Improving Area Determination of Leather Arches using Image Processing Techniques

Document Type : Research Paper

Authors

Faculty of Mechanical Engineering, Semnan University, Semnan, Iran.

Abstract

One of the key issues in image processing is calculating the area of surfaces from their images. In this article, we attempt to estimate the surface area of a piece of leather. First, the algorithm for this process will be explained, and then the results will be validated by presenting some examples and models. These images were recorded without editing and in real conditions. Using Python and the OpenCV library, pre-processing is applied to the images, and through algebraic calculations, the output of the algorithm is obtained as the surface area of the desired object. In this process, lighting and the selection of algorithms are very important. By creating favorable conditions, a result with an error margin of 1% to 3% can be achieved in the shortest possible time compared to the results of the mechanical measuring devices.

Keywords

Main Subjects


[1]
R. C. &. W. Gonzalez, Digital Image Processing (4th ed.). Pearson., (2018).
[2]
A. Haroon, "Enhanced Functionality of Footing Machine through Deep Learning," International Workshop Soft Computing Applications. Cham: Springer International Publishing, 2020..
[3]
N. Rmadi, "Exploring multisite musculoskeletal symptoms among sewing machine operators in a tunisian leather and footwear industry using decision tree models.," Clinical Epidemiology and Global Health 27, (2024): 101575..
[4]
Y. &. W. Y. Zhou, " A new method for area measurement of irregular shapes based on image processing.," Journal of Visual Communication and Image Representation, 53, 1-9., (2018).
[5]
R. &. G. R. Ranjan, " A survey of image processing techniques for area measurement.," Journal of Computer and Communications, 5(2), 1-11., (2017).
[6]
Z. Chen, "A systematic review of machine-vision-based leather surface defect inspection.," Electronics 11.15 ): 2383., 2022.
[7]
L. Zhou, "A deep learning approach for area measurement of irregular shapes," . IEEE Transactions on Image Processing, 28(6), 2913-2925., (2019).
[8]
A. &. G. R. Kumar, "Image segmentation techniques for accurate area measurement.," International Journal of Computer Vision, 131(3), 456-470., (2023). .
[9]
E. A. E. B. a. M. M. Sekehravani, "Implementing canny edge detection algorithm for noisy image," Bulletin of Electrical Engineering and Informatics 9.4 1404-1410., (2020).
[10]
H. &. Y. J. Chen, "A novel approach for area measurement using contour detection. 158, 227-234.," (2022).
[11]
P. &. S. N. Rao, " Automated area measurement using advanced image processing techniques.," IEEE 8(11), 298., (2022).
[12]
D. Cheng, "Thin and large depth-of-field compound-eye imaging for close-up photography," Photonics. Vol. 11. No. 2. MDPI,, 2024..
[13]
Q. &. Z. H. Liu, "Combining deep learning and traditional methods for area measurement.," Computers in Biology and Medicine, 153, 106-115., (2023).
[14]
M. &. D. R. Patel, "Hybrid approaches for area measurement of irregular shapes," Journal of Computational Science, 66, 101-110, (2022).
[15]
S. &. M. Ghosh, "Image processing for area estimation in environmental monitoring.," Environmental Modelling & Software, 157, 105-116., (2023).
[16]
S. &. S. R. Mishra, "Efficient algorithms for area measurement in digital images.," Journal of Digital Imaging, 36(3), 1-10., (2023).
[17]
S. &. R. K. Srinivasan, "Machine learning methodologies for area measurement in agriculture.," Computers and Electronics in Agriculture, 202, 107-119., (2023). .