The Roadmentor Image Segmentation Tool is a powerful feature within the Roadmentor platform that enables users to accurately segment and classify objects or regions within images.
With the Image Segmentation Tool, you can perform precise delineation and labeling of various components within an image, which is essential for object recognition, analysis, and computer vision tasks.
Object Segmentation: This tool allows you to segment and separate different objects or regions within an image based on their visual characteristics. By accurately delineating these components, you can extract meaningful information and facilitate subsequent analysis.
Interactive Annotation: The Image Segmentation Tool provides intuitive annotation capabilities, allowing you to manually trace the boundaries of objects or regions within the image. This interactive approach ensures precise and accurate segmentation, resulting in high-quality labeled data.
Automatic Segmentation: In addition to manual annotation, the tool offers advanced algorithms and techniques for automated image segmentation. These algorithms analyze the image content and apply segmentation based on predefined criteria, saving time and effort in the annotation process.
Multi-Class Segmentation: With the Image Segmentation Tool, you can segment images into multiple classes or categories, enabling you to differentiate and label various object types or regions simultaneously. This is particularly beneficial when dealing with complex scenes or datasets containing diverse objects.
Annotation Editing: The tool provides editing capabilities that allow you to refine and adjust the segment boundaries as needed. This ensures accurate representation of object boundaries and enhances subsequent analysis tasks.
Class Labeling: You can assign labels or classes to the segmented objects or regions, providing context and identification. This facilitates object recognition and classification tasks, enabling you to analyze and categorize the components within the image accurately.
Data Export: Data export is in development phase.
The Image segmentation tool in Roadmentor utilizes the power of our current ML model to provide you with auto-annotated segmentations. This means that the tool automatically generates initial segmentations based on the model's predictions. You can then verify and edit these segmentations as needed, ensuring accurate and precise results.
By leveraging the ML model's capabilities, the segmentation tool saves you time and effort by automatically suggesting initial segmentations. However, it also recognizes the importance of human expertise in refining the annotations. You have the flexibility to review the auto-generated segmentations and make necessary adjustments wherever required.
This interactive process allows you to combine the efficiency of automated segmentation with the accuracy and control of manual annotation. You can verify the auto-annotated segmentations, refine them to align with your desired results, and ensure the highest quality of annotations for your specific tasks.
With the segmentation tool in Roadmentor, you benefit from a collaborative workflow that combines the power of machine learning with human expertise, resulting in precise and reliable segmentations tailored to your specific needs.
- Open the Roadmentor platform and navigate to the dataset you want to view or edit. Select the dataset you wish to work with from the available options. Within the dataset, locate and click on the annotation tool.
- Switch to segmentation from dropdown.
- The segmentation tool utilizes the power of our current ML model to provide you with auto-annotated segmentations. This means that the tool automatically generates initial segmentations based on the model's predictions. You can then verify and edit these segmentations as needed, ensuring accurate and precise results.
- If using manual annotation, utilize the intuitive annotation tools to trace and delineate the boundaries of objects or regions within the image.
- Refine and adjust the segment boundaries as needed using the provided editing controls.
- Assign labels or classes to each segmented object or region to provide context and identification.
- Review the segmented image and make any necessary adjustments or refinements to ensure accurate segmentation.