Pixel-Level Semantic Segmentation Services for Deep Learning
Semantic segmentation will help the AI-based perception model to classify and detect the objects of interest with pixel-wise annotation. Cogito provides semantic segmentation annotation to classify, localize, detect and segment multiple types of objects in the image belonging to a single class.
Image Segmentation for Deep Learning
Semantic segmentation in image annotation makes multiple objects detectable through instance segmentation via computer vision to localize the object. It can visualize the different types of object in a single class as a single entity, helping perception model to learn from such segmentation and separate the objects visible in natural surroundings.
Precise Movement of Self-driving Cars
Autonomous vehicles working on computer vision-based deep learning perception model can learn better scenarios through more accurate pixels to recognize the different classes of objects on a road. While developing a self-driving car, it is providing the crucial information to make sure it can move safely avoiding all types of objects in its path.
Accurate Aerial Imagery View for Monitoring
Train your drone or autonomous flying objects for geo-sensing of farm fields, urbanization and deforestation through image segmentation deep learning. Satellite imagery annotated precisely here for monitoring such fields and gathering the useful information to mend farming efficiency and enhance the crop productivity.
Instance Segmentation for Deep Learning
To enable denser detection for Computer Vision, the image segmentation process takes object detection a step further. Indeed, instance segmentation assists in detecting objects within a defined category by creating masks for each individual object in the image. It is just like semantic, but dives a bit deeper and identifies for each pixel the object it belongs to. Cogito offers annotation for instance segmentation deep learning algorithms.
Panoptic Segmentation Datasets for AI
To make the object detection process easier and accurate, panoptic segmentation is used to annotate the images containing objects of a single class. It is the combination of instance and semantic segmentation. In this annotation technique annotators classify all the pixels in the image as belonging to a class label, while identifying the instance of class they belong to. Cogito provides panoptic segmentation dataset for all types of complex AI models.
Semantic Segmentation for Medical Images
Semantic segmentation is not only limited to automotive, robotics or autonomous flying objects but also provides accurate information for medical diagnosis through semantic segmentation of medical images. Cogito specializes in healthcare training data by offering medical image annotation service for timely diagnosis of different types of diseases.