Bounding Box
A bounding box is a rectangular border drawn around an object in an image to indicate its location and extent. Bounding boxes are the primary output format for object detection models.
Understanding Bounding Box
Bounding boxes are rectangular annotations that localize objects within images by specifying their position and extent, typically defined by the coordinates of opposite corners or a center point with width and height. They serve as the fundamental output for object detection models, from pioneering architectures like R-CNN to modern real-time detectors like YOLO and SSD. During annotation, human labelers draw bounding boxes to create ground truth labels that models learn to predict. Evaluation metrics like Intersection over Union (IoU) measure how well predicted boxes overlap with ground truth. While bounding boxes are computationally efficient and intuitive, they provide only coarse localization and cannot capture the precise shape of irregular objects, which is why computer vision has also developed instance segmentation approaches. Bounding boxes remain essential in applications like autonomous driving, surveillance, and medical imaging powered by convolutional neural networks.
Category
Computer Vision
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Computer Vision
Computer vision is a field of AI that enables machines to interpret and understand visual information from images and videos. Applications include facial recognition, autonomous driving, medical imaging, and augmented reality.
Face Recognition
Face recognition is a computer vision technology that identifies or verifies individuals by analyzing facial features in images or video. It is used in security systems, phone unlocking, and photo organization.
Image Captioning
Image captioning is the AI task of generating natural language descriptions of images. It requires both visual understanding (computer vision) and text generation (NLP) capabilities.
Image Classification
Image classification is the computer vision task of assigning a label to an entire image based on its visual content. Deep learning models like ResNet and Vision Transformers achieve near-human accuracy on this task.
Image Segmentation
Image segmentation is the process of partitioning an image into meaningful regions or classifying each pixel into a category. It is used in medical imaging, autonomous driving, and satellite analysis.
Instance Segmentation
Instance segmentation is a computer vision task that identifies each object in an image and delineates its exact pixel boundary. Unlike semantic segmentation, it distinguishes between individual instances of the same class.
Masked Autoencoder
A masked autoencoder is a self-supervised learning method that masks random patches of an image and trains the model to reconstruct them. It has proven highly effective for pre-training vision models.
Neural Radiance Field
A Neural Radiance Field (NeRF) is a deep learning method that represents 3D scenes as continuous functions, enabling photorealistic novel view synthesis from 2D images. NeRFs have transformed 3D reconstruction and rendering.