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Deep learning advances are boosting computer vision applications — but there are still clear limits

Computer Vision Applications

Computer vision applications permit the computer to interpret and process the images from the visual world in a similar way as a human does. As a way of mimicking humans, researchers in the field sense to develop a machine that can automate the tasks of humans by cognitive recognition. Finally, they reach the idea of computer vision. It’s significantly difficult for machines to predict the multi-dimensional data rather than other forms of binary. This makes AI systems more complicated to learn visual data. 

Deep learning and artificial neural networks make the computer vision appropriate to replicate the human. Advances in deep learning gave a quick boost to CV. Computer vision applications are more proficient in identifying the patters from the image rather than human visual systems. Amazing actions make the computer vision technology to be the future of every industry. Today’s computer vision technology is powered by deep learning algorithms that use special convolutional neural networks (CNN) to train the images. 

Convolutional Neural Networks are trained by providing thousands of google deep learning images to learn from them. This uses a supervised machine learning algorithm. Neural scans the image pixel by pixel to understand and identify the hidden patterns and memorize them. That memory is then used as a reference to scan more images. After each predicted output from previous data, AI models become more intelligent to provide accurate information. They are signed in classifying objects. But when it comes to understanding the context of the image and relationship between them. Computer vision applications systems fail badly. There are clear limits that still exist. Some spaces where computer vision technology is being used are:

The current state of computer vision…

Image recognition and captioning  

Image recognition and object detection has one of the major areas where computer vision has made vast progress. Neural networks are trained with machine learning algorithms to detect a wide range of objects with more accuracy. Image captioning is staying the major computer vision application. Social media like Facebook and Twitter use computer vision with natural language processing to generate the captions of images having no labeled data. The purpose is to identify the elements present in an image. Advance algorithms are now capable to classify living and non-living objects more precisely.

Google uses a huge amount of user data to trains its AI models. A great example is Google Photos, it uses its computer vision algorithms to mark them with content information relevant to the scene. You can then search your images based on the generated information. Google’s image recognition isn’t perfect. However, in one case, the computer vision algorithm mistakenly tagged a picture of two dark-skinned people as “gorilla,” causing dishonor for the company.

Image editing and enhancements

Companies like Adobe are using machine learning and deep learning to automatically enhance the features of images. Google’s line of Pixel phones utilizes a deep neural network to make auto adjustments to pictures like blurring and lightning. On zooming pictures become pixelated. Some features like ML Super Resolution use computer vision-based zooming that focuses on edges and patterns. This approach produces incredible results in image enhancements. Many companies have turned up its way to deep learning to enhance images as well as videos. 

Facial recognition applications

Initially, computer vision applications were limited to key areas limited to research labs. These technologies were too expensive to adopt. The rise in technology has affected computer vision to found its way in various computing devices. iPhone X introduces a facial recognition system that utilizes a neural network to trains its AI model. By doing so, it identifies the device owner under different situations.

The recent studies are creating brain prints as a password. Generally, deep learning algorithms are trained by the human waves to create the patterns. As self-driving cars map the surrounding. Brain prints create a map of patterns when some person looks to the picture. In terms of its uniqueness, these systems are more efficient. These systems manage and enhance security, where privacy is a major concern.

Facial recognition and biometric utilize computer vision, not just for a smartphone but also for businesses to check whether the person belongs to their organization or not. Business systems apply physiological features to verify an individual’s unique patterns. Every individual retains unique fingerprints. Deep learning algorithms learn the unique patterns and apply them to control the high-privacy, research labs, and bank vaults. Computer vision and deep learning are also great in identifying the unique patterns in a person’s retina. Rather than advancements, these systems are not much effective and still can be made a fool.

Researchers at Carnegie Mellon University discovered that by donning special glasses, they could fool facial recognition algorithms to mistake them for celebrities


Data efficient home security

Random development on the Internet of Things has made home security cameras more efficient. By security cameras, you can monitor the security of your home. Initially, to secure home, these cameras record the full-time video. As it consumes around the clock resources, so that approach was not suitable. Irrelevant footages are sent to the cloud that demands a lot of storage and electricity. To kick off the hitch, computer vision algorithms help them to become more efficient.

Whenever they observe the motion of objects in the scene. Cameras immediately start recording. Transfer it to the cloud and alert the homeowner. A part of the video is saved. As the computer vision applications are still not too efficient so don’t expect to distinguish it between combat and individual. That’s why AI have become an extra ethical issue of artificial intelligence.

Interacting with the real world

Augmented reality makes possible to interact with the real world through virtual objects. AR is now becoming the expanding market in the world. Augmented reality utilizes computer vision machine learning to predict where to place the virtual object. Objects are placed at the tracked locations in the real world. Augmented reality gives the user a virtual experience of the objects. Many computer vision applications apply the combination of augmented reality like Snapchat.

Computer vision applications also enable you to extract the information form the real world through camera. Google lens is a major example. By Google Lens, you can search the desired pictures, can read the business cards and translate the street signs and much more.

Advanced Computer Vision Applications…

Medical image processing

The computer vision algorithm interprets complex patterns to extract the information. Initially, this requires a lot of effort to do. Engineers and experts have to brainstorm to extract the relevant features. Deep learning provides end to end solutions that make the process of detection accuracy. Engineers train the neural network by MRI images. Some professionals say: soon advanced computer vision might replace the radiologist. Here is a clear limit. Machine learning predicts the patterns while humans make decisions based on their intelligence. They can’t fully mimic the human.

Computer vision aids the computer to recognize elements and objects from digital images more precisely than experts do. Computer vision detects cancer tumors from CT scan images. This detects the astonishing patterns from data that is not possible also for a radiologist to predict. Detected cancer is diagnosed to prevent it from further affecting the patient. Computer vision can identify other diseases more precisely and accurately than humans.

Playing games

Teaching computers to play games has always been a hot area of AI research. Most game-playing programs use reinforcement learning, an AI technique that develops its behavior through trial and error. Computer vision algorithms play an important role in helping these programs parse the content of the game’s graphics. For the moment, AI algorithms need huge amounts of data to learn games. For instance, OpenAI’s Dota-playing AI had to go through 45,000 years’ worth of gameplay to achieve the champion level.

Cashier-less stores

In China, many stores use facial recognition technology to enhance the customer payment experience. No need for a cashier to maintain the records. Person just has to show his/her face to the camera instead of using a credit card or mobile payments. In 2016, Amazon introduced Amazon Go. Here customers get a realistic experience without waiting for the cashier to make a list of the cart items.

Amazon Go stores are equipped with cameras that utilize computer vision to check customer behavior. Which item is being picked and then placed on the shelves? Items added to the cart are tracked by computer vision technology. On leaving the store, customers are charged from their Amazon account.

Self-driving cars

Self driving cars remain a big dream for companies like Tesla, Nissan, etc. To navigate safely on the road, self-driving cars must have a strong competency of sense. That intelligence could be achieved by computer vision machine learning. Computer vision aids in understanding the road signs and follow the traffic rules. Deep neural networks parse the information collected through LIDARS and ultrasonic sensors. The information is combined with data to train the model and create a map of the surrounding. This helps autonomous cars to navigate on the road and drive safely.

Computer vision also helps them in making critical decisions. Such as giving away to ambulance and fire engines. Challenges tend to grow with advanced computer vision. The major challenge is computer vision. To decide their surroundings seem to big hitch for self-driving cars. Different companies are tackling this situation in different ways. Tesla is creating their chips while other companies are improving lidars and cameras.

3D vision

3D vision systems are becoming gradually accessible. Diverse approaches adopted in the design of 3D Convolutional Neural Networks (3D CNNs). This emerging field is known as Geometric Deep Learning (GDL) and has multiple computer vision applications such as video classification, computer graphics, vision, and robotics. 3D computer vision adds an extra layer of difficulty as it introduces an extra dimension. FFT based methods can optimize 3D CNNs by reducing computation time. 

GDL deals with the extension of DL techniques to 3D data. 3D data can be classified as Euclidean or non-Euclidean. 3D Euclidean-structured data has an underlying grid structure that allows for a global parametrization while 3D non-Euclidean data has no global parametrization and does not have the gridded array. 3D Euclidian is more suitable for analyzing simple rigid objects such as chairs, planes, etc. 

Soon computer vision will perform a broad range of functions. Algorithms will not only be easy to train but can also distinguish the image more accurately. In some cases, image captioning combined with natural language generation to interpret the objects. AGI and ASI give the ability to process the information better than humans. The application of deep learning and computer vision will be functionally more effective.

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Written by Impressim

Impressim is a leading tech company, focusing on headphones, technology, buyers guide, products comparison, web development, and lifestyle.

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