When it comes to object recognition, AI systems can be divided into two main categories: machine learning and computer vision. Let’s take a closer look at each of these methods.
Machine learning is a powerful AI technique that can be used to solve many different types of problems. In the context of object recognition, machine learning algorithms can be used to learn how to recognize objects from a training dataset. Once the machine learning algorithm has been trained, it can then be applied to new images or videos to automatically recognize the objects in them.
One of the advantages of using machine learning for object recognition is that it can be used to learn from a large dataset of images or videos. This means that the machine learning algorithm can be trained on a wide variety of data, and can therefore be more accurate than a computer vision algorithm that is trained on a smaller dataset. Additionally, machine learning algorithms can be trained to recognize objects from different perspectives, and can therefore be more robust than computer vision algorithms.
Deep learning is a subset of machine learning that is particularly well suited for analyzing visual data. Deep learning algorithms learn to recognize patterns in data by building layers of artificial neurons, each of which performs a simple task such as detecting edges or identifying shapes. The output of the deep learning algorithm is a set of recognized objects, along with their location in the image or video.
Deep learning algorithms have a number of advantages over other algorithms for object recognition. First, deep learning algorithms can learn from a large dataset of images or videos. This means that the algorithm can be trained on a wide variety of data, and can therefore be more accurate than a computer vision algorithm that is trained on a smaller dataset. Additionally, deep learning algorithms can be trained to recognize objects from different perspectives, and can therefore be more robust than computer vision algorithms.
Computer vision is a field of AI that deals with the analysis of visual data. Computer vision algorithms can be used to extract meaning from images and videos, in much the same way that humans do. In the context of object recognition, computer vision algorithms can be used to identify the objects in an image or video, and to determine their size, shape, and location.
Computer vision algorithms have a number of advantages over other algorithms for object recognition. First, computer vision algorithms can be used to learn from a large dataset of images or videos. This means that the algorithm can be trained on a wide variety of data, and can therefore be more accurate than a machine learning algorithm that is trained on a smaller dataset. Additionally, computer vision algorithms can be used to learn from different perspectives, and can therefore be more robust than machine learning algorithms.
References:
https://machinelearningmastery.com/machine-learning-vs-computer-vision-for-image-recognition/
https://towardsdatascience.com/object-recognition-with-machine-learning-17aae5c5109b
https://www.jeremyjordan.me/object-detection/
https://towardsdatascience.com/object-detection-with-deep-learning-a4e2c46b1959