> For the complete documentation index, see [llms.txt](https://docs.algenta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.algenta.ai/vision-and-media-processing.md). # Vision & media processing - [Image feature and quality kernels](https://docs.algenta.ai/vision-and-media-processing/image-feature-and-quality-kernels.md): Sobel edge/saliency features and color histograms, deterministic blur/exposure/entropy quality scores, pixel-level similarity metrics (MSE, PSNR, SSIM), and Otsu-based foreground region boxes over fla - [Image transform and morphology kernels](https://docs.algenta.ai/vision-and-media-processing/image-transform-and-morphology-kernels.md): Binary morphology and connected components, grayscale convolution/filtering, box and bilinear downscaling with integral images, and deterministic RGB palette extraction, all on flat or nested Float64 - [Color science kernels](https://docs.algenta.ai/vision-and-media-processing/color-science-kernels.md): RGB/HSL/HSV color-space conversion, CIE colorimetry (delta-E, Lab-to-LCH, CCT, CRI, whiteness), and WCAG text-contrast accessibility checking, layered as three modules of increasing specificity. - [Document vision and layout kernels](https://docs.algenta.ai/vision-and-media-processing/document-vision-kernels.md): Pixel-level document image processing -- Otsu and adaptive binarization, skew and scan-quality scoring, and projection-based segmentation into text lines, columns, density grids, and table-likeness sc - [Video quality and accessibility kernels](https://docs.algenta.ai/vision-and-media-processing/video-quality-and-accessibility-kernels.md): Per-pixel worst-case WCAG contrast for caption overlays, dominant/mean color analysis, a composite no-reference frame-quality score, and clip-to-clip duplicate detection, all over flat row-major video - [Video content and scene kernels](https://docs.algenta.ai/vision-and-media-processing/video-content-and-scene-kernels.md): Frame-level video primitives -- scene-change scoring, motion-region extraction, storyboard/keyframe selection, and the routing gate that decides how much of a clip is worth a model call, mirroring the - [Ranging and remote-sensing kernels](https://docs.algenta.ai/vision-and-media-processing/ranging-and-remote-sensing-kernels.md): LiDAR point-cloud geometry and vegetation-return metrics, LiDAR-derived terrain/canopy mapping and survey QA, and the core radar range/Doppler/link-budget equations. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.algenta.ai/vision-and-media-processing.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.