78건 · 관련도순
Build Python image analysis with Azure AI Vision to generate captions and tags, locate objects and people, and extract text using OCR, or optical character recognition. It also provides suggested crop regions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build AI agents that inspect screenshots, choose actions, and operate a mouse and keyboard. Covers the observation and action loop, desktop control examples, and isolated environments that restrict access to files, credentials, and networks.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build visual AI systems that detect objects or divide images into labeled regions. This skill guides dataset preparation, model selection, training, evaluation, inference optimization, and deployment using frameworks such as PyTorch and Ultralytics.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when "computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation, Claude computer, OpenAI Operator, browser agent, visual agent, RPA with AI, " mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Computer Vision Specialist IA — Expert en vision par ordinateur (YOLO, OpenCV, object detection, OCR, segmentation)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Computer Vision v2 IA — Expert en vision par ordinateur (détection d'objets, segmentation, OCR, vidéo, déploiement edge)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 18+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and CoreMLRequest/VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Creates MATLAB interfaces to Python image processing and computer vision models from GitHub repositories or pip-installable packages using MPyReq. Use when asked to interface MATLAB with a Python CV/image model (segmentation, depth estimation, object detection, image generation, super-resolution, etc.), given a GitHub repo URL for an image/vision model, or asked to create an MPyReq demo for a deep-learning vision pipeline. Do NOT use for general-purpose Python-MATLAB interfacing, non-vision models (NLP, tabular, audio), model deployment/serving, or MATLAB-only image processing workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Think and work like an expert Computer Vision Scientist. Use when a task calls for Computer Vision Scientist judgment. Reasons from image formation, projective geometry (pinhole intrinsics and extrinsics, epipolar/PnP/bundle adjustment, similarity-scale ambiguity), and COCO/LVIS AP mechanics through DINOv3/SigLIP 2 foundation baselines, RF-DETR/YOLO26/SAM 3 models, COLMAP 4/GLOMAP and VGGT geometry, pycocotools/TrackEval/BOP evaluation, and CVPR reporting and EU AI Act limits while treating train–test and pretraining leakage, AP evaluation-setting gaming, preprocessing mismatches (EXIF, BGR, aliased resizing), label noise, and camera-convention and scale errors as first-class failure modes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert-level computer vision for robotics covering camera models, feature detection, object detection, pose estimation, visual SLAM, and deep learning for perception.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 16+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Computer vision expert specializing in real-time hand tracking and gesture interface designUse when "hand tracking, gesture recognition, mediapipe, hand gestures, touchless interface, sign language, hand pose, finger tracking, mediapipe, hand-tracking, gesture-recognition, computer-vision, hci, touchless, ml" mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when implementing object detection, semantic/instance segmentation, 3D vision, or video understanding - covers YOLO, SAM, depth estimation, and multi-modal visionUse when ", " mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.