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Computer vision is a broad term for the work done with deep neural networks to develop human-like vision capabilities for applications, most often run on NVIDIA GPUs. It can include specific training of neural nets for segmentation, classification and detection using images and videos for data.

Computer vision can handle many more tasks. Developed with convolutional neural networks, computer vision can perform segmentation, classification and detection for a myriad of applications.

Computer vision has infinite applications. With industry changes from computer vision spanning sports, automotive, agriculture, retail, banking, construction, insurance and beyond, much is at stake.

Computer Vision Use Cases and Benefits

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    Public Safety and Home Security

    Computer vision with image and facial recognition helps quickly identify unlawful entries or persons of interest, resulting in safer communities and a more effective way of deterring crimes.

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    Authentication and Enhanced Computer-human interaction

    Enhanced human-computer interaction improves customer satisfaction such as offering products based on customer sentiment analysis in retail outlets or faster banking services with quick authentication based on customer identity and preferences.

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    Content Management and Analysis

    With millions of images added every day to media and social channels. The use of computer vision technologies such as metadata extraction and image classification greatly improves efficiency and revenue opportunities.

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    Autonomous Driving

    Using computer vision technologies. Auto manufacturers can provide improved and safer self-driving car navigation realizing the goal of making autonomous driving a reality and a reliable transportation option.

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    Medical Imaging

    Medical image analysis with computer vision can greatly improve the accuracy and speed of a patient's medical diagnosis, resulting in better treatment outcomes and life expectancy.

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    Manufacturing Process Control

    Well-trained computer vision incorporated in robotics improves quality assurance and operational efficiencies in manufacturing applications, resulting in more reliable and cost effective products.

Following is a curated list of top 10 real-world applications of computer vision:

  • Facial Recognition

    Face Recognition algorithms capture more than just features. It is programmed in a manner that enables it to capture all the unique factors of a face and that too from multiple angels. For example, it also measures and memorizes the distance between an individual’s eyes and mouth. Siamese Network in computer vision is used to carry out facial recognition.

  • Augmented Reality

    Augmented Reality (AR) first takes a real-world environment and then adds a computer-generated input to it. Several parts from both of the environments, real and augmented, can interact together and even digitally manipulated. Therefore, AR can simply be defined as an amalgamation of both of the worlds. It renders a 3D registration of real and virtual objects.

  • Social Distancing

    Every single human life is at the risk of getting infected from the virus and social distancing can be regarded as the need of the hour. We can effectively monitor public spaces through computer vision cameras and sensors to track social distancing and impose strict rules and regulations for those who violate its norms.

  • Unmanned Aerial Vehicles (UAVs)

    Amidst COVID-19, Computer Vision-enabled drones or UAVs can be used for a variety of purposes:

    • 1. Delivering emergency food supplies and testing kits.
    • 2. Spraying disinfectant for sanitization of public places.
    • 3. Detecting unmasked citizens through cameras.
    • 4. Communicating advisory through speakers.
    • 5. Analyzing the movement of people in quarantine shelters.

  • Machine Vision

    Machine Vision is defined as a set of methods to enable image-based automation for business operations like process control, automated inspection, robot guidance, etc. It is a bifurcation of systems engineering that integrated existing technologies in new ways and use them to solve real-world problems.

  • Self-driving cars

    Self-driving cars have been at the core of the automobile industry over the past few years and computer vision brought a promise to transform this vision into reality. YOLO (You Look Only Once) is an immensely popular computer vision algorithm used for autonomous driving which can efficiently detect objects in the path.

  • Optical Character Recognition

    OCR is simply the electronic conversion of images containing handwritten text into machine-encoded text. It includes text processing in different forms, such as a photo of a document, a scanned document, subtitles superimposed on an image, or a scene-photo. Several computer vision algorithms are used for OCR technology, such as matrix matching.

  • Visual Search

    Visual search uses images as keywords as opposed to texts and searches for related images, websites, blogs, or any other posts. Visual Search Engine is programmed in a manner that bridges the time gap between your search. For example, Google Lens allows the users to look for objects through the lens and get similar results as per their image search.

  • Gesture Recognition

    It is of no surprise that multiple algorithms exist in the computer vision field to detect human gestures and postures. They can interpret human gestures originated from any motion or state of the human body. For example, a store supervisor can carry out emotion recognition to determine if customers visiting the store are happy with the services or not.

  • Computer-aided Diagnosis

    Computer Vision also finds a wide range of applications in the healthcare sector. It can assist medical professionals in training. Doctors can interpret medical images used in techniques like X-Ray and MRI using computer vision efficiently.

Computer Vision on E2E Cloud

E2E Networks helps in Accelerate Convolutional Neural Networks based deep-learning workloads like video analysis, facial recognition, medical imaging and others.

Accelerate Machine Learning and Deep Learning Workloads with up to 70% cost-savings

Multiple Use-cases, One Solution!

E2E’s GPU Cloud

is suitable for a wide range of uses

  • AI/ML/DL

    Train complex models at high speed to improve predictions and decisions of your algorithms. Use any framework or library: TensorFlow, PyTorch, Caffe, MXNet, Auto-Keras, and many more.

  • Computer Vision

    Accelerate Convolutional Neural Networks based deep-learning workloads like video analysis, facial recognition, medical imaging and others

  • Computational Finance

    Analyze and calculate large and complex financial data; performtons of transactions in real-time. Do accurate financial forecasting, faster

  • Scientific Research

    Design and implement data-parallel algorithms that scale to hundreds of tightly coupled processing units: molecular modelling, fluid dynamics and others

  • Big Data:

    Deal with large-size data sets and continuously growing data, splitting it up between processors to crunch through voluminous data sets at a quicker rate

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3 Things to Know About Computer Vision

  • Segmentation: Image segmentation is about classifying pixels to belong to a certain category, such as a car, road or pedestrian. It’s widely used in self-driving vehicle applications, including the NVIDIA DRIVE software stack, to show roads, cars and people. Think of it as a sort of visualization technique that makes what computers do easier to understand for humans.
  • Classification: Image classification is used to determine what’s in an image. Neural networks can be trained to identify dogs or cats, for example, or many other things with a high degree of precision given sufficient data.
  • Detection: Image detection allows computers to localize where objects exist. It puts rectangular bounding boxes — like in the lower half of the image below — that fully contain the object. A detector might be trained to see where cars or people are within an image, for instance, as in the numbered boxes below.