Wondering how the surveillance systems are able to identify people or how the different hurdles are verified by the autonomous vehicles? Well, Image Recognition is the technology behind such miracles. Equipped to identify various elements, including places, logos, people, objects, buildings, and several other variables in images, Image Recognition is what drives the autonomy of numerous industries.
Sprouted from the leading-edge technologies like Artificial Intelligence and Machine Learning and being a part of Computer Vision, Image Recognition is all about identifying and detecting objects or attributes in digital videos or images. It employs the large volume of digital data shared by users through different mediums like apps, social networks, and websites to deliver better and more innovative services to people accessing it.
As per the reports presented by Markets and Markets, the Image Recognition market valued at USD 26.2 billion in 2020 is expected to reach the value of USD 53.0 billion by the end of 2025. The market is expected to increase with a CAGR of 15.1%, owing to its active adoption amongst different industries like e-commerce, automotive, healthcare, and more. Let’s dive a bit deeper and explore how image recognition actually works and what applications are made possible with it.
Image Recognition: The Working Specs
Image Recognition working can be summed up into three significant steps: data collection, predictive model creation, and finally recognizing images. Find out below in detail –
Data Collection & Organizing:
In the case of Image Recognition, the computer or system classifies images in either a raster or vector image. On one side, where raster images form up to be a series of pixels with discrete binary values for colors, on the other side, vector images stand to be a collection of color-annotated polygons. Further, the geometric encoding is modified into constructs representing physical features and objects to analyze images. Post logically examining the constructs, data organization takes place, involving classification and data extraction. The initial level in image classification is to simplify the image by extracting meaningful information and dropping out the excess.
Predictive Model Creation:
Herein, image recognition technology employs feature vectors as input in a classification algorithm and provides output as a class label. Neural networks are required to build a predictive model, besides providing for numerous algorithms for image classification that helps recognize images like that of support vector machines, logistic regression, and more.
This is the final stage depicting the actual working of the image recognition. It includes two vital aspects, namely data training and data test. While training data is a type of data utilized for training a novel application, model, or system for various techniques depending on the project’s feasibility and requirements; data test, on the other side, is collected data that has been explicitly recognized for use in tests, typically of a computer program. This data is further conveyed into the model to recognize images.
The Compelling Use-Cases of Image Recognition
Image Recognition powers different industries by providing them a range of applications –
Image recognition helps the e-commerce industry with both the search and advertising aspect. It allows customers to identify specific products using mobile applications, thereby transforming your smartphone into a virtual showroom. Hence, presenting a more interactive view of the world by making everything they see searchable.
The gaming industry has found its companion in image recognition, with it allowing the gamers to use their real-location as a battlefield for virtual adventures. It also provides for generating such new user experiences and user interfaces.
With image recognition, the automotive industry is empowered to detect obstacles, warn you about the proximity to guardrails and walkways, read road signs and stop lights, amongst much more. For this, the pictures of road indications, objects, humans, roads, etc., under varying weather conditions are fed into the neural networks.
The Healthcare industry harnesses image recognition in a way that the doctors and healthcare workers get the amenity of detecting real-time emotions of patients by analyzing how they are feeling during the time they are hospitalized and the time when they’re to be discharged.
Apart from these, there are other domains such as the Retail industry, security industry, etc, which are actively fostering the benefits of Image Recognition.
The Final Lines
Industries across the world are shifting towards the autonomous arena, to which image recognition provides excellent value by way of automating business processes and enhancing productivity. It’s not feasible for every company to invest in this technology full-fledged, and even with an outstanding team, it may consume a lot of effort to generate the required results. This is where Teksun Inc comes into the picture. Our team of professional experts combined with the right tools, techniques, and technologies, help you harness the very best of Image Recognition for your specific business project.