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Product Shape Detection Method Based on Computer Image Recognition

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DOI: 10.23977/jipta.2023.060103 | Downloads: 14 | Views: 461

Author(s)

Guo Li 1

Affiliation(s)

1 Department of Journalism and Communication, Anhui Vocational College of Press and Publishing, Hefei Anhui, 230601, China

Corresponding Author

Guo Li

ABSTRACT

Compared with image recognition technology, product modeling detection in computer vision image recognition is a more complex problem, because image classification only needs to determine the general types of images, but there may be multiple processing objects in product modeling, so all object objects must be classified and located. Therefore, product shape detection and classification is more complex than image recognition. In this paper, several modeling recognition methods based on depth learning are studied, and a target shape recognition method based on computer vision is proposed. This method uses chain code method to determine the product shape, distinguish the subtle differences of product shape, and determine the deformation detection of product shape.

KEYWORDS

Computer, Image recognition, Product modeling

CITE THIS PAPER

Guo Li, Product Shape Detection Method Based on Computer Image Recognition. Journal of Image Processing Theory and Applications (2023) Vol. 6: 33-40. DOI: http://dx.doi.org/10.23977/jipta.2023.060103.

REFERENCES

[1] Dorogi Gábor, Bodnár Péter, Nagy Katalin. Automatikus csontszegmentáció szájsebészeti műtéti tervezés támogatására.[J]. Orvosi hetilap, 2022, 163(46).
[2] Palm Viktoria, Norajitra Tobias, von Stackelberg Oyunbileg, Heussel Claus P., Skornitzke Stephan, Weinheimer Oliver, Kopytova Taisiya, Klein Andre, Almeida Silvia D., Baumgartner Michael, Bounias Dimitrios, Scherer Jonas, Kades Klaus, Gao Hanno, Jäger Paul, Nolden Marco, Tong Elizabeth, Eckl Kira, Nattenmüller Johanna, Nonnenmacher Tobias, Naas Omar, Reuter Julia, Bischoff Arved, Kroschke Jonas, Rengier Fabian, Schlamp Kai, Debic Manuel, Kauczor HansUlrich, MaierHein Klaus, Wielpütz Mark O.. AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine[J]. Healthcare, 2022, 10(11).
[3] Zebari Dilovan Asaad, Ibrahim Dheyaa Ahmed, Zeebaree Diyar Qader, Haron Habibollah, Salih Merdin Shamal, Damaševičius Robertas, Mohammed Mazin Abed. Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images[J]. Applied Artificial Intelligence, 2021, 35(15).
[4] Hongyu Chen. Detection System for Mobile Phone Interface Circuit Board Assembly Based on Computer Vision[J]. Academic Journal of Engineering and Technology Science, 2021, 4.0(8.0).
[5] Lumini Alessandra, Nanni Loris, Maguolo Gianluca. Deep Ensembles Based on Stochastic Activations for Semantic Segmentation[J]. Signals, 2021, 2(4).
[6] Wheeler Bradley J., Karimi Hassan A. A semantically driven self-supervised algorithm for detecting anomalies in image sets[J]. Computer Vision and Image Understanding, 2021, 213.
[7] Lecca Michela, Torresani Alessandro, Remondino Fabio. Comprehensive evaluation of image enhancement for unsupervised image description and matching[J]. IET Image Processing, 2020, 14(16).
[8] Song Huajun, Song Jie, Ren Peng. Underwater Pipeline Oil Spill Detection Based on Structure of Root and Branch Cells[J]. Journal of Marine Science and Engineering, 2020, 8(12).
[9] V Jacintha, Jacintha V, Shakthi Murugan K H, Kumar Karanam Arun, Devi S, Saravanan G, Shyam Ganesh D. An Image Processing based Fault Detection in Fabrics[J]. IOP Conference Series: Materials Science and Engineering, 2020, 994(1).
[10] Vijay Vasanth Aroulanandam, Thamarai Pugazhendhi Latchoumi, Battula Bhavya, Shaik Sajida Sultana. Object Detection in Convolution Neural Networks Using Iterative Refinements[J]. Revue d'Intelligence Artificielle, 2020, 33(5).
[11] Asmae Ennaji, Abdellah Aarab. Ant Colony Optimization Algorithm for Lesion Border Detection in Dermoscopic Images[J]. Discontinuity, Nonlinearity, and Complexity, 2018, 74.
[12] P Archana, B Karunakar. Image Segmentation Using Improved Canny Algorithm and Mathematical Morphology[J]. Journal of Innovation in Electronics and Communication Engineering, 2018, 82.
[13] Anwar Syed Muhammad, Majid Muhammad, Qayyum Adnan, Awais Muhammad, Alnowami Majdi, Khan Muhammad Khurram. Medical Image Analysis using Convolutional Neural Networks: A Review.[J]. Journal of medical systems, 2018, 4211.
[14] Mohammad Alshibli, Ahmed Sayed, Ozden Tozanli, Elif Kongar, Tarek M. Sobh, Surendra M. Gupta. A Decision Maker-Centered End-of-Life Product Recovery System for Robot Task Sequencing[J]. Journal of Intelligent & Robotic Systems, 2018, 913-4.
[15] Kavitha Nagarathinam, Ruba Soundar Kathavarayan. Moving shadow detection based on stationary wavelet transform and Zernike moments[J]. IET Computer Vision, 2018, 126.

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