Systematic Review of Aggregation Functions Applied to Image Edge Detection

Edge detection is a crucial process in numerous stages of computer vision. This field of study has recently gained momentum due to its importance in various applications. The uncertainty, among other characteristics of images, makes it difficult to accurately determine the edge of objects. Furthermore, even the definition of an edge is vague as an edge can be considered as the maximum boundary between two regions with different properties. Given the advancement of research in image discontinuity detection, especially using aggregation and pre-aggregation functions, and the lack of systematic literature reviews on this topic, this paper aims to gather and synthesize the current state of the art of this topic. To achieve this, this paper presents a systematic review of the literature, which selected 24 papers filtered from 428 articles found in computer databases in the last seven years. It was possible to synthesize important related information, which was grouped into three approaches: (i) based on both multiple descriptor extraction and data aggregation, (ii) based on both the aggregation of distance functions and fuzzy C-means, and (iii) based on fuzzy theory, namely type-2 fuzzy and neutrosophic sets. As a conclusion, this review provides interesting gaps that can be explored in future work.

Automated summary

Edge detection is a crucial process in computer vision with increasing importance in various applications. Determining the edge of objects accurately is challenging due to the uncertainty and vagueness associated with image characteristics. This paper aims to gather and synthesize the current state of the art in image discontinuity detection through a systematic review of the literature. It identifies three approaches: multiple descriptor extraction and data aggregation, aggregation of distance functions and fuzzy C-means, and fuzzy theory such as type-2 fuzzy and neutrosophic sets. This review provides valuable insights and potential directions for future research.