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Detect and visualize edges in any image. Choose between Canny, Sobel, Laplace, Scharr, and Prewitt algorithms to highlight contours, boundaries, and structural details.
Detect edges in images using Canny, Sobel, Laplace, Scharr, and Prewitt operators — visualize boundaries instantly.
All processing runs locally in your browser. Your files never leave your device — no upload, no server, no signup required.
Every method here looks for places where image brightness changes sharply from one pixel to the next, because a strong change usually marks a boundary between objects, textures, or lighting regions. The image is converted to grayscale first, then a small convolution kernel slides across it to estimate the intensity gradient horizontally and vertically. Sobel and Prewitt use 3×3 kernels that combine smoothing with differentiation; Sobel weights the center row and column more heavily, so it suppresses noise slightly better. Scharr is a refined Sobel variant whose coefficients are tuned for rotational symmetry, giving more accurate gradient direction on fine diagonal detail. Laplace is a second-derivative operator: true edges sit at the zero-crossings of its response, which is why it reacts strongly to fine, high-frequency structure but is also far more sensitive to noise. Canny is the most complete pipeline — it applies a Gaussian blur, computes Sobel gradients, thins them with non-maximum suppression, then uses two thresholds (hysteresis) to keep strong edges plus only the weak edges connected to them.
Reach for Canny when you want clean, connected, single-pixel-wide contours — it is the default choice for tracing shapes or feeding later analysis. Its two thresholds are the main control: raise the low threshold to drop faint texture and noise, lower it to recover delicate detail. The first-derivative operators (Sobel, Scharr, Prewitt) are better when you want a continuous gradient image rather than a binary edge map, or when edge direction matters. Use Laplace to emphasize fine high-frequency structure, but smooth the image first, since the second derivative amplifies noise heavily. If the source is grainy or low-contrast, denoising or stretching contrast with a general image filter beforehand will sharpen the result far more than tweaking thresholds alone.
An edge map is rarely the final goal — it is the input to the next computer-vision step, so this tool pairs naturally with several others:
Because Pixlane runs the entire OpenCV pipeline in your browser through WebAssembly, your image is processed on your own device and never uploaded to a server. That keeps private photos, scanned documents, and proprietary product shots fully local, removes any file-size or account limits, and returns each edge map instantly — no signup, no watermark, and no waiting on a network round-trip.
Edge detection identifies boundaries in images where brightness changes sharply. It is a fundamental operation in computer vision used for object recognition, image segmentation, and feature extraction.
Canny is a multi-stage algorithm producing clean, thin edges with hysteresis. Sobel uses 3×3+ directional kernels. Scharr is an optimized 3×3 Sobel with better rotational symmetry. Laplace uses the second derivative, detecting edges in all directions. Prewitt uses simpler [-1,0,1] kernels for basic gradient estimation.
In Canny edge detection, the low threshold filters out weak edges and the high threshold identifies strong edges. Pixels between the two thresholds are kept only if connected to strong edges (hysteresis).
dx and dy control the derivative order in the X and Y directions. Setting dx=1,dy=0 detects vertical edges; dx=0,dy=1 detects horizontal edges. At least one must be non-zero.
The kernel size determines the convolution window used for gradient computation. Larger kernels (5×5, 7×7) smooth out noise but may miss fine details. 3×3 is the default for most applications.