What is computer vision in AI? Computer vision – AI examples

What is computer Vision and Computer vision Ai examples

Many times, I have ended up knowing the quality of the apples, whether it is a good or a rotten, only after cutting the apples. Sometimes even if the quality of apples is good and not rotten, I wonder how long this apple will last. Imagine an application in helping to solve these kinds of issues for an end customer in purchasing the apples at a fruit or vegetable shop.   How about the same application that can help a farmer to check the quality of apples at the farm before dispatching the apples? So Lets check with Computer vision AI examples

Let’s take a look at another example, we might have seen many people taking pictures of the food on the table in a restaurant or even at home (some health programs require the participants to upload the images of the food intake before the intake). In a modern lifestyle, some of us are health conscious, and imagine these images we take can show the nutrient values and calorie intake and check the balance of calorie intake we require of a day, and help us to take the right quantity of food!  This brings in value for those who are on weight loss programs or some health programs. A step further, if these kinds of applications are used in restaurants and given the option of being connected to the “feed the hunger” program which helps to contribute the excess food to the huger!! … I believe the benefits can be enormous and we can think of many scenarios.

Some of the existing applications in our mobile phones help us to understand what is possible in solving the above use cases. Some of us might have noticed, on some mobile phone, when we take a selfie, it tries to predict the age and display the age of the person!! Probably the uses can further extend!!

So… the key in all of the above use cases, is “digital imaging”. The digital cameras (vision) in the mobile phones or any other digital cameras play the role of capturing the videos/images. When these images/videos are fed into some kind of AI-powered analytical engine, the analytical engine output with required meaningful inferences and predictions!!

These are everyday needs or challenges that we face in life… and when we have a solution stitched around these technologies, we can see some of the solutions in reality.

Computer vision with AI

The terminology sounds very industrial & commercial in nature, which means that computers have vision like human beings have a vision.  These are the same as capturing digital images/videos with digital cameras. These digital cameras act as computer eyes (computer vision). When this is combined with AI, a new value gets generated by creating meaningful inferences and predictions in everyday life and commercial scenario as well.

Components of Computer vision

• Image Sensors:  Image sensors convert light (photon) into digital values (pixel), to convert this, the process undergoes a few steps before it gets converted to a digital value (digital images).  These image sensors are the critical component of digital cameras, as these sensors are becoming more efficient, and more intelligent the prevalence of digital cameras becomes higher.
• Machine vision camera– It is a camera device with lenses, image sensors, data storage, and data transfer capabilities. These image sensors have become the heart of digital cameras.
• Edge computing / Analytics:This is optional. Not all computer vision need not have edge computing capabilities. However, the images captured need to be transferred and the data transfer requires connectivity and bandwidth, the next evolution is to do the analytics at the edge device itself so that only the required information could be transferred. Hence, the image sensors are becoming AI-enabled and becoming more intelligent in terms of handling the necessary data at the hardware itself, and the decision making becomes faster as the process is done in the camera itself, when the decision making becomes faster, the same can be used for any time-critical applications.

The Game changer

• As the image sensors become more powerful, more efficient, and lesser in size, digital cameras in mobile phones will become powerful and more intelligent. This could be a game-changer. The reason is that analytics and automation are going to be in the hands of the common people through mobile phones, which has the possibility to transform many business segments.
• What is needed?
  • AI Solutioning – Aswe have seen, the opportunities are wide open and the applications are plenty.  Coming out with user-friendly solutions is going to be the game changer and connecting the dots from the source (producer) to the destination (end-user), will give the necessary scale.
  • For example – in the case of checking the quality of apples, when the user just scans the apple and if the information on the quality of the apple is updated throughout the supply chain. Both the seller and buyer are sure of giving and buying the right quality apples. This helps in many ways such as selling the right quality product, reducing the wastage, and thereby the possibility of optimization at different stages (from a retail store, cold storage places, transportation, and so on).

• Who can play a Vitol role?
  • Organizations that have a startup culture and experimenting culture to experiment on bold ideas are poised to grab the opportunities.

“Computer vision AI examples” – Some more use cases

• Sustainability – Monitoring the number of people in the meeting room and adjusting the air conditioner based on the occupancy. This helps to reduce the power consumption and reduce the carbon footprint
• Agriculture – Capture the growth of plants/crops and do imaging analysis and based on that utilize the fertilizers, this will help to get the optimum yield.

Computer vision market

• As the need for automation and automated decision-making is increasing, the presence of digital cameras with smart sensors is going to be on the rise and the need for edge analytics is going to be on the rise.
• The computer vision market is expected to touch $17.4 by 2023 and the growth is expected to get accelerated from 7.8% to 16% CAGR growth.
• As we can see the image sensors become more affordable, and the adoption and new solutions are expected to transform many business segments.Also check

  Hyper-Personalization – The New Possibilities In The Digital Era!

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