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A look into the advantages and disadvantages of Image Recognition

Imagine you are driving on the street and you see a signage showing the direction for different locations – your eyes are immediately able to calibrate and respond to that signage. Now, imagine replicating this visual performance of a human eye in a machine. You now have what is called image recognition.

Image recognition depends extensively on convolutional neural networks and machines need to take into account contextual knowledge as well as parallel processing. However, it is important to note that human capabilities deteriorate over a period of time but automatic recognition systems will be able to maintain their performance. Let us take a look at image recognition and why it is extremely important.

What is image recognition?

Image recognition is self-explanatory and it refers to the use of technologies to identify places, logos, people, objects, buildings, physical environment, and other variables in a digital image.

For humans, it is really easy to distinguish between an image of a cat from that of an image of a dog but it is not necessarily simple for a computer. While humans see a digital image for what it is, a computer will see the same image as the composition of various elements, also known as pixels.

Each pixel has a finite, discrete quantity of numeric representation denoting its intensity or grey level. In a nutshell, a computer basically sees each image as a numerical value of these pixels and it has to recognise these patterns in numerical data to recognise a certain image.

How does image recognition work?

The process of image recognition starts with the creation of a neural network that processes the individual pixels of an image. These neural networks are then fed with as many pre-labelled images as possible to teach them how to recognise similar images.

The process can be broken down into following simple steps:

  1. The first step is to create a dataset containing images with their respective labels. For example, we can have a dataset of images labelled as flowers or dogs or Eiffel tower, or something that is self-explanatory for the neural network.
  2. The second step is to feed these datasets into a neural network and then train the model on these datasets. For training a model on images, a convolutional neural network is preferred.
  3. The last step is to feed in the image that is not part of the training data and get predictions.

What are the various categories of image recognition tasks?

The various categories of image recognition depend on the type of information required and performance of the task at various levels of accuracy. An algorithm or model can be used to identify a specific element and it can assign an image to a large category. Image recognition tasks can be categorised into following parts:

  • Classification: This process identifies the “class” or the category to which the image belongs. An image will only belong to one class.
  • Tagging: An image recognition task used for classification with a higher degree of precision is called tagging. It helps to identify several objects within an image and assigns more than one tag to a particular image.
  • Localisation: The process of placing an image in the given class and creating a bounding box around the object to show its location in the image is called localisation.
  • Detection: This task pertains to categorising multiple objects in the image and creating a bounding box around it to locate each of them. It is a variation of the classification with localisation tasks for numerous objects.
  • Semantic Segmentation: Semantic segmentation helps to locate an element on an image to the nearest pixel. This task must be extremely precise and accurate and plays an important role in the development of autonomous vehicles.
  • Instance Segmentation: It helps in differentiating multiple objects belonging to the same class.

What are the challenges faced by image recognition models?

Image recognition models face a number of challenges including viewpoint variation, where a model can be seen struggling to predict accurate value within the images aligned in different directions. Another challenge is scale variation where the size variation majorly affects the classification of the objects in an image.

Other challenges faced by an image recognition model include deformation, where objects do not change even if they are deformed. Another challenge faced by image recognition models is variation of objects within the same class. Arguably, the most difficult challenge to overcome is occlusion.

When an object blocks the full view of the image, it is called occlusion and the result is that incomplete information gets fed to the AI model. In such a situation, it is necessary to develop an algorithm sensitive to these variations and consisting of a wide range of sample data.

Image recognition: what are its use cases?

Image recognition is already a broadly used technology that impacts a large number of business areas. It is one of those technologies that will change our lives in the real-world and thus have a never-ending list of use cases. However, here are some of the most widely applied use cases for image recognition right now.

  • Healthcare: The impact of image recognition technology in healthcare cannot be downplayed. Even with years of experience, doctors can make mistakes like any other human being but an image recognition system can assist doctors in such situations. Some of the common use cases in the healthcare industry include MRI, CT, and X-ray, which use deep learning algorithms to analyse a patient’s radiology results.
  • Manufacturing: In the manufacturing industry, we can see use of image recognition technology to detect fault lines in a process. They are also used to evaluate quality of the final product or decrease the defects during production. Some companies have also deployed image recognition to assess the condition of their workers.
  • Autonomous vehicles: The biggest impact of image recognition will be seen with autonomous vehicles where the technology will analyse signage and even the activities on the road to take necessary actions. Modern vehicles already come with cameras and image sensors but autonomous vehicles will epitomise this technology and show possibilities yet to be seen.
  • Other applications: Image recognition can be seen in military surveillance, e-commerce for brand and logo recognition, education, social media, and even visual impairment aid.

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2048 1366 Editorial Staff
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