Research on Edge Detection Algorithm of Indoor Soft Decoration Pattern

Abstract

Purpose of edge detection is to identify the points with obvious brightness change in digital image. The image edge detection effect under normal light is good, but the image display effect under dark vision is poor, and most of the edge information is lost. In order to quickly and accurately obtain the image edge part containing a large amount of feature information, many classical algorithms have appeared in the field of edge detection. This paper discusses the development of soft edge detection and intelligent edge detection technology for readers. Aiming at the image difference caused by proportion, rotation, and folding in the process of automatic recognition of indoor soft decoration patterns, this paper focuses on the collection of proportion, rotation, blur, and illumination of indoor soft decoration pattern images under five folding changes. The edge detection algorithm (EDA) is used to quickly and accurately obtain images containing a large amount of feature information at a low cost of edge parts, extract the local features of the pattern, calculate the feature matching with Euclidean distance, and eliminate the false matching with random sampling consistency algorithm. Finally, the experimental results show that EDA algorithm usually combines a variety of features to realize edge detection, and its output edge detection result is better than the traditional edge detection algorithm which only considers the gradient change. The algorithm has the highest accurate matching rate, and the average accurate matching rate is 87.10%; the robustness of this algorithm is better than other algorithms, and the speed is the fastest. Therefore, EDA algorithm has good applicability in indoor soft decoration pattern feature matching.