_{Distinguished Professor in the Department of Computer Science & Engineering at Michigan State University, Ph.D. - Ohio State University.}

_{Professor of Computer Vision Engineering in the Department of Engineering Science, University of Oxford, Ph.D. - Sunderland Polytechnic.}

_{The Iris Einheuser Professor of Computer Science, Duke University. Ph.D. - Carnegie Mellon University.}

_{Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science (EECS) at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), Ph.D. - MIT.}

_{CEO of Kitty Hawk Corporation, and chairman and co-founder of Udacity., Ph.D. - University of Bonn.}

Computer vision is a field of study that aims to enable computers to interpret and understand visual information from the world. This can include tasks such as image recognition, object detection, and scene understanding.

One of the core techniques used in computer vision is image processing. This involves manipulating and analyzing digital images to extract useful information. Techniques such as filtering, thresholding, and edge detection are commonly used to process images and extract features that are relevant for a particular task.

Another key technique used in computer vision is machine learning. This involves training models on large datasets of labeled images, in order to enable the computer to make predictions about new images. Convolutional neural networks (CNNs) are a popular type of machine learning model used in computer vision, as they are particularly well-suited to analyzing images.

Deep learning, a more advanced form of machine learning, has had a big impact in computer vision. By using deep neural networks, computer vision systems can learn to recognize objects and patterns in images with increasing accuracy. This has led to breakthroughs in areas such as image classification, object detection, and semantic segmentation.

One of the most popular application of computer vision is object detection. Object detection is the process of identifying and locating objects in an image or video. This can be used for a wide range of tasks, such as security systems, self-driving cars, and image search.

Another important application of computer vision is image recognition. Image recognition is the process of identifying objects, people, places, and things in images. This can be used for tasks such as tagging images on social media, searching for images, and automatically organizing images in a photo library.

Overall, computer vision is a rapidly growing field with many potential applications. Advancements in deep learning, have significantly improved the accuracy of computer vision systems, and this trend is expected to continue in the future. As a result, we can expect to see computer vision being used in an increasing number of areas, from autonomous vehicles to medical imaging.