calculation of directional gradient eigenvalue for support vector machine training

Directional Gradient Histogram and SVM for Identifying Concrete Vibration States

Concrete is a fundamental building material extensively used in the construction industry. During pumping, the concrete frequently develops voids that require compaction through vibration. Inadequate vibration may result in a honeycombed concrete surface, while excessive vibration can lead to concrete segregation1,2. The quality of vibration operation significantly impacts the strength3,4,5,6 and durability of the formed concrete structures7,8. Cai et al.9,10 conducted a study that combined experimental research with numerical analysis to investigate the influence mechanism of vibration on aggregate settlement and concrete durability. The findings revealed that vibration time and aggregate particles exert a substantial impact on aggregate settlement, while aggregate density and the plastic viscosity of the cement-based material have minimal effects. Vibration causes aggregate deposition at the bottom of the concrete specimens. Moreover, as the vibration time increases, the chloride ion concentration decreases at the bottom of the concrete specimens while significantly increasing at the top9,10.
Currently, the assessment of concrete vibration state relies predominantly on manual judgment. As the construction industry continues to progress through intelligent reforms, robot operations have emerged as the future direction11,12. Consequently, a crucial challenge in intelligent vibration operations is how to enable robots to identify the vibration state of concrete.
The histogram of the oriented gradient is a technique that utilizes the intensity gradient of pixels or the distribution of edge directions as a descriptor to characterize the representation and shape of objects in images13,14. This approach operates on the local grid cells of the image, providing robust stability in characterizing image changes under various geometric and optical conditions.
Zhou et al.15 proposed a method for directly extracting directional gradient features from Bayer mode images. This approach omits numerous steps in calculating the directional gradient by matching the color filter column with the gradient operator, thereby significantly reducing the computational requirements for directional gradient image recognition. He et al.16 utilized the directional gradient histogram as the underlying feature and employed the mean clustering algorithm to classify rail fasteners and determine whether the fasteners are defective. The recognition results indicated that the histogram of the oriented gradient feature exhibited high sensitivity to fastener defects, meeting the needs of railway maintenance and repair. In another study, Xu et al.17 preprocessed face image features using Gabor wavelet filtering and reduced the dimension of feature vectors through binary coding and the HOG algorithm. The average recognition accuracy of the method is 92.5%.
The support vector machine (SVM)18 is used to map the vector into a high-dimensional space and establishes a separating hyperplane with a suitable direction to maximize the distance between two parallel hyperplanes. This allows for the classification of support vectors19. Scholars have improved and optimized this classification technology, leading to its application in various fields such as image recognition20,21, text classification22, reliability prediction23, and fault diagnosis24.
Li et al.25 developed a two-stage SVM model for seismic failure pattern recognition, focusing on three seismic failure modes. The analysis results indicate that the proposed two-stage SVM method can achieve more than 90% accuracy for the three failure modes. Yang et al.26 integrated an optimization algorithm with the SVM to simulate the relationship between the five ultrasonic parameters and the stress of the loaded concrete. The performance of an unoptimized SVM is unsatisfactory, particularly in the low-stress stage. However, traversing the model optimized by the algorithm yields improved results, albeit with lengthy computation times. In comparison, the particle swarm optimization optimized SVM significantly reduces the calculation time while delivering optimal simulation results. Yan et al.27 employed SVM technology and introduced a precision-insensitive loss function to predict the elastic modulus of high-strength concrete, comparing its prediction accuracy against the traditional regression model and neural network model. The research findings demonstrate that the SVM technology produces a smaller prediction error for elastic modulus compared to other methods.
This paper collects image samples of concrete under various vibration states and describes the concrete&#39;s different states using the directional gradient histogram technique. The directional gradient is employed as a feature vector for training the SVM, and the study focuses on the viability of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. Additionally, the paper analyzes the influence mechanism between three key parameters-binarization threshold, directional gradient statistical block size, and directional gradient statistical interval number-in the feature extraction process of the directional gradient histogram and the recognition accuracy of the SVM.

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines 

Reconhecimento de Imagem e Análise de Parametrização do Estado de Vibração do Concreto Baseado em Máquina de Vetor de Suporte

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Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training  

Cálculo do Autovalor do Gradiente Direcional para Treinamento de Máquina de Vetor de Suporte

Depois de binarizar as imagens concretas, calcule o gradiente binário horizontal e vertical de cada pixel na imagem usando esta equação. Calcule a direção e o tamanho do gradiente binário de cada ponto usando esta equação. Em seguida, determine o tamanho do bloco de segmentação da imagem representado como n.Para dividir a imagem em blocos quadrados de n por n, defina uma linha de segmentação para cada n pixels ao longo do eixo horizontal e do eixo vertical. Classifique os pixels no intervalo de ângulo estatístico do gradiente apropriado de cada direção com base na direção do gradiente binário de cada pixel no bloco. Para obter o valor estatístico de gradiente desse intervalo, soma o gradiente binário dos pixels no intervalo de ângulo estatístico de gradiente de cada direção no sentido anti-horário.Registre os resultados obtidos para as estatísticas de gradiente angular intervalar direcional estatística. Em seguida, divida as amostras em áreas de cálculo precisas, onde cada área consiste em quatro blocos adjacentes. Calcule o valor estatístico do gradiente direcional dentro do intervalo angular para cada bloco na área de cálculo especificada.Em seguida, gere o vetor de feição com as estatísticas do gradiente direcional como um componente. Combine os vetores de feição de gradiente direcional derivados de cada área de cálculo para obter o vetor de feição de gradiente direcional da imagem. À medida que o tamanho do bloco é expandido até certo ponto, as características do gradiente direcional em cada bloco de amostras de imagem de concreto com diferentes estados de vibração exibem diferenças significativas.

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Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training

90 Views.  Wuhan University. After binarizing the concrete images, calculate the horizontal and vertical binary gradient of each pixel in the image using this equation. Calculate the binary gradient direction and size of each point using this equation. Then determine the size of the image segmentation block represented as n.To divide the image into square blocks of n by n, set a segmentation line for every n pixels along the horizontal axis and the vertical axis. Classify pixels into the appropriate gradient's sta...