8 Real-Life Applications of Image Recognition

A computer method called image recognition allows for the automatic identification of objects in pictures and films. It employs pattern recognition to find pre-specified target objects like people or cars. Additionally, it is independent of any prior knowledge of their features.

 

Identifying things in photos or movies can be done via image detection. It can recognize their direction and place accordingly. Then, after being tagged and classified by intelligent algorithms, the image data is made available for a wide range of uses.

 

Systems and applications for image identification and classification methods frequently use computer and machine vision software solutions. The latter is what drives robotic steering, process control, autonomous inspection, and other business applications.

 

Also Read | Image Recognition: Categories and Uses

 

 

What is Image Recognition?

 

Identifying interesting things in an image and determining the category to which it belongs are both tasks of image recognition. The words "image recognition," "photo recognition," and "picture recognition" are interchangeable. We naturally recognize items as distinct instances and link them to distinct definitions when we visually perceive an object or scene.

 

However, visual recognition is a very difficult operation that takes a lot of computing power for machines to complete. Artificial intelligence-assisted image recognition has been a long-standing research topic in the field of computer vision. The classification of detected objects into several categories is the common objective of image recognition, even though various techniques to mimic the human eye have developed over time (determining the category to which an image belongs).

 

As a result, it is often referred to as object recognition. Deep learning technology, in particular, has seen great success in the previous several years in a variety of computer vision and image understanding tasks. Therefore, in terms of performance (computed frames per second/FPS) and adaptability, deep learning image recognition approaches produce the greatest results.

 

 

How does image recognition work?

 

For the human brain, picture recognition software solutions appear to be relatively straightforward. However, the system must first be trained using the labeled data for it to recognize the stimuli. In other words, you should input photos of the necessary objects, item coordinates, position, and other information.

 

The application then uses a variety of ML-based techniques to extract features and identify patterns. Once applications for classifying images have received sufficient training, we can feed in an image that is not part of the training set and obtain predictions. 

 

Convolutional neural networks are the de facto industry standard for image recognition in AI applications. However, the latter is time-consuming and calls for huge training datasets. Engineers can therefore combine different algorithms to get the required accuracy.

 

Also Read | Machine Learning: Advantages and Disadvantages

 

 

Applications of Image Recognition In Daily Life

 

1. Automobile Industry: 

 

These devices are used for a variety of reasons, including reducing the number of traffic accidents and promoting orderly traffic norms and regulations. At CES last year, Cisco unveiled its collaboration with Hyundai, a traditional automaker, to enable over-the-air updates for autonomous vehicles.

 

 

2. Video games: 

 

The current situation allows gamer to use their actual environment as a battlefield for virtual adventures thanks to enhanced image recognition technologies. A recent rumor claimed that Microsoft was working on 4K cameras compatible with Xbox One so you could use facial recognition to sign in. The Xbox 360 has reached a whole new level thanks to Microsoft Kinect's sophisticated sensing capabilities.

 

 

3. Healthcare Sector: 

 

The healthcare sector is greatly benefited from image recognition technology. It aids in bringing about significant adjustments throughout a patient's journey. Robotic microsurgery in the healthcare sector employs computer vision and image recognition methods. Because of developments in machine learning and artificial intelligence during the past ten years, this technique has become more widely used. To understand how patients are feeling when they are in the hospital or just before being discharged, real-time emotion detection can also be utilized to identify the emotions of the patients.

 

 

4. Business process management:

 

Process management is a catch-all phrase for the efficient organization, planning, and control of corporate operations. Business applications for image recognition have emerged as a key component of it.

 

In the world of business, optical character recognition is ideally situated to enhance routine operations. It can read printed text and transform it into computer-encoded text or digital data. It means that a document printed on paper can be scanned and converted using an OCR engine into various formats including the Portable Document Format (PDF), XML, HTML, plain text, Microsoft Word, Excel, and searchable PDF files, among others. 

 

OCR applications can drastically cut down on the amount of time needed to manually go through mountains of paperwork for routine business activities.

 

 

5. Merged reality: 

 

This combines augmented reality with virtual reality. Merged reality is developed to address the drawbacks of VR and AR and provides virtual-world experiences that are more dynamic and realistic. For instance, Microsoft's Windows Holographic Shell or Intel's Project Alloy are wireless headsets that let you use 3D cameras to insert real things into the virtual environment.

 

 

6. Security Industry: 

 

This cutting-edge technology is essential to the security sector. Security measures are a crucial component of any platform, including those used in offices, smartphones, banks, and homes. Drones, security cameras, facial recognition biometric devices, and other security tools have all been developed. 

 

SimCam will showcase home automation and security cameras at CES 2019 that have artificial intelligence built in for features like pet monitoring, facial recognition, and many more via location training. A feature of the smart interior camera Netatmo allows footage to only begin recording when the system recognizes any unfamiliar faces.

 

 

7. Social Media Platforms: 

 

Image recognition is doing well here because it makes it simpler for marketers to locate images on social media. To find the proper photographs at unmatched speed and scale, image recognition technologies may search social media sites for images and compare them to vast libraries. 

 

As a result, it provides businesses with significant advantages in terms of customer service. To provide an accurate description of the photo's content and to identify who is in it without being tagged, Facebook combined face recognition technology and automatic text technologies in 2016. This function was implemented for the benefit of persons who are visually impaired.

 

 

8. Image-based search engines: 

 

For the greatest search results, this technology uses picture recognition. There are additional visual search engines that do the same tasks as the more established firms, Google and Bing being the two oldest competitors on this platform. A classic visual search engine with a large image collection, for instance, is Picsearch. Marks and Spencer, a UK retailer, just introduced a visual search style finder on their website.

 

Stock photography and video are two industries where image recognition is being used commercially to great effect. Photographers and videographers have venues to sell their work on stock websites. Large amounts of visual content need to be tagged, which takes time and is laborious. At the same time, without accurate keyword attribution, their material cannot be indexed, which prevents customers from finding it. 

 

Therefore, image recognition is important for stock websites. Every day, it supports trillions of queries on stock websites. It gives consumers the means to find visual content through searches. At the same time, picture recognition provides stock contributors with a great deal of relaxation. 

 

Automatic keyword ideas are provided to them, saving them a tonne of time and work. Additionally, image recognition can help them come up with innovative ways to more successfully and thoroughly categorize their content. To make it simple for stock contributors to describe and categorize their content, image recognition AI from Imagga has been combined with keywording software solutions like Qhero. 

 

Such programs examine visual resources and suggest pertinent keywords. Photographers no longer need to spend as much time refining their visual material. It eliminates the need for manual keywording by suggesting the best phrases to use when describing an image.

 

Also Read | Introduction to Residual Network (ResNet)

 

 

Fundamental Steps in Image Processing:

 

• Image Acquisition: 

 

The initial stage of image processing is image acquisition. In image processing, this stage is often referred to as pretreatment. The image must be retrieved from a source, typically one that is hardware-based.

 

 

• Image enhancement: 

 

Image enhancement is the technique of bringing out and emphasizing specific appealing aspects of an image that have been hidden. Changing the brightness, contrast, etc., can do this.

 

 

• Image restoration: 

 

Image restoration is the practice of enhancing the visual appeal of an image. Picture restoration, as opposed to image augmentation, is carried out utilizing specific mathematical or probabilistic models.

 

 

• Color image processing: 

 

Digital color modeling techniques are a part of color image processing. The widespread use of digital photos on the internet has given rise to the importance of this stage.

 

 

• Multiresolution Processing with Wavelets: 

 

Wavelets are used to represent images at different resolution levels. For pyramidal representation and data compression, the images are separated into wavelets or smaller sections.

 

 

• Compression: 

 

Compression is a technique used to lessen the amount of space needed to save or transmit an image. This is done especially when the photograph will be used online.

 

 

• Morphological Processing: 

 

Based on a picture's shape, a set of processing processes is used to morph the image.

 

 

• Segmentation:

 

One of the most challenging aspects of image processing is segmentation. It entails dividing an image into its objects or components. After an image has been divided into regions through the segmentation process, each region is represented and characterized in a way that is appropriate for further computer processing. The qualities and regional aspects of the image are covered via representation. It is the job of description to extract quantitative data that helps distinguish one class of items from another.

 

 

• Recognition:

 

A label is given to an object through recognition based on its description.

 

Also Read | Object recognition vs Image Recognition

 

 

Final Note:

 

The spread of image recognition algorithms is more evident and unavoidable than ever, to sum it up. The main force behind this technology has been and will remain the arms race in automation. Consequently, object identification has gained traction in every sector, including national security and e-commerce.

 

Try these picture recognition use cases yourself if you're still not convinced of the value of image recognition. More than just detecting images, image recognition has a tonne of advantages you can take advantage of. Many people are only now starting to understand its possibilities. Today, it may be used to recognize a variety of information sources, including voice recordings, text messages, and other types of media in addition to images.