Eye see you

Welcome back!  I know just how excited you are to finally finish Computer vision.  You’re probably wondering how does computer vision works?  Computer vision requires tons of information to run analyses of information over and over until it discerns distinctions and ultimately recognizes images.  Think of it this way, to train a computer to recognize automobile tires, it needs to be given many tire images and tire-related items to learn and remember a tire, especially one with no defects.  Two essential technologies are used to accomplish this: deep learning and a convolutional neural network (CNN).

Algorithmic models allow machine learning to happen, rather than someone programming it to recognize an image.  If enough data is fed through the model, the computer will “look” at the data and learn from it, allowing it to distinguish one image from another.  CNN helps machine learning or deep learning to “look” by breaking photos down into pixels which are then given tags or labels.  The labels perform convolutions and predict what it’s “looking” at.  The neural network runs the convolutions to ensure the accuracy of its prediction in a series of repetitions until the prediction starts to come true.  This is similar to how humans look at images.

A lot of research is being done in the computer vision field.  Real-world applications demonstrate how important computer vision is to business, entertainment, transportation, healthcare, and even our everyday lives.  The main component to the growth of these applications is the flood of visual information coming from smartphones, security systems, traffic cameras, and other visually instrumented devices.  This information creates a testing ground for computer vision applications to train and launch them to become part of a range of human activities.

Computer vision has created many opportunities for major companies.  Google Translate lets users point a smartphone camera at a sign in another language and almost immediately obtain a translation of the sign in their preferred language.  The development of self-driving vehicles relies on computer vision to make sense of the visual input from a vehicle’s cameras and other sensors such as identifying other vehicles, traffic signs, lane markers, bicycles, pedestrians, and any additional visual information encountered on the road.  IBM is applying computer vision technology with partners like Verizon, bringing intelligent AI to the edge, also helping automotive manufacturers identify quality defects before a vehicle is complete.

While there are many potential use cases for computer vision, here are three of the most common applications that are helping retailers to improve their operations to deliver a high-quality customer experience. Improving inventory management by putting in place cameras to monitor display shelves so when they get low on inventory, adjusting the digital signage to redirect customers to similar products to ensure inventory management efforts are always a step ahead. This reduces product shrinkage by offering retailers a simple way to implement more advanced loss-prevention measures.  Security cameras combined with point-of-sale system data, computer vision, can monitor self-checkouts and alert management in real-time of any anomalies.  Lastly, enhancing the customer experience by having computer vision by having the cameras monitor checkout lines and issue an automatic alert to the store manager when another checkout counter may need to be opened.  This reduces the wait time a teammate would otherwise have to signal for support.    Working with technology partners to install the necessary edge computing devices and provide training and support, retailers can adapt and scale this technology to help drive efficiencies as they grow their business with a vision of the future of retail.

Computer vision is the next major step in the Technology race, helping us reimagine AI to the next level.  With great power comes great responsibility.  This kind of technology also opens doors to cyber-attacks.  Since we are in a very fragile time with computer security vulnerability, this only puts a greater risk for human safety.  This opens opportunities for hackers to take systems over for ransom or malicious activity that could ultimately harm.  The future of computer technology is at our fingers tips. We need to be extra careful when creating new technologies and what they are for because they might open doors for another’s opportunity.

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