Image Recognition Software

Latest image recognition software uses deep learning networks. The most used deep learning model is an artificial neural network model called convolutional neural networks (CNN).

Before the image is recognized, it must first be preprocessed and the useless features (i.e. noise) must be filtered. The preprocessed images are evaluated pixel by pixel.

The numerical value of each pixel is associated with another pixel using an operator called convolution. The objects in the image are represented by mathematical vectors and classified as a result of this method. For example, in order to identify pictures containing cars, a set of images that contains cars is processed. Then a vector which is describing the car in images is obtained. The first set of data is called training data. Then new pictures are tested on the model to understand its accuracy. This set of data is called the test data. Check out our research to learn more about how image recognition technology works

Image recognition technology can be applied in all areas where image acquisition is possible. Our research analyzed the industries and business functions where image recognition software is used frequently:

• Automotive Industry
• Security Industry
• Healthcare
• Retail
• Visual search and e-commerce
• Marketing

For example, image recognition technology is used to enable autonomous driving from cameras integrated in cars. Another example is the diagnosis in healthcare. The software enables faster and accurate medical imaging. For an in-depth analysis of AI-powered medical imaging technology, feel free to read our research.

An exponential increase in image data and rapid improvements in deep learning techniques make image recognition more valuable for businesses.

1. Image data is increasing every day: As camera hardware becomes smaller and integrated into daily life through mobile devices and sensors, the increase in image generation continues. The rapid increase in image data increases the demand for processing this data and making it useful. There are image processing applications in e-commerce, supply chain, retail, automotive and other industries.

1. Increased effectiveness of deep learning: Deep learning enables fast and accurate image processing. Deep learning is becoming more powerful thanks to both advances in hardware and algorithms. As it gets cheaper and faster, businesses can integrate image recognition solutions into their business. According to MarketsandMarkets “image recognition market is estimated to grow from USD 16 billion in 2016 to USD 39 billion by 2021, at the CAGR of 20% during the forecast period.”. For more on why deep learning is impactful, feel free to check out our article on the topic

While choosing image recognition software, the software's accuracy rate, recognition speed, classification success, continuous development and installation simplicity are the main factors to consider.

• Accuracy: Most of the times, this is the most important factor. However, in real time usage, speed can be as important. We have explained a few ways to measure accuracy of machine learning models.
• Continuous learning: Every AI vendor boasts of continuous learning but few achieve it. The ideal solution should be learning from its incorrect predictions (inferences in machine learning jargon). The necessary volume for learning is also important. A model that requires thousands of examples for improving its model would be slow to improve itself.
• Speed:The solution must be fast enough for the necessary application. While a customer-facing solution may require a response within milliseconds, a solution for internal use can be OK to be produced within a few hours or even days.
• Flexibility: It is important to foresee the constraints of the future and adaptability of the solution for the future needs is important.
• Ease of setup and integration: The solution should be easy to setup and use. Since most solutions will be API endpoints, they tend to be easy-to-setup.