The Application Of Computer Vision

The word computer vision means the ability of a computer to see and perceive the surrounding environment. Many applications can be covered by computer vision — Object detection and recognition, self-driving cars, facial recognition, ball tracking, photo tagging, and more. Before plunging into the specialized terminology, let’s first cover the complete line of computer vision. The whole pipeline is divided into 5 essential steps, each with a distinct role. First, information is required for the computer vision algorithms to process which can be either an appearance or a stream of images (picture frames). The next step is pre-processing. In this step, the function is used for the incoming image so that the algorithm can sufficiently comprehend the image.

Some of its functions include noise reduction, image scaling, dilation, erosion, removing color spots, etc. The next step is to select the desired area or desired area. Below are object detection and image segmentation algorithms. Next, we have feature extraction which means retrieving relevant information/features from the image which is required to reach the final destination. The last step is recognition or prediction, where we recognize objects in a certain picture frame or predict the probability of objects in a certain picture frame.

Let’s take a look at the real-world application of the computer vision pipeline. Facial expression recognition is a computer vision application used by many research laboratories to get an idea of the effects of certain products on their users. Again, we have the input data for which we apply a preprocessing algorithm. The next step involves detecting a face in a particular frame and cropping that part of the frame. Once this is achieved, facial markers are identified like mouth, eyes, nose, etc. — the main feature for emotion recognition. In the end, the predictive model (trained model) classifies the image based on the extracted features in an intermediate step.