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Caltech 101
Caltech 101 is a data set of digital images created in September 2003 and compiled by Fei-Fei Li, Marco Andreetto, Marc 'Aurelio Ranzato and Pietro Perona at the California Institute of Technology. It is intended to facilitate Computer Vision research and techniques and is most applicable to techniques involving image recognition classification and categorization. Caltech 101 contains a total of 9,146 images, split between 101 distinct object categories (faces, watches, ants, pianos, etc.) and a background category. Provided with the images are a set of annotations describing the outlines of each image, along with a Matlab script for viewing. Purpose Most Computer Vision and Machine Learning algorithms function by training on example inputs. They require a large and varied set of training data to work effectively. For example, the real-time face detection method used by Paul Viola and Michael J. Jones was trained on 4,916 hand-labeled faces. Cropping, re-sizing and hand-marking ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Set
A data set (or dataset) is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files. In the open data discipline, data set is the unit to measure the information released in a public open data repository. The European data.europa.eu portal aggregates more than a million data sets. Some other issues ( real-time data sources, non-relational data sets, etc.) increases the difficulty to reach a consensus about it. Properties Several characteristics define a data set's structure and properties. These include the number and types of the attributes or variables, and various statistical measures applic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Vision
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The scientific discipline of computer vision is concerned with the theory ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Overhead Imagery Research Data Set
The Overhead Imagery Research Data Set (OIRDS) is a collection of an open-source, annotated, overhead images that computer vision researchers can use to aid in the development of algorithms. Most computer vision and machine learning algorithms function by training on a large set of example data.Caltech 101 Wikipedia article, Caltech 101 Further, for many academic and industry researchers, the availability of truth-labeled test data helps drive algorithm research. While a great deal of terrestrial imagery is available on the Internet from various sources, there are few (if any) repositories of overhead imagery. The limited overhead imagery that is found via sources such as Google Earth or Google Maps is copyrighted or may have limited use.Google Permissions – http://www.google.com/permissions/geoguidelines.html Vehicle Data Set The initial ~1,000 images in the OIRDS is focused on an Automatic Target Detection (ATD) task for passenger vehicles. Passenger vehicles in the OIRDS co ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
MIT Computer Science And Artificial Intelligence Laboratory
Computer Science and Artificial Intelligence Laboratory (CSAIL) is a research institute at the Massachusetts Institute of Technology (MIT) formed by the 2003 merger of the Laboratory for Computer Science (LCS) and the Artificial Intelligence Laboratory (AI Lab). Housed within the Ray and Maria Stata Center, CSAIL is the largest on-campus laboratory as measured by research scope and membership. It is part of the Schwarzman College of Computing but is also overseen by the MIT Vice President of Research. Research activities CSAIL's research activities are organized around a number of semi-autonomous research groups, each of which is headed by one or more professors or research scientists. These groups are divided up into seven general areas of research: * Artificial intelligence * Computational biology * Graphics and vision * Language and learning * Theory of computation * Robotics * Systems (includes computer architecture, databases, distributed systems, networks and networked sy ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
LabelMe
LabelMe is a project created by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) which provides a dataset of digital images with annotations. The dataset is dynamic, free to use, and open to public contribution. The most applicable use of LabelMe is in computer vision research. As of October 31, 2010, LabelMe has 187,240 images, 62,197 annotated images, and 658,992 labeled objects. Motivation The motivation behind creating LabelMe comes from the history of publicly available data for computer vision researchers. Most available data was tailored to a specific research group's problems and caused new researchers to have to collect additional data to solve their own problems. LabelMe was created to solve several common shortcomings of available data. The following is a list of qualities that distinguish LabelMe from previous work. * Designed for recognition of a class of objects instead of single instances of an object. For example, a traditional dataset may hav ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Caltech 256
The California Institute of Technology (branded as Caltech or CIT)The university itself only spells its short form as "Caltech"; the institution considers other spellings such a"Cal Tech" and "CalTech" incorrect. The institute is also occasionally referred to as "CIT", most notably in its alma mater, but this is uncommon. is a private university, private research university in Pasadena, California. Caltech is ranked among the best and most selective academic institutions in the world, and with an enrollment of approximately 2400 students (acceptance rate of only 5.7%), it is one of the world's most selective universities. The university is known for its strength in science and engineering, and is among a small group of Institute of Technology (United States), institutes of technology in the United States which is primarily devoted to the instruction of pure and applied sciences. The institution was founded as a preparatory and vocational school by Amos G. Throop in 1891 and began ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Aliasing
In signal processing and related disciplines, aliasing is an effect that causes different signals to become indistinguishable (or ''aliases'' of one another) when sampled. It also often refers to the distortion or artifact that results when a signal reconstructed from samples is different from the original continuous signal. Aliasing can occur in signals sampled in time, for instance digital audio, or the stroboscopic effect, and is referred to as temporal aliasing. It can also occur in spatially sampled signals (e.g. moiré patterns in digital images); this type of aliasing is called spatial aliasing. Aliasing is generally avoided by applying low-pass filters or anti-aliasing filters (AAF) to the input signal before sampling and when converting a signal from a higher to a lower sampling rate. Suitable reconstruction filtering should then be used when restoring the sampled signal to the continuous domain or converting a signal from a lower to a higher sampling rate. For spa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Compression Artifact
A compression artifact (or artefact) is a noticeable distortion of media (including images, audio, and video) caused by the application of lossy compression. Lossy data compression involves discarding some of the media's data so that it becomes small enough to be stored within the desired disk space or transmitted (''streamed'') within the available bandwidth (known as the data rate or bit rate). If the compressor cannot store enough data in the compressed version, the result is a loss of quality, or introduction of artifacts. The compression algorithm may not be intelligent enough to discriminate between distortions of little subjective importance and those objectionable to the user. The most common digital compression artifacts are DCT blocks, caused by the discrete cosine transform (DCT) compression algorithm used in many digital media standards, such as JPEG, MP3, and MPEG video file formats. These compression artifacts appear when heavy compression is applied, and o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cordelia Schmid
Cordelia Schmid is computer vision researcher, currently Head of the THOTH project team at INRIA (French Institute for Research in Computer Science and Automation), Montbonnot, France. Schmid obtained a degree in Computer Science from the University of Karlsruhe, and her doctorate from the Institut National Polytechnique de Grenoble, with a prizewinning thesis on "Local Greyvalue Invariants for Image Matching and Retrieval". Schmid was named Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2012 ''for contributions to large-scale image retrieval, classification and object detection''. She was a co-winner of the Longuet-Higgins Prize in 2006, in 2014, and again in 2016. In 2017, she became a member of the Academy of Sciences Leopoldina. She won the 2020 Milner Award The Royal Society Milner Award, formally the Royal Society Milner Award and Lecture, is awarded annually by the Royal Society, a London-based learned society, for "outstanding achieve ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Svetlana Lazebnik
Svetlana Lazebnik (born 1979) is a Ukrainian-American researcher in computer vision who works as a professor of computer science and Willett Faculty Scholar at the University of Illinois at Urbana–Champaign. Her research involves interactions between image understanding and natural language processing, including the automated captioning of images, and the development of a benchmark database of textually grounded images. Education and career Lazebnik was born in Kyiv in 1979 to a family of Ukrainian Jews, and emigrated with her family to the US as a teenager. She majored in computer science at DePaul University, minoring in mathematics and graduating with the highest honors in 2000. She completed her Ph.D. in 2006 at the University of Illinois at Urbana–Champaign, with the dissertation ''Local, Semi-Local and Global Models for Texture, Object and Scene Recognition'' supervised by Jean Ponce. After postdoctoral research at the University of Illinois, she became an assistant pro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CVPR
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. According to Google Scholar Metrics (2022), it is the highest impact computing venue. Affiliations CVPR was first held in Washington DC in 1983 by Takeo Kanade and Dana Ballard (previously the conference was named Pattern Recognition and Image Processing). From 1985 to 2010 it was sponsored by the IEEE Computer Society. In 2011 it was also co-sponsored by University of Colorado Colorado Springs. Since 2012 it has been co-sponsored by the IEEE Computer Society and the Computer Vision Foundation, which provides open access to the conference papers. Scope CVPR considers a wide range of topics related to computer vision and pattern recognition—basically any topic that is extracting structures or answers from images or video or applying mathematical methods to data to extract or r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Jitendra Malik
Jitendra Malik is an Indian-American academic who is the Arthur J. Chick Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is known for his research in computer vision. Academic biography Malik was born in Mathura, India, on October 11, 1960. He did his schooling from Jabalpur, at the St. Aloysius Senior Secondary School. He received the BTech degree in electrical engineering from Indian Institute of Technology Kanpur in 1980 and the PhD degree in computer science from Stanford University in 1985. In January 1986, he joined the University of California, Berkeley, where he is currently the Arthur J. Chick Professor in the Computer Science Division, Department of Electrical Engineering and Computer Sciences (EECS).Biography from UC Berkeley EECS Faculty Homepages, retrieve ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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