Development of a Novel Image Analysis and Classification Algorithms to Separate Tubers from Clods and Stones

Khazaee, Yousef; Kheiralipour, Kamran; Hosainpour, Adel; Javadikia, Hossein; Paliwal, Jitendra

doi:10.1007/s11540-021-09528-7

Cited by 11 publications

(14 citation statements)

References 29 publications

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“…In this study, the lighting condition was fixed using the four halogen lamps. Different lighting conditions and angles were used for the deep learning method in our previous studies [31,32]. The number of samples was selected based on these previous studies.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…where I is a mono color image [31], and H, S, and V are the different channels of HSV color space. Equation (1) was developed by the trial-and-error method.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…The processed image (in RGB color space) was transformed into different spaces including L * a * b *, HSV, NRGB, CrCgCb, I1I2I3, and gray (Figure 2) [31][32][33][34]. Nineteen image channels from each color space were obtained: R, G, B, L *, a *, b *, H, S, V, NR, NG, NB, Cr, Cg, Cb, I1, I2, I3, and gray.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…It provides the spatial relationship between image pixels. Then, the energy, entropy, correlation, homogeneity [31][32][33][34][35], and contrast features were extracted.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…where P d is the co-occurrence matrix, P(i, j) is the color values at i column and j row, N g is the number of pixels, and σ i,j is the standard deviation associated with i column and j row. The values of eccentricity, elongation, roundness, [31][32][33][34][35], centroid length, length of the small and large axes, and length of the large axes as shape features were calculated.…”

Section: Image Preprocessingmentioning

confidence: 99%

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Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing

2022

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The existence of dockage, unripe kernels, and foreign materials in chickpea mixtures is one of the main concerns during chickpea storage and marketing. Novel algorithms based on image processing were developed to detect undesirable, foreign materials, and matured chickpea kernels in the chickpea mixture. Images of 270 objects including 54 sound samples and 36 samples of each undesired object were prepared and features of these acquired images were extracted. Different models based on linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural networks (ANN) methods were developed by using MATLAB. Three classification algorithms based on LDA, SVM, and ANN methods were developed. The classification accuracy in training, testing, and overall detection showed the superiority of ANN (99.4, 92.6, and 94.4%, respectively) and LDA (91.1, 94.0, and 91.9%, respectively) over the SVM (100, 53.7, and 88.5%, respectively). The developed image processing technique can be incorporated with a vision-based real-time system.

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Section: Image Acquisitionmentioning

confidence: 99%

“…where I is a mono color image [31], and H, S, and V are the different channels of HSV color space. Equation (1) was developed by the trial-and-error method.…”

Section: Image Preprocessingmentioning

confidence: 99%

Section: Image Preprocessingmentioning

confidence: 99%

“…It provides the spatial relationship between image pixels. Then, the energy, entropy, correlation, homogeneity [31][32][33][34][35], and contrast features were extracted.…”

Section: Image Preprocessingmentioning

confidence: 99%