Fast and robust superpixel generation method

Abstract

Superpixel segmentation approach, as a preprocessing step in computer vision tasks, groups pixels into perceptually coherence atomic regions to replace the pixel grid in images and reduce the primitives and redundancy of subsequent works. In this study, the authors proposed a fast and robust superpixel generation method based on non‐iterative framework with the constraint of linear path. They collected neighbouring pixels as the initial superpixels one by one in the conventional order at first. To make the superpixels attach to the most object boundaries well and robust to noise, they defined a new distance measurement between pixels and superpixel seeds by considering the colour difference of pixels in the neighbourhood and along the linear path from the pixel to the seed. Meanwhile, they proposed a new way to set parameters adaptively based on the intrinsic quality of images. Then, they refined the initial superpixels by merging the smallest ones until the number of superpixel meets the expectation. The experimental results on clean and noisy images demonstrate that the proposed method is effective and presents a competitive performance in computational efficiency with the state‐of‐the‐art real‐time method.

Strategies for improving the detection accuracy of computerized machine vision considering spatial applications

“…Considering the conditions of spatial factors, when computer detection is carried out with the help of computer vision technology, it is necessary to pay attention to the image pre-processing technology, which is closely related to the subsequent image processing and analysis [17]. Figure 1 shows the image preprocessing process, to first extract the relevant information data of the image, and then effectively integrate the image preprocessing technology and template technology, thus reducing the technical difficulty of the actual monitoring.…”

The Application of FLICSP Algorithm Based on Multi-Feature Fusion in Image Saliency Detection

“…This technology has broad applications, which involve high processing requirements, and some tasks require human assistance. Therefore, how to use intelligent devices such as computers to quickly and accurately analyze and process extensive images, so that they can independently complete various visual tasks like humans, is currently the most interesting issue in computer vision [4][5][6]. The clearer the picture, the more details it requires, and the more memory it requires.…”