Image recognition and Pattern recognition

Image recognition and pattern recognition

Image recognition and pattern recognition are specific subtypes of AI and Deep Learning. They are based on high-dimensional data, such as images. This means that a single data point – e.g. a picture or video frame – contains lots of information. A single image can easily contain millions of pixels. The high-dimensional nature of this type of data makes neural networks particularly suited for further processing and analysis – whether you are looking for image classification or object or pattern recognition.

Within the family of neural networks, there are multiple types of algorithms and data processing tools available to help you find the most appropriate model for your business case. Below, we have set out a few popular steps for this process. We will use image processing as an example, although the corresponding approach can be used for different kinds of high-dimensional data and pattern recognition.

Practical business applications

- Detect production errors
- Recognise faces or shapes in images
- Detect fraudulent activities

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Image and Pattern recognition in the AI Flow

1. Connect

2. Analysis

Image and Pattern recognition

Image and Pattern recognition is one of the available Trendskout AI Flow analysis-functions.

3. Automate

How does this work technically?

Feature transformation and feature extraction

In this step, some filters and pre-processing steps are applied to images. This allows neural networks to discover correct patterns faster. Trendskout applies different types of feature transformation and extraction, in interaction with the hyper-tuning step. For example, a photo can first be transformed via PCA to a lower dimensional structure, high contrast filters can be applied to it, or certain features can be pre-selected via feature extraction. This step is similar to the data processing applied to data with a lower dimensionality, but uses different techniques. As with classification, annotated data is also often required here, i.e. training data on which the system can learn which patterns, objects or images to recognize.

Image and Pattern Recognition

Selection of the algorithm and hypertuning

machine learning algoritmes

After the images, or other high-dimensional data, have been transformed via feature extraction and feature transformation, the selection of the algorithm is started. Trendskout performs this autonomously, just as with other AI and Deep Learning applications. Within the family of neural networks, the underlying algorithm, there are many different subtypes, each with their own set of parameters; number of neurons, number of layers of neurons, how the information between neurons is processed and many others. This means that an infinite number of configurations are possible, of which only a few give an optimal result, for example, able to correctly recognize an object in an image or data. Trendskout performs this hypertuning in interaction with feature transformation and feature extraction. In other words, through advanced Deep Learning techniques, Trendskout can find and implement the optimal data processing, feature extraction and transformation, and set up the best performing algorithm and associated parameter configuration. The combined process of data processing, algorithm selection and hyper-tuning provides an optimal model that can apply the desired image recognition or pattern detection to new data in the next step.

Real-time image and pattern recognition

When the training phase, the previous 2 steps, is completed, the model can be deployed in real-time on new data, for example new images in which patterns or objects must be recognized. The output of this step depends on the chosen Trendskout AI flow and can be: identification of the pattern found, a label, a similar image or other output.
The model is periodically re-evaluated and the entire process from the previous two steps is repeated in the background. If a more efficient model is found, the previous model is replaced.

Image and pattern recognition + Trendskout

As with other AI functions, AI flows can be set up via drag & drop to implement image recognition and pattern recognition use cases. This allows different types of input sources and locations, depending on where the images or data are accessible, or they can be loaded directly into Trendskout, which is practical for training data. In addition, different types of output are possible; displaying the recognized object, labeling the image according to recognized annotations from the training step, signaling when a certain pattern has been discovered, etc. To support this, Trendskout offers numerous output and automation options; from sending communication via e-mail or sms, controlling an external system via API or Plugin, writing a result into a database, generating a report, etc. Every step in the AI ​​flow can be operated via a visual interface in a no-code environment.

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