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Automatic Plankton Image Recognition
Artificial Intelligence Review, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiaoou Tang +6 more
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Image recognition with occlusions
1996We study the problem of how to detect “interesting objects” appeared in a given image, I. Our approach is to treat it as a function approximation problem based on an over-redundant basis. Since the basis (a library of image templates) is over-redundant, there are infinitely many ways to decompose I. To select the “best” decomposition we first propose a
Tyng-Luh Liu +3 more
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6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07), 2007
The authentication of people using iris-based recognition is a widely developing technology. Iris recognition is feasible for use in differentiating between identical twins. Though the iris color and the overall statistical quality of the iris texture may be dependent on genetic factors, the textural details are independent and uncorrelated for ...
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The authentication of people using iris-based recognition is a widely developing technology. Iris recognition is feasible for use in differentiating between identical twins. Though the iris color and the overall statistical quality of the iris texture may be dependent on genetic factors, the textural details are independent and uncorrelated for ...
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Statistical recognition of color images
Applied Optics, 1987The feasibility of classification of stochastic images for color vision in real time has been investigated with two approaches. First, a hybrid incoherent optical correlator based on a quasi-monochromatic cathode ray tube (CRT) is sequentially operated on red, green, and blue channels for statistical pattern recognition.
Z H, Gu, S H, Lee, Y, Fainman
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Synthesized images for pattern recognition
Pattern Recognition, 1995Abstract Since there is no generic procedure for machine pattern recognition due to its complexity, ad hoc computer algorithms have been developed for each class of problems. In visual pattern recognition, depending on the area of investigation, it is difficult to obtain test images with the desired characteristics.
Mario Miyojim, Heng-Da Cheng
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Ensemble learning for image recognition
2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2017With the continuous development of the Internet and information technology, data has penetrated into every area of today's industry and business functions. Now, data has been already one of the most valuable assets in the Internet and a core element of a company's competitiveness. There seems to have endless data on the Internet, then most of it cannot
Xu Chen, Long Hong, Guofang Huang
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Image approach to voice recognition
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017Systems for user verification are constantly developed, for which we need novel methods and approaches. In this article we present our research on the model for sound processing. Input signal is transformed by the use of Discrete Fourier Transform into spectrogram.
Dawid Polap, Marcin Wozniak
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Invariant recognition in hyperspectral images
Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 2003The spectral radiance measured for a material by an airborne hyperspectral sensor depends strongly on. The illumination environment and the atmospheric conditions. This dependence has limited the success of material identification algorithms that rely exclusively on the information contained in hyperspectral image data.
Glenn Healey, David Slater
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Image Processing and Recognition
1979Publisher Summary The chapter presents the basic techniques used for image processing and pictorial pattern recognition by digital computer. One of the principal goals of image processing is to improve the appearance of the picture by increasing contrast, reducing blur, or removing noise.
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Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications - HotMobile '10, 2010
We argue that the most desirable architecture for mobile image recognition runs the complete algorithm on the mobile device. Alternative solutions that run the recognizer on a remote server will not be as desirable because of the delay between image capture and receipt of a result that can cause users to abandon the technique.
Jonathan J. Hull +4 more
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We argue that the most desirable architecture for mobile image recognition runs the complete algorithm on the mobile device. Alternative solutions that run the recognizer on a remote server will not be as desirable because of the delay between image capture and receipt of a result that can cause users to abandon the technique.
Jonathan J. Hull +4 more
openaire +1 more source