Computer Vision: Challenges and Opportunities

With the increased use of CCTV cameras and social media platforms like Instagram, Tik-Tok and YouTube etc where images and videos are all about the data, its the need of hour to develop the algorithms/models by which computers can analyze these data items just like text.

Computers can easily understand, analyze, classify and filter text data but it’s not easy for computers to match the quality of human perceptual capabilities as input parameters, their relationships and processing is complex enough to be programmed in computers.

In more simple terms we can say that, implementing the human perception behavior in computers is the goal of Computer Vision but its not as simple as described in this single statement.

It needs an inter-disciplinary model able to understand and analyze image contents with the help of some image processing framework working on rough images and then applying Deep learning mechanisms for object identification and then applying statistical modeling to find patterns or relevant information out of image/video data. So its way beyond the capabilities of image processing framework.

Computer Vision is the field of study which seeks to develop models by which computers can index, analyze, classify and interpret the images/videos just like any other dataset with minimal human intervention. Computer vision has its use cases in medical imaging, OCR software, image classification, self-driving cars, fingerprint recognition, face recognition and surveillance systems.

Few more articles on computer vision which I find as interesting reads are:

https://towardsdatascience.com/everything-you-ever-wanted-to-know-about-computer-vision-heres-a-look-why-it-s-so-awesome-e8a58dfb641e


https://machinelearningmastery.com/what-is-computer-vision/

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