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