Topic
Haar
About: Haar is a research topic. Over the lifetime, 636 publications have been published within this topic receiving 6410 citations.
Papers published on a yearly basis
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Papers
Book•
27 Sep 2001
TL;DR: A comprehensive presentation of the conceptual basis of wavelet analysis, including the construction and analysis of wavelets bases, can be found in this article, along with a detailed exposition of the Haar series.
Abstract: This book provides a comprehensive presentation of the conceptual basis of wavelet analysis, including the construction and analysis of wavelet bases. It motivates the central ideas of wavelet theory by offering a detailed exposition of the Haar series, then shows how a more abstract approach allows readers to generalize and improve upon the Haar series. It then presents a number of variations and extensions of Haar construction.
413 citations
302 citations
TL;DR: An efficient numerical method for solution of nonlinear evolution equations based on the Haar wavelets approach is proposed and tested in the case of Burgers and sine-Gordon equations, demonstrating that the accuracy of theHaar wavelet solutions is quite high even in the cases of a small number of grid points.
275 citations
10 Apr 2006
TL;DR: Empirical results showed that the Haar+Adaboost method yields AU recognition rates comparable to those of the Gabor+SVM method but operates at least two orders of magnitude more quickly.
Abstract: We examined the effectiveness of using Haar features and the Adaboost boosting algorithm for FACS action unit (AU) recognition. We evaluated both recognition accuracy and processing time of this new approach compared to the state-of-the-art method of classifying Gabor responses with support vector machines. Empirical results on the Cohn-Kanade facial expression database showed that the Haar+Adaboost method yields AU recognition rates comparable to those of the Gabor+SVM method but operates at least two orders of magnitude more quickly.
188 citations
1 Jan 2006
TL;DR: In this paper, the effectiveness of using Haar features and the Adaboost boosting algorithm for FACS action unit (AU) recognition was examined. But, the Haar+Adaboost method yielded AU recognition rates comparable to those of the Gabor+SVM method but operates at least two orders of magnitude more quickly.
Abstract: We examined the effectiveness of using Haar features and the Adaboost boosting algorithm for FACS action unit (AU) recognition. We evaluated both recognition accuracy and processing time of this new approach compared to the state-of-the-art method of classifying Gabor responses with support vector machines. Empirical results on the Cohn-Kanade facial expression database showed that the Haar+Adaboost method yields AU recognition rates comparable to those of the Gabor+SVM method but operates at least two orders of magnitude more quickly.
165 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 59 |
| 2024 | 110 |
| 2023 | 88 |
| 2022 | 138 |
| 2021 | 34 |
| 2020 | 26 |