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The intersubjective applications of computer vision and deep learning to watch out for in 2021

The intersubjective applications of computer vision
The main aim of computer vision is to make the two ends of digital and physical interaction meet. That said, it is difficult to envision the different applications of computer vision without the aid of deep learning architecture. In this article, we take a close look at the applications which utilize both computer vision and deep learning techniques.

The detection and tracking of pose

Computer vision services can be used to detect patterns of movement in the human body. Such moments can be frozen into different types of poses. The estimation of these poses through different frames helps in pose estimation and tracking. This type of frame recording can be used to assess the performance of players. If we look more closely at frame detection and pose tracking, we observe that convolution neural networks automatically infer pose information making the entire task easier.

The capturing of motion

Traditionally, we used to make use of optical markers and specialized cameras to capture the various instances of motion. With the advancement of computer vision services, we became capable to automatically detect and recognize the motion of the human skeleton. Such technology is also used to garner extra information which was not possible earlier.

Behavior analysis

Data analysts and sports coaches increasingly rely on behavioral analysis to detect tactical game plan. While manual behavior analysis is a time-consuming task, it becomes quite easy with the help of computer vision services. The dual combination of computer vision and deep learning is used to extract invaluable pieces of data for analytical measurements.

Detailed diagnosis

The first important medical application of computer vision and deep learning technology is the detection of cancer. Deep learning is critical for the detection of breast and skin cancer while computer vision is pivotal for its identification. By the utilization of image detection and processing techniques, computer vision can differentiate between cancerous and non-cancerous images. Next in line is the diagnosis and control of coronavirus using computer vision as well as deep learning techniques. With the help of computer vision, we can design different models for diagnostic analysis of covid 19. X-ray-based diagnosis and x-ray radiography enable digital diagnosis of Covid 19 with the help of Covid Net.

Apart from its use in disease detection, deep learning and computer vision also find applications in cell classification. Deep learning techniques are used to classify T- lymphocytes and this process of classification is pivotal for the detection of colon cancer.

Face detection procedure

The procedure of face detection is one of the most prominent examples of computer vision. Based on a particular subset of training data, we can classify a new specimen into a specific cluster. The technique of face detection becomes even more important after protocols like mask-wearing are made necessary. With the help of deep learning and computer vision technology, we can spot samples in the crowd that are devoid of masks. This helps in the quick identification of miscreants by raising an alarm. The technique of face detection with masks has also been used by travel apps like Uber. Such apps can detect in advance if passengers are wearing masks or not. Consequently, passengers who are not wearing a mask are denied rides at an early stage.

Monitoring of crops and detection of flowering

One of the most important domains where decisions are taken in a traditional way is crop monitoring. Based on mere intuition and subjective human judgment, important decisions related to sowing and harvesting are taken. With the aid of computer vision, we can continuously monitor the life cycle of plant growth and apply micronutrients at the appropriate time. In this way, any effect of the disease can be negated at an early stage. One more important advantage of using growth monitoring mechanism is that the plant growth indicators can be accurately measured. Coming to the detection of flowering in plants, the technique of computer vision can be used to detect the harvesting period. Lastly, we can also keep an eye on the final product during the storage process.

Concluding remarks

The applications of both deep learning and computer vision are growing at a great pace. It has also been observed that there is a lot of intersubjectivity in these applications. For instance, the application domains of farm automation, insect detection, plant disease detection, retail, manufacturing, and transportation are showing great promise for computer vision. As such, the growth of these application domains needs to be associated with deeper research for a brighter future of computer vision services.

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