Information visualization or information visualisation is the study of (interactive) visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. The naming of subfields is sometimes confusing. One accepted definition is that it's information visualization when the spatial representation is chosen, whereas it's scientific visualization when the spatial representation is given.
Scopus is Elsevier’s abstract and citation database launched in 2004. Scopus covers nearly 36,377 titles from approximately 11,678 publishers, of which 34,346 are peer-reviewed journals in top-level subject fields: life sciences, social sciences, physical sciences and health sciences. It covers three types of sources: book series, journals, and trade journals. All journals covered in the Scopus database, regardless of who they are published under, are reviewed each year to ensure high quality standards are maintained. Searches in Scopus also incorporate searches of patent databases. Scopus gives four types of quality measure for each title; those are h-Index, CiteScore, SJR and SNIP.
Shahjalal University of Science and Technology (SUST) is a public research university based in Sylhet, Bangladesh. It is the 8th oldest university of the country and the first university to adopt American credit system. The university is known for its pioneering research and education in the physical sciences and engineering. In 2016, SUST ranked as the top research university in Bangladesh by Scopus-SCImago institution ranking. In 2017, the university had highest research expenditure among all universities in Bangladesh. Following the success of SUST, twelve more STEM universities have been established in the country by the Government of Bangladesh.
Thomas Shi-Tao Huang was a Chinese-born American electrical engineer and computer scientist. He was a researcher and professor emeritus at the University of Illinois at Urbana-Champaign (UIUC). Huang was one of the leading figures in computer vision, pattern recognition and human computer interaction.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
The International Conference on Computer Vision (ICCV) is a research conference sponsored by the Institute of Electrical and Electronics Engineers (IEEE) held every other year. It is considered to be one of the top conferences in computer vision, alongside CVPR and ECCV, and it is held on years in which ECCV is not.
The Conference on Computer Vision and Pattern Recognition (CVPR) is an annual conference on computer vision and pattern recognition, which is regarded as one of the most important conferences in its field.
Matti Kalevi Pietikäinen is a computer scientist. He is currently Professor (emer.) in the Center for Machine Vision and Signal Analysis, University of Oulu, Finland. His research interests are in texture-based computer vision, face analysis, affective computing, biometrics, and vision-based perceptual interfaces. He was Director of the Center for Machine Vision Research, and Scientific Director of Infotech Oulu.
The MNIST database is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning. It was created by "re-mixing" the samples from NIST's original datasets. The creators felt that since NIST's training dataset was taken from American Census Bureau employees, while the testing dataset was taken from American high school students, it was not well-suited for machine learning experiments. Furthermore, the black and white images from NIST were normalized to fit into a 28x28 pixel bounding box and anti-aliased, which introduced grayscale levels.
Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of principal component analysis (PCA) which works well with respect to grossly corrupted observations. A number of different approaches exist for Robust PCA, including an idealized version of Robust PCA, which aims to recover a low-rank matrix L0 from highly corrupted measurements M = L0 +S0. This decomposition in low-rank and sparse matrices can be achieved by techniques such as Principal Component Pursuit method (PCP), Stable PCP, Quantized PCP, Block based PCP, and Local PCP. Then, optimization methods are used such as the Augmented Lagrange Multiplier Method (ALM), Alternating Direction Method (ADM), Fast Alternating Minimization (FAM) or Iteratively Reweighted Least Squares (IRLS ).
Demetri Terzopoulos is a Distinguished Professor of Computer Science in the Henry Samueli School of Engineering and Applied Science at the University of California, Los Angeles, where he directs the UCLA Computer Graphics & Vision Laboratory.
The Institute of Electrical and Electronics Engineers sponsors more than 1,600 annual conferences and meetings worldwide. IEEE is also highly involved in the technical program development of numerous events including trade events, training workshops, job fairs, and other programs.
Ted V. Shaneyfelt is an American Computer Scientist and engineer known for producing the user interface on the first dual-mode cellular telephone to be commercially deployed in North America.
Bedrich Benes is a computer scientist and a researcher in Computer Graphics.
Pamela Cosman is a professor of Electrical and Computer Engineering at the University of California, San Diego. She has conducted pioneering research on the quality of compressed images for application in medical diagnostic imaging. At UCSD, Cosman currently researches ways to improve wireless video transmission.
The MICCAI Society is a professional organization for scientists in the areas of Medical Image Computing and Computer Assisted Interventions. Due to the multidisciplinary nature of these fields, the society brings together researchers from several scientific disciplines. including computer science, robotics, physics, and medicine. The society is best known for its annual flagship event, The MICCAI Conference, which facilitates the publication and presentation of original research on MICCAI-related topics. However, the society provides endorsements and sponsorships for several scientific events each year.
Energy-based generative neural networks is a class of generative models, which aim to learn explicit probability distributions of data in the form of energy-based models whose energy functions are parameterized by modern deep neural networks. Its name is due to the fact that this model can be derived from the discriminative neural networks. The parameter of the neural network in this model is trained in a generative manner by Markov chain Monte Carlo (MCMC)-based maximum likelihood estimation. The learning process follows an ''analysis by synthesis'' scheme, where within each learning iteration, the algorithm samples the synthesized examples from the current model by a gradient-based MCMC method, e.g., Langevin dynamics, and then updates the model parameters based on the difference between the training examples and the synthesized ones. This process can be interpreted as an alternating mode seeking and mode shifting process, and also has an adversarial interpretation. The first energy-based generative neural network is the generative ConvNet proposed in 2016 for image patterns, where the neural network is a convolutional neural network. The model has been generalized to various domains to learn distributions of videos, and 3D voxels. They are made more effective in their variants. They have proven useful for data generation, data recovery, data reconstruction.
