Computer vision: An introduction to, what is computer vision? Its applications, use cases, color tracking algorithms, facial recogniton systems and future perspective.
Computer vision stands as a transformative field within artificial intelligence, empowering machines with the ability to interpret and understand the visual world. From aiding medical diagnoses to enabling autonomous vehicles, computer vision's applications span numerous industries, reshaping how we understand, analyze, and interact with visual data. In general terms most people rely on vision to prepare food, walk around obstacles, read street signs, watch videos and do hundreds of other tasks. Vision is the highest bandwidth sense and it provides a fire hose of information about the state of the world and how to act on it. For this reason computer scientists have been trying to give computers vision for half a century now, the goal is to give computers the ability to extract high level understanding from digital images and videos. As anyone with a digital camera or smartphone knows, computers are already really good at capturing photos with incredible detail, much better than humans. Images on computers are most often stored as big grids of pixels. Each pixel is defined by a color stored as a combination of three additive primary colors: red, green and blue. By combining different intensities of these three colors we can represent any color which are called rgb values.
What is Computer Vision?
Computer vision is a sub domain of artificial intelligence that gives computers the ability to make sense of visual data, enabling them to identify and understand objects and people in images and videos. Similar to other types of Artificial Intelligence based algorithms, computer vision based algorithms seek to automate tasks that imitate human capability of vision and make sense of what they perceive through it. In general computer vision models focus on analyzing, processing and interpreting visual data. Computer vision empowers computers and systems to extract significant insights from digital images, videos, and visual inputs. This capability enables them to take actions or offer recommendations based on the derived information. The principles of computer vision mirror human vision, although humans have an innate advantage. Human sight benefits from a lifetime of contextual learning to differentiate objects, gauge distance, assess motion, and detect anomalies within images. Computer vision based algorithms train machines to execute these tasks, albeit within a compressed time frame, using cameras, data, and algorithms in place of retinas, optic nerves, and a visual cortex. Through this training, systems can rapidly analyze thousands of products or processes per minute, detecting minute defects or issues that might elude human perception. This capacity allows computer vision to swiftly surpass human capabilities, especially in tasks involving inspecting products or monitoring production assets. At its core, computer vision seeks to enable machines to process and comprehend visual information from the surrounding environment, much like the human visual system. Leveraging algorithms, machine learning, and deep neural networks, computer vision extracts meaningful insights from images or video data, allowing machines to perceive, recognize, and interpret objects, scenes, patterns, and gestures.
Computer Vision - Color Tracking Algorithms
If we want to track a colored object like a bright pink ball on a screen, the first thing we need to do is record the ball's color, for that will take the rgb value of the centermost pixel. With this value saved, we can give a computer program an image and ask it to find the pixel with the closest color match. An algorithm like this might start in the upper right corner and check each pixel one at a time, calculating the difference from our target color. Having looked at every pixel, the best match is very likely a pixel from our ball. This simple algorithm would be applicable to every frame in a video, allowing us to track the ball over time. Due to variations in lighting, shadows and other effects the ball on the field is almost certainly not going to be the exact same rgb value as our target color. The tracking might be poor and if one of the team's jerseys use the same color as the ball our algorithm might get totally confused for these reasons color marker tracking and similar algorithms are rarely used unless the environment can be tightly controlled. Color tracking algorithms are able to search pixel by pixel because colors are stored inside of single pixels, but this approach doesn't work for features larger than a single pixel like edges of objects, which are inherently made up of many pixels. In order to identify these types of features in images computer vision algorithms have to consider small regions of pixels called patches.
Computer Vision - Facial Recognition Systems
Convolutional neural networks use banks of neurons to process image data, each outputting a new image essentially digested by different learned kernels. These outputs are then processed by subsequent layers of neurons allowing for convolutions on subsequent convolutions. The very first convolutional layer might find things like edges as that's what a single convolution can recognize. The next layer might have neurons that convolve on those edge features to recognize simple shapes composed of edges like corners. A layer beyond that might convolve on those corner features and contain neurons that can recognize simple objects like mouths and eyebrows, say in using facial recognition dataset. This keeps on going, building up in complexity until there's a layer that does a convolution that puts it together, eyes, ears, mouth and eyebrows into a face. Convolutional neural networks aren't required to be many layers deep but they usually are in order to recognize complex objects and scenes. Convolutional neural networks can be applied to many image recognition problems like recognizing handwritten text, spotting tumors and monitoring traffic flow on roads. Continuing with facial recognition problems, once we've isolated a face in a photo we can apply more specialized computer vision algorithms to pinpoint facial landmarks like the tip of the nose and corners of the mouth. This data can be used for determining things like if the eyes are open. We can also track the position of the eyebrows; their relative position to the eyes can be an indicator of surprise or delight. Smiles are also pretty straightforward to detect based on the shape of mouth landmarks. All of this information can be interpreted by emotion recognition algorithms giving computers the ability to infer when you're happy, sad, frustrated, confused and so on. This could in turn allow computers to intelligently adapt their behavior, maybe offer tips when you're confused and not ask to install updates when you're frustrated. This is just one example of how vision can give computers the ability to be context sensitive that is aware of their surroundings and not just the physical surroundings like if you're at work or on a train but also your social surroundings like if you're in a formal business meeting or at a friend's birthday party. Humans tend to behave differently in different surroundings and so should their computing devices if they're smart. Facial landmarks also capture the geometry in the face like the distance between eyes and the height of the forehead. This is one form of biometric data and it allows computers with cameras to recognize faces. Whether it's smartphones automatically unlocking itself when it sees your face or governments tracking people using cctv cameras. The applications of facial recognition seem limitless; there have also been recent breakthroughs in landmark tracking for hands and whole bodies giving computers the ability to interpret a user's body language.
Computer Vision - Other General Applications and use cases
Computer vision is used in industries ranging from energy and utilities to manufacturing and automotive – and the market is continuing to grow. A huge range of practical applications for computer vision technology makes it a central component in many modern innovations and solutions. Computer vision can be run in the cloud or on premises. Some of these applications are listed below:
Computer vision assists in medical diagnoses through image analysis, enabling the identification of anomalies in medical scans, aiding in disease detection, and guiding surgical procedures.
Powering the perception systems of autonomous cars, drones, and robots, computer vision helps in navigating and recognizing objects, pedestrians, and traffic signs, ensuring safe and efficient transportation.
From identifying everyday objects in photos to recognizing specific items in real-time video feeds, computer vision supports various applications in retail, security, and manufacturing.
Enhancing immersive experiences, computer vision enables the overlay of digital information onto the real world (AR) and the creation of simulated environments (VR).
Computer vision aids in monitoring and analyzing video feeds for surveillance purposes, detecting suspicious activities, and enhancing security systems.
In manufacturing, computer vision facilitates quality control, assembly line automation, and robotic vision systems, optimizing production processes and ensuring product quality.
Computer Vision - Future Perspective
The future of computer vision holds promise, with ongoing research focusing on explainable AI, multi-modal perception, edge computing for real-time analysis, and ethical frameworks governing its use across domains. Recent advancements in deep learning have significantly enhanced computer vision capabilities, enabling more accurate and complex visual understanding. Integrating vision with other senses like audio and touch will enable more comprehensive and human-like understanding of the environment, enhancing interaction and perception capabilities. However, challenges persist, including ethical considerations in facial recognition, interpretability of AI-driven decisions, and the need for robustness against adversarial attacks.The evolution of computer vision is reshaping industries, driving innovation, and transforming human experiences, providing solutions to complex challenges while creating new opportunities for societal progress and technological advancement. Computer vision continues to redefine how machines perceive and interact with the visual world. As technology advances, its applications are poised to further transform industries, drive innovation, and enhance human-machine interactions. The journey of computer vision represents an exciting frontier in artificial intelligence, reshaping our world through the lens of intelligent visual perception. At the hardware level you have engineers building better and better cameras giving computers improved sight. Using camera data and computer vision algorithms we are able to recognize faces, navigate smart TVs and intelligent shooting systems that respond to hand gestures and emotion. Each of these are active areas of research with breakthroughs happening every year and that's just the tip of the iceberg today computer vision is everywhere, whether it's bar codes being scanned at stores, self driving cars waiting at red lights or snapchat filters superimposing mustaches and the most exciting thing is that computer scientists are really just getting started enabled by recent advances in computing like super fast gps, computers with human like ability to see, are going to totally change how we interact with them.
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