Top 3 Inspirational applications of deep learning for computer vision
Deep learning in computer vision has gained rapid progress over a short period of time frame. A portion of the applications where deep learning is used in computer vision contain face recognition systems, self-driving cars, security cameras, and so on. The idea to mix deep learning with computer vision has existed for an extensive period. Since the beginning days of Artificial Intelligence (AI), computer scientists have been working on creating machines that can see and comprehend the world as humans do. The strong efforts have led to the disentanglement of computer vision, a huge subfield of AI and computer science that deals with processing the content of visual data. In recent years, computer vision has taken a jump to advances of deep learning. Computer vision refers to the entire process of imitating human vision in a non-biological apparatus. The technology has played a significant role in providing innovative vision in sci-fi movies. Deep learning is a branch of AI that is especially good at handling unstructured data such as images and videos. As deep learning shows advantages in the feature extraction, it has been extensively used in the field of computer vision and is continuously replacing machine learning algorithms.
Image classification is the way toward foreseeing a specific class or label for something that is characterized as a set of data points. It is a subset of classification problems where a whole image is assigned a label. There are endless categories or classes in which a particular image can be classified. Consider a manual process where images are compared and comparable ones are grouped according to like-attributes, however without necessarily knowing ahead of time what you are looking for. The deep learning architecture for image classification generally comprises convolutional layers, making it a convolutional neural network (CNN). Several hyperparameters, like the number of convolutional layers and the activation function for each layer will have to be set. One can typically select these values based on existing research. The classification process is turning simple with deep learning providing insights to computer vision.
Facial recognition applications
Facial recognition turned in to a common name recently. Earlier, it was a costly technology restricted to police research labs. Be that as it may, the recent technology improvements in computer vision algorithms have made facial recognition discover its way into different computing devices.Face recognition is the common matrix behind face unlock in the mobile phone. An authentication system uses an on-device neural network to open the lock in phone when it sees its owner’s face and works appropriately under various lighting conditions, facial hair, haircuts, hats, and glasses. In China, numerous stores are using facial recognition technology to give a smoother payment experience to customers instead of using credit cards or mobile payment apps. Notwithstanding, regardless of advances, current facial recognition is not perfect. AI and security researchers have found various approaches to make facial recognition systems to commit mistakes.
Item and logistic classification
Deep learning in computer vision is of big assistance to the industrial sector, particularly in logistics. Scanners have for quite some time been used to track stock and deliveries and improve shelf space in stores. When deep learning is applied, a camera can not only read a bar code, yet additionally detects if there is any sort of label or code in the object. The camera understands it and classifies the object based on the information associated with the label. For example, the Dynam.AI team using deep learning gave a solution for their expert club fitters that could distinguish the type of club and then identify the specific model of club head and shaft that a client is using.