Results for “image segmentation”

Use when editing footage that already exists on Scenario through MCP with a tool model instead of a new generation: 3D LUT grading, color correction, film grain, vignette, blur, sharpen, glow, tint, desaturate, posterize, solarize, cubism, crystallize, oilify, chromatic aberration or dodge and burn on a clip; trimming to a time range, splitting at cut points, reversing, resizing, re-encoding, frame extraction, image sequences, background removal, segmentation masks, subjects as layers.

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Use when finished Scenario assets have to give something back: a caption for a dataset or alt text, a reusable style description, a verdict against a brief, a canny, depth, pose, or segmentation control map for the next model, or the asset itself found again by text, tags, or visual similarity and filed into a collection. Keywords: caption, describe, analyze, QA, control map, find similar, reverse image search, semantic search, tag, collection.

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Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language models.

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Computational pathology toolkit for whole-slide images (WSIs): load slides, extract tiles, stain normalization, nuclear segmentation, feature extraction, and ML training. Supports H&E and multiplex. For end-to-end pipelines from raw WSIs to quantitative outputs.

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Guide DeepMedic 0.8.4 workflows for multi-modal 3D NIFTI medical-image segmentation, including data preparation, model configuration, training, checkpoint continuation, inference, and CPU/CUDA troubleshooting.

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Route BiRefNet image segmentation, matting, inference, configuration, training, evaluation, and model-export workflows.

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Segment overlapping visual parts from source images, wireframes, texture atlases, and decals before mesh reconstruction. Use when a mascot/logo/template contains touching or overlapping components and exact structural part masks are needed before contour-to-mesh, UV fitting, or landmark repair.

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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.

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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).

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SAM: zero-shot image segmentation via points, boxes, masks.

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), pr...

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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.

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End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end.

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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).

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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).

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