Computer Vision (CV) is a subfield of Artificial Intelligence (AI) that creates sight for machines. By analyzing a series of images, developers can attempt to mimic the human’s visual cognitive systems using neural networks and Deep Learning (DL). 

With sight enabled, machines are capable of an increased number of relevant applications, including self-driving cars, camera filters, and security surveillance systems. However, this also brings many ethical concerns due to an overabundance of cameras in our digital world. So, what is Computer Vision and how does it work?

Understanding the Basics of Computer Vision Techniques

Deep and Machine Learning (ML) are behind many advances in Computer Vision, making it possible to design more robust vision systems. By applying Neural Networks, machines are able to extract information from visual features in many ways:

• Image recognition: The most common CV task, machines are tasked with identifying objects or features within an image. 

• Object detection: Taking image recognition one step further, algorithms must also identify the size and location of an object within an image. 

• Image segmentation: Breaking down an image into sections or regions, helping machines to distinguish different parts of an image. 

• Motion analysis: Applying image recognition to video, allowing algorithms to develop better object tracking. 

Key Computer Vision Techniques

Looking more closely, CV employs a number of advanced techniques to help bring vision to machines. New developments are easily applicable to different industries as well, making innovation in CV some of the most exciting.

• Edge Detection: Fundamental to image processing, edge detection is when a machine must identify points in an object that signal the edge of an object by analyzing pixel brightness. The technique is crucial for medical imaging and autonomous driving. 

• Color Analysis: Processing images to gather quantitative information about the color content, converting color spaces to simplify feature extraction. This technique is commonly found in digital editing and surveillance. 

• Scale-Invariant Feature Transform (SIFT): An algorithm that extracts key points from an image that can be used to find matching key points in other images, contributing greatly to 3D modeling when changing an image’s scale.  

• Convolutional Neural Networks (CNN): A deep learning model that is used to analyze grid-like visual data to detect patterns. CNNs are commonly used as the backbone for most image recognition and object detection systems. 

Emerging Trends in Computer Vision Techniques

Deep Learning is helping to revolutionize the field of Computer Vision by replacing the need for manual feature engineering. This allows human developers to take a step back from the algorithm, giving it the space to process, analyze, and learn directly with the given image data. As a DL model, this means that the machine can process images at a faster rate because it is less handicapped by human intervention.

General Adversarial Networks (GANs) are also helping to innovate computer vision. GANs, which consist of two components - a generator and a discriminator - compete against each other to create the best results. In Computer Vision, GANs can help with image inpainting, style transfers, and super-resolutions. 

Reinforcement Learning is another subset of AI that uses CV to help agents understand the world they are put into. By using CV, agents can learn how to visualize their environment, leading to more optimal learning. 

Challenges and Future Opportunities in Computer Vision Techniques

As innovative as Computer Vision is, there are still a number of challenges that need to be solved including ethical concerns over privacy. CV has the potential to vastly improve surveillance networks around the world with face recognition. However, this level of recording people who are not aware they are being monitored and have not consented to it poses major legal problems for governments and corporations that use the technology in bad faith. 

Computer Vision also poses a risk towards deep fakes - artificially generated content using real people - and their inherent risk to fragile societies that may not be fully aware of AI’s use which can lead to spurious misrepresentation and libel disputes across the world. 

Technological obstacles also exist, with many new systems becoming resource-heavy and depending on increasingly expensive hardware to operate when attempting to analyze real-world or real-time information that may include variables like weather and natural lighting that are unlike simulated conditions made in controlled training environments. 

Despite its challenges though, Computer Vision is ripe with innovation and is finding numerous applications in our everyday lives. Edge computing like smart and IoT devices are some of the most notable examples of how CV is integrating into consumer products, but we can also expect to see more development in explainable AI as well with CV’s growing ethical concerns.