Deep learning models have the ability to learn patterns and to derive meaning from images. Thus, they reduce the need for methods based on hand-crafted features. Deep learning methods are used in a wide range of different computer vision applications such as motion detection, face recognition, and image synthesis. Let’s take a look at some of the most popular computer vision applications that are powered by deep learning.
Deep learning is widely used in computer vision systems for face recognition tasks. Such systems can be used for a variety of purposes, for example within a security feature of a mobile device. The mobile device can be unlocked when a face that belongs to a registered user appears in the camera’s field of view. Moreover, face recognition is used by social media sites to allow users to automatically tag subjects in photos that are uploaded to the site.
Computer vision systems can be trained to recognize various objects or people. This functionality can be used to improve the efficiency of image search applications. When provided with an image of an object or a person, the computer vision system can find other images that contain the same object or person. Computer vision systems can also take the semantic meaning into account. This is used for search functions that take text input and return images that match the query topic.
Another common use of computer vision algorithms is to create vision systems that enable machines to “see”. Such systems are for example used in navigation procedures for autonomous vehicles. In order to navigate within a certain environment, autonomous vehicles must be able to identify the objects around them and to react appropriately. The vision system of such an autonomous vehicle must be able to distinguish between roadways, other cars, pedestrians, street signs, and many other objects. Machine vision systems that power autonomous vehicles are among the most powerful applications of deep learning for computer vision.
Computer vision algorithms can be employed to sort or cluster photos in a large database. This functionality is for example used by companies that are producing mobile phones whenever they provide the option to automatically create albums for the photos you captured.
Computer vision algorithms can be applied to video data as well. One computer vision application using video data is motion detection. By comparing the location of an object in a given frame of a video to its location in subsequent frames, conclusions about the movement of an object can be drawn. These methods are also applied within the design of user interfaces for apps, to change the placement of UI elements as objects move around the screen.
Computer vision algorithms can be used for security purposes, to enable real-time threat detection at public places such as airports. For this purpose, computer vision methods are used to track the movement of people and objects. If a suspicious bag is left behind, the security system can detect such a suspicious object based on how long it has been in a certain location and can flag the object for inspection by a security team.
Computer vision systems can be used to support smartphone users in taking better photos. Object recognition algorithms can be used to focus on certain objects and to automatically tag objects in images. Computer vision algorithms can also be used to process images and to apply filters such as color correction to images that were taken using the phone’s camera.
Speaking of color correction, the colorization of old black and white images is another area of use for computer vision algorithms. Neural colorization is the process of converting grayscale images to color images using neural networks. Consider this task as applying a transformation or filter to an image. This specific filter colors the images based on probability values. Training sets for such tasks often involve the transformation of color images into grayscale versions to let the model learn the correlation between the grayscale images and the colors of the original image.
Image reconstruction involves filling in missing portions of an image or correcting corrupted parts of an image. Much like image colorization, image reconstruction can be seen as a filter that is applied to the image. Old damaged photos can be reconstructed and missing portions of the image can be filled in. Training data for image reconstruction tasks involve creating artificially corrupted images that a model uses to learn the appropriate corrections.
Image synthesis - the process of generating new image content - is another computer vision application. The image content can either be entirely new or it can be generated based on previous images. Examples of image synthesis that is carried out on existing images include altering the style of an image or adding objects. Altering the style of an image is also referred to as neural style transfer. Images can be generated from scratch as well. Neural networks have for example been used to generate artificial images of landscapes, buildings, and even faces.