Journal of Machine Learning and Applications

Computer Vision

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A machine learning system may automatically learn to interpret visual input by utilizing pre-programmed computational frameworks. The system is subjected to numerous data analyses until it can distinguish between features and recognize images. The primary techniques used are convolutional neural networks, an integral neural network, and deep learning, a subset of machine learning. Algorithms enable the system to learn independently to replace human labor in tasks like picture recognition. Computer vision can be applied to Self-driving cars, facial recognition, augmented and mixed reality, and healthcare. Computer vision applications have been effectively used in categorizing images, retrieving images based on contents, detecting specific objects, and even detecting motion for object tracking and recognition.