Explore Best Practices and Application of Computer Vision in Artificial Intelligence! Discover Essential Skills for Success!!

Abstract:

Computer vision is a field of artificial intelligence (AI) that trains computers to see, interpret and understand the world around them through machine learning techniques.

Computer vision is a field of artificial intelligence (AI) that applies machine learning to images and videos to understand media and make decisions about them. With computer vision, we can, in a sense, give vision to software and technology.

Computer vision programs use a combination of techniques to process raw images and turn them into usable data and insights.

The basis for much computer vision work is 2D images, as shown below. While images may seem like a complex input, we can decompose them into raw numbers. Images are really just a combination of individual pixels and each pixel can be represented by a number (grayscale) or combination of numbers such as (255, 0, 0—RGB).

Keywords:

Computer Vision, RGB, Pixels, 2D images, Artificial Intelligence (AI), Machine Learning 

Learning Outcomes :

After undergoing this article you will be able to understand the following 

1. What is computer vision?

2. Why computer vision is important?

3. How does computer vision work?

4. What things are important to understand computer vision?5. Real-world applications of computer vision

6. Benefits that computer vision could bring

7. Limitations of computer vision 

8. Strategies for leveraging computer vision and their corresponding best practices

9. Conclusions 

10. FAQs

References 

1. What is computer vision?

Computer vision is a field of computer science that focuses on enabling computers to identify and understand objects and people in images and videos. Like other types of AI, computer vision seeks to perform and automate tasks that replicate human capabilities.

Computer vision, a type of artificial intelligence, enables computers to interpret and analyze the visual world, simulating the way humans see and understand their environment. It applies machine learning models to identify and classify objects in digital images and videos, then lets computers react to what they see.

2. Why computer vision is important?

Human vision extends beyond the mere function of our eyes; it encompasses our abstract understanding of concepts and personal experiences gained through countless interactions with the world.

Computer Vision is important due to the following Key Aspects 

1. Image Recognition: This is the most common application, where the system identifies a specific object, person, or action in an image.
2. 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.
3. 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.
4. 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.
5. Motion Analysis: This involves understanding the trajectory of moving objects in a video, commonly used in security, surveillance, and sports analytics.
6. Machine Vision: This combines computer vision with robotics to process visual data and control hardware movements in applications such as automated factory assembly lines.

3. How does computer vision work?

Modern computer vision systems combine image processing with machine learning and deep learning techniques. Hence, developers combine different software (etc., OpenCV or OpenVINO) and AI algorithms to create a multi-step process, a computer vision pipeline. The organization and setup of a computer vision system vary based on the application and use case. However, all computer vision systems contain the same typical functions.

Generally, computer vision works in three basic steps: 

Step #1: Acquiring the image/video from a camera Step #2: Processing the image, and 

Step #3: Understanding the image.  

Step #1: Image acquisition. 

The digital image of a camera or image sensor provides the image data or video. Technically, any 2D or 3D camera or sensor can be used to provide image frames. 

Step #2: Pre-processing. The raw image input of cameras needs to be preprocessed to optimize the performance of the subsequent computer vision tasks. Pre-processing includes noise reduction, contrast enhancement, re-scaling, or image cropping. 

Step #3: Computer vision algorithm. 

The image processing algorithm, most popularly a deep learning model (DL model), performs image recognition, object detection, image segmentation, and image classification on every image or video frame. 

Step #4: Automation logic. 

The AI algorithm output information needs to be processed with conditional rules based on the use case. This part performs automation based on information gained from the computer vision task. 

For example, pass or fail for automatic inspection applications, match or no-match in recognition systems, and flag for human review in insurance, surveillance and security, military, or medical recognition applications.

4. What things are important to understand computer vision?

Computer vision is the ability of computers to recognize and extract data/information from objects in images, videos, and real-life events. Unlike humans, computers have a hard time processing visual data. While we can interpret what we perceive depending on our memories and prior experiences, computers cannot. To bridge the gap between what they see and understand, computers employ artificial intelligence (AI), neural networks, deep learning (DL), parallel computing, and machine learning (ML).

Computer vision algorithms focus on one pixel blob at a time and use a kernel or filter that contains pixel multiplication values ​​for edge detection of objects. The computer recognizes and distinguishes the image by observing all aspects of it including colors, shadows, and line drawings.

Today, we use convolutional neural networks (CNNs) for modeling and training. CNNs are special neural networks, specifically designed for processing pixel data, used for image recognition and processing. The convolutional layer contains multiple neurons and tensors. These process large datasets by learning to adjust their values ​​to match characteristics that are important for distinguishing different classes. This is done by extensively training the model.

One way to help computers learn pattern recognition is to feed them numerous labeled images so that they can look for patterns in all the elements.

5. Real-world applications of 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.

Some prominent area of application of computer vision are the following:

• Cancer Detection.
• COVID-19 diagnosis.
• Cell Classification.
• Movement Analysis.
• Mask Detection.
• Tumor Detection.
• Disease Progression Score.
• Healthcare and rehabilitation.

• Transportation

• Healthcare

• Manufacturing

• Agriculture

• Retail

Computers are used at homes for several purposes like online bill payment, watching movies or shows at home, home tutoring, social media access, playing games, internet access, etc.

Computer vision applications have a major role in product and component assembly in the manufacturing space. As a part of industry 4.0 automation, most of the manufacturing industry has been implementing computer vision to conduct fully automated product assembly and management processes.

6. Benefits that computer vision could bring

Improved data precision is one of the main advantages of using computer vision in business intelligence. Large databases can be analyzed by computer vision programs with high levels of consistency and precision, removing prejudice and mistakes brought on by human perception.

The use of Computer Imagining grows rapidly thanks to the discovery of advantages for industries. There are five main advantages of computer vision:

• Process in a simpler and faster way: it allows the clients and industries to check. Also, it gives them access to their products. It’s possible thanks to the existence of Computer Vision in fast computers.
• Reliability: computers and cameras don’t have the human factor of tiredness, which is eliminated in them. The efficiency is usually the same, it doesn’t depend on external factors such as illness or sentimental status.
• Accuracy: the precision of Computer Imagining, and Computer Vision will ensure a better accuracy on the final product.
• A wide range of use: We can see the same computer system in several different fields and activities. Also, in factories with warehouse tracking and shipping of supplies, and in the medical industry through scanned images, among other multiple options.
• The reduction of costs: time and error rate are reduced in the process of Computer Imagining. It reduces the cost of hire and train special staff to do the activities that computers will do as hundreds of workers.

7. Limitations of computer vision 

Despite all the advantages of computer vision thanks to the capacity of Machine Learning, we have to consider some disadvantages:

• Necessity of specialists: there is a huge necessity of specialist related to the field of Machine Learning and Artificial Intelligence. A professional that knows how those devices work and take full advantage of Computer Vision. Also, the person can repair them when necessary. There are a lot of work opportunities after doing a Master in Artificial Intelligences. However, companies still wait for those specialists.
• Spoiling: eliminate the human factor may be good in some cases. But when the machine or device fails, it doesn’t announce or anticipate that problem. Whereas a human person can tell in advance when the person won’t come.
• Failing in image processing: when the device fails because of a virus or other software issues, it is highly probable that Computer Vision and image processing will fail. But if we do not solve the problem, the functions of the device can dissapear. It can froze the entire production in the case of warehouses.

8. Strategies for leveraging computer vision and their corresponding best practices

Strategies about AI implementation

Defining Clear Business Objectives

Align AI strategies with defined business objectives

Prioritize Objectives Based on Immediate Value and ROI

Ensuring Data Readiness

Selecting the right AI solutions for your organization

Resource Planning & Training and Budgeting

Conduct Proof-of-Concept Tests

Monitoring KPIs and AI implementation success factors

Governance and Compliance

The best Strategies for implementing computer vision are as follows

1. Learn the basics of implementing AI

2. Discover the trends

3. Improve needed skills

4. Remain updated

5. Update continuously

6. Plan, Do, Check then Act.

7. Apply relationship chart to understand the roots 

Successful machine vision system implementations yield valuable insights and best practices, including:

• Collaboration: Engaging all relevant stakeholders in the planning and implementation process ensures a shared understanding of the project’s goals and requirements.
• Detailed scope: Developing a comprehensive scope is critical for effective project management and successful implementation.
• Component selection: Careful evaluation and selection of cameras, lenses, and accessories are crucial for optimizing system performance.
• Integration and compatibility: Ensuring seamless integration with existing systems and infrastructure minimizes disruption and facilitates efficient operation.
• Continuous improvement: Embracing advanced technologies, such as AI and computer vision, enables businesses to adapt and evolve their machine vision systems in response to changing needs and challenges.

9. Conclusions 

Today’s computer vision systems support a range of industries, from manufacturing to retail to finance, helping businesses extend and enhance AI at the edge. Object detection, object recognition, and object classification are the key functions in computer vision systems today.

10. FAQs

How Smart Cities will be embedded with technologies applying computer vision?

Modern cities continue to grow at a rapid pace, with 55 percent of the world’s population living in urban areas today and a projected 13 percent increase to that number by 2050.Cities around the world are being challenged to provide tangible and equitable economic, social, and environmental benefits for their citizens. They must also provide better quality and more sustainable services, improve public safety, address congestion and environmental issues, reduce costs, and promote local economic competitiveness. Scaling existing infrastructure and human resource-intensive processes is cost prohibitive, unmanageable, and unsupportable, so cities are looking to technological solutions to alleviate these pressures.

As population has grown, technology has taken unprecedented strides. With the introduction of Internet of Things (IoT) devices, edge computing, machine learning, artificial intelligence (AI), and 5G communications networks, the technological tools needed are now available, and transitioning to a smart, technology-supported city is now possible.

Embedded smart city technologies (sensors, cameras, and edge computing) can now provide near real-time awareness of issues requiring attention, and data collected and analyzed from these devices can be used to optimize city operations. Smart city solutions can improve basic services, enhance public safety, increase sustainability, and inform planning and policy making. They can also be used to enhance public experiences and optimize operational efficiency at local sports stadiums, theme parks, and resorts or improve parking and safety on university campuses. The reach and impact are broad, but every application helps result in a better quality of life for citizens.

References 

• Ballard, Dana H. and Christopher M. Brown (1982) Computer Vision, Prentice-Hall, Englewood Cliffs NJ (ISBN 0-13-165316-4).
• Davies, E.R. (1997) Machine Vision: Theory, Algorithm, Practicalities (2nd edition), Academic Press, San Diego. (ISBN: 0-12-206092-X)
• Haralick, Robert M. and Linda G. Shapiro (1992-93) Computer and Robot Vision (2 volumes), Addison-Wesley, Reading MA (ISBN 0-201-10877-1 and 0-201-56943-4).
• Horn, Berthold K.P. (1986) Robot Vision, MIT Press, Cambridge MA (ISBN 0-262-08159-8).
• Jain, Ramesh, Rangachar Kasturi, and Brian G. Schunck (1995) McGraw-Hill, New York (ISBN 0-07-032018-7).