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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] |
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] |
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] |
Internet
The Internet (or internet) is the global system of interconnected computer networks that uses the Internet protocol suite (TCP/IP) to communicate between networks and devices. It is a '' network of networks'' that consists of private, public, academic, business, and government networks of local to global scope, linked by a broad array of electronic, wireless, and optical networking technologies. The Internet carries a vast range of information resources and services, such as the inter-linked hypertext documents and applications of the World Wide Web (WWW), electronic mail, telephony, and file sharing. The origins of the Internet date back to the development of packet switching and research commissioned by the United States Department of Defense in the 1960s to enable time-sharing of computers. The primary precursor network, the ARPANET, initially served as a backbone for interconnection of regional academic and military networks in the 1970s to enable resource shari ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Google Earth
Google Earth is a computer program that renders a 3D computer graphics, 3D representation of Earth based primarily on satellite imagery. The program maps the Earth by superimposition, superimposing satellite images, aerial photography, and geographic information system, GIS data onto a 3D globe, allowing users to see cities and landscapes from various angles. Users can explore the globe by entering addresses and coordinates, or by using a Computer keyboard, keyboard or computer mouse, mouse. The program can also be downloaded on a smartphone or Tablet computer, tablet, using a touch screen or stylus to navigate. Users may use the program to add their own data using Keyhole Markup Language and upload them through various sources, such as forums or blogs. Google Earth is able to show various kinds of images overlaid on the surface of the earth and is also a Web Map Service client. In 2019, Google has revealed that Google Earth now covers more than 97 percent of the world, and has c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Google Maps
Google Maps is a web mapping platform and consumer application offered by Google. It offers satellite imagery, aerial photography, street maps, 360° interactive panoramic views of streets ( Street View), real-time traffic conditions, and route planning for traveling by foot, car, bike, air (in beta) and public transportation. , Google Maps was being used by over 1 billion people every month around the world. Google Maps began as a C++ desktop program developed by brothers Lars and Jens Rasmussen at Where 2 Technologies. In October 2004, the company was acquired by Google, which converted it into a web application. After additional acquisitions of a geospatial data visualization company and a real-time traffic analyzer, Google Maps was launched in February 2005. The service's front end utilizes JavaScript, XML, and Ajax. Google Maps offers an API that allows maps to be embedded on third-party websites, and offers a locator for businesses and other organizations in numero ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Comparison Of Datasets In Machine Learning
These datasets are applied for machine learning research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce. Image data These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification. Facial recognition In computer vision, face images have been used extensively to develop facial recognition syste ... [...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] |
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