Open CV and Deep Learning – The Perfect Combination

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

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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.