Automatic image recognition: with AI, machines learn how to see

In 1982, neuroscientist David Marr established that vision works hierarchically and introduced algorithms for machines to detect edges, corners, curves and similar basic shapes. Concurrently, computer scientist Kunihiko Fukushima developed a network of cells that could recognize patterns. The network, called the Neocognitron, included convolutional layers in a neural network. If AI enables computers to think, computer vision enables them to see, observe and understand.

In the agricultural sector, the crop yield, vegetation quality, canopy etc. are important factors for enhanced farm output. For better crop yield farmers are using AI-based image recognition systems. These systems use images to assess crops, check crop health, analyze the environment, map irrigated landscapes and determine yield. Companies can use it to increase operational productivity by automating certain business processes. Consequently, image recognition systems with AI and ML capabilities can be a great asset. The goal is to train neural networks so that an image coming from the input will match the right label at the output.

The AI Image Recognition Process

For instance, a dog image needs to be identified as a “dog.” And if there are multiple dogs in one image, they need to be labeled with tags or bounding boxes, depending on the task at hand. Lawrence Roberts is referred to as the real founder of image recognition or computer vision applications as we know them today. In his 1963 doctoral thesis entitled “Machine perception of three-dimensional solids”Lawrence describes the process of deriving 3D information about objects from 2D photographs. The initial intention of the program he developed was to convert 2D photographs into line drawings. These line drawings would then be used to build 3D representations, leaving out the non-visible lines.

These systems leverage machine learning algorithms to train models on labeled datasets and learn patterns and features that are characteristic of specific objects or classes. By feeding the algorithms with immense amounts of training data, they can learn to identify and classify objects accurately. Image recognition algorithms use deep learning and neural networks to process digital images and recognize patterns and features in the images.

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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. The deeper network structure improved accuracy but also doubled its size and increased runtimes compared to AlexNet. Despite the size, VGG architectures remain a popular choice for server-side computer vision models due to their usefulness in transfer learning. VGG architectures have also been found to learn hierarchical elements of images like texture and content, making them popular choices for training style transfer models.

With the new ANPR software, an artificial intelligence software was trained to accurately and reliably identify number plates with hundreds of thousands of images in a GDPR-compliant manner. There’s also the app, for example, that uses your smartphone camera to determine whether an object is a hotdog or not – it’s called Not Hotdog. It may not seem impressive, after all a small child can tell you whether something is a hotdog or not. But the process of training a neural network to perform image recognition is quite complex, both in the human brain and in computers.

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In simple terms, the process of image recognition can be broken down into 3 distinct steps. Retail is now catching up with online stores in terms of implementing cutting-edge techs to stimulate sales and boost customer satisfaction. Object recognition solutions enhance inventory management by identifying misplaced and low-stock items on the shelves, checking prices, or helping customers locate the product they are looking for.

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