@article {16718,
	title = {The Bible, Truth, and Multilingual OCR Evaluation},
	year = {1998},
	month = {1998/12//},
	institution = {University of Maryland, College Park},
	abstract = {Multilingual OCR has emerged as an important information technology, thanks to the increasing need for cross-language information access. While many research groups and companies have developed OCR algorithms for various languages, it is difficult to compare the performance of these OCR algorithms across languages. This difficulty arises because most evaluation methodologies rely on the use of a document image dataset in each of the languages and it is difficult to find document datasets in different languages that are similar in content and layout. In this paper we propose to use the Bible as a dataset for comparing OCR accuracy across languages. Besides being available in a wide range of languages, Bible translation are closely parallel in content, carefully translated, surprisingly relevant with respect to modern-day language, and quite inexpensive. A project at the University of Maryland is currently implementing this idea. We have created a scanned image dataset with groundtruth from an Arabic Bible. We have also used image degradation models to create synthetically degraded images of a French Bible. We hope to generate similar Bible datasets for other languages, and we are exploring alternative corpora such as the Koran and the Bhagavad Gita that have similar properties. Quantitative OCR evaluation based on the Arabic Bible dataset is currently in progress.},
	keywords = {*BIBLE, *CORPUS, *DATASETS, *GROUNDTRUTH, *OPTICAL CHARACTER RECOGNITION, *TEST SETS, *TRANSLATIONS, accuracy, algorithms, CYBERNETICS, DOCUMENT IMAGES, DOCUMENTS, IMAGES, INFORMATION SCIENCE, LANGUAGE, linguistics, MULTILINGUAL OCR(OPTICAL CHARACTER RECOGNITION), SYMPOSIA, TEST AND EVALUATION},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458666},
	author = {Kanungo,Tapas and Resnik, Philip}
}
@article {12051,
	title = {Computing Intrinsic Images.},
	year = {1986},
	month = {1986/08//},
	abstract = {Low level modern computer vision is not domain dependent, but concentrates on problems that correspond to identifiable modules in the human visual system. Several theories have been proposed in the literature for the computation of shape from shading, shape from texture, retinal motion from spatiotemporal derivatives of the image intensity function and the like. The problems with the existing approach are basically the following: (1) The employed assumptions are very strong and so most of the algorithms fail when applied to real images. (2) Usually the constraints from the geometry and the physics of the problem are not enough to guarantee uniqueness of the computed parameters. (3) In most cases the resulting algorithms are not robust, in the sense that if there is a slight error in the input this results in a catastrophic error in the output. In this thesis the problem of machine vision is explored from its basics. A low level mathematical theory is presented for the unique robust computation of intrinsic parameters. The computational aspect of the theory envisages a cooperative highly parallel implementation, bringing in information from five different sources (shading, texture, motion, contour and stereo), to resolve ambiguities and ensure uniqueness and stability of the intrinsic parameters. The problems of shape from texture, shape from shading and motion, visual motion analysis and shape and motion from contour are analyzed in detail.},
	keywords = {*ARTIFICIAL INTELLIGENCE, *COMPUTERS, *IMAGE PROCESSING, *VISION, algorithms, CAMERAS, CATASTROPHIC CONDITIONS, COMPUTATIONS, CYBERNETICS, ERRORS, HUMANS, IMAGES, INTENSITY, LOW LEVEL, MATHEMATICS, MOTION, RETINA, Robots, SHADOWS, SHAPE, TEXTURE, THEORY, THESES.},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA189440},
	author = {Aloimonos, J.}
}
@article {11945,
	title = {Correspondence from Correspondence},
	journal = {Optical Society of America, Topical Meeting on Machine Vision},
	year = {1980},
	month = {1980///},
	pages = {46 - 51},
	abstract = {Image matching (or registration), is in general terms, aligning two identical or similar images or parts of images, and it is basic to a number of vision problems. These include optical nagivation (also known as motion analysis), stereo image interpretation, object analysis, change detection and others. The difficult part of all these problems, is the matching problem, i.e., the computation of the correspondence of elements in the different views. The computation of correspondence, in both the stereo and the motion problem is impossible without the use of some assumptions about the visual world and various researchers have proposed several theories for the computation of visual correspondence that worked well for the domains that fitted their assumptions. Studied here is the problem of the computation of visual correspondence, under the assumption that some values of the correspondence function are already known. The mathematical constraints that are needed for growing a solution for the correspondence function from points where its value is known are developed.},
	keywords = {ALIGNMENT, COMPUTATION, Error analysis, IMAGE PROCESSING, IMAGES, STEREOSCOPIC VISION, VECTORS (MATHEMATICS)},
	url = {http://adsabs.harvard.edu/abs/1980osa..meet...46A},
	author = {Aloimonos, J. and Kamgar-Parsi,Behrooz}
}
