Identification of image edges using edge detection is done to obtain images that are sharp and clear. The selection of the edge detection algorithm will affect the result. Canny operators have an advantage compared to other edge detection operators because of their ability to detect not only strong edges but also weak edges. Until now, Canny edge detection has been done using classical computing where data are expressed in bits, 0 or 1. This paper proposes the identification of image edges using a quantum Canny edge detection algorithm, where data are expressed in the form of quantum bits (qubits). Besides 0 or 1, a value can also be 0 and 1 simultaneously so there will be many more possible values that can be obtained. There are three stages in the proposed method, namely the input image stage, the preprocessing stage, and the quantum edge detection stage. Visually, the results show that quantum Canny edge detection can detect more edges compared to classic Canny edge detection, with an average increase of 4.05%.
The cancer or object detection on image Computed Tomography, X-Ray, and Mammography often use segmentation methods. The most common segmentation method used is based on edge detection. The edge detection method which is proposed in this study is Quantum based with Canny operator. The quality improvement of edge detection results is emphasized on the method to generate the gradient magnitude value which is done by observing the strength of neighbour pixel values. The strength of this pixel is obtained by calculating the pixel change value and the distance of neighbour pixels to the centre pixel. The results of the implementation show that the proposed method produces a better PSNR value than the original Canny, Sobel, Robert, and Prewitt Operator.
<span lang="IN">In the production process, quality checking is very important, one of which is on the wire. In the process of making brass-coated steel tire straps sometimes produce quality goods not in accordance with the desired standard values. Checks that are carried out manually have low efficiency and quite high errors occur. So it is necessary to check by measuring the wavelength on the brass plated steel cord automatically. In this study, used 3 automatic measurement methods using 2 evaluations, namely RMSE and Cosine Similarity. The results showed the best measurement using RMSE with method 2. Whereas the worst method uses RMSE with method 1. The smallest RMSE value is 0.0098 and the largest RMSE is 0.0966. The lowest Cosine Similarity value is 0.1253, while the highest Cosine Similarity value is 0.2079.</span>
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