[*images maybe holding images until release date]
Artificial Intelligence can recognise and react in multiple ways to all types of image regardless of the sensitivity, speed and accuracy required.
Being able to automatically recognise patterns, shapes, sizes, colours and orientation of objects within images is an extremely powerful tool in a wide range of businesses, operations, tasks and processes.
By learning from known and highly variable image shapes, textures, dimensions, positions in space (2D/3D) including all the in-between highly variable and unpredictable object attributes; image recognition becomes more insightful, more meaningful, more accurate and much more beneficial when used for tasks and processes like: e.g. object classification, automatic and rapid image-based diagnosis, anomaly detection, quality control, computer vision, self-driving vehicles, threat assessment, robotics, security, investigations, and responding to visual changes in any type of environment, amongst many more cases.
The accuracy of image recognition is directly proportional to what is already known about certain objects (e.g. their full range of attributes and dimensions): the more past images to learn from the more accurate and informed we can conduct future image recognition.
Learning from past images in order to recognise the same type of image/object in the future might sound straightforward - it is of course exactly what a human brain does - but making this process automatic and asking a machine to do this is extremely complex and demanding, mainly due to the huge amount of variability involved: size, shape, orientation, perspective, direction, distance, colour and lighting - and these are variations that can happen for just one object, let alone when you have multiple types of object in the same image.
If a traditional software programming approach was used, the computer would continually be asking "if this, then learn that; if x and y, learn z". This stepwise methodology is extremely limited, inefficient and resource-heavy as the program will only do what it has explicitly been told to look for by the programmer; making it next to impossible to account for all possibilities and variations, even in the simplest of images and objects. You certainly couldn't use this approach for complex and diverse scenarios e.g. multiple objects in the same image, or indeed where a single object showed more than a couple of variations, unlike most objects that contain many hundreds, if not thousands of variable attributes.
With AI and Machine Learning technology it is now possible to learn from near infinite amounts of variable, constantly changing image data in order to carry out much more accurate and rapid image recognition, automatically.
ELDR-I Image is built around our powerful ELDR-I AI Engine, which is a Deep Learning Convolutional Neural Network. ELDR-I Image uses Supervised Learning and Image Classification to learn how to recognise all types of images thrown at it, regardless of size, complexity and granular detail (down to the single pixel level).
When ELDR-I Image has learnt (trained) from the data, it is then primed to receive current-status image data from which to rapidly process images and recognise/classify objects within - in order to give a response - and that response can range from a simple classification to a "yes/no" to triggering sophisticated downstream events.
ELDR-I Image can handle and learn from multiple sources, sizes and complexities of image data for numerous environments and requirements simultaneously. Data can be changed at any time and it can continually learn.
By default ELDR is plug and play - you can simply give it appropriately formatted image data and it will automatically learn from it, including self optimisation, self scaling and classification.
In some cases you may be happy with plug and play, however almost everything in ELDR-I Image is configurable; from labelling of images, to colours, displays, output format, learning modes, learning accuracy, all the way through to Convolutional Neural Network dynamics and dimensions.
ELDR-I Image uses a rich intuitive GUI Dashboard from which to manage the whole AI process (image data preparation, learning, outputting and testing), including a comprehensive suite of gamified charts and other visual displays to monitor everything.
AI Integration is our speciality. We understand that AI can be used in a variety of ways and in numerous system-types and processes. We build our software to be entirely modular and there are multiple integration methods and points ranging from network-based RESTful API integration to direct coupling at the code level, depending on the response time required, amongst other considerations.
As well as Image Recognition software for the Telecommunications industry, we provide a comprehensive set of other Artificial Intelligence, Machine Learning, Deep Learning and Data Science software:
Whether you are starting out on your first AI project, just interested in the possibilities of AI or are wanting to expand your existing AI suite, we are here to help.
We will discuss with you where you are, where you want to be, and how we can achieve it with AI - whether by a bespoke solution or using one of our off-the-shelf products
We will work with you to gather, analyse and prepare all your relevant data sources for use in the AI system(s)
We will run and tune the AI throughout the AI learning process and enable the AI to produce a real time visual output to confirm the AI is producing beneficial results
When you are satisfied the AI is delivering the results you desire, we will integrate the AI with your new or existing systems
Fennaio has the expertise in the Telecommunications sector to get you up and running with Image Recognition AI and Machine Learning in your new or existing systems, software and operations.Get Started