Computer Vision can be defined as the field that aims to describe the world through images by interpreting, reconstructing, and extracting properties from images, such as shapes, textures, densities, and distances. CVSs are also known as machine vision systems, visual image systems, or just image systems. Therefore, Computer Vision is essentially the development of artificial systems to handle visual problems of interest, and for such, it uses image processing and analysis techniques. Along with image analysis and processing, other areas such as Machine Learning and Pattern Recognition are also highly interconnected with Computer Vision.
1. Image Classification
2. Object Detection
3. Object Tracking
4. Semantic Segmentation
5. Instance Segmentation
Deep learning and machine learning innovations are at the core of the ongoing revolution in Artificial Intelligence for the
interpretation and analysis of multimedia data. The convergence of large-scale datasets and more affordable Graphics Processing Unit (GPU)
hardware has enabled the development of neural networks for data analysis problems that were previously handled by traditional handcrafted
features. Several deep learning architectures such as
1. Convolutional Neural Networks (CNNs),
2. Recurrent Neural Networks (RNNs),
3. Long Short Term Memory (LSTM)/Gated Recurrent Unit (GRU),
4. Deep Believe Networks (DBN), and
5. Deep Stacking Networks (DSNs)
have been used with new open source software and libraries options to shape an entirely new scenario in computer vision processing.