Image recognition is a result of the incredible fusion between artificial intelligence and computer vision that has led to the emergence of this technology. Image recognition software or applications take the help of camera technology and various AI models.
Image recognition technologies can distinguish and identify objects, people, texts and so on by extracting information from the images it captures. The emergence of this technology has revolutionised the industrial platform, be it pharma companies or retail shops and made brilliant opportunities for building a career in data analytics.
How does computer vision help in image recognition?
The role of computer vision is very pivotal in image recognition. It incorporates various processes like Optical Character Recognition (OCR) with the help of which it extracts textual data from images it captures. Two technologies are employed in this case: convolutional neural networks (CNN) and deep learning which generally use Python programming.
With the aid of machine learning, computers can distinguish between images without the necessity of detailed programming. This is done by breaking down images into minute pixels and then making predictions based on that. By undergoing repetitive iterations, the predictions are made more accurate which is almost parallel to human perception.
Using CNN for image recognition
The Convolutional Neural Network inculcates a strong algorithm which it uses for image processing. It uses three different layers for analysing the images namely, Convolutional Layer, Pooling Layer and Fully-Connected Layer.
In the Convolutional Layer, a small portion of input neurons is connected to hidden neurons. The dimensionality of the feature map is reduced by the Pooling Layer which follows the Convolutional Layer. In the end, the Fully-Connected Layer assesses the input data from the previous two layers and helps make assessments through predictions based on memory.
How does image recognition work?
There are a series of steps that are followed to convert images into textual data. These steps include:
- Acquisition of image: this is the first step where the image is retrieved from an external source
- Enhancement of image: this step involves changing the picture quality for better assessment
- Restoration of image: this utilises certain mathematical tools for improving the quality of the image
- Multiresolution processing: here the image is divided into smaller wavelets for the compression of data
- Morphology-based processing and segmentation: analysis of images is done based on their shapes and then subdivided into smaller individual components
- Description and representation: each component is analysed and quantitative information is derived
At the end of these steps, image recognition is made possible where the objects are tagged with a label centred on their characteristic features.
This entire process includes pre-determined signal processing methods which are employed to derive the information from the captured images. These methods include object visualisation, recognition, pattern measurement and so on.
Challenges faced in image recognition
The evolution in image recognition has brought with it several technological advancements. However, the advancements are augmented with various challenges and limitations which need to be overcome. The challenges are as follows:
- Model generalisation improvement: the challenge here is to ensure that the system can run well in real-world scenarios that can differ from training and test sets. One finds varying distributions in real-world scenarios like different viewing angles, size of the objects and camera features.
- Failure to read small and huge data sets: here the challenge is to enable the system to learn new data by introducing it to small and limited datasets in the beginning and utilising deep learning and machine learning to learn new information and ultimately recognise new objects. Similarly, another challenge here is that the current models lack the efficiency to read huge datasets to perform critical tasks.
- Limitations to cognitive understanding: the challenge here is the inefficiency to go beyond just object recognition and achieving a cognitive understanding of objects to interpret inter-relationships between objects like humans to humans, humans to cars and so on.
- Limitations to automate engineering of networks: the challenge here is that, instead of focusing on some specific features, the efforts are now to build novel network architectures. However, this is quite a difficult task involving myriads of parameters and choices.
Applications of image recognition
Some of the major arenas where image recognition is used are as follows:
- Face Recognition: This is used in surveillance and security works.
- Remote Sensing: various sensors are used to extract information about a distant object. This is used in ships, aircraft and satellites to name a few.
- Medical sectors: image recognition is being used in image diagnosis of a disease in medical sectors. It is also used in augmenting Computational Tomography (CT) scans and Magnetic Resonance Imaging (MRI).
- Processing of Video: it is used to process visual data in television sets and other visual electronic systems.
A massive revolution in the industrial sector has been brought about by the advancements in technologies supporting image recognition and computer vision. Utilising deep learning and machine learning integrated neural connecting systems have been developed which aim at getting much better in the coming days.
However, it has yet to overcome a number of challenges to attain its maximum potential. To gain expertise in such technological backgrounds you can check out Postgraduate Program In Data Science And Analytics provided by Imarticus. This 6-month long program will help data science aspirants with a better chance of securing a career in data analytics with a machine learning certification.