Technological Eyesight

With the growing amount of data being generated in this digital world, Technological Eyesight is evolving at a rapid pace.

Making use of general learning algorithms, artificial intelligence, and machine learning a multidisciplinary field has been created to develop techniques that enable Technological Eyesight to see and understand the content of digital images such as photographs and videos and process and provide useful results based on the observation. The idea is to develop methods to reproduce the capability of human vision by understanding the content of digital images.

Once the data is provided by a computer vision system, the technology acts as a vision sensor and provides needed information to interpret and understand the visual world. The models are trained with the help of artificial intelligence and by being fed thousands of labelled or pre-identified images. Images are acquired in real-time through digital images such as video, photos or 3D technology for analysis and deep learning models automate the process to identify an object. The deep learning model helps machines to accurately identify and classify objects to finally react to it.

Computer vision focuses on replicating parts of the complexity of the human visual system and enabling computers to identify and process objects in images and videos.

Technological Eyesight is all about pattern recognition which is used for image analysis in the industries of smartphones, web, VR/ AR, medical imaging, media, and insurance for image segmentation, object detection, facial recognition, edge detection, image classification, and feature matching.

The advancement in artificial intelligence and innovations in deep learning and neural networks in recent time has taken computer vision to a level to surpass human vision related to detecting and labeling objects. Convolution Neural Networks, a Deep Learning based computer vision algorithms is one such example. Moreover, with new hardware and algorithms has made it capable of providing accuracy rates for object identification.

This advancement in the scientific discipline has made obtaining information from images or multi-dimensional data easier than ever as it is built on the theory and technology of modern systems and lifestyle


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