The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack

Post capture refocusing effect in smartphone cameras is achievable by using focal stacks. However, the accuracy of this effect is totally dependent on the combination of the depth layers in the stack. The accuracy of the extended depth of field effect in this application can be improved significantly by computing an accurate depth map which has been an open issue for decades. To tackle this issue, in this paper, a framework is proposed based on Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth from the focal stack and synthetic defocus application. In addition to its ability to provide high structural accuracy and occlusion handling, the optimization function of the proposed method can, in fact, converge faster and better than state of the art methods. The evaluation has been done on 21 sets of focal stacks and the optimization function has been compared against 5 other methods. Preliminary results indicate that the proposed method has a better performance in terms of structural accuracy and optimization in comparison to the current state of the art methods.Comment: 15 pages, 8 figure

Fast Ewald summation for free-space Stokes potentials

We present a spectrally accurate method for the rapid evaluation of free-space Stokes potentials, i.e. sums involving a large number of free space Green's functions. We consider sums involving stokeslets, stresslets and rotlets that appear in boundary integral methods and potential methods for solving Stokes equations. The method combines the framework of the Spectral Ewald method for periodic problems, with a very recent approach to solving the free-space harmonic and biharmonic equations using fast Fourier transforms (FFTs) on a uniform grid. Convolution with a truncated Gaussian function is used to place point sources on a grid. With precomputation of a scalar grid quantity that does not depend on these sources, the amount of oversampling of the grids with Gaussians can be kept at a factor of two, the minimum for aperiodic convolutions by FFTs. The resulting algorithm has a computational complexity of O(N log N) for problems with N sources and targets. Comparison is made with a fast multipole method (FMM) to show that the performance of the new method is competitive.Comment: 35 pages, 15 figure

Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation

Stain variation is a unique challenge associated with automated analysis of digital pathology. Numerous methods have been developed to improve the robustness of machine learning methods to stain variation, but comparative studies have demonstrated limited benefits to performance. Moreover, methods to handle stain variation were largely developed for H&E stained data, with evaluation generally limited to classification tasks. Here we propose Stain Consistency Learning, a novel framework combining stain-specific augmentation with a stain consistency loss function to learn stain colour invariant features. We perform the first, extensive comparison of methods to handle stain variation for segmentation tasks, comparing ten methods on Masson's trichrome and H&E stained cell and nuclei datasets, respectively. We observed that stain normalisation methods resulted in equivalent or worse performance, while stain augmentation or stain adversarial methods demonstrated improved performance, with the best performance consistently achieved by our proposed approach. The code is available at: https://github.com/mlyg/stain_consistency_learnin

Comparación del rendimiento de los métodos de detección de daños estructurales basados en la función de respuesta en frecuencia y la densidad espectral de potencia

[EN] Recent catastrophic events have aroused great interest in the scientific community regarding the evaluation and prediction of the structural response along the life cycle of infrastructures. Efforts are put into developing adequate health monitoring systems to help prevent future human life and economic losses. Here, two non-destructive damage detection methods are presented: the Frequency Response Function-based and the Spectral Density Function-based methods. The damage detection performance of both methods is compared through a particular case study, where different damage scenarios are analyzed in a 2D truss bridge. The reliability of each method is studied in terms of different prediction errors. Numerical results show that the PSD method for damage detection on a steel truss bridge structure provides more accurate and robust results when compared to that based on FRF.Grant PID2020-117056RB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by ERDF A way of making Europe.Hadizadeh-Bazaz, M.; Navarro, I.; Yepes, V. (2022). Performance comparison of structural damage detection methods based on Frequency Response Function and Power Spectral Density. DYNA Ingeniería e Industria (Online). 97(5):493-500. https://doi.org/10.6036/1050449350097

Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. As opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise, SVM is considered an effective method of data classification. One of the techniques used to overcome this inefficiency is fuzzy logic, with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies. The result further showed that using FSVM with a Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value. Doi: 10.28991/HIJ-2021-02-04-02 Full Text: PD

Towards automatic extraction of harmony information from music signals

PhDIn this thesis we address the subject of automatic extraction of harmony information from audio recordings. We focus on chord symbol recognition and methods for evaluating algorithms designed to perform that task. We present a novel six-dimensional model for equal tempered pitch space based on concepts from neo-Riemannian music theory. This model is employed as the basis of a harmonic change detection function which we use to improve the performance of a chord recognition algorithm. We develop a machine readable text syntax for chord symbols and present a hand labelled chord transcription collection of 180 Beatles songs annotated using this syntax. This collection has been made publicly available and is already widely used for evaluation purposes in the research community. We also introduce methods for comparing chord symbols which we subsequently use for analysing the statistics of the transcription collection. To ensure that researchers are able to use our transcriptions with confidence, we demonstrate a novel alignment algorithm based on simple audio fingerprints that allows local copies of the Beatles audio files to be accurately aligned to our transcriptions automatically. Evaluation methods for chord symbol recall and segmentation measures are discussed in detail and we use our chord comparison techniques as the basis for a novel dictionary-based chord symbol recall calculation. At the end of the thesis, we evaluate the performance of fifteen chord recognition algorithms (three of our own and twelve entrants to the 2009 MIREX chord detection evaluation) on the Beatles collection. Results are presented for several different evaluation measures using a range of evaluation parameters. The algorithms are compared with each other in terms of performance but we also pay special attention to analysing and discussing the benefits and drawbacks of the different evaluation methods that are used