# Image Edge Detection Apply professional edge detection algorithms (Sobel, Prewitt, Laplacian, Canny) with adjustable thresholds and visualization options > Canonical page: https://elysiatools.com/en/tools/image-edge-detect - **Category:** Design - **Keywords:** image, edge, detection, sobel, prewitt, laplacian, canny, contour, gradient, boundary ## Overview Extract and visualize structural boundaries from your images using professional-grade algorithms like Canny, Sobel, and Laplacian. This tool allows you to isolate contours and gradients with precise control over thresholds and output styles for design, computer vision, or artistic projects. ## Inputs - **Image File** (file): Upload image for edge detection - **Edge Detection Method** (select) - **Edge Threshold** (number): Threshold for edge detection (0-255) - **Canny High Threshold** (number): High threshold for Canny detector (0-255) - **Aperture Size** (number): Kernel aperture size (odd number, 3-15) - **Output Mode** (select) - **Edge Color** (color): Color for detected edges (hex or R,G,B) - **Show Original Comparison** (checkbox): Display original image alongside edge detection results - **Output Format** (select) - **Quality (1-100)** (number): Output quality for lossy formats ## When to use - Isolating object outlines for graphic design or vector tracing preparation. - Preprocessing images for machine learning models that require feature extraction. - Analyzing structural patterns or surface textures in technical or scientific photography. ## How it works - Upload your image file in PNG, JPEG, TIFF, WebP, or BMP format. - Select your preferred detection algorithm, such as Canny for high-precision edges or Sobel for gradient analysis. - Adjust the threshold values and aperture size to fine-tune the sensitivity of the edge detection. - Choose your output mode and format, then process the image to generate the final result. ## Use cases - Preparing image masks for background removal or object segmentation. - Creating stylized artistic effects by highlighting the structural skeleton of a photograph. - Extracting geometric features from technical diagrams or blueprints for further analysis. ## Frequently asked questions ### Which algorithm should I choose? Canny is best for clean, distinct object edges, while Sobel and Prewitt are better for analyzing gradient intensity. Laplacian is ideal for detecting fine details and noise. ### What does the aperture size do? The aperture size determines the size of the kernel used for the derivative calculation. Larger values capture broader edges, while smaller values are more sensitive to fine details. ### Can I see the original image next to the result? Yes, enable the 'Show Original Comparison' checkbox to view the source image alongside the processed edge output. ### What is the difference between Edge Threshold and Canny High Threshold? The Edge Threshold sets the base sensitivity for standard operators, while the Canny High Threshold specifically controls the upper bound for the Canny detector's hysteresis process. ### Can I change the color of the detected edges? Yes, you can use the 'Edge Color' picker to customize the color of the extracted contours in your output image. ## Related tools - [Image Convolution Kernel](https://elysiatools.com/en/tools/image-convolve): Apply standard convolution kernels (blur, sharpen, edge detection, emboss) for image processing and effects - [Image Emboss Effect](https://elysiatools.com/en/tools/image-emboss): Apply professional emboss effects with adjustable depth, angle, and lighting direction for 3D surface appearance - [Image Gaussian Blur](https://elysiatools.com/en/tools/image-gaussian): Apply Gaussian blur kernel for smooth noise reduction and image softening with precise control - [Image Gradient Operation](https://elysiatools.com/en/tools/image-gradient): Apply gradient operations (Sobel, Prewitt, Roberts) for edge detection and image enhancement - [Image Laplacian Operator](https://elysiatools.com/en/tools/image-laplacian): Apply Laplacian operator for edge detection, sharpening, and image enhancement with different kernel variants - [Image Roberts Cross Edge Detection](https://elysiatools.com/en/tools/image-roberts): Apply Roberts Cross operator for simple and fast edge detection, particularly effective for diagonal edges - [Image Sharpening](https://elysiatools.com/en/tools/image-sharpen): Enhance image clarity and sharpness with adjustable sharpening intensity and multiple sharpening methods - [Image Box Blur](https://elysiatools.com/en/tools/image-box-blur): Apply box blur kernel for simple uniform averaging and fast noise reduction ## Samples - [Android Image Processing Java Samples](https://elysiatools.com/en/samples/android-image-processing-java): Android Java image processing examples including reading/saving images, scaling, and format conversion - [Android Image Processing Kotlin Samples](https://elysiatools.com/en/samples/android-image-processing-kotlin): Android Kotlin image processing examples including reading/saving images, scaling, and format conversion - [Web Image Processing Python Samples](https://elysiatools.com/en/samples/web-image-processing-python): Web Python image processing examples using PIL/Pillow including reading, saving, resizing, and format conversion - [Web Image Processing Rust Samples](https://elysiatools.com/en/samples/web-image-processing-rust): Web Rust image processing examples including image read/save, scaling, and format conversion ## Related content - [Image Edge Detection and Convolution Kernel Lab](https://elysiatools.com/en/hubs/image-edge-detection-and-convolution-kernels): Extract the luminance channel, suppress noise with a recorded radius, compare Sobel, Prewitt, Roberts, and Laplacian operators, tune thresholded edge detection, and reproduce the winning result as an explicit convolution kernel.