<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Condados Blog</title><description>Computer Vision tooling, tutorials, and field notes from the AI frontier.</description><link>https://condados.ai/</link><item><title>From Photons to Pixels: Image Formation and Color Spaces, with OpenCV in Python and C++</title><link>https://condados.ai/blog/image-formation-and-color-spaces/</link><guid isPermaLink="true">https://condados.ai/blog/image-formation-and-color-spaces/</guid><description>How a camera turns light into an array of numbers — projection, sampling, quantization, the Bayer sensor — and the color spaces (RGB/BGR, grayscale, HSV, YCrCb, Lab) you convert between every day. The equations, plus runnable OpenCV in both Python and C++.</description><pubDate>Thu, 04 Jun 2026 00:00:00 GMT</pubDate><category>computer-vision</category><category>image-processing</category><category>opencv</category><category>color-spaces</category><category>fundamentals</category></item><item><title>CNN vs Transformer, quantized: YOLO26-seg and RF-DETR-Seg race for instance masks on an Intel iGPU</title><link>https://condados.ai/blog/yolo26-rfdetr-seg-openvino/</link><guid isPermaLink="true">https://condados.ai/blog/yolo26-rfdetr-seg-openvino/</guid><description>Two opposite small segmentation models — a 2026-era CNN and a DETR transformer — both quantized to INT8 with NNCF and run on a laptop Intel iGPU. YOLO26-seg&apos;s forward pass is ~7.4× faster; RF-DETR-Seg keeps its masks more faithful under INT8. The whole shootout, numbers first.</description><pubDate>Wed, 03 Jun 2026 00:00:00 GMT</pubDate><category>computer-vision</category><category>instance-segmentation</category><category>openvino</category><category>IntelSoftwareInnovator</category><category>quantization</category><category>edge-ai</category><category>yolo</category><category>rf-detr</category></item></channel></rss>