Home▸Computer Science▸Artificial Intelligence Computer Vision Simulation

Computer Vision: Edge Detection Kernels

Interactive 3D computer-vision simulator: slide Sobel and Laplacian convolution kernels across a live pixel grid, watch the gradient magnitude rise as a relief map, and threshold it into a real edge map — the classic feature-extraction step behind self-driving car perception and facial recognition.

Computer Science2DModerate60 FPS📱 Mobile-adapted

This simulation explores the core convolution mechanics that let an artificial-intelligence system interpret visual data: a small kernel matrix slides across every pixel of an image, and the weighted sum it produces reveals structure the raw grayscale grid hides. Choose a synthetic test scene, apply the Sobel horizontal, Sobel vertical, combined gradient-magnitude, or Laplacian kernel, and watch a live 3D relief map rise wherever intensity changes sharply — exactly the boundary-finding step that feeds early-stage feature extraction in self-driving car perception stacks and facial-recognition pipelines. An animated scan window sweeps the grid one 3×3 neighbourhood at a time while live readouts track the gradient magnitude, the current scan position, and the fraction of the image flagged as an edge at your chosen threshold.

⚙ Under the hood

This simulation explores the core concepts of computer vision within an artificial intelligence framework. Users will manipulate image data and observe how algorithms interpret and respond to visual information, mimicking processes used in self-driving cars and facial recognition systems.

AI Computer VisionSimulation

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install