Segment Anything ,On-Premise Floor Plan Recognition ,Liner.ai ,Label Studio ,KawniX ,FaceSymAI ,DirectAI ,CloudStudio ,AI Co-pilot for Healthcare ,arivis Cloud , are the best paid / free image segmentation tools.
Image segmentation is a computer vision technique that involves partitioning an image into multiple segments or regions, each representing a specific object or part of the image. The goal is to simplify the representation of an image into something more meaningful and easier to analyze. Image segmentation has a long history in computer vision, with early methods dating back to the 1970s. It has become increasingly important in various applications, such as medical image analysis, autonomous driving, and object recognition.
image segmentation already has over 11 AI tools.
image segmentation already boasts over 554.5K user visits per month.
image segmentation already exists at least 0 AI tools with more than one million monthly user visits.
| Core Features | Price | How to use | |
|---|---|---|---|
| arivis Cloud |
AI toolkit for researchers' image processing tasks. |
1. Sign up for an arivis Cloud account. 2. Upload your images or data. 3. Access the AI toolkit and select the desired tasks. 4. Train AI models or apply pre-trained models. 5. Customize the workflow and automate the image processing tasks. |
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| Annotab Studio |
Annotab Studio is a web-based tool for labeling and annotating data, specifically images. |
To use Annotab Studio, simply sign up for the beta version and start leveraging its features. Upload your data and easily create annotations by labeling objects in the images. You can track your annotation progress, version control your dataset, and design your own workflow or choose one from the provided library. |
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| AI Co-pilot for Healthcare |
AI-powered medical assistance for healthcare professionals. |
1. Sign in to the platform. 2. Upload x-ray images for analysis. 3. Receive AI-generated insights and recommendations. |
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| CloudStudio |
AI-enabled video editor for professional videos. |
To use CloudStudio, simply sign up on the website and start creating your videos. The intuitive controls and advanced AI tools make it easy to edit and enhance your videos. You can resize, trim, add audio and text, merge, remove background, and export your videos with just a few clicks. |
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| DirectAI |
Build computer vision models instantly with DirectAI, no code or training data needed. |
1. Get API Access: Users can obtain API access to DirectAI's computer vision models. 2. View Docs: Explore the documentation to understand the usage and capabilities. 3. Build with Plain Language: Describe the desired computer vision model using plain language, eliminating the need for coding. 4. Deploy and Iterate: Deploy the model in seconds and iterate as needed. |
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| FaceSymAI |
Discover your face symmetry with AI! |
Upload a photo and let the AI analyze your facial symmetry |
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| KawniX |
AI-powered data access & coding assistance |
Sign up for an account, upload geospatial data, access AI coding assistance, and automate workflows |
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| Label Studio |
Label Studio: open-source tool for labeling data in various models. |
To use Label Studio, you can follow these steps: 1. Install the Label Studio package through pip, brew, or clone the repository from GitHub. 2. Launch Label Studio using the installed package or Docker. 3. Import your data into Label Studio. 4. Choose the data type (images, audio, text, time series, multi-domain, or video) and select the specific labeling task (e.g., image classification, object detection, audio transcription). 5. Start labeling your data using customizable tags and templates. 6. Connect to your ML/AI pipeline and use webhooks, Python SDK, or API for authentication, project management, and model predictions. 7. Explore and manage your dataset in the Data Manager with advanced filters. 8. Support multiple projects, use cases, and users within the Label Studio platform. |
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| Liner.ai |
Liner.ai is a free tool for building and deploying machine learning applications without coding. |
To use Liner.ai, follow these steps: 1. Import your data or use a pre-labeled dataset. 2. Train your model with a press of a button in Liner. 3. Deploy your trained model by exporting it to various platforms for easy integration with your application. |
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| On-Premise Floor Plan Recognition |
AI-powered floor plan recognition for blueprints and hand-sketches. |
1. Upload a blueprint or hand-sketch floor plan image. 2. Let our AI engine analyze and identify the walls, doors, and windows. 3. Customize and integrate the recognition results into your own application. |
Segment Anything is an AI platform for advanced data segmentation and analysis.
AI-powered floor plan recognition for blueprints and hand-sketches.
Liner.ai is a free tool for building and deploying machine learning applications without coding.
A user uploads an image of a skin lesion to a medical image analysis application, which uses image segmentation to identify and isolate the lesion from the surrounding skin. The application then analyzes the segmented lesion to determine if it is potentially cancerous.
A user captures an image using a smartphone camera, and an image editing application applies image segmentation to separate the foreground objects from the background. The user can then easily apply different effects or filters to the foreground and background separately.
A user uploads an image of a skin lesion to a medical image analysis application, which uses image segmentation to identify and isolate the lesion from the surrounding skin. The application then analyzes the segmented lesion to determine if it is potentially cancerous.. A user captures an image using a smartphone camera, and an image editing application applies image segmentation to separate the foreground objects from the background. The user can then easily apply different effects or filters to the foreground and background separately.
{/if]Simplifies the representation of an image, making it easier to analyze and understand.
Enables the extraction of object-level information, such as shape, size, and location.
Facilitates tasks like object recognition, tracking, and scene understanding.
Helps in reducing the computational complexity of subsequent image processing tasks.
