Computer Vision is a field of research that studies the capacity of computers to interpret and extract meaningful information from visual data, such as photographs, movies, and real-world events. This is in contrast to the human ability to comprehend and contextualise visual information through past experiences. To bridge this gap, cutting-edge technologies such as Artificial Intelligence (AI), Neural Networks, Deep Learning (DL), Parallel Computing, and Machine Learning (ML) have been developed, allowing computers to construct meaning from visual data.
In this blog post, we will delve deeper into the concept of computer vision, exploring the algorithms that form its foundation, the various approaches it can take, and other related topics.
Seeing how computer vision works
In order to determine if a picture contains pertinent information, a computer utilises a process of computer vision algorithms which examines the individual pixels of the image by multiplying neighbouring pixels with a kernel or philtre. This process is known as analysing the RGB value of each pixel, and is different from how humans perceive the entire image at once. By taking into account all the components of the image, such as colours, shadows, and line drawings, the computer is able to differentiate and detect the picture in detail.
We are currently utilising convolutional neural networks (CNNs) to model and train our system. CNNs are highly specialised neural networks designed for image recognition and processing; they are equipped with a convolutional layer that contains multiple neurons and tensors that are responsible for analysing pixel input. Through this process, the model is able to adjust its values to differentiate among classes when presented with a massive dataset. In order to ensure the model is properly trained, it must undergo intensive training.
Computers may be taught to recognise patterns by being presented with a large number of tagged pictures.
In order to build a profile of a lion, a computer must be able to extract data from a set of images, such as a million photographs of a lion. This data should include information about colour, form, distance between shapes, boundaries, and other features. With this knowledge, the computer can then be used to determine if an unlabeled picture is that of a lion.
For clarity, allow me to describe a situation that really occurred.
Progress in computer vision
Prior to the emergence of deep learning, the capabilities of computer vision were severely limited, requiring a great deal of human input and monitoring. For instance, when seeking to utilise face recognition, the process was extremely labour-intensive and typically involved a variety of manual steps.
- Commit the Information to memory: Create a database that includes the photos of all the people we’re keeping tabs on.
- The Annotation of images: It is important to maintain the distinctive features of each individual’s facial structure, such as the distance between the lips and the nose, the width of the face, the length of the face, and the length of the eyes. These key characteristics are unique to every person and should be respected and preserved.
- Incorporate learnings into a fresh round of pictures: Annotate a fresh set of photographs once again and examine the differences between them.
The utilisation of machine learning has enabled us to recognise and address challenges that were previously unsolvable in computer vision. Therefore, developers no longer need to painstakingly include each possible rule into their vision programs. Features are miniature, specialised algorithms which can identify exclusive patterns in pictures. An analytic mathematics learning approach, such as k-means or logistic regression, is used to categorise photographs and detect objects.
Deep learning is an innovative approach to machine learning that has revolutionised the field. Neural networks are the foundation of this method, as they are a universal algorithm capable of solving any problem that can be represented by examples. Neural networks are trained over time by providing them with repetitively labelled samples of relevant data; this allows them to make generalisations about the wider context of the data, and they can then use this information to accurately classify new data as it becomes available.
For example, the development of a face recognition software utilising Deep Learning (DL) requires the construction or selection of a pre-defined algorithmic rule, as well as training it using examples of the faces of the persons it should identify. With a sufficient number of samples, the neural network is able to detect faces without any additional constraints or measures.
Computer vision’s practical uses
Nowadays, many computer vision applications rely heavily on deep learning technology, extending to areas such as facial recognition, self-driving vehicles, and even cancer detection. This technology has enabled us to make significant advances in these fields, and its continued development promises to bring even more solutions to the world.
Face recognition algorithms heavily rely on computer vision’s ability to detect patterns. After analysing the collected data, the algorithm can make suggestions for changes or take some form of action.
Autonomous vehicles can use computer vision to better understand their surroundings by constructing 3D models from live video feeds. The vehicles are equipped with multiple cameras to capture footage which is then processed by computer vision algorithms. This data is used to detect the end of roads, read traffic signs, and identify other vehicles, obstacles, and pedestrians. This enables the vehicle to autonomously navigate through streets and highways, detecting and avoiding any potential obstructions.
The application of computer vision in healthcare is becoming increasingly popular as a way of improving clinical decision-making. One example of this is the use of radiologic imaging and analysis which produces images of targeted organs or tissues to enable accurate diagnosis.
Real-world environments with virtual enhancements (AR)
Augmented Reality (AR) is a technology that uses computer vision to integrate digital content into physical environments. This is achieved by utilising the camera on a device, such as a smartphone, to identify physical objects in the environment and to process the data. As an example, a smartphone’s camera can be used to calculate the height of a table. With AR, we can create a more immersive experience by combining the physical world with digital content.
Imaging at a Super-Resolution (SR)
Enhance the clarity of your photos with Super-Resolution (SR) methods. There are several techniques available, 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 have already been pre-trained, making them easy to download and use.
Due to the fact that the model assigns various interpretations to the numerous low-resolution images in the context of super-resolution imaging, each image is treated as if it were completely distinct. After the model has determined the disparities between the photographs, it will start generating a continuous flow of much higher quality images.
Recognising text with a camera (OCR)
Optical Character Recognition (OCR) is a process used to extract text from scanned documents, images, or image-only PDFs. Utilising various thresholding and contouring methods, OCR is able to recognise characters and organise them into readable sentences and phrases. Many libraries have software available for this purpose, with OpenCV being a popular choice.
Digitalizing text, scanning passports for automated check-in, analysing consumer data, etc. all benefit greatly from OCR technology’s widespread use.
Applications of computer vision techniques
Computer Vision encompasses a broad range of techniques, including semantic segmentation, object localization, object recognition, instance segmentation, and many more. These techniques can be used to perform a variety of tasks, such as determining an object’s velocity in a video, constructing a three-dimensional model of a user-defined environment, or eliminating distracting noise from an image, such as blurring.
Semantic segmentation is a method of classifying pixels into distinct categories and labelling them accordingly. This technique can be used to identify which pixels in an image belong to a certain type of object. For example, it can be used to identify whether a certain pixel is part of a picture of a cat or a dog, and the label of the image (cat or dog) can then be revealed.
In order to localise an image, each object within it is assigned a descriptive name that can assist in locating it. Once the desired object is identified, a box is drawn around it, providing a reference point to be used as a standard for identifying other objects.
Object detection is a method of locating representations of tangible entities, such as people, bicycles, and buildings, in digital media. This technique can be employed with the use of learning methods in order to track down individual instances of these items.
After adhering to the preceding steps, the process of instance segmentation is employed to identify individual instances in an image, assigning labels to the corresponding pixels accordingly. This method of image segmentation has become increasingly popular in many industries, such as autonomous vehicles, smart agriculture, and medical imaging.
This article aimed to provide a comprehensive overview of computer vision, outlining its principles, methods, and applications. Despite the significant progress made in this field, there are still a number of challenges that must be addressed in order to make further progress, such as dealing with poor data quality, technological constraints, and optimising deep learning models. Fortunately, the future of computer vision is looking very promising due to increasing demand, ongoing research, and new technological advancements.