Semantic segmentation assigns a label to each pixel in an image, based on the class of the object it belongs to. For example, in a street scene, all pixels that belong to cars would have the same ...

Semantic segmentation is a technique that assigns a label to every pixel in an image, indicating what object or region it belongs to. This can be very useful for robotics, as it can help robots to ...

Semantic segmentation and object detection are critical tasks for enabling self-driving cars to perceive and navigate their surroundings safely. Semantic segmentation involves labeling each pixel in ...

Semantic Segmentation with Object Detection - This is an extension of the project to include object detection as well using the pretrained YOLOv3 model. Image Preprocessor - This notebook is useful to ...

To avoid overfitting, some image augmentation methods could be used to ensure input sufficient data size including flipping, cropping, rotation, translation, noise injection, random erasing, mixing ...

Image segmentation forms the basis of numerous Computer Vision projects. It segments the visual input in order to process it for tasks such as image classification and object detection. However, all ...

Despite its numerous characteristics, YOLOv3 has to be combined with appropriate image segmentation technologies to achieve effective 2D object extraction in real-time monitoring, robot navigation, ...

It has been well demonstrated that adversarial examples, i.e., natural images with visually imperceptible perturbations added, cause deep networks to fail on image classification. In this paper, we ...

The team has shared how Semantic-SAM tackles the problem of semantic awareness by using a decoupled categorization strategy for parts and objects. The model individually encodes objects and parts ...