Traditional Approach in Computer Vision
Before 2012, the working of computer vision was quite different from what we are experiencing now. For example, if we wanted the computer system to recognize the image of a dog, we had to include the understanding and explanation of the dog in the system itself for the output. A dog consists of several different features: head, ears, four legs, and a tail. All these details were stored in the system’s memory for conceptual understanding for recognizing the dog, which further triggered the output. The object’s explanation used to be stored in the form of pixels, i.e., most minor units of visual data.
When the object needed to be recognized in the future, the system divided the digital image into subparts of raw data and matched it with the pixels in its memory. This process was not efficient enough as the system would fail if the slightest change were observed in the color of the object or even if the level of lightness was changed. Also, it became difficult to store the detail of every single object individually in the system for its future recognition. Eventually, it became burdensome for the engineers to craft the rules to detect the features of images manually.
Modern Approach in Computer Vision
Eventually, after lots of research and modern automation systems, this traditional computer vision technique was replaced with advanced machine learning, specifically deep learning algorithms that make more effective use of computer vision. Traditional computer vision techniques follow the top-down flow for identifying the image using its features, whereas deep learning models work vice versa.
The neural network model of machine learning trains the system to use a bottom-up approach. The algorithm analyzes the dog’s features in general and classifies it with previously unseen images to draw the most accurate results. This process happens by training the model using massive datasets and countless training cycles.
Neural network-backed computer vision is possible because of the abundance of image data available today and the reduced computing power required to process the datasets. Millions of image databases are accurately labeled for deep learning algorithms to work on. It has helped deep learning models successfully surpass the hard work of traditional machine learning models for manual feature detectors.
Therefore, the significant difference between the traditional vision system versus the new neural network model is that humans have to train the computer “what should be there” in the image in the conventional computer vision system. In contrast, in the modern neural network model, the deep learning algorithm trains itself for analyzing “what is there” in the image.
This modern neural network algorithm is precious for various things like diagnosing tissue samples because, as per studies, human visuals limit the image resolution to 2290 pixels per inch. Hence, even the slightest change in the density can change the final results and mislead the experts.
Moreover, when it comes to humans working excessively on the exact image resolution for over a long time, it creates human fatigue, which results in poor business outcomes and risks of lives when the problems are related to infrastructures or aircraft maintenance. But this problem comes to an end by improving the ability of computer vision systems to get precise results and perform continuously over a long time using neural network models.
