Computer Vision

Introduction:

Everyone knows what computer vision is but did you know that the concept and development of computer vision has been going on for more than 60 years!!! It initially started with a digital image scanner converting images into grids of numbers for computers to recognize them. After that Lawrence Roberts is know for his paper " for the development of the internet" also took up computer vision explored the idea of extracting 3-D geometrical information from a 2-D perspective. It was then the idea of computer vision really picked up and brough a lot of researchers to explore the field with the key idea of making computers understand images by recognizing them. But as time progressed the interest in the field slowly disappeared like everything in the AI field due to the lack of computational resources to carry out such heavy tasks. In the 1990s. Yann LeCun introduced the concept of Convolutional Neural Networks, which became the backbone of computer vision for years to come.

What is Computer Vision:

Computer vision is a multidisciplinary field of study that enables computers to interpret and understand visual information from the world, just like humans do. It involves the development of algorithms and techniques that allow machines to extract meaningful insights and make decisions based on images and videos.

Overview of How It Works: Computer vision systems work by analyzing digital images or video frames. They follow a series of steps, including:

1. Image Acquisition: First, a camera or sensor captures an image or a series of images from the real world.
2. Preprocessing: These raw images are then processed to enhance their quality and remove any unnecessary information, like noise or unwanted colors.
3. Feature Extraction: Computer vision algorithms identify important features in the images, such as edges, shapes, colors, and textures.
4. Object Recognition: The system matches these features with known patterns or objects in its database. This helps it recognize what's in the image, like a cat, a car, or a tree.
5. Interpretation and Decision-Making: Once the system recognizes objects, it can make decisions or provide insights based on the information. For example, it might identify a stop sign in a traffic image and signal a self-driving car to stop.

Domains in Computer Visions:

There are many domains inside the computer visions, but I will try to define the main ones that cover 90% of the current use-cases:

1. Image Classification: This domain focuses on teaching computers to categorize images into predefined classes or labels. For example, recognizing whether an image contains a cat or a dog.
2. Object Detection: Object detection goes beyond classification and identifies specific objects within an image while also providing their location. It's used in applications like surveillance and autonomous vehicles to detect pedestrians, cars, or other objects.
3. Image Segmentation: Image segmentation divides an image into meaningful segments or regions, often based on similarities in color, texture, or shape. It's used in medical imaging, where it can help identify and separate different structures in the body.
4. Face Recognition: Face recognition is a specialized domain that focuses on identifying and verifying individuals based on their facial features. It's used in security systems and unlocking devices like smartphones.
5. Gesture Recognition: Gesture recognition involves interpreting human gestures or movements captured through cameras. It's used in gaming, sign language recognition, and human-computer interaction.
6. Object Tracking: Object tracking follows the movement of objects or people within a sequence of images or video frames. It's used in video surveillance, sports analysis, and robotics.

Currently algorithms of Computer Vision:

Here are the top 5 deep learning architectures that are used in computer vision, with a brief explanation of each:

1. Convolutional Neural Networks (CNNs) : CNNs are the most widely used deep learning architecture for computer vision tasks. They are particularly well-suited for image classification, object detection, and image segmentation. CNNs work by extracting features from images using convolutional and pooling layers. These features are then fed to fully connected layers to make predictions. CNNs are able to learn complex features from images because they use a hierarchical structure of layers. Each layer learns a different set of features, and the features from each layer are combined to form more complex features in the next layer. This process continues until the final layer, which makes the prediction. CNNs have achieved state-of-the-art results on a variety of computer vision benchmarks, including the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). CNNs are used in a wide range of real-world applications, such as self-driving cars, medical imaging, and social media.

2. Residual Neural Networks (ResNets) : ResNets are a type of CNN that addresses the problem of vanishing gradients, which can make it difficult to train deep networks. ResNets use shortcut connections to allow information from earlier layers to flow directly to later layers. This helps to improve the accuracy and trainability of deep networks. ResNets have achieved state-of-the-art results on a variety of computer vision benchmarks, including ILSVRC. ResNets are used in a wide range of real-world applications, such as self-driving cars, medical imaging, and social media.

3. Vision Transformers (ViTs) : ViTs are a type of transformer-based model that is specifically designed for computer vision tasks. ViTs treat images as a sequence of patches and then use the transformer architecture to learn long-range dependencies between the patches. ViTs have achieved state-of-the-art results on a variety of computer vision benchmarks, including ILSVRC. ViTs are used in a variety of real-world applications, such as self-driving cars, medical imaging, and robotics.

Business Applications of Computer Vision

The current market of computer vision is estimated to be worth more than $50 Billion and expect to grow at a tremendous pace. Computer Vision has the ability to impact almost every industry, all the way from identifying cancer cell's to autonomous self-driving cars. Let me take you through some of the impacts that computer vision already has on the industries:

1. Health and Safety : Computer vision serves a crucial role in identifying potential hazards and triggering alarms when anomalies occur. It has been instrumental in developing methods for computer systems to detect unsafe practices at construction sites, such as workers not wearing hard hats or safety harnesses. Additionally, it monitors environments where heavy machinery, like forklift trucks, operates near humans, with the ability to automatically halt operations if someone enters their path. Considering that workplace accidents cause around 2.7 million injuries annually, as reported by the US Bureau of Labor Statistics, businesses are increasingly investing in computer vision to mitigate the human and financial toll of oversights or lapses in attention. Moreover, computer vision is playing a pivotal role in combating the spread of illnesses, especially in the context of viral infections. Today, these technologies are being extensively deployed to ensure compliance with social distancing guidelines and mask-wearing mandates. Furthermore, during the current pandemic, computer vision algorithms have been developed to aid in diagnosing infections through the analysis of chest x-rays, searching for signs of infection and lung damage within the images.
2. Retail and consumer goods : In 2022, the impact of computer vision technology on the shopping and retail sectors has become increasingly evident. Amazon has led the way with its innovative cashier-less Go grocery stores, where cameras seamlessly recognize the items customers select from the shelves. Throughout the year, we have witnessed the expansion of this concept, with more branches emerging. Notably, Tesco has introduced the UK's inaugural checkout-free supermarket, joining the trend. Beyond streamlining the checkout process, computer vision plays a multifaceted role in retail. It aids in efficient inventory management by employing cameras to monitor stock levels on store shelves and in warehouses, triggering automatic restocking when necessary. Furthermore, computer vision contributes to optimizing store layouts by tracking customer movement patterns, ensuring optimal product placement. Security systems in retail have also harnessed this technology to deter shoplifting. Another increasingly popular application allows customers to access product information by simply scanning barcodes using their mobile phones. In the realm of fashion retail, an exciting innovation is the "virtual fitting room." Here, shoppers can virtually try on garments without physical contact. Mirrors equipped with cameras overlay clothing images on the customer's reflection and can even suggest matching accessories for the items being tried on, making shopping a more interactive and enjoyable experience.
3. Manufacturing and Transportation : Computer vision serves as a fundamental component within the interconnected systems of modern automobiles. While we often associate it with upcoming autonomous vehicles, its utility extends to the existing fleet of "connected" cars already traversing roads and occupying our garages. Ingenious systems have emerged employing cameras to track facial expressions, aiming to detect signs of driver fatigue that could lead to potential drowsy driving accidents. Given that such fatigue contributes to as much as 25% of fatal and severe road incidents, it's evident that technologies like this have the potential to save lives. This technology has already found application in commercial vehicles, including freight trucks, and in 2022, it may begin to integrate into personal vehicles as well. Furthermore, computer vision holds promise for various other applications in automobiles, transitioning from concept to reality. This includes monitoring seatbelt usage and even ensuring passengers don't leave behind their keys and phones when exiting taxis and ride-sharing vehicles. In the realm of self-driving cars, computer vision assumes a pivotal role, emerging as the primary onboard element for autonomous navigation. Notably, Tesla recently announced its intention to rely primarily on computer vision, diverging from the use of lidar and radar, which employ laser and radio waves to construct a model of the car's surroundings.

Future of Computer Vision

Looking ahead, the future of computer vision is bound to be transformative. As technology continues to advance, we can anticipate even more innovative applications. Imagine a world where computer vision aids in advanced medical diagnostics, enhances search and rescue missions by identifying survivors in disaster-stricken areas, or revolutionizes the way we interact with our environment through augmented reality experiences. With the potential to create safer and more efficient transportation systems, improve healthcare outcomes, and enhance our daily lives, computer vision is poised to have a profound and positive impact on society in the years to come. As we harness its potential and ensure responsible development, the possibilities for a brighter future, where machines can perceive and understand the world as we do, are truly exciting.

About the Author:

I am passionate about AI and relentless in my pursuit of solving real-world problems through personal projects. ?? Since the tender age of 13, I've been captivated by the endless possibilities of programming, and I haven't looked back since! ??

With an insatiable curiosity, I immerse myself in the latest developments, always eager to explore out-of-the-box ideas that push the boundaries of what AI can achieve. ??? I thrive on showcasing the true potential of AI and its impact on our ever-changing society.

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