Computer Vision delves into the study of computers’ ability to interpret and extract valuable insights from visual data, including real-world events, movies, and photographs. This differs from human capability, as we contextualise and understand visual information based on past experiences. To narrow this gap, advanced technologies like Artificial Intelligence (AI), Neural Networks, Deep Learning (DL), Parallel Computing, and Machine Learning (ML), have been developed and integrated to enable computers to infer context from visual data.
This blog post aims to provide an in-depth understanding of computer vision by examining its underlying algorithms, diverse approaches, and other relevant topics.
Visualizing the workings of computer vision
To determine the presence of relevant information in an image, computer vision utilises a set of algorithms which scrutinize each pixel of the image by multiplying adjacent pixels with a kernel or filter. This process involves analysing the RGB value of every pixel, which is distinct from how humans perceive the whole image at once. By considering all aspects of the image like colours, shadows, and line drawings, the computer can differentiate and recognise details in the picture.
Our current system is modelled and trained using convolutional neural networks (CNNs), which are highly specialised neural networks that are ideal for image processing and recognition. These networks have a convolutional layer that includes several neurons and tensors that assess pixel input. The model adjusts its values to distinguish between classes when presented with substantial data through this process. It is crucial to subject the model to extensive training to guarantee proper training.
By presenting computers with a considerable number of labelled pictures, they can be trained to identify patterns.
To create a lion’s profile, a computer needs to extract information from tens of thousands of images, such as a million photographs of a lion. This information encompasses features like colour, shape, edges, distances between shapes, boundaries, among others. Having absorbed this information, the computer can then identify an untagged picture as a lion or not.
To provide clear understanding, let me narrate an actual instance.
Advancements in Computer Vision
Before the advent of deep learning, computer vision had restricted functionality and needed a significant degree of human observation and input. For instance, when utilising face recognition, it was an extremely labour-intensive process, involving several manual steps.
Memorise the Information:Develop a database that consists of photos of all the individuals we are monitoring.
Annotation of Images:Preserving the unique features of an individual’s facial structure, like the distance between the nose and lips, the width and length of the face, and the size of the eyes, among others, is crucial. These distinct characteristics set individuals apart from each other and should be carefully maintained.
Integrating learnings into a new series of images:Repeat the process of annotating a new batch of photographs and analyze the differences between them.
Thanks to the use of machine learning, we can now resolve challenges that were previously unsolvable in computer vision. Developers no longer need to include every possible rule, thanks to features which are miniature, specialised algorithms that can detect unique patterns in pictures. Through mathematical methods for analysis and learning, such as k-means or logistic regression, images are grouped and objects are identified.
Deep learning, an advanced machine learning approach, has transformed the field. Neural networks are central to this method, as they are a universal algorithm designed to solve problems that can be presented through examples. Neural networks undergo training over time, using repeatedly tagged appropriate data samples which enable them to make inferences about the broader context of the data. Utilizing this information, they can accurately classify new data as it becomes available.
For instance, the process of developing a face recognition software using Deep Learning (DL) involves creating or selecting a pre-existing algorithmic rule and then training it with instances of the faces it is supposed to recognise. Given enough samples, the neural network can identify faces without the need for extra constraints or measures. To learn more about DevOps and why you should learn it in 2022, click on this link.
Practical Applications of Computer Vision
Currently, several computer vision applications extensively use deep learning technology in areas like facial recognition, self-driving cars, and cancer detection. These innovations have helped us make substantial progress in these fields, and with further development, this technology holds the potential to provide even more solutions to the world.
Facial recognition algorithms depend on computer vision’s pattern detection capabilities. By analyzing the gathered data, the algorithm can propose modifications or take relevant action based on the results.
Computer vision enables self-driving vehicles to gain a better understanding of their surroundings by developing 3D models from real-time video feeds. The cars are fitted with multiple cameras that capture footage, which computer vision algorithms process. This data is used to identify the end of roads, read traffic signs, spot other vehicles, roadside obstructions, and pedestrians. Such insights allow the vehicles to traverse streets and highways autonomously, detecting and avoiding potential obstacles.
The healthcare sector is increasingly using computer vision to enhance clinical decision-making. Radiologic imaging and analysis is one such example, which generates images of specific organs or tissues to facilitate accurate diagnoses.
Augmented Reality (AR): Enhancing Real-World Environments
Augmented Reality (AR) is a computer vision technology that superimposes digital content on the physical environment. The technology leverages a device’s camera, such as a smartphone, to recognize and process data about objects in the environment. For instance, an AR-powered smartphone camera can measure the height of a table. Combining the physical world and digital content through AR offers a more immersive experience.
Super-Resolution (SR) Imaging
Augment photo clarity using Super-Resolution (SR) techniques. You can choose from several techniques, such as the Enhanced Deep Super-Resolution Network (EDSR), the Efficient Sub-Pixel Convolutional Neural Network (ESPCNN), the Fast Super-Resolution Convolutional Neural Network (FSRCNN), and the Laplacian Pyramid Super-Resolution Network (LapSRN). These models are pre-trained and easy to download and use.
In super-resolution imaging, the model evaluates each low-resolution image individually and associates it with diverse interpretations. Once the model has identified the differences between the images, it produces an uninterrupted stream of higher quality images.
Optical Character Recognition (OCR): Camera-Based Text Recognition
Optical Character Recognition (OCR) is a technique that extracts text from scanned documents, images, or PDFs containing image-only pages. By employing multiple thresholding and contouring techniques, OCR recognizes characters and assembles them into legible sentences and phrases. Several libraries offer software for this, with OpenCV being a widely used option.
OCR technology has many applications, including digitizing text, scanning passports for automated check-in, and analyzing consumer data. Its widespread use offers significant benefits.
Utilizing Computer Vision Techniques: Applications
Computer Vision techniques include semantic segmentation, object localization, object recognition, instance segmentation, and many others. These techniques can be applied to an array of tasks, like calculating object acceleration rate in videos, building three-dimensional models of user-defined environments, or removing image noise, such as blurring.
Semantic segmentation is a technique that classifies pixels into different categories and assigns corresponding labels. This method identifies which pixels in an image belong to a specific object type. For instance, it can distinguish whether a pixel is part of a cat or a dog image, and subsequently, the image label (cat or dog) can be displayed.
To localize an image, each object within it is attributed a descriptive label that aids in locating it. Once the specific object is identified, a box is drawn around it, serving as a reference point to identify other objects.
Object detection is a technique to detect tangible objects like people, bicycles, and buildings, in digital media. This method can be combined with learning algorithms to track down individual occurrences of these objects.
Subsequent to completing the prior steps, the instance segmentation process is utilized to recognize separate instances in an image and label respective pixels accordingly. This image segmentation technique is gaining popularity in numerous industries, like self-driving cars, smart farming, and medical imaging.
The article aimed to offer a comprehensive summary of Computer Vision, highlighting its principles, techniques, and applications. Despite the significant advancements made in this field, it still has some challenges to overcome, such as addressing poor data quality, technological limitations, and optimizing deep learning models. However, the growing demand, continued research, and new technological developments point to a promising future for Computer Vision.