Fast and Accurate Computation of 3D Charlier Moment Invariants for 3D Image Classification

Yamni, Mohamed; Daoui, Achraf; ogri, O. El; Karmouni, Hicham; Sayyouri, Mhamed; Qjidaa, Hassan; Maâroufi, Mustapha; Alami, Badreeddine

doi:10.1007/s00034-021-01763-0

Cited by 17 publications

(3 citation statements)

References 42 publications

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“…The Figure 2 is the input glaucoma image obtained from MESSIDOR database. Since the CLAHE does not execute, the RGB image is converted into a gray scale image [15]. The Figure 3 is the gray scale image.…”

Section: Simulation Resultsmentioning

confidence: 99%

Effectiveness of Feature Extraction by PCA-Based Detection and Naive Bayes Classifier for Glaucoma Images

Christobel

Vimala

Athanesious

et al. 2022

International Journal of Digital Multimedia Broadcasting

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After cataract, glaucoma is one of the second leading retinal diseases in the world. This paper presents the methodology to detect the glaucoma using principal component analysis. The images are involved in dilation as a preprocessing, enhancement using the contrast limited adaptive histogram equalization method, and followed by the extraction of features using principal component analysis. The extracted features are classified using support vector machine, Naive Bayes, and K-nearest neighbor classifiers. Comparing with other classifiers, the Naive Bayes provides high accuracy of 95% which demonstrates the effectiveness of the feature extraction and the classifier.

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Section: Simulation Resultsmentioning

confidence: 99%

Effectiveness of Feature Extraction by PCA-Based Detection and Naive Bayes Classifier for Glaucoma Images

Christobel

Vimala

Athanesious

et al. 2022

International Journal of Digital Multimedia Broadcasting

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“…These studies provide valuable insights into monitoring cattle behavior and have practical applications in the agricultural industry 41 – 43 …”

Section: Research Statusmentioning

confidence: 99%

UD-YOLOv5s: Recognition of cattle regurgitation behavior based on upper and lower jaw skeleton feature extraction

Gao,

Wang,

Wang

et al. 2023

J. Electron. Imag.

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Rumination plays a pivotal role in assessing the health status of ruminants. However, conventional contact devices such as ear tags and pressure sensors raise animal welfare concerns during rumination behavior detection. Deep learning offers a promising solution for non-contact rumination recognition by training neural networks on datasets. We introduce UD-YOLOv5s, an approach for bovine rumination recognition that incorporates jaw skeleton feature extraction techniques. Initially, a skeleton feature extraction method is proposed for the upper and lower jaws, employing skeleton heatmap descriptors and the Kalman filter algorithm. Subsequently, the UD-YOLOv5s method is developed for rumination recognition. To optimize the UD-YOLOv5s model, the traditional intersection over the union loss function is replaced with the generalized one. A self-built bovine rumination dataset is used to compare the performance of three deep learning techniques: mean shift algorithm, mask region-based convolutional neural network, and you only look once version 3 (YOLOv3). The results of the ablation experiment demonstrate that UD-YOLOv5s achieves impressive precision (98.25%), recall (97.75%), and a mean average precision of 93.43%. We conducted a generalization performance evaluation in a controlled experimental environment to ensure fairness, indicating that UD-YOLOv5s converges faster than other models while maintaining comparable recognition accuracy. Moreover, our work reveals that when convergence speed is equal, UD-YOLOv5s outperforms other models regarding recognition accuracy. These findings provide robust support for accurately identifying cattle rumination behavior, showcasing the potential of the UD-YOLOv5s method in advancing ruminant health assessment.

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“…To overcome this problem raised by continuous moments, many researchers have begun to apply discrete polynomials as the basis of discrete moments which are fitted for digital image studies. Examples of discrete orthogonal moments include Tchebichef [22,23], Krawtchouk [24,25], Charlier [26,27], Meixner [28,29], Hahn/Dual-Hahn [30,31], and Racah [32].…”

Section: Introductionmentioning

confidence: 99%

Four-Term Recurrence for Fast Krawtchouk Moments Using Clenshaw Algorithm

Asli

Rezaei²

2023

Electronics

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Krawtchouk polynomials (KPs) are discrete orthogonal polynomials associated with the Gauss hypergeometric functions. These polynomials and their generated moments in 1D or 2D formats play an important role in information and coding theories, signal and image processing tools, image watermarking, and pattern recognition. In this paper, we introduce a new four-term recurrence relation to compute KPs compared to their ordinary recursions (three-term) and analyse the proposed algorithm speed. Moreover, we use Clenshaw’s technique to accelerate the computation procedure of the Krawtchouk moments (KMs) using a fast digital filter structure to generate a lattice network for KPs calculation. The proposed method confirms the stability of KPs computation for higher orders and their signal reconstruction capabilities as well. The results show that the KMs calculation using the proposed combined method based on a four-term recursion and Clenshaw’s technique is reliable and fast compared to the existing recursions and fast KMs algorithms.

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Fast and Accurate Computation of 3D Charlier Moment Invariants for 3D Image Classification

Cited by 17 publications

References 42 publications

Effectiveness of Feature Extraction by PCA-Based Detection and Naive Bayes Classifier for Glaucoma Images

Effectiveness of Feature Extraction by PCA-Based Detection and Naive Bayes Classifier for Glaucoma Images

UD-YOLOv5s: Recognition of cattle regurgitation behavior based on upper and lower jaw skeleton feature extraction

Four-Term Recurrence for Fast Krawtchouk Moments Using Clenshaw Algorithm

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