For the most part, computer vision has become the hottest field when it concerns deep learning. Today, computer vision is well integrated with academic subjects, such as engineering, mathematics, physics, computer science, and psychology. Computer vision is designed to represent visual understanding, along with the context of an environment. For this reason, computer vision is paving the field for artificial intelligence, accrediting the cross-domain configuration.
Computer Vision – What Is It?
According to the textbook meanings, computer vision is the development of meaningful yet explicit descriptions of physical objects through the images. In addition, it plays an influential role in making productive decisions for sensed images and physical objects. Computer vision is designed with extensive applications, such as face recognition, image retrieval, surveillance, gaming, and controls, along with smart cars and biometrics.
In the recent past, deep learning and neural networks have developed and advanced the performance of recognition systems (visual systems). There are deep learning architectural details that have optimized the computer vision. Computer vision operates through the recognition techniques, such as object tracking, segmentation, character recognition, and captioning. In the section below, we will be looking at some of the applications of computer vision!
The deep learning approaches and technological advancements have increased the value and capacity of visual recognition systems. Computer vision technologies have been adopted by various companies while the successful utilization is evident in various sections, hence widespread applications. In addition, it will improve the demand for computer vision technologies and tools.
Applications of Computer Vision
Human Pose Estimation
This is surely an influential application of image segmentation. Posenet is an open-source model, designed for human pose estimation. This is the computer vision technique that infers the object’s or person’s pose (these are collected through the image or video). It is defined as the set of coordinates that help outline the person’s pose. Likewise, two coordinates are called pose while pose estimation is conducted through identification, tracking, and locating the skeleton’s key points from the video/image.
There are multiple applications for human pose estimation. For instance, the human pose estimation has applications in gaming, animation, AR experiences, in-training robots, and activity recognition for surveillance systems and sports. For the development of human pose estimation, MPII, HUMANEVA, and COCO are the most-used datasets.
If you haven’t been living under the roof, you would know how Faceapp was being used by everyone out there. This trending app is actually very interesting. In essence, the application is an image manipulation tool that optimizes the image through filters. These filters are designed with a gender swap and aging filter. Such apps are developed through the adversarial networks of deep convolution. These are commonly known as GAN; it’s an innovation in the world of computer vision.
The training utilizes the neural nets pair that’s essential for generating new data through the distribution of training data. It’s a beneficial application for learning purposes, be it semi-supervised or supervised paradigms. The utilization of GAN can be implemented for image editing, semantic imaging to the translation, super-resolution of images, generation of text to image, and translation of image to image; photo in painting is there as well.
Development of Social Distancing Tools
With the COVID-19 hitting the world, the precautionary measures are already integrated, such as face masks and hand sanitizers. That being said, computer vision will play an influential role in optimizing the social distancing tools. It can help track people in specific areas or proximity to outline if they are following the precautionary measures or not. These tools are working with tracking and object detection on a real-time basis.
With this being said, the bounding box is used to detect people in the video. Besides, it tracks the movement in the frame and measures the distance between these boxes. In case there is someone breaking the rules, the bounding boxes will be used for highlighting. The transfer learning techniques are being used for making the tools accurate and advanced. For instance, the detection models, such as Mask R-CNN and YOLO are being utilized.
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Creation of 3D Model Through 2D Images
This is a rather interesting application of computer vision where 2D images are converted into 3D models. This is being accomplished through an AI system, known as a generative query network with which the image is perceived through various angles. For instance, Nvidia has developed an AI architecture that can outline the 3D capacities.
Facebook has also developed a 3D photo feature with artificial intelligence configuration. The datasets like IKEA, ObjectNet3D, and NYU Depths can be used for this application. In addition, the applications include robotics, gaming, animation, surgical operations, medical diagnosis, and self-driving cars.
Medical Image Analysis
X-rays and CT scans have become the applications of computer vision in the medical field. However, the recent developments in the field of computer vision have empowered doctors to understand medical reports by converting them into interactive models (3D), hence easier interpretation and analysis. The most recent example is the ability of medical health experts to differentiate between pneumonia and COVID-19.
Computer vision has been increasingly used by engineers for detecting the defects in bad prints, metal cranks, and paint, if the sizes are smaller than 0.05mm, it’s pretty precise. It is conducted through the vision cameras that are integrated with algorithms, known as the intelligent brain. It will be able to variate between the defects. Also, the algorithm is designed to align with specific applications through pictures and images.
Computer vision has been applied to outline the well-integrated laser metrology equipment. The users can now make adjustments to the measurements. Besides, there is an abundance of sufficient lighting that aligns with the environment and material. With the implication of computer vision, straightness, size, and parallelism can be detected or measured.