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Image Recognition, operation and uses of the technology

In recent years the great growth in the use of smartphones and cameras, which can now be purchased at more accessible prices than in the past, has allowed a strong improvement in technology also thanks to the use of new approach criteria based on Deep Learning It is on Machine Learning.

The speed at which the learning methodology performs image detection has improved dramatically from 20 seconds to 2 seconds on average to newer versions that can reach and even exceed 20 millisecond speeds.

These highly improved performances have consequently favored its use in various sectors which have seized the opportunity by developing new solutions.

In recent years the increased use of applications that leverage technology Image Recognition, together with the use of broadband data services, have driven its growth. At the same time, analysts believe that two conditions are crucial for the continuous development of technology in the years to come, namely a growing demand for the analysis of big data and the Brand Recognition.

Image Recognition - Smart Car

In fact, many marketers look with interest at the notoriety that their brand can achieve, for example through tracking of your logo on social platforms. L’Image Recognition therefore it can be identified as one of the functions of using theArtificial intelligence; according to a ranking drawn up by the Observatory of the Polytechnic of Milan in 2022 the Computer Vision in Europe it ranks in the top positions with a market share that reaches 10% of the total AI market.

The Image Recognition process

To understand the technology behind theImage Recognition it is important to understand how it works. We mentioned earlier how theImage Recognition use the Machine Learning to process digital images and recognize patterns and figures in images. The algorithms are trained with large datasets to learn to distinguish different objects; subsequently the trained model is able to classify the images into the different categories.

Machine Learning

The process of Image Recognition it typically consists of the following phases:

  1. data collection via a large set of described images, subsequently used to train the algorithm;
  2. cleaning and removing distortions or other artifacts to make the image usable. The process may require the optimization of some peculiarities of the image such as brightness or contrast.
  3. feature extraction, identifying isolated and relevant parts of the image, which will be used to distinguish the different images by the algorithm;
  4. model training: once the features have been extracted the algorithm is trained on those images;
  5. Model testing and evaluation occurs after the algorithm has been trained. This step helps identify errors or weaknesses in the model to improve;
  6. il deployment occurs when the model has been tested and validated to classify new images into new categories with accuracy.

The process that is used for theImage Recognition and the Machine Learning, usually composed of an algorithm loaded on a computing platform that allows the end user to recognize an image or a sequence of images. L’Image Recognition can be seen as a technical form of Machine Learning.

Machine Learning

There are three types of methodologies that can be trained forImage Recognition that is to say: the Supervised Learningl’Unsupervised Learning and the Self-Supervised Learning.

If the data has been pre-categorized using the Supervised Learningalgorithms are used to distinguish between different objects and the categories within which objects are recognized are already known.

In case the data has not been categorized, theUnsupervised Learning to determine similarities and differences between images; the methodology is useful when categories are unknown.

Finally with the Self-Supervised Learningsimile all’Unsupervised Learning, with the fact that both do not use pre-categorized data, on the other hand the model is trained using pseudo categorizations created on the data; this type of model is selected when there is scarcity of data.

Today there are giants Tech and some startup which offer APIs that allow you to integrate software Image Recognition. Among the best known software there are Amazon Rekognition, Clarifis, Google Cloud Vision API, Microsoft Azure Face API, Open CV, Simple CV, Scikit-image, Syte, Dataloop.

Face Recognition

Image Recognition software and services on the market

Companies offering software solutions forImage Recognition they are often specialized by market segment, including enterprise (for large customers), mid-market and small-business. The factors by which customer satisfaction can be measured are: object recognition functionality, ease of use and personalized image recognition.

The advantages for a business that uses software Image Recognition can be recognized in higher productivity and a clear cut in costs in addition to new ones insights which can be deducted.

The most widespread features of these software components are the categorization of an image, the recognition of text that can be translated and rendered in a readable format, the Facial Recognition and the development of the activity of recognizing inappropriate or unsafe content.

Various industries have adopted Image Recognition technology as well as smartphone applications.

With the’Image Recognition a machine can identify different objects, once a model has learned to recognize the distinctive elements the machine can be programmed to perform a particular action, this leads to the technology being adopted by several technology-oriented sectors including: the sector automotive, security, healthcare, retail and marketing.

Image Recognition in the automotive sector

The automotive sector benefits fromImage Recognition with regards to the driving of autonomous vehicles; cameras, sensors and lidar (light detection and ranging) produce images which are then analyzed by software Image Recognition to identify, for example, vehicles, traffic lights and other objects. The lidar is activated to measure the distance between objects using a laser light using photodetectors and readout integrated circuits.

The aforementioned components, such as cameras, sensors and lidar, allow the car to also obtain 3D (high resolution) information. This gives the vehicle the ability to have a detailed view of everything around it; this occurs through a mapping that allows the vehicle to follow the road, positioning itself correctly in relation to other cars and identifying possible threats.

Finally, by means of long-range cameras it is possible to scan in greater detail what had been learned through radar and sensors such as colours, very important elements for the mobility of cars to recognize, for example, the brightest side lights from those darker braking zones.

Traffic Image Recognition

Other applications of Computer Vision

As far as the retail sector is concerned the technology of Image Recognition it can be used to personalize the customer’s shopping experience, recommending clothes that match the customer’s style through the use of technologies such as the virtual mirror that allows you to see how certain items look without actually wearing them.

Other examples of the use of technology come from the medical field where doctors can be helped in the recognition of various diseases.

From the vastness of uses and functions to which theImage Recognition it is clear that there are countless mobile applications that use the technology. An example is certainly the Google application that I use Google Lens gives the possibility to perform real-time searches via the camera: read texts in images and translate them into different languages, find similar items of clothing, identify plants and animals.

There are also numerous applications for the care of green spaces and gardens; in fact it is possible to recognize and identify unknown flowers in the garden, have a green thumb and keep the plants thriving, for example through the application Plant Parentwhich allows the recognition of diseases and the optimization of the position of plants also based on the light test.

Currently the leading segment in the world in the various application categories is that of Health & Wellness; there are approximately 400 thousand apps available on iOS and Android that can be recognized as such, with a high number of new apps being released on a daily basis. Of course also within this sector the technology of Image Recognition is used as in the case of SnapCalorie which identifies the types of foods that make up a meal using a photo, measuring the size of the portion to estimate its calorie content.

The frontiers in which we witness the use of technology Image Recognition they are increasingly extensive; last but not least is the use of technology at sea to recognize sharks, with the aim of safeguarding swimmers and surfers.

This technology certainly has many positive aspects but limitations must also be kept in mind.

For the end user and businesses that decide to implement the software, it is good to keep in mind that the use of this technology must be planned with good time-to-market knowing the required timescales and taking into due consideration all the pitfalls that may arise in terms of data security.

Finally, at the moment, the focus onImage Recognition is focused on the development of Machine Learning in order to improve the quality of the results and thus increase the satisfaction of the end customer.

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