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In computing, a system image is a serialized copy of the entire state of a computer system stored in some non-volatile form such as a file. A system is said to be capable of using system images if it can be shut down and later restored to exactly the same state. In such cases, system images can be used for backup.

If a system has all its state written to a disk, then a system image can be produced by simply copying that disk to a file elsewhere, often with disk cloning applications. On many systems a complete system image cannot be created by a disk cloning program running within that system because information can be held outside of disks and volatile memory, for example in non-volatile memory like boot ROMs.

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A process image is a copy of a given process's state at a given point in time. It is often used to create persistence within an otherwise volatile system. A common example is a database management system (DBMS). Most DBMS can store the state of its database or databases to a file before being closed down (see database dump). The DBMS can then be restarted later with the information in the database intact and proceed as though the software had never stopped. Another example would be the hibernate feature of many operating systems. Here, the state of all RAM memory is stored to disk, the computer is brought into an energy saving mode, then later restored to normal operation.

Similar, Lisp Machines were booted from Lisp images, called Worlds. The World contains the complete operating system, its applications and its data in a single file. It was also possible to save incremental Worlds, that contain only the changes from some base World. Before saving the World, the Lisp Machine operating system could optimize the contents of memory (better memory layout, compacting data structures, sorting data, ...).

Assuming I have two images, A and B, I pretty much want as much data as possible about each so I can later programmatically compare them. Things like "general color", "general shape", etc. would be great.

EDIT: The end goal here is to be able to have a computer tell me how "similar" too pictures are. If two images are the same but in one someone blurred out a face; they should register as fairly similar. If two pictures are completely different, the computer should be able to tell.

There are space domain and frequency domain descriptors of the image which each can be useful here. I can probably name more than 100 descriptors but in your case, only one could be sufficient or none could be useful.

So assuming that the question here is not identifying objects and there is no transform, translation, scale or rotation involved and we are only dealing with the two images which are the same but one could have more noise added upon it:

2) Frequency domain: Convert the image to frequency domain image (using FTT in an image processing tool such as OpenCV) which will be 2D as well. Do the same above squared diff as above, but perhaps you want to limit the frequencies. Then normalise by the number of pixels. This fares better on noise and translation and on a small rotation but not on scale.

As a child, David Chapman spent hours in front of his family computer, playing StarCraft, Spyro the Dragon, and other video games that used high-quality graphics to take players on fantastic adventures.

Just a year ago, Chapman joined the computer science faculty at the University of Miami, in the College of Arts and Sciences. Since then, he has collaborated with colleagues across the University to help accelerate research projects. Chapman also enjoys teaching students how to formulate their own computer algorithms to delve into data and uncover new insights.

This summer, Chapman was named the first Knight Foundation Junior Chair in Data Science and Artificial Intelligence at the Frost Institute for Data Science and Computing (IDSC). It is the second of six endowed chair positions the John and James L. Knight Foundation funded to foster more data science and artificial intelligence research at the University. In the role, Chapman will be working closely with his former mentor at University of Maryland, Baltimore County (UMBC), computer science professor Yelena Yesha, who holds the only other named Knight Chair at IDSC.

Today, Chapman spends most of his time focused on an area of artificial intelligence called computer vision, which helps computers find meaningful information from images. It also includes image recognition, or the ability for computers to sort through pictures and quickly find specific objects, people, or other entities.

But Chapman wants to improve upon these tools. In working on computer vision algorithms, he has noticed they often spit out a definitive answer, even if the new dataset is different than the one they were trained on. Often, this means the results are biased.

Yet, Chapman is also using his computer vision expertise to help colleagues in the College of Engineering create a program that will detect microcracks in concrete. Finding these early will ideally help buildings maintain their structural integrity before they become larger issues, he said.

Computer Vision is the study of dealing with how computers can gain their understanding from digital images, videos or other visual inputs. It then provide actions or recommendations based on that information. While AI trains computers and smart devices to reason, Computer Vision helps them to see, observe and understand. The key topics presented in Computer Vision include cameras and sensors, detection and recognition, learning, basic geometry-based vision and video analysis. Meanwhile, Image Processing aims to develop algorithms to tackle the popular tasks in digital images and videos, including: image enhancement, image restoration, image compression and feature extraction.

Computers work in binaryclosebinaryA number system that contains two symbols, 0 and 1. Also known as base 2.. All data must be converted into binary in order for a computer to process it. Images are no exception.

Many images need to use colours. To add colour, more bits are required for each pixelclosepixelPicture element - a single dot of colour in a digital bitmap image or on a computer screen.. The number of bits determines the range of colours. This is known as an image's colour depthclosecolour depthThe amount of bits available for colours in an image..

Image quality is affected by the resolutioncloseresolutionThe fineness of detail that can be seen in an image - the higher the resolution of an image, the more detail it holds. In computing terms, resolution is measured in dots per inch (dpi). of the image. The resolution of an image is a way of describing how tightly packed the pixels are.

In a low-resolution image, the pixels are larger and therefore, fewer are needed to fill the space. This results in images that look blocky or pixelated. An image with a high resolution has more pixels, so it looks a lot better when it is enlarged or stretched. The higher the resolution of an image, the larger its file size will be.

Files contain extra data called metadataclosemetadataData about data, eg photo image files have data about where the photo was taken and which camera took the picture.. Metadata includes data about the file itself, such as:

To present mathematics relevant to image processing and analysis using real image computing objectives, as illustrated by computer implementations. The image computing objectives of concern include sharpening blurred images, denoising images, rotating or scaling objects in images, changing the number of image pixels, and locating object edges and bars in images. This mathematics is important in understanding methods that improve image quality, that find, recognize, or visualize objects in images, or that register images.

The School of Chemical Sciences has announced the 2022 Science Image Challenge list of winners and finalists, which includes several images highlighting innovative research projects in the Department of Chemistry.

The winning images and finalists' images from the 2022 Science Image Challenge will go on exhibit at Willard Airport in Champaign. The School of Chemical Sciences, the Department of Chemistry and the Department of Chemical and Biomolecular Engineering thank Willard Airport for hosting the winning images exhibit.

Image title: Driving a muscle

Image description: An immunofluorescence image of innervation between a motor neuron (green) and skeletal muscles (red). During neural innervation of skeletal muscle, a neuromuscular junction forms at the interface becoming a chemical synapse for neuromuscular communication. Our work aims to discover the crosstalk between nerves and muscles.

Image title: Anisotropic Art

Image description: Presented is a false colored transmission electron microscopy image of gold nanorods. Their absorption peaks are tunable throughout the visible and near-infrared light regions. Additionally, gold nanorods can undergo a variety of surface modifications. This adaptability makes them highly useful for applications such as biosensing and photothermal therapy.

The School of Chemical Sciences invites graduate and undergraduate students, postdoctoral associates/fellows, and staff members within the school, including the departments of chemistry and chemical and biomolecular engineering, to submit computer-assisted or traditional scientific images designed to inform, educate, and inspire.

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at -freiburg.de/people/ronneber/u-net . 006ab0faaa