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Comparison of surface extraction techniques performance in computed tomography for 3D complex micro-geometry dimensional measurements

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第1-4期97卷 441-453页

作者： Torralba, Marta Jimenez, Roberto Yague-Fabra, Jose A. Ontiveros, Sinue Tosello, Guido AGM Ctr Univ Defensa Carretera Huesca S-N Zaragoza 50090 Spain Univ Zaragoza I3A Maria de Luna 3 Zaragoza 50018 Spain Autonomous Univ Baja California Dept Ind Engn San Fernando 21460 Tecate Mexico Tech Univ Denmark Dept Mech Engn Lyngby Denmark

The number of industrial applications of computed tomography (CT) for dimensional metrology in 10(0)-10(3) mm range has been continuously increasing, especially in the last years. Due to its specific characteristics, CT has the potential to be employed as a viable solution for measuring 3D complex micro-geometries as well (i.e., in the sub-mm dimensional range). However, there are different factors that may influence the CT process performance, being one of them the surface extraction technique used. In this paper, two different extraction techniques are applied to measure a complex miniaturized dental file by CT in order to analyze its contribution to the final measurement uncertainty in complex geometries at the mm to sub-mm scales. The first method is based on a similarity analysis: the threshold determination;while the second one is based on a gradient or discontinuity analysis: the 3D canny algorithm. This algorithm has proven to provide accurate results in parts with simple geometries, but its suitability for 3D complex geometries has not been proven so far. To verify the measurement results and compare both techniques, reference measurements are performed on an optical coordinate measuring machine (OCMM). The systematic errors and uncertainty results obtained show that the 3D canny adapted method slightly lower systematic deviations and a more robust edge definition than the local threshold method for 3D complex micro-geometry dimensional measurements.

关键词： 3D complex geometry Computed tomography Surface extraction canny algorithm

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Application of crack detection algorithm using convolutional neural network in concrete pavement construction

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2022年第2-3期13卷 154-163页

作者： Wei, Wuqiang Xu, Xiaoyan Zhengzhou Univ Sci & Technol Zhengzhou 450064 Henan Peoples R China Henan Univ Technol Inst Econ & Trade Zhengzhou 450001 Henan Peoples R China

Concrete pavement cracks detection is specifically studied based on the application principle of Convolutional Neural Network (CNN) in large-scale image recognition and processing. Firstly, the feature extraction and selection principle are introduced for sample data of concrete pavement crack, and the sample Image Processing (IP) method is expounded. Secondly, the pre-processing of concrete pavement crack images is proposed. Through the establishment of the functional relationship between Crack Rate (CP) and Crack Rate Index (CRI), the samples are trained, and the crack detection model based on CNN is implemented. The CNN model is evaluated by comparing the accuracy and loss rate of the CNN model with the traditional Alex model in processing different numbers of images. Afterward, a concrete pavement crack detection platform is developed with cross-platform Python, OpenCV, and QT framework, combined with Deep Learning (DL), graphical interface development, and image processing. Thereupon, a concrete pavement health evaluation method is proposed. Regression analysis shows that the evaluation method can reasonably evaluate concrete pavement crack.

关键词： convolutional neural network crack detection Gaussian filtering algorithm canny algorithm concrete pavement

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Automatic image segmentation of oil palm fruits by applying the contour-based approach

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SCIENTIA HORTICULTURAE 2020年 261卷

作者： Septiarini, Anindita Hamdani, Hamdani Hatta, Heliza Rahmania Anwar, Khoerul Mulawarman Univ Fac Comp Sci & Informat Technol Dept Comp Sci Samarinda Indonesia STMIK PPKIA Pradnya Paramita Informat Engn Malang Indonesia

Nowadays, computer vision-based automation systems in the field of oil palm plantations are still being developed. The ripeness grading for quality control of oil palm fruit and the fruit picking robots are the implementation examples of those systems. Such a system requires a pre-processing called segmentation. Segmentation is a complex process and will be more challenging if the image of oil palm fruit is captured in the natural background moreover with uneven brightness. In this work, the segmentation method of oil palm fruit is proposed by applying the contour-based approach due to the oil palm fruit that has various shapes and colors. This method implemented the canny algorithm combining with several operations of morphology and reconstruction to remove the noise. The proposed method is evaluated using a local dataset consisting of 160 images with three kinds of ripeness grading (60 raw, 50 under-ripe, and 50 ripe). Overall, the average of segmentation accuracy achieves of 90.13% with the error rates of false positive and false negative are 2.92% and 5.20%, respectively.

关键词： Oil palm Fruit segmentation Edge detection canny algorithm Morphology operation Reconstruction

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Modified dual channel PCNN algorithm with hybrid edge enhancement approach for multimodality brain image fusion

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2018年第2期28卷 120-146页

作者： Kavitha, S. Bharathi, B. Sasikala, P. Chandraleka, D. Ashwini, V SSN Coll Engn Dept CSE Madras 603110 Tamil Nadu India BA Continuum India Pvt Ltd Madras 600113 Tamil Nadu India Accenture Serv Private Ltd Madras Tamil Nadu India Accenture Serv Private Ltd Bangalore Karnataka India

Image fusion plays a vital role for many applications in the field of computer vision, remote sensing, image robotics and medical imaging. This paper is focused on the fusion of multimodality brain images, using a Modified Dual Channel Pulse Coupled Neural Network (MDCPCNN) algorithm along with hybrid edge enhancement approach namely canny and Ant Colony Optimisation (ACO). In general, the fused image derived from PCNN algorithm has better tissue information and contrast even though a loss occurs in edge information. To overcome this drawback, a hybrid edge enhancement approach is proposed and applied along with MDCPCNN fusion algorithm. The proposed model is validated using four datasets of brain images from different modality, with the subjective and objective measures. The fused image constructed from the proposed model consistently retains the edge information, contrast and texture than the existing PCNN's without false information or information loss.

关键词： image fusion dual channel pulse coupled neural network multimodality brain images canny algorithm ant colony optimisation

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Processing the Bouguer anomaly map of Biga and the surrounding area by the cellular neural network: application to the southwestern Marmara region

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EARTH PLANETS AND SPACE 2007年第4期59卷 201-208页

作者： Aydogan, Davut Istanbul Univ Fac Engn Dept Geophys TR-34310 Istanbul Turkey

An image processing technique called the cellular neural network (CNN) approach is used in this study to locate geological features giving rise to gravity anomalies such as faults or the boundary of two geologic zones. CNN is a stochastic image processing technique based on template optimization using the neighborhood relationships of cells. These cells can be characterized by a functional block diagram that is typical of neural network theory. The functionality of CNN is described in its entirety by a number of small matrices (A, B and I) called the cloning template. CNN can also be considered to be a nonlinear convolution of these matrices. This template describes the strength of the nearest neighbor interconnections in the network. The recurrent perceptron learning algorithm (RPLA) is used in optimization of cloning template. The CNN and standard canny algorithms were first tested on two sets of synthetic gravity data with the aim of checking the reliability of the proposed approach. The CNN method was compared with classical derivative techniques by applying the cross-correlation method (CC) to the same anomaly map as this latter approach can detect some features that are difficult to identify on the Bouguer anomaly maps. This approach was then applied to the Bouguer anomaly map of Biga and its surrounding area, in Turkey. Structural features in the area between Bandirma, Biga, Yenice and Gonen in the southwest Marmara region are investigated by applying the CNN and CC to the Bouguer anomaly map. Faults identified by these algorithms are generally in accordance with previously mapped surface faults. These examples show that the geologic boundaries can be detected from Bouguer anomaly maps using the cloning template approach. A visual evaluation of the outputs of the CNN and CC approaches is carried out, and the results are compared with each other. This approach provides quantitative solutions based on just a few assumptions, which makes the method more powerful

关键词： CNN derivative method cross-correlation canny algorithm cloning template gravity anomaly geological features southwestern Marmara region Turkey

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Image Edge Enhancement Detection Method of Human-Computer Interaction Interface Based on Machine Vision Technology

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MOBILE NETWORKS & APPLICATIONS 2022年第2期27卷 775-783页

作者： Jin, Yi Wei, Wei Suzhou Vocat Univ Jiangsu Prov Support Software Engn R&D Ctr Modern Dept Comp Engn Suzhou 215104 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian Peoples R China

Aiming at the problems of low detection accuracy and long detection time of existing image edge detection technologies, an image edge detection method of human-computer interaction interface based on machine vision technology is proposed. Based on machine vision technology, the image weight is calculated by iterative repeated weighted least square method, the image is Gaussian filtered by improved canny algorithm, and the optimal threshold is calculated by iterative method to judge the effective edge. Through comparative experiments, it is proved that the maximum detection accuracy of the man-machine interface image edge enhancement detection method based on machine vision technology proposed in this paper is 100%, the detection time is always kept below 0.2S, and the fastest detection time is 0.1 s, which has wide applicability.

关键词： Machine vision technology Image edge enhancement detection canny algorithm Least square method Gaussian filtering

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Visual retrieval of digital media image features based on active noise control

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第6期22卷 2107-2120页

作者： Zhang, Ying Luo, Xiaobo Huaihua Polytech Coll Dept Informat & Art Design Huaihua 418000 Hunan Peoples R China

In order to improve the recall and precision of image retrieval, a visual retrieval method of digital media image features based on active noise is proposed. In this paper, canny algorithm is used to detect the edge of the image to get the feature information of the edge of the image. The RGB color space model is used to decompose the color information of the image, and the color characteristic information of the image is obtained. Extracting image features to retain the useful information contained in the image as much as possible;In order to facilitate the visual retrieval of image features, reduce the retrieval complexity and further fuse image features, SPCA and ReliefF algorithms are used to reduce the dimensionality of image features, and the active noise control method is used to sharpen the image. After processing the results, a digital media image feature visual retrieval platform is established to realize the visual retrieval of digital media image features. Experimental results show that the proposed method has a high accuracy of over 80% and a high recall rate of 95.2%.

关键词： Active noise control image features visual retrieval canny algorithm wavelet PCA ReliefF algorithm

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Shape reconstruction of delamination defects using thermographic infrared signals based on an enhanced canny approach

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INFRARED PHYSICS & TECHNOLOGY 2020年 111卷 103527-103527页

作者： Ratsakou, Almpion Skarlatos, Anastassios Reboud, Christophe Lesselier, Dominique Univ Paris Saclay List CEA F-91120 Palaiseau France Univ Paris Saclay Lab Signaux & Syst CentraleSupelec CNRS F-91190 Gif Sur Yvette France

The canny shape-reconstruction algorithm is combined with the thermographic signal reconstruction (TSR) technique for the characterisation of delamination defects in planar multilayer structures. The adopted approach consists in fitting the raw thermographic images to a low-degree polynomial in the log-log representation of the time axis, according to TSR technique, and then apply the canny algorithm to the reconstruction of the original signals. The proposed juxtaposition of the two treatments benefits from the inherent signal de-noising and compression of the TSR algorithm, thus enhancing the performance of the overall reconstruction process. The proposed approach has been applied using synthetic signals obtained via a 3D semi-analytical simulation code and the obtained reconstructed images have been compared with the (known) delamination geometry demonstrating satisfactory results.

关键词： Infrared testing Delamination Image reconstruction canny algorithm TSR Semi-analytical solution

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Cervical cell recognition based on AGVF-Snake algorithm

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2019年第11期14卷 2031-2041页

作者： Dong, Na Zhao, Li Wu, Aiguo Tianjin Univ Sch Elect & Informat Engn 92 Weijin Rd Tianjin Peoples R China

PurposeIn recent years, with the increasing incidence of cervical cancer, it is a tedious and time-consuming task with unsatisfying accuracy to manually recognize the cells. Machine recognition can be a good solution, but it suffers from the difficulty of obtaining precise edges of cells, which directly influence the final recognition accuracy. To improve the recognition accuracy and shorten the time used for cell recognition, an AGVF-Snake (Adaptive Gradient Vector Flow-Snake) model for the extraction of cell edges has been proposed in this ***, the cell is initially located by the improved canny algorithm. Then, the adaptive initial contour model and gradient vector model are used to obtain accurate cell edges. Finally, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) classifier is selected to recognize the cervical *** dataset is used to verify the AGVF-Snake algorithm;the accuracy of two and seven classifications are recorded. Six other classification methods are introduced for comparison. According to the experimental results, the accuracy of two and seven classifications can achieve up to 99%, which are better than other six *** experiment results show that the proposed algorithm has obvious recognition advantages, and thus provides an effective methodological framework for the diagnosis of cervical cancer diseases.

关键词： canny algorithm AGVF-Snake PSO-SVM Cervical cancer recognition

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Edge detection algorithm in complex image text information extraction

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2023年第3期23卷 1381-1393页

作者： Li, Zhen Criminal Invest Police Univ China Sch Forens Sci & Technol Shenyang 110854 Liaoning Peoples R China

With the rapid development of network technology and information technology, the amount of information contained in images has increased significantly. How to effectively extract text information from complex images has become the focus of current research in this field. Firstly, the canny algorithm in the edge detection algorithm is improved and applied to the edge detection of complex images. Then the K-means algorithm is optimized to achieve better clustering effect of pixels. Finally, the text information in the image is extracted from the two. The results show that under the influence of salt and pepper noise from 0% to 90%, the quality factor obtained by the improved canny algorithm is at least 0.4, and the detection accuracy is higher;The minimum peak signal-to-noise ratio of the algorithm is 38, and the maximum mean square error is 30, which are both better than the LOG algorithm and the traditional canny algorithm, and have better noise reduction effect and image fidelity. It is used together in the extraction process of image text information, and the text recognition accuracy rate of the combined algorithm reaches a maximum of 93%, and is stable at more than 90%, indicating that this method has a high text recognition accuracy rate and provides text extraction for complex images. A reference path is available.

关键词： Edge detection canny algorithm text extraction K-means algorithm

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