With the power of Internet technology, people’s eyes have seen diversity online. The digital form of texts, images, and videos quickly capture our attention. But did you know that deep down, these visual reflections on us are an enormous complexity of the human vision system resolved by computer scientists through an Artificial Intelligence called computer vision?
Curious enough about this sophisticated branch of computer science? No worries because we’ll dig into its awe-inspiring complexities that data scientists rigidly studied and pushed until the actual materialisation of its subset, computer vision, a breakthrough.
Table of Contents
Computer vision: what is it?
In many ways, computer vision has made mounting benefits, especially in pursuing a higher level of modernisation. Its conceptualisation was influenced by the experiment conducted by neurophysiologists on a cat’s visual cortex. They found a strong interconnection between the computer vision and the human brain. But this happened after the phase of an analogue computer vision in the 1950s conducted by universities that initiated Artificial Intelligence.
Historically, computer vision had gone a long way with varying challenges before it finally achieved groundbreaking success.
How does it work?
Understanding its function in full perspective will require well-founded computer science and artificial intelligence knowledge. But you don’t need to be a well-versed person.
Learning how it works and gives positive output insufficient background knowledge to hold on to, which are the following:
● Acquire image
It is the first stage that needs not be complicated. Any data set, small or large, can be acquired for analysis using a real-time camera or video. A 3D photo technology can also be used for this purpose.
● Process the image
Image processing is a subset of computer vision that focuses on enhancing and understanding images. In essence, it is a precursor to the existence of machine vision that analyses and manipulates visual content produced by algorithms.
Machine vision performs a complex task as it deals with image pixels. Each pixel contains a set of values, such as the primary colours: red, green, and blue. Their intensity is also figured out, and a digital image, called a matrix, is formed when all are together. Such instances classify computer vision as a study of matrices.
Matrices can be manipulated in the simplest “linear algebra” algorithm and by using complicated applications such as convolutions and other operations.
● Understand the image
Interpreting the image through pixels is beyond the normal mind’s reach. Computer vision has to do complex operations to calculate matrices and interpret the relationship of neighbouring pixels to conclude that such an image is of a person, animal, place, or object.
This complex stage requires advanced algorithms to analyse patterns, a fact that shows how remarkable our brain power is on many levels.
These three are basic tasks of computer vision. There’s more to be learned, such as the involved operations in the process:
- Convolution – makes use of a learnable kernel convolved with an image.
- Pooling – reduces image dimensions.
- Non-linear Activations – involves pooling and convolution operations to increase model depth.
Computer vision has crucial stages of visual processing content. Each stage follows in-depth procedures that only computer data scientists can fully understand. But let’s get a quick overview of them which are as follows:
- Image Classification – is far simpler than object detection and image segmentation tasks. In this process, the group of images are classified and predefined using the sample images that were already classified.
- Object Detection – a process that detects and localizes objects using bounding boxes. It focuses on identifying the class-specific details of visual content like video or image. It is commonly used in security, surveillance, and automated vehicle parking lot systems.
- Image Segmentation – divides images into subparts, and each image represents a particular class that neural networks have already identified.
- Face and Person Recognition – follows the same task as object detection does with the face as the primary object. The system will process the image information by finding the common facial features: lips, eyes, or nose.
- Edge Detection – involves algorithms and mathematical methods to detect the object’s boundaries. Convolutions are part of this task, along with special detection filters.
- Image Restoration – takes various levels of reconstructing old and damaged images. It reduces unnecessary elements using mathematical tools, inpainting, and realistic colourisation.
- Feature Matching – an essential task of computer vision as it looks for the relevance of an image with another using all indicators such as edges or localised corners. It follows three steps:
- Detecting the regions in focus.
- Forming the local descriptors surrounding the key points.
- Matching the features with their local descriptors in an image.
Conclusion
Computer vision as a study is quite a deep and complicated subject matter. It entails a number of tasks, mathematical functions, and a series of complex processes. It takes a genius mind to fully understand how it works from the start to the core levels. Many challenging routes need to be overcome before visual content is finally brought to our eyesight with precise class and descriptions.