The two main motivators in computer vision research are to develop algorithms to solve vision problems and to understand and model the human visual system. This work focuses on developing… Más…
The two main motivators in computer vision research are to develop algorithms to solve vision problems and to understand and model the human visual system. This work focuses on developing solutions to vision problems from the computer vision and pattern recognition community's point of view. Empirical Evaluation Techniques in Computer Vision covers methods that allow comparative assessment of algorithms and the accompanying benefits: Places computer vision on solid experimental and scientific grounds Assists the development of engineering solutions to practical problems Allows accurate assessments of computer vision research Provides convincing evidence that computer vision research results in practical solutions Empirical evaluations are divided into three basic categories providing useful insights into computer vision algorithms. Independently administered evaluations make up the first category. The second is evaluations of a set of classification algorithms by one group. The third category is composed of problems where the ground truth is not self evident. A major component of the evaluation process is to develop a method of obtaining the ground truth. Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms that determine the strengths and weaknesses of different approaches while identifying necessary further research. Successful evaluations ca algorithms,computer science,computer vision and pattern recognition,computers,computers and technology,electrical and electronics,electronics,engineering,engineering and transportation,programming Engineering & Transportation, IEEE Computer Society Press<
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The two main motivators in computer vision research are to develop algorithms to solve vision problems and to understand and model the human visual system. This work focuses on developing… Más…
The two main motivators in computer vision research are to develop algorithms to solve vision problems and to understand and model the human visual system. This work focuses on developing solutions to vision problems from the computer vision and pattern recognition community's point of view. Empirical Evaluation Techniques in Computer Vision covers methods that allow comparative assessment of algorithms and the accompanying benefits: Places computer vision on solid experimental and scientific grounds Assists the development of engineering solutions to practical problems Allows accurate assessments of computer vision research Provides convincing evidence that computer vision research results in practical solutions Empirical evaluations are divided into three basic categories providing useful insights into computer vision algorithms. Independently administered evaluations make up the first category. The second is evaluations of a set of classification algorithms by one group. The third category is composed of problems where the ground truth is not self evident. A major component of the evaluation process is to develop a method of obtaining the ground truth. Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms that determine the strengths and weaknesses of different approaches algorithms,computer science,computer vision and pattern recognition,computers,computers and technology,electrical and electronics,electronics,engineering,engineering and transportation,programming Engineering & Transportation, Wiley-IEEE Computer Society Pr<
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Wiley-IEEE Computer Society Pr. PAPERBACK. 0818684011 - BOOK IN GREAT CONDITION - -Choose Expedited Shipping for fastest delivery! Used Texts May Have Used Book Stickers on the Cover. Free USPS Tracking Number. Used texts may NOT contain supplemental materials such as CD's, info-trac, access codes, etc... Satisfaction Guaranteed! (Hawaii,Alaska, Puerto Rico and APO's, allow additional time for delivery.) . Good., Wiley-IEEE Computer Society Pr<
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Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms … Más…
Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms that determine the strengths and weaknesses of different approaches while identifying necessary further research. Successful evaluations can help convince potential users that an algorithm has matured to the point that it can be successfully fielded. Livre - Livre, [PU: IEEE Computer Society Press]<
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The two main motivators in computer vision research are to develop algorithms to solve vision problems and to understand and model the human visual system. This work focuses on developing… Más…
The two main motivators in computer vision research are to develop algorithms to solve vision problems and to understand and model the human visual system. This work focuses on developing solutions to vision problems from the computer vision and pattern recognition community's point of view. Empirical Evaluation Techniques in Computer Vision covers methods that allow comparative assessment of algorithms and the accompanying benefits: Places computer vision on solid experimental and scientific grounds Assists the development of engineering solutions to practical problems Allows accurate assessments of computer vision research Provides convincing evidence that computer vision research results in practical solutions Empirical evaluations are divided into three basic categories providing useful insights into computer vision algorithms. Independently administered evaluations make up the first category. The second is evaluations of a set of classification algorithms by one group. The third category is composed of problems where the ground truth is not self evident. A major component of the evaluation process is to develop a method of obtaining the ground truth. Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms that determine the strengths and weaknesses of different approaches while identifying necessary further research. Successful evaluations ca algorithms,computer science,computer vision and pattern recognition,computers,computers and technology,electrical and electronics,electronics,engineering,engineering and transportation,programming Engineering & Transportation, IEEE Computer Society Press<
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The two main motivators in computer vision research are to develop algorithms to solve vision problems and to understand and model the human visual system. This work focuses on developing… Más…
The two main motivators in computer vision research are to develop algorithms to solve vision problems and to understand and model the human visual system. This work focuses on developing solutions to vision problems from the computer vision and pattern recognition community's point of view. Empirical Evaluation Techniques in Computer Vision covers methods that allow comparative assessment of algorithms and the accompanying benefits: Places computer vision on solid experimental and scientific grounds Assists the development of engineering solutions to practical problems Allows accurate assessments of computer vision research Provides convincing evidence that computer vision research results in practical solutions Empirical evaluations are divided into three basic categories providing useful insights into computer vision algorithms. Independently administered evaluations make up the first category. The second is evaluations of a set of classification algorithms by one group. The third category is composed of problems where the ground truth is not self evident. A major component of the evaluation process is to develop a method of obtaining the ground truth. Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms that determine the strengths and weaknesses of different approaches algorithms,computer science,computer vision and pattern recognition,computers,computers and technology,electrical and electronics,electronics,engineering,engineering and transportation,programming Engineering & Transportation, Wiley-IEEE Computer Society Pr<
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Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms … Más…
Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms that determine the strengths and weaknesses of different approaches while identifying necessary further research. Successful evaluations can help convince potential users that an algorithm has matured to the point that it can be successfully fielded. Livre - Livre, [PU: IEEE Computer Society Press]<
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Empirical evaluations of algorithms are slowly emerging as a serious subfield in computer vision. The text builds a foundation for developing accepted practices for evaluating algorithms that determine the strengths and weaknesses of different approaches while identifying necessary further research. Successful evaluations can help convince potential users that an algorithm has matured to the point that it can be successfully fielded.
Detalles del libro - Empirical Evaluation Techniques in Computer Vision
EAN (ISBN-13): 9780818684012 ISBN (ISBN-10): 0818684011 Tapa blanda Año de publicación: 1998 Editorial: INST OF ELECTRICAL & ELEC 255 Páginas Peso: 0,363 kg Idioma: eng/Englisch
Libro en la base de datos desde 2007-05-18T02:44:20-05:00 (Mexico City) Página de detalles modificada por última vez el 2019-12-29T13:35:45-06:00 (Mexico City) ISBN/EAN: 0818684011
ISBN - escritura alterna: 0-8186-8401-1, 978-0-8186-8401-2 Mode alterno de escritura y términos de búsqueda relacionados: Autor del libro: kevin phillips, bowyer Título del libro: computer vision, eva
