This article deals with machine vision techniques applied to timber grading singularities. Timber used for architectural purposes must satisfy certain mechanical requirements, and, therefore, must be mechanically graded to ensure the manufacturer that the product complies with the requirements. However, the timber material has many singularities, such as knots, cracks, and presence of juvenile wood, which influence its mechanical behavior. Thus, identifying those singularities is of great importance. We address the problem of timber defects segmentation and classification and propose a method to detect timber defects such as cracks and knots using a bag-of-words approach. Extensive experimental results show that the proposed methods are efficient and can improve grading machines performances. We also propose an automated method for the detection of transverse knots, which allows the computation of knot depth ratio (KDR) images. Finally, we propose a method for the detection of juvenile wood regions based on tree rings detection and the estimation of the tree’s pith. The experimental results show that the proposed methods achieve excellent results for knots detection, with a recall of 0.94 and 0.95 on two datasets, as well as for KDR image computation and juvenile timber detection.
In the present work, a novel signal denoising technique for piecewise constant or linear signals is presented termed as "signal split." The proposed method separates the sharp edges or transitions from the noise elements by splitting the signal into different parts. Unlike many noise removal techniques, the method works only in the nonorthogonal domain. The new method utilizes Stein unbiased risk estimate (SURE) to split the signal, Lipschitz exponents to identify noise elements, and a polynomial fitting approach for the sub signal reconstruction. At the final stage, merging of all parts yield in the fully denoised signal at a very low computational cost. Statistical results are quite promising and performs better than the conventional shrinkage methods in the case of different types of noise, i.e., speckle, Poisson, and white Gaussian noise. The method has been compared with the state of the art SURE-linear expansion of thresholds denoising technique as well and performs equally well. The method has been extended to the multisplitting approach to identify small edges which are difficult to identify due to the mutual influence of their adjacent strong edges.
In this work, we attempt to propose a signal restoration technique from the noise corrupted signal. The main
diculty in most of the noise removal approaches is the extraction of singularities which are part of the signal
from noise elements. In order to over come this problem, the propose method measures the Lipschitz exponent of
the transitions to extract the noise elements. Unlike many noise removal techniques, the present method works
in the non orthogonal domain. These noise elements were identied from the decaying slope of modulus maxima
lines and is termed as Lipschitz exponents. The main contribution of the work is the reconstruction process.
By utilizing the property of Lipschitz exponents, it is possible to reconstruct the smooth signal by non linear
functioning. Statistical results are quite promising and performs better than conventional shrinkage methods
in the case of high variance noise. Furthermore, in order to extract noise elements the proposed method is not
limited with the selection of wavelet function for the addressed signal as well.
KEYWORDS: Electrocardiography, Denoising, Signal analysis, Signal detection, Heart, Interference (communication), Wavelet transforms, Fourier transforms, Signal processing, Signal analyzers
In this article, we present the method of empirical modal decomposition (EMD) applied to the electrocardiograms and
phonocardiograms signals analysis and denoising. The objective of this work is to detect automatically cardiac anomalies
of a patient. As these anomalies are localized in time, therefore the localization of all the events should be preserved
precisely. The methods based on the Fourier Transform (TFD) lose the localization property [13] and in the case of
Wavelet Transform (WT) which makes possible to overcome the problem of localization, but the interpretation remains
still difficult to characterize the signal precisely.
In this work we propose to apply the EMD (Empirical Modal Decomposition) which have very significant properties on
pseudo periodic signals. The second section describes the algorithm of EMD. In the third part we present the result
obtained on Phonocardiograms (PCG) and on Electrocardiograms (ECG) test signals. The analysis and the interpretation
of these signals are given in this same section. Finally, we introduce an adaptation of the EMD algorithm which seems to
be very efficient for denoising.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.