In this paper, a method for adaptive Canny edge detection algorithm is proposed. Adaptive Canny algorithm is used to increase the accuracy of output objects. In traditional Canny need to set two threshold values manually, so there are some defects to different images but this paper puts faorward an adaptive threshold values based on mean and median values. Our proposed adaptive Canny edge detection method can detect edges successfully which is divided into several steps. First, Gaussian filter is used to smooth and remove noise. Second, gradient magnitude is computed. Third, nonmaximum suppression is applied in which the algorithm removes pixels that are not part of an edge. Finally, hysteresis thresholding is applied which uses two threshold values, upper and lower. A pixel will be marked as an edge if it's gradient lies in between of lower and upper threshold values. A pixel will be discarded if it's gradient below the lower or beyond the upper threshold values. Eventually, the pixels gradient is between the two threshold values will be connected as marked edge. The experimental results show the efficacy of the proposed method.
The shortest path-searching with the minimal weight for multiple destinations is a crucial need in an indoor applications, especially in supermarkets, warehouses, libraries, etc. However, when it is used for multiple item searches, its weight becomes higher as it searches only the shortest path between the single sources to each destination item separately. If the conventional Dijkstra algorithm is modified to multi-destination mode then the weight is decreased, but the output path is not considered as the real shortest path among multiple destinations items. Our proposed algorithm is more efficient for finding the shortest path among multiple destination items with minimum weight, compared to the single source single destination and modified multi-destinations of Dijkstra’s algorithm. In this research, our proposed method has been validated by real-world data as well as by simulated random solutions. Our advancement is more applicable in indoor environment applications based on multiple items or destinations searching.
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