A Guide to Computer Vision And Its Applications

Introduction

Computer vision is a field of technical study that promotes computer systems to replicate the human optical visual system. It is considered a subdivision of artificial intelligence that gathers information from images and/ or videos and processes to define specific attributes. The entire computer vision process implicates image acquisition, data screening, repetitive analysis, and the identification and extraction of information. This comprehensive digital process enables computer systems to comprehend a diverse range of visual content and operate on it accordingly.

Recent advances in the computer vision process and methodology have created a significant market opportunity for those enterprises wanting to embrace this technology. In addition, social media discussions have indicated a constant increase in the appeal of using computer vision across different industries since 2019. However, there are inherent challenges associated with computer vision technology that varies from computational data discrepancies to digital data privacy issues.

What is computer vision?

In essence, computer vision projects translate digital visual data into detailed, informative descriptions to construct multi-dimensional data. This constructed or assembled data is then transformed into a computer-readable language to aid complicated computer-modeled decision-making. The main objective of this branch of AI is to “instruct” devices to collect information from pixels correctly.

As computer vision vendors strive to differentiate their offerings, they scrutinize emerging technologies to build superior capabilities that enhance relevant service delivery and promote a positive end-user experience.

As previously stated, computer vision is a subset of deep learning and AI where we humans “teach” computer systems to notice and interpret the visual world around them and us. While our visual capabilities develop naturally over time, assisting and supporting machines to decode and comprehend their surroundings via vision remains a predominantly unresolved challenge.

Unfortunately, the complexity of the human visual system and its dynamic interaction with the environment, make machine computer vision a very challenging project to realize.

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How does Computer Vision Work?

Computer vision systems utilize input from auto-sensing devices, machine learning, AI, and deep learning to reproduce or imitate how the organic human vision system functions. Computer vision systems operate on complex algorithms that are trained on enormous amounts of visual images and data. These intricate systems recognize patterns present within the digital visual data and utilize those routines to decide and define the content of other similar images.

Let us look at an example: software programmers upload and feed millions of bird images into the computer instead of training a computer to look for beaks, wings, talons, and colorful feathers that constitute what a bird looks like. This scenario allows the computer system to understand – over time and with repetition – what the different features are that create a bird so as to recognize it immediately.

While this concept – outlining the basics of how computer vision works – may seem uncomplicated, processing and understanding an image via machine vision is actually fairly challenging.

Why is computer vision difficult?

A digital image consists of thousands of pixels, with a single-pixel existing as the smallest item into which an image is divided.

Computers process images using an array of pixels, where each individual pixel has a value set, representing the existence and intensity of the three constituent primary colors that it contains: red, green, and blue. Combining all of the pixels together will form a digital image.

This digital image is essentially a mathematical matrix that computer vision applications are trained to study and learn. Even the most straightforward computer vision algorithm will use linear algebra to manipulate these digital pixel matrices, and complex computer vision applications involve mathematical operations like matrix convolutions with learnable kernels that will consistently evolve over time.

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Computer Vision History and Research

The earliest experiments in computer vision occurred in the early 1950s, using the inaugural neural algorithms and networks that could detect the edges of an image and then attempt to sort simple items into categories like squares and circles.

In the mid-1970s, the first commercial application of a computer vision system was designed and built to interpret typed and/or handwritten text using Optical Character Recognition (or OCR) technology. This improvement was primarily used to analyze and decipher written text for the blind.

As the Information Superhighway matured into the Internet in the 1990s, creating, assembling, and posting large images online for detailed analysis and facial recognition systems blossomed and prospered. This growing data set allowed computer systems to identify distinctive people in videos and photo images. These applications were predominantly used by law enforcement agencies and at airports.

As the Internet became more than just a centerpiece, computer scientists attained more access to large volumes of data than ever before. The costs decreased as computing hardware continued to improve, making superior hardware models effortless to procure and employ for digital science activities. Fundamental algorithms and neural networks developed and improved into the 1980s-90s, and nowadays, more than seventy years after it began, the field of AI has consistently advanced and progressed both in science ingenuity and commercial application.

Computer Vision Applications

The evolution of computer vision applications has witnessed the large-scale systemization of complex issues into widespread solvable problem statements. The methodical division of computer vision topics into distinctive formed classes with appropriate nomenclature has allowed data scientist researchers to identify the particular challenges and work on the globally and efficiently.

The most prevalent computer vision assignments that are generally found in AI include the following:

Digital Image Classification

Digital Image Classification has proven to be one of the most popular study topics ever since the breakthrough ImageNet computer vision datasets were released back in 2010.

As a result of being one of the most popular computer vision projects undertaken by both beginners and experts, digital image classification as a problem statement is actually relatively straightforward. For a group of images, the assignment is to classify them into a set of predefined subclasses using exclusively a set of sample images that have already been previously classified.

Compared to complex subjects such as digital object detection and digital image segmentation, which must be identified and localized with the features they detect, digital image classification works by processing the entire digital image and allocating specific data labels to it.

Digital Object Detection

Digital Object Detection refers to the assessment object tracking, detection, and localization of distinct objects using digital bounding box techniques.

These techniques search for class-specific attributes in a digital image or video to identify them if they happen to materialize. These classes, or subclasses, can be automobiles, animals, people, or anything which the model has been trained to search and identify.

Previously these methods used the Haar feature set to detect attribute features within a digital image and categorize them based on machine learning detection procedures.

It should be noted that the process is both time-consuming and highly error-prone. In addition, there are inherent limitations on the number of objects that can be accurately detected.

As a result, complex deep learning standards such as SSD that utilize millions of attribute parameters to remove these limitations are often deployed for this assignment. Digital object detection is usually accompanied by digital object recognition, which is also known as object classification.

Digital Image Segmentation

Digital Image Segmentation divides a digital image into sub-portions or sub-objects to exemplify that the computer vision system can distinguish an identifiable object from the background or another digital object within the same image.

A digital “segment” of a digital image represents a particular subclass of an object that the neural network has identified within an image, typically represented by a pixel mask that can be utilized to extract it from the image.

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Advancements in Computer Vision

Contemporary computer vision applications are moving away from basic statistical procedures when analyzing digital imagery and are increasingly dependent on deep learning models. With deep learning, a computer vision system operates on a neural network algorithm, providing an even more accurate analysis of the digital image dataset. Additionally, deep learning allows an application to retain the analyzed information from each image processed – so it subsequently learns and becomes more precise the more often it is employed.

Let's highlight the recent advancements that have significantly impacted the field and its applications:

1. Deep Learning and Neural Networks: Integrating deep learning with computer vision has been a game-changer. Convolutional Neural Networks (CNNs) have become the backbone of many computer vision applications. These networks are especially adept at analyzing visual imagery and have been fundamental in achieving image and video recognition breakthroughs.

2. Generative Adversarial Networks (GANs): GANs are a class of machine learning frameworks where two neural networks contest with each other to generate new, synthetic realities. In computer vision, GANs have been used for image generation, creating realistic, indistinguishable images from real photographs. They are also used in image enhancement, such as super-resolution, and art creation.

3. Transfer Learning: Transfer learning involves taking a pre-trained model (usually trained on a large dataset) and using it as a starting point for a new, similar problem. This has allowed for the rapid development of effective computer vision models without the need for massive datasets or extensive training times.

4. Real-time Object Detection and Recognition: The development of algorithms like YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector) has enabled object detection and recognition in real-time. This is crucial for applications like autonomous driving, where timely and accurate object recognition can be a matter of life and death.

5. 3D Vision and Depth Perception: Advancements in 3D vision have enabled computers to perceive depth in images and videos. This is particularly important in robotics, where depth perception is necessary for robots to interact with their environment effectively.

6. Edge Computing in Computer Vision: With the advancements in hardware, computer vision models are now being deployed on edge devices like smartphones, drones, and IoT devices. This allows for real-time processing without the need for data to be sent to a central server, reducing latency and allowing for applications in remote areas.

7. Improved Facial Recognition Technologies: Facial recognition has seen significant advancements, with new algorithms that are capable of recognizing faces with higher accuracy and from different angles and under various lighting conditions. This has applications in security, authentication, and surveillance.

8. Data Augmentation Techniques: New data augmentation techniques have been developed that allow for the expansion of datasets by applying transformations and adding variability. This has enabled models to be more robust and generalize better to new data.

9. Explainable AI (XAI): The need for explainability and transparency has grown as computer vision systems are being used in more critical applications. XAI in computer vision is an area that aims to make the decision-making process of AI models more understandable and interpretable for humans.

10. Integration with Other Technologies: Computer vision is increasingly being integrated with other technologies such as augmented reality (AR), virtual reality (VR), and natural language processing (NLP) to create more immersive and interactive experiences.

These advancements in computer vision are driving innovation across various industries including healthcare, automotive, entertainment, manufacturing, and agriculture. As research continues, we can expect even more groundbreaking developments that will further expand the capabilities and applications of computer vision.

Computer Vision Examples

Most enterprises cannot wholly utilize computer vision systems due to a lack of clear business strategy, guidance, and execution challenges. Therefore, business data and analytics leaders must evaluate their business value chain to identify those areas where they can effectively leverage current computer vision capabilities.

Having said that, there are various industrial applications of computer vision systems that have commercially recognized the benefits.

Face and Person Recognition

Facial recognition is a subset of digital object detection where the direct object under the detection guise is a human face. Like object detection where elements are both noticed and localized, digital facial recognition achieves positive detection and recognition of the face in question.

Facial recognition techniques search for the most common facial features such as a nose, eyes, and lips to categorize a face using these features.

Edge Detection

Edge detection is the process of successfully detecting borders in digital objects. It is achieved algorithmically, with the assistance of mathematical operations, that helps detect distinct changes in a digital image's illumination or brightness.

This process is often employed as a pre-processing action for many ensuing computer vision tasks. Edge detection is mainly accomplished by conventional image processing-based algorithms that detect specifically designed edge filters.

Similarly, the edges in a digital image offer vital information about the actual image contents, thereby resulting in all deep learning methods performing edge detection internally for the global capture of low-level learnable kernel features. And the end result? A self-learning computer vision system that uses characteristic edge formulation knowledge to learn image detection and recognition.

Digital Image Restoration

Digital Image Restoration is the reconstruction process of old and faded hard-copy images, typically old photographs. Standard digital image restoration procedures involve the reduction of additive digital noise through mathematical means. Concurrently, digital reconstruction requires significant image modifications, leading to advanced digital analysis and the use of image restoration.

Damaged components of an image are replenished with the assistance of digital generative models that assess and evaluate what the image is attempting to communicate. The restoration process is frequently followed by a colorization procedure that colors the picture's subject (if non-black and white) in the most natural and realistic form possible.

Digital Scene Reconstruction

One of the most difficult challenges is to remediate is digital scene reconstruction, which is the digital 3D restoration or rebuilding of an entity from a photo.

Most computer vision algorithms in scene restoration approximately perform by creating a “point cloud” around a specific object's (or entity's) surface and reconstructing a digital “mesh” from within – and outwards – from the object's point cloud. The challenges arise from the quality (or lack thereof) of the original image with which to work; however, the algorithmic technology is continually improving, year-on-year.

Computer vision technology applied

Analyzing and enhancing digital images is quite beneficial in numerous fields and industries. The following are some of the main use cases:

Medical Diagnostic Imagery: Digital image classification and pattern matching detection are comprehensively used healthcare computer vision to create software systems that assist medical professionals with diagnosing harmful illnesses such as cancer. A group of medical researchers has taught and “trained” a computer vision system to analyze scans of oncology patients. The AI algorithm showed upwards of 95% accuracy with recognition and detection of cancer scans.

Factory and Supply Chain Management: It is essential to find and identify defects in the manufacturing process with maximum accuracy. However, this is a challenging exercise because it requires intense and constant product monitoring. A computer vision system can use real-time detected data from digital cameras and apply machine learning algorithms to analyze the data streams of the recently manufactured products on a conveyor belt. This automated method is more painless and cost-efficient for checking low-quality product items than human product inspection.

Security System Management: Digital facial image recognition is used basically anywhere where real-time security is essential. In the United States, schools use facial recognition technology to identify and prevent sex offenders and other criminals from entering school premises, thus reducing any potential threats. Similar styled computer vision software can recognize weapons on students to prevent violent acts in schools. Airlines use facial recognition systems for passenger identification and check-in, saving time and reducing the stress associated with checking in and ticketing.

Self-Driving Automobiles: Computer vision sensors and digital cameras assist cars to learn to recognize objects such as pedestrians, car bumpers, trees, and parked vehicles in their vicinity. Computer vision facilitates these self-driving vehicles to proceed freely – and safely – in an environment without human supervision.

Retail Management: Amazon was the foremost enterprise to open a store without cashiers or cashier machines. Amazon is fitted with multiple computer vision cameras, and these devices identify and track the items customers place in their shopping carts. Such devices can track and identify whether the customer returns the product to the shelf, or removes it entirely from the virtual shopping cart. These devices prevent shoplifting and prevent stock or product shortages before they occur.

Animal Conservation Systems: Computer vision assists ecologists in retrieving data about the wildlife, and tracking the locations, activities, and behaviors of rare species without directly disturbing the animals.

The Challenges Computer Vision Currently Faces

Computer vision systems greatly assist humans across various occupations and industries, and their development opportunities are endless. However, as with all complex and intricate computer systems, there is no technology that is free from bugs, flaws, or poor algorithmic implementation – which is true for automated computer vision systems and procedures.

Generic Challenges

The following are the main limitations that befall computer vision systems:

Lack of Specialists: Businesses must have an internal team of highly trained professionals with profound knowledge of the differences between AI and machine learning and deep learning technologies to “teach” and train computer vision systems. There is a demand for highly skilled specialists who can help shape this future of this technology. This assertion does not mean that outside or external consultants cannot be advised and hired – however, this must be a short-term exercise only, due to both the cost and the valuable intellectual property that such consultants have and demand.

The necessity of Regular Monitoring: If a computer vision system encounters a technical issue or an unplanned outage, this can cause immense disruption to businesses. Therefore, companies must have a dedicated in-house team to monitor and evaluate computer vision systems. Besides regular and unplanned maintenance activities, updates and changes to computer vision algorithms will be required. Also, testing such changes before the release into computer vision production environments is a long and time-intensive process.

One of the most significant challenges in machine computer vision systems is that they lack an understanding of how the human brain and visual optical system works.

Our enhanced and intricate sense of sight is something that develops naturally. Yet, even though we use this extraordinary ability, we are still unable to explain to computer systems, via algorithms, the entire process by which we can understand what we see.

Furthermore, straightforward, our everyday life operations such as studying the time on the clock, walking across the street at the pedestrian crossing, or pointing at something in the sky, require us to sufficiently understand the objects around us so we can understand and comprehend our environment.

Such natural aspects are entirely different from the simple operations of computer vision programs, but are nevertheless largely inseparable. The simulation – and understanding – of human vision through algorithms and mathematical models thus requires the identification of an object within an image, as well as comprehending its presence and its expected behavior. This is seemingly simple for humans to do but is – at present – quite challenging to model with absolute certainty within a computer vision system.

Scene Comprehension Challenges

Computer vision systems are adequate at locating and identifying digital objects. However, they do experience difficulties when attempting to understand a scene's overall context, especially if it is a non-trivial scenario.

Thus, computer vision systems do not understand or comprehend postmodern art, or bizarre digital craftwork that attempts to expose both the meaninglessness and meaningfulness of certain circumstances.

Unfortunately, AI is an exact, precise, and concise process with no margin for computational discrepancies. To improve the overall position, we would require a “general” AI capability, with problem-solving abilities that are basically equal to that of an intelligent human being. However, we are quite far from accomplishing that scenario.

Privacy Issue Challenges

Computer vision systems must adhere to both local and international privacy laws and regulatory bodies. Computer vision systems for facial recognition purposes are primarily adopted by governments to promote their national security. AI ethicists are still attempting to determine the consequences of universal computer vision systems for public well-being, where facial recognition data is ubiquitous, especially via cloud-based systems.

Conclusion

Computer vision science is undoubtedly an ingenious and innovative field that employs the latest machine learning algorithms and technologies to create complex software systems. These complex systems assist us, humans, across various industries and fields. From wildlife conservation to retail application, to smart algorithms that are required to solve the problems of image classification, and facial pattern recognition. Computer vision technology illustrates a crucial new phase our civilization is pushing toward in creating artificial intelligence that will one day be as sophisticated as humans.

Resources

• AI-Based Visual Inspection Systems: Next-Generation Quality Control

• Active Learning For Object Detection

• Aerial Object Using YoloV5 And Kili Technology

• Benefits Of Computer Vision For Defect Detection And Scaling