AI Computer vision is transforming the way we live and work. In the last decade, companies have explored and shaped how artificial intelligence (AI) can be leveraged to transform and enhance daily operations. Having a clear strategy to implement AI can support the achievement of operational excellence, the generation of data-driven insights, the fostering of customer-centric approaches, the promotion of enterprise-wide innovation and agility, and the reinforcement of ethical and risk management functions. As AI continuously evolves to meet more advanced business requirements, a myriad of technologies have emerged and have completely transformed the digital landscape.
Amongst these technologies, computer vision has become increasingly significant and performant, enabling modern machines to capture, to interpret, to understand, and to make decisions based on visual data. In simpler terms, computer vision grants machines the ability to respond to visual information, much like the power of sight.
What is computer vision?
Computer vision is a rapidly growing field in artificial intelligence that focuses on enabling computers to interpret and understand the visual world. By using algorithms and machine learning techniques, computer vision aims to give machines the ability to process and analyze visual information, such as images and videos. This technology is used in a wide range of applications, including facial recognition, autonomous vehicles, medical imaging, and augmented reality.
Importance of computer vision in the field of AI
Computer vision plays a crucial role in the field of AI by enabling machines to understand and interpret visual information, similar to the way humans do. This technology is powered by AI and machine learning algorithms that allow computers to analyze, process, and make sense of visual data. The significance of AI and machine learning in enabling computer vision cannot be overstated, as they enable machines to learn from and improve their ability to understand visual data over time.
The industry of computer vision is rapidly growing, with applications in various fields such as healthcare, automotive, retail, and security. The need for computers to understand the human environment through visual data processing is driving this growth, as it enables machines to perceive and recognize objects, people, and environments.
The potential applications of computer vision technology are vast, including image recognition, video analytics, autonomous vehicles, augmented reality, and medical imaging. As the technology continues to advance, computer vision is poised to transform various industries and create new opportunities for innovation and advancement.
Object detection and visual recognition
In the field of computer vision, object detection and recognition play a crucial role in identifying and localizing objects within images or video. This technology is widely used in various applications, including industrial processes and connected home camera systems. Object detection is essential for automating tasks in manufacturing, enabling robots to identify and handle items with precision and efficiency.
Visual recognition systems are particularly important in manufacturing, where accuracy and speed are critical. By using object detection and recognition, robots can effectively locate and manipulate objects, optimizing production processes.
In contrast, image recognition systems go beyond simple object detection by interpreting and understanding the content of images. Examples of this technology in action include autonomous cars identifying traffic signs and obstacles, as well as smartphone apps recognizing and sorting images. Such systems enable machines to make critical decisions based on visual input, enhancing safety and performance in various scenarios.
Understanding digital images
Digital images in computer vision are processed using various techniques to extract useful information. Image segmentation involves dividing an image into multiple segments to simplify and analyze the image. Object detection is used to locate and classify objects within an image, while facial recognition focuses on identifying and verifying a person's face within an image. Pattern detection is utilized to identify recurring patterns or shapes within an image.
Image-understanding systems operate at three levels of abstraction: low-level, mid-level, and high-level. Low-level processing involves basic image features such as edges and textures, while mid-level processing focuses on grouping and organizing these features into meaningful objects or regions. High-level processing incorporates semantic understanding and reasoning, allowing the system to interpret and comprehend the content of the image.
Representational requirements for image-understanding systems include the ability to encode and store visual information, while inference/control requirements involve the system's capacity to reason, make decisions, and take actions based on the processed image data. These systems rely on complex algorithms and machine learning techniques to achieve accurate and efficient image processing.
How computer vision enhances business operations
Computer Vision adoption can be leveraged to enable a multitude of benefits and can support the shaping of business operations, including:
- Efficiency through automation: Standardizing and streamlining key business processes while reducing manual intervention.
- Enhanced accuracy: Performing tedious tasks requiring a high-level of precision.
- Improved security: Bettering access control, identity verification, and surveillance solutions.
- Enabled real-time decision making: Making decisions based on the environment and surroundings.
- Personalized and improved user experiences: Creating personalized customer experiences and augmenting reality.
These benefits will considerably reduce the need for manual intervention in repetitive and time-consuming tasks, allowing for labor to upskill and focus on high value-added tasks.
Computer vision applications
Computer vision has revolutionized the way we interact with modern computer systems, enabling their answer to the visual world. Its applications know no boundaries and span across:
- Image and video analysis: object recognition, image classification, and video tracking
- Medical imaging: diagnosis and analysis of medical images like X-rays, MRIs, and CT scans
- Autonomous vehicles: visual perception for self-driving cars and drones
- Facial recognition: biometric security, emotion detection, and identity verification
- Augmented reality (AR) and virtual reality (VR): enhancing real-world experiences with computer-generated information
- Industrial automation: quality control, defect detection, and process optimization in manufacturing
- Retail: shelf monitoring, customer tracking, and cashier-less checkout systems
- Security and surveillance: intrusion detection, crowd monitoring, and anomaly detection
- Agriculture: crop monitoring, disease detection, and yield estimation
- Document processing: document classification and sorting, enhanced search and retrieval, document layout understanding, and enhanced data extraction while minimizing risk of error
Computer vision market growth
The global computer vision market is on the rise and continues to grow: according to a report by Markets and Markets, the market size for computer vision in 2023 reached USD 17.2 billion, and it is expected to exceed USD 45 billion by 2028. Key players in Asia, such as China and India, are expected to lead this growth to meet increasing global demand for greater efficiency through automation, particularly in the manufacturing, agricultural, and national security sectors.
Computer vision key innovators
Key technology organizations with a share of the American computer vision market include NVIDIA, Intel, Microsoft, Alphabet, and Amazon. These companies continuously launch new products and new solutions in this space, but also rely on strategic partnerships and collaboration with large corporations with a need for AI and computer vision. This strategy allows them to better understand evolving business requirements and to innovate accordingly.
How can you approach computer vision implementation?
Implementing computer vision is an iterative process, and ongoing monitoring, testing, and refinement is often necessary to adapt to the changing conditions and requirements. As an initial playbook, the following steps can be followed to successfully implement innovative computer vision solutions in your operations.
- Define objectives: Align on the desired goals and objectives your organization wants to achieve through leveraging computer vision. It is important that the business and technical requirements of the solution, along with the expected performance metrics, are also clearly defined to meet these goals and objectives.
- Data collection: Gather tagged data for training and testing of computer vision solutions. This is an integral part of achieving your goals and objectives, as the solution will be increasingly performant as more quality data is provided for training and testing.
- Preprocessing: Enhance and complement data through curation to augment robustness of the training.
- Model selection: Design or select a model that suits your business and technical requirements, leveraging off-the-shelf solutions, or partially/completely design your own solution.
- Model training: Configure or fine-tune the selected solution, leveraging the tagged dataset collected and curated.
- Validation and testing: Evaluate the solution’s performance on additional datasets to ensure that it meets all requirements and satisfactory performance results.
- Integration: Integrate the trained model into your company's processes - this can involve incorporating it into existing software or developing new applications to be leveraged by end-users.
- Deployment: Deploy the solution in production, ensuring it meets the performance results and is reliable.
- Monitoring and maintenance: Continuously monitor the deployed solution and its performance metrics in order to update it as needed. If issues arise, responsibilities and a clear process need to be defined to identify and remediate the issue.
- Scaling and optimizing: Consider scaling the solution to additional applications, assuming that the initial implementation is successful and consistently meets the expected performance requirements.
Next steps for computer vision innovation
Computer vision has revolutionized modern systems, providing them with the ability to perceive and understand our visual world. With rising technology applicability and organizations’ awareness of it, one can only expect further development in the space, including:
- 3D computer vision: Capturing and interpreting 3D information from images and videos. This will generate significant improvement in augmented reality and autonomous vehicle applications.
- Semantic segmentation: Classifying each pixel in an image to improve labeling and analysis. This process will improve autonomous vehicle navigation and medical imaging analysis.
- Generative adversarial networks (GANs): Combining GANs with computer vision aims to enable systems to learn patterns from the visual data provided, and to enhance a wide range of solutions through improved image resolution or generating content based on learned patterns for training purposes.
- Few-shot learning: Improving the performance of computer vision models, even if trained on small amounts of labeled data. This will be particularly useful when models are being developed without the availability of a large training dataset.
- Continual/zero-shot learning: Enabling continuous learning and model adaptability to new information for which the solution was not explicitly trained for will significantly improve models implemented in a dynamic and diverse environment.
- Responsible AI: Identifying and mitigating risks of robustness, security, fairness, explainability, transparency, and accountability when developing, implementing, and monitoring computer vision solutions.
Application highlight: intelligent document processing
Intelligent document processing is a great example that highlights the impact of computer vision in an organization’s key business processes. Across all industries, large volumes of documents are still captured and processed to extract key data. The critical information in these documents can be conveyed as machine-based or handwritten text and can be structured and/or unstructured.
Computer vision technology is an integral part of intelligent document processing and can unlock significant improvements in document-centric business processes.
Computer vision in intelligent document processing
By implementing computer vision technologies in intelligent document processing, organizations can achieve a more robust and versatile solution for automating document workflows, extracting information, and gaining deeper insights from both textual and visual content.
Enhance data extraction
Intelligent document processing aims to extract structured and unstructured data from documents for which the accuracy can be enhanced, leveraging computer vision through the understanding of visual context, layout, and the structure of documents. Information can be quickly and consistently extracted from text fields, tables, charts, and images to provide a holistic view of document content.
Improve document analysis
Intelligent document processing focuses on understanding the semantic meaning of the information captured in the documents, and computer vision enables the processing of visual elements (e.g., images, logos, signatures) supporting a comprehensive analysis of the documents.
Handle and adapt to document variability
Intelligent document processing is designed to account for various document formats and structures, leveraging computer vision technology to recognize and to interpret diverse visual elements. The result is the ability of the intelligent document processing solution to accommodate several variations of document layouts and designs.
Intelligent document processing aims to standardize and enhance workflows through automation and the reduction of manual tasks, and computer vision plays an integral role by automating visual analysis to trigger key processes (e.g., classification) and inform decision-making.
Enforce security and compliance
Intelligent document processing supports security and compliance efforts by performing document checks and detecting anomalies in the content. Computer vision enhances these checks by analyzing visual features and identifying irregularities.
Augment user experiences
The objective of intelligent document processing is to improve user efficiency through the visual representations and insights generated via computer vision technologies. This process contributes to a more intuitive understanding of document content without the need for time-intensive consumption and analysis.
Computer vision represents a profound shift in the technological landscape. Over the last decade, companies have begun to harness its transformative potential to achieve operational excellence, data-driven insight, customer-centricity, innovation acceleration, and ethical risk management. Its core function lies in its capacity to automate repetitive tasks and free up human resources, and its applications drive market growth and efficiency in multiple sectors.
The global computer vision market is rapidly expanding, and the future promises continued innovation that will deliver even greater impacts, particularly in respect to intelligent document processing. Computer vision delivers substantial improvements in automating workflows and extracting insights from textual and visual content, and organizations that embrace it will better position themselves for success.