Computer Vision: turning images into information

Thanks to advances in artificial intelligence, machine learning and deep learning technologies, computer vision has evolved by leaps and bounds, revolutionizing many industries with its ability to interpret and understand visual data. The computer vision market is set for double-digit growth (between 10 and 20%, depending on statistical sources) between 2024 and 2030. But what is Computer Vision, and what are the challenges involved in getting to grips with this exciting technology?

Computer Vision in a nutshell

Computer Vision is a field of artificial intelligence that enables machines to understand and interpret visual data contained in images or videos. The processing and analysis of relevant information from these data is carried out by sophisticated algorithms integrated into the software and hardware of computer vision systems.

This discipline has progressed considerably since the development of the first computer image digitization technologies in the early 60s. From simple image analysis, computer vision has evolved to handle extremely complex scenes. In some fields, such as industry and healthcare, it can match or even surpass human visual analysis capabilities.

Applications and Impact of Computer Vision in the Modern Technological Landscape

Computer Vision covers all fields of application involving the analysis of visual content and requiring software to understand the pixels that make up the image.
In particular, it enables the following tasks to be carried out:

• Classification of objects according to their visual characteristics, such as edges, textures, shapes and colors.
• Detecting and tracking the movement of objects or individuals, a complex process involving differentiation between static and moving parts, as well as determining the direction, speed and trajectory of moving objects.
• Image segmentation to facilitate image analysis and understanding, or to enable 3D reconstruction of a scene.
• Image optimization to eliminate noise, restore blurred or low-resolution images or fill in missing parts.

Each task requires the development of specific algorithms, ranging from relatively simple mathematical methods to complex sets of machine learning and deep learning algorithms.

Computer vision occupies an essential place in the modern technological landscape. This discipline is already used in a wide variety of sectors. For example, it is transforming industries by automating tedious tasks, thereby increasing efficiency, quality and productivity. It enhances safety by enabling the creation of ever more efficient and reliable driver assistance systems (ADAS). In the healthcare sector, it contributes to the diagnosis, monitoring and treatment of diseases.

Key stages of Computer Vision

Computer Vision comprises three key processes:

1. Capturing visual data: this process, which relies on different sensor technologies, combines the data acquisition stages, transforming real-world images into digital formats, and pre-processing, which refines this data to ensure uniformity and improve image quality for subsequent analysis.
2. Image processing and analysis: this stage involves identifying certain characteristics of the images in order to recognize specific patterns or objects.
3. Machine learning: this process improves the ability of systems to interpret and interact with visual data. The emergence of deep learning-based methods, such as convolutional neural networks (CNNs), has greatly contributed to major advances in the realization of complex tasks such as object recognition, semantic segmentation and face detection, often surpassing traditional methods in terms of accuracy and robustness. These advanced techniques are distinguished by their ability to learn hierarchical representations of visual data from large sets of collected and annotated data.

Challenges and Points of Vigilance in the Computer Vision Domain

However, technological and ethical challenges remain in the field of computer vision.

Technological point of view

• Quality of visual data

Image clarity and condition are key parameters influencing system accuracy. Light variability and the intrinsic quality of the sensors used can interfere with the interpretation of visual data, producing low-quality images. To minimize these effects, pre-processing techniques such as normalization, denoising and contrast adjustment must be applied to facilitate data interpretation by the computer vision system.

Another difficulty lies in managing depth of field and perspective variability, requiring the integration of sophisticated, flexible algorithms. Partially hidden objects can also distort scene interpretation. Training models to deal with object occlusion and using techniques such as RPCA (Robust Principal Component Analysis) can improve object recognition even when they are partially hidden.

Generally speaking, guaranteeing data quality throughout the acquisition and pre-processing stages is essential for developing reliable and accurate machine learning models.

• Hardware computing capacity

The quality of the hardware platform supporting computer vision systems is also a key factor. The computational complexity involved in processing large volumes of visual data and integrating computer vision with other technologies poses significant challenges for hardware systems. Advanced algorithms such as YOLO and Faster R-CNN require robust hardware capable of processing real-time data and handling high-resolution imagery. In particular, the use of GPUs (graphics processing units) and suitable specialized processors (such as NPUs: neural processing units) can speed up data processing and analysis.

Ethical point of view

It is vital to develop and implement robust ethical and regulatory frameworks for the use of computer vision. This includes implementing privacy protections, reducing algorithmic biases (which can result from data that is not representative of the population’s diversity), improving transparency and accountability, and preventing malicious uses of the technology.