Image Data Collection – Helping Create The Internet Of Things With Eyes

Have you ever stopped to think about the truly wonderful thing that is vision? Vision can be seen as a process of natural image data collection. We use visual data to make informed decisions. Every time we avoid tripping over an object for example we’re using the combined resources of our eyes and our brains to see, process and send instructions to our muscles to respond appropriately. When we identify something or someone, we are again using visual data collected by our eyes. This information is analyzed by our brain, which then directs us to respond in a particular way.

Now this same principle is being used to create the next generation of AI algorithms. Artificial intelligence is not only capable of coming up with the answer to something by following a series of predefined steps, it can also ‘learn’ new things as it goes along. In fact, the more data that is presented to an intelligent algorithm, the smarter it can become. This is Artificial Intelligence at work.

This new generation of super smart intelligent algorithms though goes beyond being able to process the ‘flat’ statistical data fed back from things like sensors and chips installed in devices hooked up to the Internet of Things. An example of ‘simple’ IoT technology at work is the use of equipment-generated operational and performance data to spot maintenance issues. So your autonomous car can tell you when it needs to drive itself to an auto repair shop! The latest generation of algorithms however can use information fed back from cameras located in that equipment. Meaning that your autonomous car could be trained to recognize you as its owner through facial recognition technology.

Learned Behavior – Natural Image Data Collection

Whilst humans, and indeed any living creature with eyes and a brain of sorts, are born with many instinctive behaviors already programmed into them, they still need to learn many new things. This type of acquired knowledge is described as learned behavior. It begins the minute we’re born – we quickly learn to recognize which face belongs to our mother. Likewise intelligent algorithms have to learn things. They need to learn to recognize certain patterns of data. They have to be ‘taught’ right from wrong so they can recognize the right data. And much like a living creature is taught these things by being presented with multiple sets of data throughout life that help them learn to make intelligent or informed decisions, so too are intelligent algorithms trained. This data can be statistical or text based, or it can be visual.

Quality Image Data Collection Matters When Training Algorithms Correctly

Ultimately though the quality of what an algorithm learns is only as good as the data that it learns from. This is particularly the case with algorithms that need to process visual data, like facial recognition software. To train these algorithms effectively it requires high quality visual data, which in turn needs good image data collection processes. The algorithms also require appropriately and expertly labeled data to assist with the learning process.

In other words, what it all boils down to is the fact that if image data collection processes used to collect visual data for training algorithms is poor the resulting algorithm won’t be anywhere near as effective at its job. Your autonomous car or your security camera for example will struggle to recognize you.