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What is a PDF annotator?
The process of labeling image data to help the computer vision-based models easily identify the object of interest is known as an image annotation. Labeling is done using tags and meta tags to enable the smart models to calculate attributes easily. There are different types of image annotation techniques. Some of these are mentioned below. Semantic Segmentation - Unlike other annotation techniques, semantic segmentation focuses on specifications and precision. It is the process of adding tags to each pixel in the image. This is used to prepare training data for autonomous vehicles, medical imaging devices. 2D Bounding Boxes - In this technique, boxes are drawn around the objects of interest within the image. This box should be drawn close to every edge of the object. One specific application of the bounding boxes technique is autonomous vehicle development wherein annotators draw boxes around elements such as vehicles, pedestrians, and cyclists. 3D Cloud Point Labeling - There is a slight difference in the 2D bounding boxes and 3D cloud point labeling technique. The first one depicts only length and width whereas the latter one labels length, width, and approximate depth. Polygons - With polygons, dots are placed around the outer edge of the object. It is just like connecting the dots while placing the dots at the same time.
Annotate PDF: All You Need to Know
Polygons come in various shapes like rectangle, cube, sphere, hexagon, octagon, etc. These shapes help to make bounding boxes easier to place on the image. Autonomous Vehicle Training Model — Using a deep learning (deep neural network) based training model that is optimized for image classification/training, it allows the model to make predictions about what it sees in the image. Training with this training model is easy for a computer vision application because the computer can use these predictions to make further classification/training updates. Learning Curve — Once the training is complete and the computer has learned the object features, it can make predictions about the object in the image. The learning curve and the computer's ability to learn to vary from one application to another. Autonomous Vehicle Prediction — A lot of work has been done on automated vehicle driverless technology. The process involves collecting and analyzing.
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