'computer vision' 검색 결과

Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reconstructed reference notes on US NRC software assurance guidance for digital computer software in nuclear power plant safety systems, built from seven public-domain sources (Regulatory Guide 1.168 Revision 2, July 2013; Regulatory Guides 1.169, 1.170, 1.171, 1.172, and 1.173 Revision 1, July 2013; NUREG-0800 Branch Technical Position 7-14 Revision 6, August 2016; pinned 2026-10-12). Use for software life cycle process questions, software requirements specifications, software configuration management plans, verification and validation with reviews and audits, test documentation and unit testing, and what NRC staff examine in a BTP 7-14 software review of plans, implementation, and design outputs. SCOPE LIMITS: NRC Regulatory Guides and the BTP 7-14 staff review position only; the endorsed IEEE standards (IEEE Std 603-1991, 7-4.3.2-2003, 828-2005, 829-2008, 830-1998, 1008-1987, 1012-2004, 1028-2008, 1074-2006), IEC 60880, IEC 61513, IEC 62138, and IAEA guides are cited by designation and edition only, with no standard text; RG 1.152 Rev 4, BTP 7-18, BTP 7-19, other SRP Chapter 7 sections, and NUREG/CR contractor reports are outside the pack; reflects the pinned 2013 and 2016 editions, not later agency actions; synthesized reference notes, not legal or licensing advice and not a substitute for the source documents. LICENCE: Public Domain (US Government work, 17 U.S.C. § 105).

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.

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.

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.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).

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.

Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create masks that identify object regions in images using points, boxes, or existing masks. The skill guides automatic mask generation, transparent-background extraction, annotation workflows, and deployment, without requiring training for each specific task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting objects with transparent backgrounds
• Building annotation tools with point and box selection
• Generating object masks automatically

Use when implementing ANY computer vision feature — image analysis, pose detection, person segmentation, subject lifting, text recognition, barcode scanning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run a custom computer-vision prompt against an image with a local vision LLM. Wraps kuzco's tool_llm_image_custom() ellmer tool. Use when none of the specialized kuzco image skills (classification, OCR, recognition, sentiment, alt text) fit the task.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MATLAB R2026a workflow for signal processing, DSP, image processing, computer vision, lidar, medical imaging, deep learning, and AI-assisted reproducible experiments. Use for filters, spectrograms, wavelets, object detection, segmentation, classification, training, inference, and generated figures.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use SAM to identify object regions in images from points, boxes, or existing masks. The skill provides implementation examples for transparent cutouts, automatic mask generation, annotation tools, and model selection based on available resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting objects with transparent backgrounds
• Building annotation tools with point or box selection
• Generating object masks automatically

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Build JavaScript or TypeScript applications that run text, image, and audio models in browsers or server runtimes. Guides compatible model selection, GPU or CPU execution, caching, and memory cleanup without requiring a Python server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add text classification or translation to a browser app
• Implement image classification and object detection
• Transcribe audio using JavaScript

Draft and revise machine learning, computer vision, and natural language processing papers. Improve section structure, paragraph flow, and figures or tables, then check major claims against experimental evidence and review the draft from a skeptical reviewer’s perspective.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting or revising an abstract and introduction
• Improving paragraph flow and terminology consistency
• Checking whether experiments support major claims

Hunt NTLM/Negotiate information disclosure on internet-reachable IIS/SharePoint/Exchange. Anonymous NTLM Type-2 challenge capture leaks NetBIOS domain, internal DNS forest, computer name, AD timestamp via AV_PAIRS structure. Default Windows-installer hostnames (WIN-XXXXXXXXXXX pattern) signal lazy provisioning. Use when target advertises `WWW-Authenticate: NTLM` or `Negotiate` headers anonymously.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Count occurrences of an object in the image using computer vision algorithm.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Display images and annotations for image processing, computer vision, and visual inspection. Use when displaying images with imageshow, creating image viewers with viewer2d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming video frames, or building apps with image display.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.