Open CV and Deep Learning – The Perfect Combination
Computer vision is one of the most intriguing and perplexing areas in AI. We have entered a new era of intelligence as a result of the exponential growth of data, most of it is in the form of pictures and movies. The rapid velocity at which humans produce visual data is altering how observational science is practised. The creation of automated techniques that can sift through the many visual datasets and generate valuable insights is necessary to advance this field. Given the sensitivity needed to examine pictures, typical methods of analysis are impractical when faced with an overwhelming flow of visuals.
Computer Vision is crucial in identifying insightful patterns in picture data when it comes to the possibilities of harnessing image data. It is a technique that addresses a wide range of issues that might arise when dealing with ambiguity, leading to the production of more useful data, interpretable outcomes, and varied insights. The world of visuals is made up of a variety of forms that interact with one another in a space that is surrounded by other items, including colours, shadows, illuminations, textures, and objects. The complexity and sophistication of effective modelling are borrowed from machine learning in an effort to control the opacity of the visual environment. What do you perceive when you consider the well-known Rabbit-Duck illusion? A duck? maybe a rabbit? Reasoning might be difficult because of the abundance of ambiguity in the visible world. Uncertainty in visual data can be caused by similar species, distorted lighting, colours, similarities between objects, and partial occlusion, among other things. It transforms what seemed to be a straightforward issue into a challenging one.
Figure 1. What do you see? A Duck? A Rabbit? (Source: Wikipedia R-D illusion)
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The goal is to teach computers to carry out difficult visual cognitive tasks as effectively as people. Even if an image is presented in an unusual or extraordinary way, a machine may rapidly learn to recognise the image without necessarily understanding its surroundings or context. A computer needs training in order to comprehend a picture in the same way that a person would in terms of its physicality.
Machine Learning and Computer Vision
Computer Vision (CV) and Machine learning are both mutually inclusive. CV is a process that enables machines to understand videos and images, how to store, manipulate, and retrieve information from them. Combining it with Deep Learning has provided developers with automated methods to train and deploy image processing algorithms for detecting patterns and recognizing objects.
Figure 2. Human Vision Vs Computer Vision (Source: Mobidev.biz)
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The combination of deep learning, neural networks, and computer vision is said to have exceeded the capability of human vision by replicating a more intelligent human visual cognitive system. When computer vision or machine learning are leveraged, they aid in generating diverse solutions capable of estimating rich insights from mountains of cryptic and multi-dimensional data. Its power can be observed across diverse industries, for example: in health care, automated processing of medical images such as CT scans detected neurological illness at a faster rate than the radiologists. In self-driving cars, if a car is integrated with computer vision, it is capable of responding on the road by identifying objects, processing data, while making swift decisions on how to react in case of an anomaly. Facial recognition technology has opened opportunities for security including financial transactions, security at airports, police checkpoints, etc. The industry of agriculture and manufacturing can gain valuable inputs through this technology in recognizing the quality of yield, identifying areas of crop fields that need maintenance, identifying defective products, etc. Geographic or Geospatial data have provided crime rate insights, disaster predictions, etc. Computer vision systems are applied in countless ways to benefit every aspect of business, industry, and society.
Figure 3. Computer Vision Use Cases
There are many diverse and distinctive visual domains, such as image classification, face recognition, object detection, semantic segmentation, posture estimation, video tracking, picture restoration, and others. It takes specialised models and methods that take into account the traits and traits of the visual source domains to achieve amazing results. These unthinkable possibilities are definitely attainable using computer vision and deep learning. As visual beings, humans process visual information through a combination of sensor controls, including "hardware sensors" (the eyes), which mechanically control light and tune the reception of image in the retina, as well as by allowing the brain to draw conclusions from experience and cross associations of years spent living in the world. By combining a number of machine learning algorithms with hardware components, computer vision processes pictures and classifies, recognises, reacts to, labels, and evaluates things in order to obtain deeper insights and produce outcomes.
Figure 4. Machine Learning in training Computer Vision (Source: HQSoftware Lab
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Technologists have developed a wide range of capabilities such as tools and software libraries to facilitate and power various computer vision applications and initiatives. The ‘library’ provides developers, data scientists, and technologists with a set of mathematical functions to build and train neural networks for deep learning and apply them in computer vision applications. Think of it as a toolbox of functions, various programs, and a framework to support image processing. It is simply a collection of accessible resources to gain effective and efficient visual inferences. It provides a platform for building and executing image processing algorithms. The tools facilitate an environment for connecting various software, services, and technologies of computer vision that are openly accessible to a community of developers and CV enthusiasts. Click here to learn Artificial Intelligence Training
Figure 5. Basic Structure of OpenCV Library (Source: Gary B & Adrian K, Learning OpenCV)
OpenCV and Visual Computing
OpenCV, or the Open-Source Computer Vision Library, is one of several libraries. Intel developed a sizable image processing library. It includes a variety of interfaces and tools for building and deploying image processing models. The purpose of OpenCV is to create an intuitive computer vision infrastructure that may allow us to construct complex vision applications quickly and with agility. Computer vision is enabling mankind with unique insights. OpenCV's use cases are numerous; here are just a handful of them: preparing visual data by performing operations such noise reduction, scaling, and augmentation. Create state-of-the-art computer vision and machine learning models. Real-time monitoring and intelligent video analytics. The following operations make up the Computer Vision (CV) section of the OpenCV Library.
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