Computer Vision

Computer vision is a field of artificial intelligence (AI) that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images, videos and other visual inputs—and to make recommendations or take actions when they see defects or issues.

If AI enables computers to think, computer vision enables them to see, observe and understand. 

Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving or something is wrong with an image.

Computer vision trains machines to perform these functions, but it must do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.

Computer vision is used in industries that range from energy and utilities to manufacturing and automotive—and the market is continuing to grow. It is expected to reach USD 48.6 billion by 2022.

Key Aspects of Computer Vision

• Image Recognition: This is the most common application, where the system identifies a specific object, person, or action in an image.
• Object Detection: This involves recognizing multiple objects within an image and identifying their location with a bounding box. This is widely used in applications such as self-driving cars, where it’s necessary to recognize all relevant objects around the vehicle.
• Image Segmentation: This process partitions an image into multiple segments to simplify or change the representation of an image into something more meaningful and easier to analyze. It is commonly used in medical imaging.
• Facial Recognition: This is a specialized application of image processing where the system identifies or verifies a person from a digital image or video frame.
• Motion Analysis: This involves understanding the trajectory of moving objects in a video, commonly used in security, surveillance, and sports analytics.
• Machine Vision: This combines computer vision with robotics to process visual data and control hardware movements in applications such as automated factory assembly lines.

How does computer vision work?

Computer vision needs lots of data. It runs analyses of data over and over until it discerns distinctions and ultimately recognize images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects.

Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).

Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data. If enough data is fed through the model, the computer will “look” at the data and teach itself to tell one image from another. Algorithms enable the machine to learn by itself, rather than someone programming it to recognize an image.

A CNN helps a machine learning or deep learning model “look” by breaking images down into pixels that are given tags or labels. It uses the labels to perform convolutions (a mathematical operation on two functions to produce a third function) and makes predictions about what it is “seeing.” The neural network runs convolutions and checks the accuracy of its predictions in a series of iterations until the predictions start to come true. It is then recognizing or seeing images in a way similar to humans.

Much like a human making out an image at a distance, a CNN first discerns hard edges and simple shapes, then fills in information as it runs iterations of its predictions. A CNN is used to understand single images. A recurrent neural network (RNN) is used in a similar way for video applications to help computers understand how pictures in a series of frames are related to one another.

Challenges of Computer Vision

Computer vision, despite its advances, faces several challenges that researchers and practitioners continue to address:

• Variability in Lighting Conditions: Changes in lighting can dramatically affect the visibility and appearance of objects in images.
• Occlusions: Objects can be partially or fully blocked by other objects, making detection and recognition difficult.
• Scale Variation: Objects can appear in different sizes and distances, complicating detection.
• Background Clutter: Complex backgrounds can make it hard to distinguish and segment objects properly.
• Intra-class Variation: Objects of the same category can look very different (e.g., different breeds of dogs).
• Viewpoint Variation: Objects can appear different when viewed from different angles.
• Deformations: Flexible or soft objects can change shape, and it is challenging to maintain consistent detection and tracking.
• Adverse Weather Conditions: Fog, rain, and snow can obscure vision and degrade image quality.
• Limited Data and Annotation: Training advanced models requires large datasets with accurate labeling, which can be costly and time-consuming.
• Ethical and Privacy Concerns: Facial recognition and other tracking technologies raise significant privacy and ethical questions.
• Integration with Other Sensors and Systems: Combining computer vision data with other sensor data can be challenging but is often necessary for applications like autonomous driving.

Computer Vision Benefits

Computer vision offers numerous benefits across various industries, transforming how organizations operate and deliver services. Here are some of the key benefits:

• Automation of Visual Tasks: Computer vision automates tasks that require visual cognition, significantly speeding up processes and reducing human error, such as in manufacturing quality control or sorting systems.
• Enhanced Accuracy: In many applications, such as medical imaging analysis, computer vision can detect anomalies more accurately and consistently than human observers.
• Real-Time Processing: Computer vision enables real-time processing and interpretation of visual data, crucial for applications like autonomous driving and security surveillance, where immediate response is essential.
• Scalability: Once developed, computer vision systems can be scaled across multiple locations and devices, making expanding operations easier without a proportional labor increase.
• Cost Reduction: By automating routine and labor-intensive tasks, computer vision reduces the need for manual labor, thereby cutting operational costs over time.
• Enhanced Safety: In industrial environments, computer vision can monitor workplace safety, detect unsafe behaviors, and ensure compliance with safety protocols, reducing the risk of accidents.
• Improved User Experience: In retail and entertainment, computer vision enhances customer interaction through personalized recommendations and immersive experiences like augmented reality.
• Data Insights: By analyzing visual data, businesses can gain insights into consumer behavior, operational bottlenecks, and other critical metrics, aiding in informed decision-making.
• Accessibility: Computer vision enhances accessibility by helping to create assistive technologies for the visually impaired, such as real-time text-to-speech systems or navigation aids.
• Innovation: As a frontier technology, computer vision drives innovation in many fields, from developing advanced healthcare diagnostic tools to creating interactive gaming systems.

Computer Vision Disadvantages

• Complexity and Cost: Developing and deploying computer vision systems can be complex and costly, requiring specialized expertise in machine learning, significant computational resources, and substantial investment in data collection and annotation.
• Privacy Concerns: Computer vision, particularly in applications like facial recognition and surveillance, raises significant privacy concerns regarding data collection, surveillance, and potential misuse of personal information.
• Ethical Implications: Computer vision algorithms may inadvertently perpetuate biases in the training data, leading to unfair or discriminatory outcomes, such as facial recognition systems that disproportionately misidentify certain demographic groups.
• Reliance on Data Quality: The precision and efficiency of computer vision systems rely greatly on the caliber and variety of the training data. Biased or inadequate data may result in erroneous outcomes and compromise the system's dependability.
• Vulnerability to Adversarial Attacks: Computer vision systems are susceptible to adversarial attacks, where minor perturbations or modifications to input data can cause the system to make incorrect predictions or classifications, potentially leading to security vulnerabilities.