Edge Location and Identification Method for Electronic Components based on Improved Canny Algorithm

The position identification of SMD electronic components mainly uses Canny edge detection algorithm to detect the edges of specific elements, benefited from its computational simplicity. The traditional Canny algorithm lacks the adaptability in gradient calculation and double thresholds selection, which may affect the location and identification accuracy of specific elements in electronic components. In this paper, an improved canny edge detection algorithm is proposed. The gradient magnitude is calculated in four directions, i.e., horizontal, vertical, and diagonal. Both the high and low thresholds can be adaptively determined based on the grayscale distribution information, to increase the adaptability of edge identification. The experimental results show that the proposed method can better locate the true edges of specific elements in electronic components with a reasonable processing speed, compared with the traditional Canny algorithm, and has been successfully applied on practical real-time vision inspection on SMD electronic components.

Application of Image Reconstruction Based on Inverse Radon Transform in CT System Parameter Calibration and Imaging

According to the requirements of CT system parameter calibration and imaging, using OpenCV and MATLAB software, the reverse Radon transform and the Canny edge detection algorithm in the projection edge methods can determine the position and geometry of two different media in the square tray based on given data. Besides, it can apply and analyze the shape and the absorption rate of the specified point, the accuracy, and stability of the template calibration parameters by this way, which enables designing a new template and calibrating the new parameters.

Echocardiography Segmentation by Fractional Differential and Improved Canny, Analysis by Fourier Descriptor

The omnidirectional M-mode echocardiogram provides a new method for human heart functional analyses. In this article, to sharpen object edges, we designed image processing kernel based on Fractional differential for image enhancement. After that, the contour of the left ventricle in a short axis is first extracted using both an improved Canny edge detection algorithm and the gray level searching algorithm in the radial direction as auxiliary. The modified Canny edge detection algorithm with the matching method between adjacent frames then is adopted for the subsequent frames to extract the left ventricular contours. The non-functional movements in the B-ultrasonic plane are determined by using the movement extracting method based on Fourier descriptors and the mass center with the inertia axis method, and the movements are removed from a compound motion. The Fourier descriptors are applied to get a series of image contour curves with the principal translation and rotation. Hence the curve of the cardiac motion can accurately show functional movements in any location of the heart. Using our technique, we can reduce multi-lines and excursion, as well as correct the omnidirectional M-mode echocardiography.

Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

The traditional canny edge detection algorithm has its limitations in the aspect of antinoise interference, and it is susceptible to factors such as light. To solve these defects, the Canny algorithm based on morphological improvement was proposed and applied to the detection of agricultural products. First, the algorithm uses the open and close operation of morphology to form a morphological filter instead of the Gaussian filter, which can remove the image noise and strengthen the protection of image edge. Second, the traditional Canny operator is improved to increase the horizontal and vertical templates to 45° and 135° to improve the edge positioning of the image. Finally, the adaptive threshold segmentation method is used for rough segmentation, and on this basis, double detection thresholds are used for further segmentation to obtain the final edge points. The experimental results show that compared with the traditional algorithm applied to the edge detection of agricultural products, this algorithm can effectively avoid the false contour caused by illumination and other factors and effectively improve the antinoise interference while more accurate and fine detection of the edge of real agricultural products.

Automated Web Design And Code Generation Using Deep Learning

Excited by ground-breaking progress in automatic code generation, machine translation, and computer vision, further simplify web design workflow by making it easier and productive. A Model architecture is proposed for the generation of static web templates from hand-drawn images. The model pipeline uses the word-embedding technique succeeded by long short-term memory (LSTM) for code snippet prediction. Also, canny edge detection algorithm fitted with VGG19 convolutional neural net (CNN) and attention-based LSTM for web template generation. Extracted features are concatenated, and a terminal LSTM with a SoftMax function is called for final prediction. The proposed model is validated with a benchmark based on the BLUE score, and performance improvement is compared with the existing image generation algorithms.