Computer Vision

compiled By Obeng Stephen (5181040221)

One of the most powerful and compelling subfield of Artificial Intelligence is COMPUTER VISION. We all in a number of ways have been experiencing computer vision without knowing.

We will be looking at what computer vision is and how it works. Also, Recent advance in Artificial Intelligence and innovation in deep learning and neural networks, have moved us to a field that has been able to surpass humans in some tasks related to detecting and labelling of object.

Computer Vision is a field that includes method for acquiring, processing, analyzing, and understanding images Computer vision is a subfield of deep learning and Artificial Intelligence where humans teach to see and interpret the world around them. Duplicate the abilities of human vision by electronically perceiving and understanding an image. Theory for building artificial systems that obtain information from images.

On a certain level Computer vision is all about pattern recognition. So one way to train a computer how to understand visual data is to feed it images, lots of images thousands, millions if possible that have been labeled, and then subject those to various software techniques, or algorithms, that allow the computer to hunt down patterns in all the elements that relate to those labels.

Pixel data diagram. At left, our image of Lincoln; at center, the pixels labeled with numbers from 0–255, representing their brightness; and at right, these numbers by themselves.

Human Vision                                             Computer Vision

Vision is an amazing feat of natural intelligence. More human brain devoted to vision than anything else. Machines Can’t Replicate Human Image Recognition, yet. They do not posses our ability to recognize distorted images.

• Automotive

Some of the most famous application of computer vision have been done by Tesla with their Autopilot function

• Manufacturing

Computer vision coupled with sensors can work wonders for critical equipment. Today, the technology is being used to check on important plants or equipment.

• Healthcare

Today, we as patients rely on the knowledge bank of medical personnel to analyze all that data and produce a correct diagnosis. This can be difficult at times so we turn to computer vision.

• Agriculture

Computer vision can offer helping hand in mapping, analyzing soil, counting livestock, evaluating crop yield, ripeness and more.

• CV In Self-Driving Cars

Computer vision enables self-driving cars to make sense of their surroundings. Cameras capture video from different angles around the car and feed it to computer vision software, which then processes the images in real-time to find the extremities of roads, read traffic signs, detect other cars, objects and pedestrians.

• CV In Facial Recognition

Computer vision also plays an important role in facial recognition applications, the technology that enables computers to match images of people’s faces to their identities. Computer vision algorithms detect facial features in images and compare them with databases of face profiles.

• CV In Augmented Reality & Mixed Reality

Computer vision also plays an important role in augmented and mixed reality, the technology that enables computing devices such as smartphones, tablets and smart glasses to overlay and embed virtual objects on real world imagery. 

• Object Classification: What broad category of object is in this photograph?
• Object Identification: Which type of a given object is in this photograph?
• Object Verification: Is the object in the photograph?
• Object Detection: Where are the objects in the photograph?
• Object Landmark Detection: What are the key points for the object in the photograph?
• Object Segmentation: What pixels belong to the object in the image?
• Object Recognition: What objects are in this photograph and where are they?

Despite the recent progress, which has been impressive, we’re still not even close to solving computer vision. However, there are already multiple healthcare institutions and enterprises that have found ways to apply CV systems, powered by CNNs, to real-world problems. And this trend is not likely to stop anytime soon.