Computer vision is a relatively developing section of computer science that attempts to obtain as much as possible information from the various sort of images or sequences of images.
Being a subject of growing interest and precise research for decades that is broadly deployed in scene restoration, object modeling, visualization, navigation, recognition, surveillance, virtual reality, or similar tasks.
"Real learning, attentive, deep learning, is playful and frustrating and joyful and discouraging and exciting and sociable and private all the time, which is what makes it great." – Eleanor Duckworth
This article is a quick tutorial on how to redefine surveillance with the power of computer vision that itself comprises excellent applications, about which we will also discuss in a further section of the blog, and yeah now I'm gonna start with a sketch of computer vision.
Being a subfield of Artificial Intelligence, Computer Vision makes computers capable enough to understand and evaluate the real world.
Applying the Deep Learning models, systems can now meticulously recognize and allocate objects form digital images and then respond accordingly as of what images depict.
Additionally, the way it is simulating a human visual system, rigorous research implies to design machines that can automate work that needs visual cognition. Withal, due to a huge amount of multi-dimensional data that requires scrutiny, the process of deciphering image become too complex in comparison to a separate form of binary information.
The practice of deep learning and neural networks have made computer vision capable enough of emulating human vision. Moreover, computer vision is adapting to identifying patterns from images in comparison to the human visual cognitive system.
With more advanced technology, computer vision is powered by the deep learning algorithms that are designed by neural networks, called convolutional neural networks (CNN) to understand the sense of images.
These neural networks are trained by deploying an enormous sample of images that aid down the algorithm to acquire and determine everything that is present in the images.
After that these neural networks examine images pixel by pixel to finger out patterns and recollect(memorize) them along with the optimal output that should be afforded for each input image as in the case of supervised learning, also to categorize image elements via checking contours and colors.
"Computers are able to see, hear, and learn. Welcome to the future."– Dave Waters
Then, this memory acts as a reference by the systems while browsing other images, and with each iteration, the AI system is capable to provide the right output.
Face Recognition: Face -detection algorithms are applied to filter and identify an individual in the pictures, especially used by Snapchat and Facebook. (If talking about face recognition, have a look at similar technology: What is Deepfake Technology and how it may be harmful?)
Image Retrieval: In order to search relevant images on the basis of content-based inquiries like in Google Images, the algorithm first examines the content in the inquiry image and then give outputs depending upon best-matched content.
Smart Cars: Computer Vision is the elementary pool of information when you want to catch traffic lights and signals and other visual appearances.
Biometric Systems: Some of the commonly deployed methods in the biometric description such as fingerprint, iris, and face matching.
Surveillance: It is the most beneficial application, Surveillance cameras are omnipresent at each public location that is used to observe doubtful activities.
Following are major domains where computer vision technology is being used or tested:
Being adept at identifying components and objects from digital images accurately as human can do, computer vision can also identify patterns that can be unnoticed by a human visual system, like;
To diagnose lung cancer finer than human radiologists, experts can build an AI that deploys computer vision and finds out cancer tumors from CT scan images.
It can also identify other forms of cancer and diseases with higher perfection than humans, this kind of practice prevents patients from late detection of cancer and provides treatment on time.
Computer Vision can be implemented to check live or recorded surveillance clips that help in law and order and security authorities with essential information such as;
In order to scan live footage of public places to figure out suspicious behavior or to identify injurious objects,
Any kind of doubtful patterns or activities that indicate any illegal action by an individual depending upon prior data, and
To search huddles of people to mark the existence of an individual of interest or wanted persons of the concerned officers, also to prevent crimes.
(Have a look at how Artificial Intelligence (AI) can be used in Politics & Government?)
Applications of the Computer Vision
Manufacturing involves large-scale usage of automation and robotics which are turning to fully-automated manufacturing and demands extensive intelligent systems to monitor industrial operations and results like;
IoT is transforming the manufacturing field and creating methods more automated and computer vision helps in improving them,
Computer Vision can analyze manufactured items for imperfections and non-compliance.
Computer Vision applications and technologies have blended into the CRM realm from sales and marketing to customer assistance services and holdings, more and more;
Computer vision is a permissive technology that aids security system for encountering enemy troop and increase the possibilities to meet targets quickly like;
Situational awareness in the military heavily depends on image-based sensors in order to convey arena-intelligence, used for complex decision-making,
In automated vehicles to navigate disputed terrain and identify adversaries, and
For supporting drivers and pilots and allowing them to escape enemy troops.
Worthwhile after the flood of online shopping, brick-and-mortar retail stores are still flourishing across the world where physical stores are facing the challenges of huge issues like averting shoplifting, refund scam, customer theft, etc. Here is where Computer Vision technology can provide assistance.
It can recognize and transform a great number of faces with enhanced efficiency, the technology is primarily focused on automating and emulating the cognitive processes of the human vision systems. After getting clues and info from videos and images, the computer vision systems implement various methods of machine learning in order to train computers for transforming and evaluating patterns over multiple faces.
For example, retailers can now be able to spot fishy behavior or unusual activities in their stores regardless of if customers are not glancing directly at security cameras, or wearing glasses, growing beards or changing hairstyles.
A glimpse of computer Vision technology
Basically, surveillance components are pictorial that is handled by us at a huge scale, even the diagnosing and cross-examination of these components are time-consuming and maintenance is quite expensive, computer vision helps us to overcome these issue.
The demand for computer vision and its application is growing rapidly and as the technology becomes more economical, there must be continuous growth in the use of Computer Vision either in image recognition, transportation, manufacturing, or gaming. With the implementation of deep learning neural networks, the dream of smart cities could plausibly become a reality, but the huge innovation is well afoot.
“Anything that could give rise to smarter-than-human intelligence—in the form of Artificial Intelligence, brain-computer interfaces, or neuroscience-based human intelligence enhancement – wins hands down beyond contest as doing the most to change the world. Nothing else is even in the same league.” -Eliezer Yudkowsky
Since, it is the beginning only, though exhilarating humankind’s capability to watch, a countless array of Computer Vision applications will briskly hit the spotlight. For more fascinating technology in Artificial Intelligence and Deep Learning, connect with us at Facebook, Twitter, and LinkedIn
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