AI Image Recognition and Its Impact on Modern Business

We take a look at its history, the technologies behind it, how it is being used and what the future holds. On the other hand, facial recognition consists of the automatic recognition of a face within an image to determine its identity. As image recognition continues to evolve, it holds great promise for reshaping industries and enhancing our daily lives. In this newsletter, we will explore the fascinating world of image recognition in depth, looking at its applications, challenges, and future developments.

Despite their differences, both image recognition & computer vision share some similarities as well, and it would be safe to say that image recognition is a subset of computer vision. It’s essential to understand that both these fields are heavily reliant on machine learning techniques, and they use existing models trained on labeled dataset to identify & detect objects within the image or video. AlexNet [38] is the first deep architecture introduced by Geoffrey Hinton and his colleagues. The VGG network [39] was introduced by the researchers at Visual Graphics Group at Oxford. GoogleNet [40] is a class of architecture designed by researchers at Google. ResNet (Residual Networks) [41] is one of the giant architectures that truly define how deep a deep learning architecture can be.

What’s the Difference Between Image Classification & Object Detection?

You can use a variety of machine learning algorithms and feature extraction methods, which offer many combinations to create an accurate object recognition model. A machine learning approach to image recognition involves identifying and extracting key features from images and using them as input to a machine learning model. There are many methods for image recognition, including machine learning and deep learning techniques.

ResNeXt [42] is said to be the current state-of-the-art technique for object recognition. R-CNN architecture [43] is said to be the most powerful of all the deep learning architectures that have been applied to the object detection problem. YOLO [44] is another state-of-the-art real-time system built on deep learning for solving image detection problems. The squeezeNet [45] architecture is another powerful architecture and is extremely useful in low bandwidth scenarios like mobile platforms.

Data collection

At about the same time, a Japanese scientist, Kunihiko Fukushima, built a self-organising artificial network of simple and complex cells that could recognise patterns and were unaffected by positional changes. This network, called Neocognitron, consisted of several convolutional layers whose (typically rectangular) receptive fields had weight vectors, better known as filters. These filters slid over input values (such as image pixels), performed calculations and then triggered events that were used as input by subsequent layers of the network. Neocognitron can thus be labelled as the first neural network to earn the label “deep” and is rightly seen as the ancestor of today’s convolutional networks. AI Image recognition is a computer vision task that works to identify and categorize various elements of images and/or videos.

Learning from past achievements and experience to help develop a next-generation product has traditionally been predominantly a qualitative exercise. Engineering information, and most notably 3D designs/simulations, are rarely contained as structured data files. Using traditional data analysis tools, this makes drawing direct quantitative comparisons between data points a major challenge.

Security and Safety

There’s no denying that the coronavirus pandemic is also boosting the popularity of AI image recognition solutions. As contactless technologies, face and object recognition help carry out multiple tasks while reducing the risk of contagion for human operators. A range of security system developers are already working on ensuring accurate face recognition even when a person is wearing a mask.

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