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Wavelets and adaptive thresholding
Journal of Communications Technology and Electronics, 2014The discrete wavelet transform and its application for signal denoising is considered. The article is oriented to readers unfamiliar with the wavelet theory and, therefore, basic definitions and theorems required for the understanding of the material below are presented at the beginning of the article.
M. V. Obidin, A. P. Serebrovski
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Analysis of an Adaptive Threshold Logic Unit
IEEE Transactions on Computers, 1970In this paper an adaptive threshold logic unit is analyzed. The unit consists of a set of self-adjusting weights, a summing device, and a comparator. Its dynamic and steady-state behavior is made clear by investigating the solutions of a system of nonlinear differential equations which describes changes in the weights.
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Adaptive estimation with soft thresholding penalties
Statistica Neerlandica, 2002We show that various robust nonparametric regression estimators, such as the least absolute deviations estimator, can be made adaptive (up to logarithmic factors), by adding a soft thresholding type penalty to the loss function. As an example, we consider the situation where the roughness of the regression function is described by a single parameter p.
Loubes, Jean-Michel, van de Geer, Sara
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Plant Cell Segmentation with Adaptive Thresholding
2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 2018There are many approaches to plant cell segmentation, but there is no established method to segment plant cell for a portable, USB-powered optical microscope. Existing methods leverage on sophisticated microscope such as confocal laser scanning microscope or electron microscope may not be applicable for a portable setup.
Zhong Hoo Chau +3 more
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2016 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW), 2016
The article considers the development of an algorithm to detect targets in the presence of noise radar method using accumulation between periods.
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The article considers the development of an algorithm to detect targets in the presence of noise radar method using accumulation between periods.
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Wavelet thresholding and adaptation
1998This chapter treats in more detail the adaptivity property of nonlinear (thresholded) wavelet estimates. We first introduce different modifications and generalizations of soft and hard thresholding. Then we develop the notion of adaptive estimators and present the results about adaptivity of wavelet thresholding for density estimation problems. Finally,
Wolfgang Härdle +3 more
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Self-adapting threshold of pulmonary parenchyma
2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2016Segmentation for pulmonary parenchyma is a crucial step for computer aided diagnosis (CAD) systems. The accuracy of pulmonary parenchyma segmentation can have a great impact on further steps of CAD systems, such as pulmonary nodule detection and feature extraction.
Xin-Yue Li +5 more
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Efficient adaptive thresholding with image masks
SPIE Proceedings, 2014Adaptive thresholding is a useful technique for document analysis. In medical image processing, it is also helpful for segmenting structures, such as diaphragms or blood vessels. This technique sets a threshold using local information around a pixel, then binarizes the pixel according to the value.
Young-Taek Oh +3 more
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An efficient architecture for adaptive progressive thresholding
Asia-Pacific Conference on Circuits and Systems, 2003A new pipelined architecture for adaptive progressive thresholding (APT) is proposed. Unlike the conventional architectures that rely heavily on multipliers and dividers to evaluate the maximum between-class variance, our method employs a reconfigurable logarithmic computing unit to simplify the circuitry and increases the application's ability to ...
Hui Tian +3 more
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ADAPT: Adaptive Thresholds for Feature Extraction
2017Threshold-based feature definitions remain one of the most widely used and most intuitive choice in a wide range of scientific areas. However, it is well known that in many applications selecting a single optimal threshold is difficult or even impossible.
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