Edge Detection
Detect edges and outlines in your images to reveal boundaries, contours, and object shapes. Perfect for computer vision, sketch effects, and image analysis.
or drop images here or paste
Detect edges and outlines in your images to reveal boundaries, contours, and object shapes. Perfect for computer vision, sketch effects, and image analysis.
or drop images here or paste
Edge detection identifies boundaries where image brightness changes sharply, revealing object outlines, contours, and structural features. Using Sobel operator algorithms, this technique calculates gradient magnitude to find areas of rapid intensity change. Edge detection forms the foundation of computer vision applications including object recognition, image segmentation, facial feature detection, and autonomous vehicle navigation systems. The resulting edge maps highlight shapes and boundaries while eliminating texture details.
Upload any JPG, PNG, or WebP image to apply edge detection instantly. The Sobel algorithm analyzes pixel intensity gradients in horizontal and vertical directions, computing edge strength at every point. Adjust the threshold slider to control sensitivity - lower values (0-50) detect subtle edges and fine details, medium values (50-100) capture prominent boundaries and contours, higher values (100-255) isolate only the strongest edges for simplified outlines. Download as PNG for crisp edge maps or WebP for smaller file sizes.
Threshold determines which edges appear in the final image based on gradient strength. Low thresholds (0-40) reveal all edges including texture and noise, ideal for detailed analysis and feature extraction. Medium thresholds (40-80) balance clarity with completeness, perfect for sketch effects and general edge visualization. High thresholds (80-150) show only dominant boundaries, useful for object recognition and simplified contour extraction. Very high values (150-255) isolate extreme contrasts for minimal line art effects.
Apply edge detection for computer vision preprocessing before object recognition, creating pencil sketch effects and line art from photographs, analyzing medical images to identify anatomical structures, processing satellite imagery for terrain and building detection, developing augmented reality applications requiring boundary identification, extracting contours for 3D modeling and reconstruction, preparing images for coloring book templates, or studying image composition and structure in photography education.
Export edge maps up to full original resolution maintaining precise boundary details. PNG format provides lossless quality ideal for further image processing, computer vision pipelines, and applications requiring exact edge coordinates. JPEG works for general viewing with smaller files but may introduce compression artifacts around thin edges. WebP offers optimal balance - 40-60% smaller than PNG while preserving sharp edge definition, perfect for web applications, machine learning datasets, and digital archives.