Edge Detection Algorithms in Computer Vision
Edge detection in computer vision is used to identify the points in a digital image at which the brightness changes sharply or has discontinuities. These points are typically organized into curved line segments termed edges. Here we discuss several key algorithms for edge detection:
Developed by John Canny in 1986, the Canny edge detector is one of the most widely used edge detection algorithms due to its robustness and accuracy. It involves several steps:
- Noise Reduction: Typically using a Gaussian filter to smooth the image.
- Gradient Calculation: Finding the intensity gradients of the image.
- Non-maximum Suppression: Thin edges by applying non-maximum suppression to the gradient magnitude.
- Double Thresholding: Potential edges are determined by high and low thresholds.
- Edge Tracking by Hysteresis: Final edge detection using the threshold values to track and link edges.
These operators detect edges by looking for the maximum and minimum in the first derivative of the image.
- Roberts Operator: The Roberts Cross operator performs 2-D spatial gradient measurement on an image. Edge points are detected by applying a diagonal difference kernel, highlighting regions of high spatial gradient that correspond to edges.
- Prewitt Operator: The Prewitt operator emphasizes horizontal and vertical edges by using a set of 3×3 convolution kernels. It is based on the concept of calculating the gradient of the image intensity at each point, thus highlighting regions with high spatial frequency that correspond to edges.
- Sobel Operator: Sobel operator also uses two sets of 3×3 convolution kernels, one for detecting horizontal edges and another for vertical. It provides more weight to the central pixels and is better at smoothing noise.
The Laplacian of Gaussian combines Gaussian smoothing and the Laplacian method. First, the image is smoothed by a Gaussian blur to reduce noise, and then the Laplacian filter is applied to detect areas of rapid intensity change. This method is particularly effective at finding edges and zero crossings, making it useful for edge localization.
Computer Vision Algorithms
Computer vision seeks to mimic the human visual system, enabling computers to see, observe, and understand the world through digital images and videos. This capability is not just about capturing visual data. Still, it involves interpreting and making decisions based on that data, opening up myriad applications that span from autonomous driving and facial recognition to medical imaging and beyond.
This article delves into the foundational techniques and cutting-edge models that power computer vision, exploring how these technologies are applied to solve real-world problems. From the basics of edge and feature detection to sophisticated architectures for object detection, image segmentation, and image generation, we unravel the layers of complexity in these algorithms.
Table of Content
- Edge Detection Algorithms in Computer Vision
- Canny Edge Detector
- Gradient-Based Edge Detectors
- Laplacian of Gaussian (LoG)
- Feature Detection Algorithms in Computer Vision
- SIFT (Scale-Invariant Feature Transform)
- Harris Corner Detector
- SURF (Speeded Up Robust Features)
- Feature Matching Algorithms
- Brute-Force Matching
- FLANN (Fast Library for Approximate Nearest Neighbors)
- RANSAC (Random Sample Consensus)
- Deep Learning Based Computer Vision Architectures
- Convolutional Neural Networks (CNN)
- CNN Based Architectures
- Object Detection Models
- RCNN (Regions with CNN features)
- Fast R-CNN
- Faster R-CNN
- Cascade R-CNN
- YOLO (You Only Look Once)
- SSD (Single Shot MultiBox Detector)
- Semantic Segmentation Architectures
- UNet Architecture
- Feature Pyramid Networks (FPN)
- PSPNet (Pyramid Scene Parsing Network)
- Instance Segmentation Architectures
- Mask R-CNN
- YOLACT (You Only Look At CoefficienTs)
- Image Generation Architectures
- Variational Autoencoders (VAEs)
- Generative Adversarial Networks (GANs)
- Diffusion Models
- Vision Transformers (ViTs)