Computer vision is an emerging interdisciplinary field that deals with computers that can be made to attain a high-level understanding from digital images or videos. Computer vision analyses certain information from images and videos and applies the interpretations for decision-making tasks. Deep learning is most widely used in computer vision that allows neural networks to focus on the most relevant feature in an image. Deep learning possesses the capability of handling complex computer vision models accurately. Deep learning algorithms for computer vision are Convolutional Neural Network(CNN), Deep Belief Network, Recurrent Neural Network, and Deep Generative models. Computer Vision tasks in deep learning include image classification and recognition, text spotting, and image caption generation.
The application areas of computer vision are image classification, object detection, object segmentation, image reconstruction, image synthesis, image super-resolution, face recognition, image classification with localization, and 3D object generation. The sub-application areas of computer vision are Drone surveillance, self-driving vehicles, weather records, and many more. Advances in a deep neural network for computer vision are Zero-shot learning, self-supervised learning, reinforcement learning, quantum convolutional neural networks, and also in the field of entomology.
• Over the last years, the development of deep learning technology is greatly due to the strides it has enabled in the field of computer vision.
• Computer vision is experiencing a great-leap-forward development today because computer vision tasks such as image classification, object detection, and image segmentation.
• Deep learning algorithms enable computer systems to automatically identify and extract the relevant information through understanding the visual world.
• In particular, CNN is the crux of deep learning algorithms in computer vision.
• For image classification, the deep CNN model aims to transform the high-dimension input image into low-dimension yet highly-abstracted semantic output through filtering mechanisms by performing convolutions in multi-scale feature maps.