Optical lenses are lenses manufactured by optics. One of typical lenses are cylindrical optical lenses. Optical lenses have specific optical properties such as refractive index, dispersion, transmittance, spectral transmittance, and light absorption. They are lenses with uniform optical properties. Optical lenses can change the direction of light propagation and change the relative spectral distribution of ultraviolet, visible, or infrared light. Optical lenses in a narrow sense refer to colorless optical lenses; in a broad sense, they also include colored optical lenses, laser lenses, quartz optical lenses, anti-radiation lenses, ultraviolet and infrared optical lenses, fiber optics lenses, acousto-optic lenses, magneto-optical lenses, and photochromic lens. Optical lenses can manufacture lenses, prisms, mirrors, and windows in optical instruments. Components composed of optical lenses are key elements in optical instruments.

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Computer visionexamples

Computer vision systems use artificial intelligence (AI) technology to mimic the capabilities of the human brain that are responsible for object recognition and object classification. Computer scientists train computers to recognize visual data by inputting vast amounts of information. Machine learning (ML) algorithms identify common patterns in these images or videos and apply that knowledge to identify unknown images accurately. For example, if computers process millions of images of cars, they will begin to build up identity patterns that can accurately detect a vehicle in an image. Computer vision uses technologies such as those given below.

Computer visioncourse

AWS provides the broadest and most complete set of artificial intelligence and machine learning (AI/ML) services connected to a comprehensive set of data sources for customers of all levels of expertise.

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Computer vision basicsw3schools

For customers that lack ML skills, need faster time-to-market, or want to add intelligence to an existing process or an application, AWS offers a range of ML-based computer vision services. These services allow you to easily add intelligence to your AI applications through pre-trained APIs. Amazon Rekognition automates your image and video analysis with ML and analyzes millions of images, live streams, and stored videos in seconds.

Image processing uses algorithms to alter images, including sharpening, smoothing, filtering, or enhancing. Computer vision is different as it doesn't change an image, but instead makes sense of what it sees and carries out a task, such as labeling. In some cases, you can use image processing to modify an image so a computer vision system can better understand it. In other cases you use computer vision to identify images or parts of an image and then use image processing to modify the image further.

Computer visionalgorithms

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Computer vision can analyze images and extract metadata for business intelligence, creating new revenue opportunities and operational efficiencies. For example, it can:

Computer Visionppt

Content-based image retrieval is an application of computer vision techniques that can search for specific digital images in large databases. It analyzes metadata like tags, descriptions, labels, and keywords. Semantic retrieval uses commands such as 'find pictures of buildings' to retrieve appropriate content.

Recurrent neural networks (RNNs) are similar to CNNs, but can process a series of images to find links between them. While CNNs are used for single image analysis, RNNs can analyze videos and understand the relationships between images.

Optical lenses are made by mixing high purity silicon, boron, sodium, potassium, zinc, lead, magnesium, calcium, barium, and other oxides according to a specific formula. Melt it at high temperature in a platinum crucible, stir evenly with ultrasonic to remove bubbles, then slowly cool it down for a long time to avoid internal stress in the lens block. Optical instruments must measure the cooled lens block to check whether the purity, transparency, uniformity, refractive index, and dispersion rate meet the specifications. The qualified lens block is heated and forged to form an optical lens photo.

The first lenses used to make lenses were lumps on ordinary windows or wine bottles. The shape resembled a crown, and the name or crown lens was derived from this. The lenses were highly uneven and had a lot of foam. In addition to crown lenses, there is another crushed stone lens with higher lead content. In 1790, Frenchman Pierre Louis Junaid discovered that mixing lens paste can produce uniform lenses. In 1884, Ernst Abbe and Otto Schott of Zeiss founded the Schott lens factory in Jena, Germany. Within a few years, they developed dozens of optical lenses. The barium crown lenses with high refractive index Invention is one of the essential achievements of SCHOTT Lens Factory.

Convolutional neural networks (CNNs) utilize a labeling system to categorize visual data and comprehend the whole image. They analyze images as pixels and give each pixel a label value. The value is inputted to perform a mathematical operation called convolution and make predictions about the picture. Like a human attempting to recognize an object at a distance, a CNN first identifies outlines and simple shapes before filling in additional details like color, internal forms, and texture. Finally, it repeats the prediction process over several iterations to improve accuracy.

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Governments and enterprises use computer vision to improve the security of assets, sites, and facilities. For example, cameras and sensors monitor public spaces, industrial sites, and high-security environments. They send automatic alerts if something out of the ordinary occurs, such as an unauthorized individual entering a restricted area.

Computer visionapplications

From boosting productivity to reducing costs with intelligent automation, computer vision applications enhance the overall functioning of the agricultural sector. Satellite imaging as well as UAV footage help to analyze vast tracts of land and improve farming practices. Computer vision applications automate tasks like monitoring field conditions, identifying crop disease, checking soil moisture, and predicting weather and crop yields. Animal monitoring with computer vision is another key strategy of smart farmiing.

Healthcare is one of the leading industries applying computer vision technology. Notably, medical image analysis creates a visualization of organs and tissues to help medical professionals make speedy and accurate diagnoses, resulting in better treatment outcomes and life expectancy. For example:

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Image classification enables computers to see an image and accurately classify which class it falls under. Computer vision understands classes and labels them, for instance trees, planes, or buildings. One example is that a camera can recognize faces in a photograph and focus on them.

Segmentation is a computer vision algorithm that identifies an object by dividing images of it into different regions based on the pixels seen. Segmentation also simplifies an image, such as placing a shape or outline of an item to determine what it is. By doing so, segmentation also recognizes if there is more than one object in an image or frame.

In semiautonomous vehicles, computer vision uses machine learning (ML) to monitor driver behavior. For example, it looks for signs of distraction, fatigue, and drowsiness based on the driver's head position, eye tracking, and upper body movement. If the technology picks up on certain warning signs, it alerts the driver and reduces the chance of a driving incident.

Today, progress in the field combined with a considerable increase in computational power has improved both the scale and accuracy of image data processing. Computer vision systems powered by cloud computing resources are now accessible to everyone. Any organization can use the technology for identity verification, content moderation, streaming video analysis, fault detection, and more.

Object detection is a computer vision task for detecting and localizing images. It uses classification to identify, sort, and organize images. Object detection is used in industrial and manufacturing processes to control autonomous applications and monitor production lines. Connected home camera manufacturers and service providers also rely on object detection to process live video streams from cameras to detect people and objects in real-time and provide actionable alerts to their end users.

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For example, if there is a cat and a dog in an image, segmentation can be used to recognize the two animals. Unlike object detection, which builds a box around an object, segmentation tracks pixels to determine the shape of an object, making it easier to analyze and label.

While visual information processing technology has existed for some time, much of the process required human intervention and was time consuming and error prone. For example, implementing a facial recognition system in the past required developers to manually tag thousands of images with key data points, such as the width of the nose bridge and the distance between the eyes. Automating these tasks required extensive computing power because image data is unstructured and complex for computers to organize. Vision applications were thus expensive and inaccessible to most organizations.

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Computer visiontutorial

Computer vision is a technology that machines use to automatically recognize images and describe them accurately and efficiently. Today, computer systems have access to a large volume of images and video data sourced from or created by smartphones, traffic cameras, security systems, and other devices. Computer vision applications use artificial intelligence and machine learning (AI/ML) to process this data accurately for object identification and facial recognition, as well as classification, recommendation, monitoring, and detection.

For customers who want to create a standard computer vision solution across their business, Amazon SageMaker makes it easy to prepare data and build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows, including no-code offerings for business analysts.

Numerous computer vision applications are used in entertainment, business, healthcare, transportation, and everyday life. We look at some use cases below:

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Deep learning is a type of ML that uses neural networks. Deep learning neural networks are made of many layers of software modules called artificial neurons that work together inside the computer. They use mathematical calculations to automatically process different aspects of image data and gradually develop a combined understanding of the image.

Computer vision basicspdf

Autonomous vehicle technology uses computer vision to recognize real-time images and build 3D maps from multiple cameras fitted to autonomous transport. It can analyze images and identify other road users, road signs, pedestrians, or obstacles.

Similarly, computer vision can improve personal safety at home as well as in the workplace. For example, recognition technology can monitor myriad safety-related issues. These include at-home real-time streams detecting pets, or live front-door cameras detecting visitors or packages delivered. In the workplace, such monitoring includes wearing of appropriate personal protective equipment by workers, informing warning systems, or generating reports.

For customers building on frameworks and managing their own infrastructure, we optimize versions of the most popular deep learning frameworks, including PyTorch, MXNet, and TensorFlow. AWS provides a broad and deep portfolio of compute, networking, and storage infrastructure ML services with a choice of processors and accelerators to meet unique performance and budget needs.

Object tracking uses deep learning models to identify and track items belonging to categories. It has several real-world applications across multiple industries. The first element of object tracking is object detection; the object has a bounding box created around it, is given an object ID, and can be tracked through frames. For example, object tracking can be used for traffic monitoring in urban environments, human surveillance, and medical imaging.