Computers & Graphics is a peer-reviewed scientific journal that covers computer graphics and related subjects such as data visualization, human-computer interaction, virtual reality, and augmented reality. It was established in 1975 and originally published by Pergamon Press. It is now published by Elsevier, which acquired Pergamon Press in 1991. From 2018 to 2022 Graphics and Visual Computing was an open access sister journal sharing the same editorial team and double-blind peer-review policies. It has since merged into GMOD, the International Journal of Graphical Models. == History == The journal was established in 1975 by founding editor-in-chief Robert Schiffman (University of Colorado, Boulder), as Computers & Graphics-UK. Schiffman, who co-organized the first SIGGRAPH conference in 1974, had the conference proceedings published as the first issue of the journal. He was succeeded in 1978 by Larry Feeser (Rensselaer Polytechnic Institute). In 1983 José Luis Encarnação (Technische Hochschule Darmstadt) took over. Joaquim Jorge (University of Lisbon) has been Editor-in-Chief since 2007. == Replicability == The journal is working with the Graphics Replicability Stamp Initiative to promote replicable results in publication. == Abstracting and indexing == The journal is abstracted and indexed in: Current Contents/Engineering, Computing & Technology EBSCO databases Ei Compendex Inspec ProQuest databases Science Citation Index Expanded Scopus Chinese Computer Federation/Recommended List of International Conferences and Journals on CAD & Graphics and Multimedia. According to the Journal Citation Reports, the journal has a 2022 impact factor of 2.5.
Empowerment (artificial intelligence)
Empowerment in the field of artificial intelligence formalises and quantifies (via information theory) the potential an agent perceives that it has to influence its environment. An agent which follows an empowerment maximising policy, acts to maximise future options (typically up to some limited horizon). Empowerment can be used as a (pseudo) utility function that depends only on information gathered from the local environment to guide action, rather than seeking an externally imposed goal, thus is a form of intrinsic motivation. The empowerment formalism depends on a probabilistic model commonly used in artificial intelligence. An autonomous agent operates in the world by taking in sensory information and acting to change its state, or that of the environment, in a cycle of perceiving and acting known as the perception-action loop. Agent state and actions are modelled by random variables ( S : s ∈ S , A : a ∈ A {\displaystyle S:s\in {\mathcal {S}},A:a\in {\mathcal {A}}} ) and time ( t {\displaystyle t} ). The choice of action depends on the current state, and the future state depends on the choice of action, thus the perception-action loop unrolled in time forms a causal bayesian network. == Definition == Empowerment ( E {\displaystyle {\mathfrak {E}}} ) is defined as the channel capacity ( C {\displaystyle C} ) of the actuation channel of the agent, and is formalised as the maximal possible information flow between the actions of the agent and the effect of those actions some time later. Empowerment can be thought of as the future potential of the agent to affect its environment, as measured by its sensors. E := C ( A t ⟶ S t + 1 ) ≡ max p ( a t ) I ( A t ; S t + 1 ) {\displaystyle {\mathfrak {E}}:=C(A_{t}\longrightarrow S_{t+1})\equiv \max _{p(a_{t})}I(A_{t};S_{t+1})} In a discrete time model, Empowerment can be computed for a given number of cycles into the future, which is referred to in the literature as 'n-step' empowerment. E ( A t n ⟶ S t + n ) = max p ( a t , . . . , a t + n − 1 ) I ( A t , . . . , A t + n − 1 ; S t + n ) {\displaystyle {\mathfrak {E}}(A_{t}^{n}\longrightarrow S_{t+n})=\max _{p(a_{t},...,a_{t+n-1})}I(A_{t},...,A_{t+n-1};S_{t+n})} The unit of empowerment depends on the logarithm base. Base 2 is commonly used in which case the unit is bits. === Contextual Empowerment === In general the choice of action (action distribution) that maximises empowerment varies from state to state. Knowing the empowerment of an agent in a specific state is useful, for example to construct an empowerment maximising policy. State-specific empowerment can be found using the more general formalism for 'contextual empowerment'. C {\displaystyle C} is a random variable describing the context (e.g. state). E ( A t n ⟶ S t + n ∣ C ) = ∑ c ∈ C p ( c ) E ( A t n ⟶ S t + n ∣ C = c ) {\displaystyle {\mathfrak {E}}(A_{t}^{n}\longrightarrow S_{t+n}{\mid }C)=\sum _{c{\in }C}p(c){\mathfrak {E}}(A_{t}^{n}\longrightarrow S_{t+n}{\mid }C=c)} == Application == Empowerment maximisation can be used as a pseudo-utility function to enable agents to exhibit intelligent behaviour without requiring the definition of external goals, for example balancing a pole in a cart-pole balancing scenario where no indication of the task is provided to the agent. Empowerment has been applied in studies of collective behaviour and in continuous domains. As is the case with Bayesian methods in general, computation of empowerment becomes computationally expensive as the number of actions and time horizon extends, but approaches to improve efficiency have led to usage in real-time control. Empowerment has been used for intrinsically motivated reinforcement learning agents playing video games, and in the control of underwater vehicles.
Direct Graphics Access
Direct Graphics Access is a plug-in for the X display servers that allows client programs direct access to the frame buffer. Graphics hardware communicates via a chunk of memory called a frame buffer. This is an array of values that represent pixel color values on the screen. Writing the appropriate values into the frame buffer therefore allows a program to paint areas of the screen. However, as with any shared resource, problems occur when multiple programs attempt to access the same resource, as they tend to write over each other's work. In the X Window System, this is solved by having a central display server that mediates between programs that want to draw on the screen. The display server also used to perform a lot of the drawing work, allowing programs to say Draw me a circle of this radius filled with this pattern or draw this text in this font. The X server does all this work, freeing programmers from having to write their own drawing code. Another advantage of the X architecture is that it works over a network, allowing programs on one machine to display output on the screen of another. Direct Graphics Access allows direct access to the frame buffer and the X-server hands over control of the frame buffer to the client program and waits for the client to hand it back. This means that the client program has control of the whole screen, and so it is mostly used for full-screen video/games.
Conservative morphological anti-aliasing
Conservative morphological anti-aliasing (CMAA) is an antialiasing technique originally developed by Filip Strugar at Intel. CMAA is an image-based, post processing technique similar to that of morphological antialiasing. CMAA uses 4 main steps which are image analysis for color discontinuities, locally dominant edge detection, simple shape handling, and lastly symmetrical long edge shape handling. A couple of years after CMAA was introduced, Intel unveiled an updated version which they named CMAA2.
IRows
iRows was a web-based spreadsheet in beta with a GUI similar to the traditional desktop-based spreadsheet applications, such as Microsoft Excel and OpenOffice.org. It was shut down on December 31, 2006, after it was announced that its two founders had been hired by Google. iRows used Ajax and XML. It was described as an example of a Web 2.0 system. iRows supported conventional spreadsheet features functions, value formatting and charts and added web oriented spreadsheet capabilities like collaboration (multiple people using a shared spreadsheet, sending a spreadsheet as a link instead of an attachment and ability to publish spreadsheets on other web pages (e.g. blogs).
Color normalization
Color normalization is a topic in computer vision concerned with artificial color vision and object recognition. In general, the distribution of color values in an image depends on the illumination, which may vary depending on lighting conditions, cameras, and other factors. Color normalization allows for object recognition techniques based on color to compensate for these variations. == Main concepts == === Color constancy === Color constancy is a feature of the human internal model of perception, which provides humans with the ability to assign a relatively constant color to objects even under different illumination conditions. This is helpful for object recognition as well as identification of light sources in an environment. For example, humans see an object approximately as the same color when the sun is bright or when the sun is dim. === Applications === Color normalization has been used for object recognition on color images in the field of robotics, bioinformatics and general artificial intelligence, when it is important to remove all intensity values from the image while preserving color values. One example is in case of a scene shot by a surveillance camera over the day, where it is important to remove shadows or lighting changes on same color pixels and recognize the people that passed. Another example is automated screening tools used for the detection of diabetic retinopathy as well as molecular diagnosis of cancer states, where it is important to include color information during classification. == Known issues == The main issue about certain applications of color normalization is that the result looks unnatural or too distant from the original colors. In cases where there is a subtle variation between important aspects, this can be problematic. More specifically, the side effect can be that pixels become divergent and not reflect the actual color value of the image. A way of combating this issue is to use color normalization in combination with thresholding to correctly and consistently segment a colored image. == Transformations and algorithms == There is a vast array of different transformations and algorithms for achieving color normalization and a limited list is presented here. The performance of an algorithm is dependent on the task and one algorithm which performs better than another in one task might perform worse in another (no free lunch theorem). Additionally, the choice of the algorithm depends on the preferences of the user for the end-result, e.g. they may want a more natural-looking color image. === Grey world === The grey world normalization makes the assumption that changes in the lighting spectrum can be modelled by three constant factors applied to the red, green and blue channels of color. More specifically, a change in illuminated color can be modelled as a scaling α, β and γ in the R, G and B color channels and as such the grey world algorithm is invariant to illumination color variations. Therefore, a constancy solution can be achieved by dividing each color channel by its average value as shown in the following formula: ( α R , β G , γ B ) → ( α R α n ∑ i R , β G β n ∑ i G , γ B γ n ∑ i B ) {\displaystyle \left(\alpha R,\beta G,\gamma B\right)\rightarrow \left({\frac {\alpha R}{{\frac {\alpha }{n}}\sum _{i}R}},{\frac {\beta G}{{\frac {\beta }{n}}\sum _{i}G}},{\frac {\gamma B}{{\frac {\gamma }{n}}\sum _{i}B}}\right)} As mentioned above, grey world color normalization is invariant to illuminated color variations α, β and γ, however it has one important problem: it does not account for all variations of illumination intensity and it is not dynamic; when new objects appear in the scene it fails. To solve this problem there are several variants of the grey world algorithm. Additionally there is an iterative variation of the grey world normalization, however it was not found to perform significantly better. === Histogram equalization === Histogram equalization is a non-linear transform which maintains pixel rank and is capable of normalizing for any monotonically increasing color transform function. It is considered to be a more powerful normalization transformation than the grey world method. The results of histogram equalization tend to have an exaggerated blue channel and look unnatural, due to the fact that in most images the distribution of the pixel values is usually more similar to a Gaussian distribution, rather than uniform. === Histogram specification === Histogram specification transforms the red, green and blue histograms to match the shapes of three specific histograms, rather than simply equalizing them. It refers to a class of image transforms which aims to obtain images of which the histograms have a desired shape. As specified, firstly it is necessary to convert the image so that it has a particular histogram. Assume an image x. The following formula is the equalization transform of this image: y = f ( x ) = ∫ 0 x p x ( u ) d u {\displaystyle y=f(x)=\int \limits _{0}^{x}p_{x}(u)du} Then assume wanted image z. The equalization transform of this image is: y ′ = g ( z ) = ∫ 0 z p z ( u ) d u {\displaystyle y'=g(z)=\int \limits _{0}^{z}p_{z}(u)du} Of course p z ( u ) {\displaystyle p_{z}(u)} is the histogram of the output image. The formula to find the inverse of the above transform is: z = g − 1 ( y ′ ) {\displaystyle z=g^{-1}(y')} Therefore, since images y and y' have the same equalized histogram they are actually the same image, meaning y = y' and the transform from the given image x to the wanted image z is: z = g − 1 ( y ′ ) = g − 1 ( y ) = g − 1 ( f ( x ) ) {\displaystyle z=g^{-1}(y')=g^{-1}(y)=g^{-1}(f(x))} Histogram specification has the advantage of producing more realistic looking images, as it does not exaggerate the blue channel like histogram equalization. === Comprehensive Color Normalization === The comprehensive color normalization is shown to increase localization and object classification results in combination with color indexing. It is an iterative algorithm which works in two stages. The first stage is to use the red, green and blue color space with the intensity normalized, to normalize each pixel. The second stage is to normalize each color channel separately, so that the sum of the color components is equal to one third of the number of pixels. The iterations continue until convergence, meaning no additional changes. Formally: Normalize the color image f ( t ) = [ f i j ( t ) ] i = 1... N , j = 1... M {\displaystyle f^{(t)}=[f_{ij}^{(t)}]_{i=1...N,j=1...M}} which consists of color vectors f i j ( t ) = ( r i j ( t ) , g i j ( t ) , b i j ( t ) ) T . {\displaystyle f_{ij}^{(t)}=(r_{ij}^{(t)},g_{ij}^{(t)},b_{ij}^{(t)})^{T}.} For the first step explained above, compute: S i j := r i j ( t ) + g i j ( t ) + b i j ( t ) {\displaystyle S_{ij}:=r_{ij}^{(t)}+g_{ij}^{(t)}+b_{ij}^{(t)}} which leads to r i j ( t + 1 ) = r i j ( t ) S i j , g i j ( t + 1 ) = g i j ( t ) S i j {\displaystyle r_{ij}^{(t+1)}={\frac {r_{ij}^{(t)}}{S_{ij}}},g_{ij}^{(t+1)}={\frac {g_{ij}^{(t)}}{S_{ij}}}} and b i j ( t + 1 ) = b i j ( t ) S i j . {\displaystyle b_{ij}^{(t+1)}={\frac {b_{ij}^{(t)}}{S_{ij}}}.} For the second step explained above, compute: r ′ = 3 N M ∑ i = 1 N ∑ j = 1 M r i j ( t + 1 ) {\displaystyle r'={\frac {3}{NM}}\sum _{i=1}^{N}\sum _{j=1}^{M}r_{ij}^{(t+1)}} and normalize r i j ( t + 2 ) = r i j ( t + 1 ) r ′ . {\displaystyle r_{ij}^{(t+2)}={\frac {r_{ij}^{(t+1)}}{r'}}.} Of course the same process is done for b' and g'. Then these two steps are repeated until the changes between iteration t and t+2 are less than some set threshold. Comprehensive color normalization, just like the histogram equalization method previously mentioned, produces results that may look less natural due to the reduction in the number of color values.
GitHub Codespaces
GitHub Codespaces is a cloud-based online integrated development environment developed by GitHub. It allows users to create and manage development environments directly within the browser or through Visual Studio Code desktop. Codespaces is tightly integrated with GitHub repositories and enables on-demand coding, debugging, and testing in a full-featured development container hosted in the cloud. == Features == Instant development environments integrated with GitHub Browser-based and desktop access via Visual Studio Code Configurable Dockerfile or devcontainer.json environments Built-in support for GitHub Copilot, extensions, snippets, and SSH. == Licensing == GitHub Codespaces is proprietary software and available to GitHub users under various subscription plans. Codespaces includes a monthly usage quota for free tier users of 120 hours, and expanded access for GitHub education, Pro, Team, and GitHub Enterprise plans. == GitHub Classroom == GitHub Classroom is an educational tool developed by GitHub to streamline the process of managing programming assignments and coursework. Integrated with GitHub repositories, it allows instructors to distribute starter code, automate grading workflows, and track student progress. GitHub Classroom is widely used in computer science education and supports integration with GitHub Codespaces for cloud-based development environments. == Programming languages supported == == Extensions == Some of the popular extensions include: