This paper provides an overview of edge detection techniques in computer vision and image processing. Edge detection involves localizing significant variations in the grey level of an image and identifying the physical phenomena that caused these variations. The process typically includes smoothing to reduce noise and differentiation to detect edges, but these steps are challenging due to the ill-conditioned nature of differentiation and the loss of information in smoothing. Various edge detectors have been developed, each with different mathematical and algorithmic properties, to address these challenges. The paper discusses the definition of edges, including step edges, lines, and junctions, and the properties of edge detectors, such as smoothing filters and differentiation operators. It explores the mutual influence between detectors and edges, considering how image characteristics and detector properties affect edge detection performance. The paper also surveys existing edge detectors and their implementations, highlighting the importance of multi-scale and multi-detector approaches for better edge localization and false edge suppression. Key topics include: 1. **Edge Definition**: Step edges, lines, and junctions are defined and their characteristics are discussed. 2. **Properties of Edge Detectors**: Smoothing filters and differentiation operators are analyzed, including their linear and rotational properties. 3. **Edge Labeling**: Techniques for localizing edges and suppressing false edges are described. 4. **Multi-Detector and Multi-Scale Approaches**: The benefits of using multiple detectors and scales are explained, along with methods for combining edge information from different scales. 5. **Mutual Influence Between Detectors and Edges**: The impact of detector properties and image characteristics on edge detection performance is examined. The paper aims to provide a comprehensive understanding of edge detection techniques and their applications in various image processing tasks.This paper provides an overview of edge detection techniques in computer vision and image processing. Edge detection involves localizing significant variations in the grey level of an image and identifying the physical phenomena that caused these variations. The process typically includes smoothing to reduce noise and differentiation to detect edges, but these steps are challenging due to the ill-conditioned nature of differentiation and the loss of information in smoothing. Various edge detectors have been developed, each with different mathematical and algorithmic properties, to address these challenges. The paper discusses the definition of edges, including step edges, lines, and junctions, and the properties of edge detectors, such as smoothing filters and differentiation operators. It explores the mutual influence between detectors and edges, considering how image characteristics and detector properties affect edge detection performance. The paper also surveys existing edge detectors and their implementations, highlighting the importance of multi-scale and multi-detector approaches for better edge localization and false edge suppression. Key topics include: 1. **Edge Definition**: Step edges, lines, and junctions are defined and their characteristics are discussed. 2. **Properties of Edge Detectors**: Smoothing filters and differentiation operators are analyzed, including their linear and rotational properties. 3. **Edge Labeling**: Techniques for localizing edges and suppressing false edges are described. 4. **Multi-Detector and Multi-Scale Approaches**: The benefits of using multiple detectors and scales are explained, along with methods for combining edge information from different scales. 5. **Mutual Influence Between Detectors and Edges**: The impact of detector properties and image characteristics on edge detection performance is examined. The paper aims to provide a comprehensive understanding of edge detection techniques and their applications in various image processing tasks.