Procesamiento de Señales e Imágenes Digitales
URI permanente para esta colecciónhttp://54.81.141.168/handle/123456789/31440
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Ítem Texto completo enlazado Separable dictionary learning for convolutional sparse coding via split updates(Pontificia Universidad Católica del Perú, 2019-05-16) Quesada Pacora, Jorge Gerardo; Rodriguez Valderrama, Paul AntonioThe increasing ubiquity of Convolutional Sparse Representation techniques for several image processing tasks (such as object recognition and classification, as well as image denoising) has recently sparked interest in the use of separable 2D dictionary filter banks (as alternatives to standard nonseparable dictionaries) for efficient Convolutional Sparse Coding (CSC) implementations. However, existing methods approximate a set of K non-separable filters via a linear combination of R (R << K) separable filters, which puts an upper bound on the latter’s quality. Furthermore, this implies the need to learn first the whole set of non-separable filters, and only then compute the separable set, which is not optimal from a computational perspective. In this context, the purpose of the present work is to propose a method to directly learn a set of K separable dictionary filters from a given image training set by drawing ideas from standard Convolutional Dictionary Learning (CDL) methods. We show that the separable filters obtained by the proposed method match the performance of an equivalent number of non-separable filters. Furthermore, the computational performance of this learning method is shown to be substantially faster than a state-of-the-art non-separable CDL method when either the image training set or the filter set are large. The method and results presented here have been published [1] at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018). Furthermore, a preliminary approach (mentioned at the end of Chapter 2) was also published at ICASSP 2017 [2]. The structure of the document is organized as follows. Chapter 1 introduces the problem of interest and outlines the scope of this work. Chapter 2 provides the reader with a brief summary of the relevant literature in optimization, CDL and previous use of separable filters. Chapter 3 presents the details of the proposed method and some implementation highlights. Chapter 4 reports the attained computational results through several simulations. Chapter 5 summarizes the attained results and draws some final conclusions.Ítem Texto completo enlazado Corn crops identification using multispectral images from unmanned aircraft systems(Pontificia Universidad Católica del Perú, 2019-04-08) Trujillano Asato, Fedra Catherine; Racoceanu, DanielClimate change and migration of population from rural to urban areas are affecting the agricultural production around the world. This study was based in the particular department of Ancash - Peru where corn is one of the most important crops of the region. Authorities in this region are concerned in finding a method, different from census; that can constantly monitor corn crops areas. This data is important to evaluate how these two causes will impact on food security in Ancash. The first part of the present thesis reviews the current techniques in the recognition of crop areas using remote sensing and multispectral images. The second part explains the methodology developed for this study, considering the data acquisition using Unmanned Aircraft Systems, the preparation of the acquired data and two deep learning model approaches. The first approach is based on binary classification of corn patches using Le Net model with near infrared images. The second one describes the segmentation of corn areas in different stages using the U-net model, in this case five band images were considered. The third part shows the results of both approaches. From these results it is concluded that training a model with data from different stages and scenarios of two campaigns (2016 and 2017) can achieve a 95% of accuracy in corn segmentation.Ítem Texto completo enlazado Characterization of healthy skin with high-frequency ultrasound using quantitative ultrasound(Pontificia Universidad Católica del Perú, 2018-08-20) Saavedra Bazán, Ana Cecilia; Castañeda Aphan, BenjamínThe skin is the largest organ of the body that protects it from the external environment. High- frequency ultra sound (HF-US) has been used to visualize the skin in depth and to diagnose some pathologies in dermatological applications. Quantitative ultrasound (QUS) includes several techniques that provide values of particular physical properties. In this thesis work, three QUS parameters are explained and used to characterize healthy skin through HF-US: attenuation coefficient slope (ACS), backscatter coefficient (BSC) and shear wave speed (SWS). They were estimated with the regularized spectral-log difference (RSLD) method, the reference phan- tom method, and the crawling wave sonoelastography method, respectively. All the three parameters were assessed in phantoms, ex vivo and in vivo skin. In calibrated phantoms, RSLD showed a reduc- tion of up to 93% of the standard deviation concerning the estimation with SLD, and BSC showed an agreement with the Faran’s theoretical curve. In gelatin-based phantoms, surface acoustic waves (SAWs) were estimated in two interfaces: solid-water and solid-US gel, which all owed corroborating SAWs presence and finding an empirical compensation factor when the coupling interface is US gel. A correction factor of 0:97 for SAW-to-shear was found to avoid underestimation in phantoms. Porcine thigh was calculated in the range from 8 to 27 MHz, where the ACS was 4:08 _+_0:43 dB cm -1 MHz-1 and BSC was in the range from 10 1 to 10° sr-1 _cm-1. Crawling wave sonoelastography method was applied for the vibration frequencies between 200 Hz and 800 Hz, where SWS was in the range from 4:6 m/sto9:1 m/s. In vivo ACS and BSC were assessed in the healthy forearm and thigh, whereas SWS only in the thigh. The average ACS in the forearm dermis was 2.07dB cm-1 _MHz-1, which is in close agreement with the literature. A significant difference (p < 0.05) was found between the ACS in the forearm dermis and the thigh dermis (average ACS of 2.54dB cm-1 _MHz-1). The BSC of the forearm and thigh dermis were in the range from 10 -1 to 10° sr-1 _cm-1, and in the range from 10-1 to 10° sr-1 _cm-1, respectively. The SWS in the thigh dermis was 2:4 _+_0:38 m/s for a vibration frequency of 200Hz, with an increasing trend as frequency increases. Results suggest that these QUS parameters have the potential to be used as a tool for in vivo skin characterization and show potential for future application in skin lesions.Ítem Texto completo enlazado Robust Minimmun Variance Beamformer using Phase Aberration Correction Methods(Pontificia Universidad Católica del Perú, 2017-04-28) Chau Loo Kung, Gustavo Ramón; Lavarello Montero, Roberto Janniel; Dahl, Jeremy J.The minimum variance (MV) beamformer is an adaptive beamforming method that has the potential to enhance the resolution and contrast of ultrasound images. Although the sensitivity of the MV beamformer to steering vector errors and array calibration errors is well-documented in other fields, in ultrasound it has been tested only under gross sound speed errors. Several robust MV beamformers have been proposed, but have mainly reported robustness only in the presence of sound speed mismatches. Additionally the impact of PAC methods in mitigating the effects of phase aberration in MV beamformed images has not been observed Accordingly, this thesis report consists on two parts. On the first part, a more complete analysis of the effects of different types of aberrators on conventional MV beamforming and on a robust MV beamformer from the literature (Eigenspace-based Minimum Variance (ESMV) beamformer) is carried out, and the effects of three PAC algorithms and their impact on the performance of the MV beamformer are analyzed (MV-PC). The comparison is carried out on Field II simulations and phantom experiments with electronic aberration and tissue aberrators. We conclude that the sensitivity to speed of sound errors and aberration limit the use of the MV beamformer in clinical applications, and that the effect of aberration is stronger than previously reported in the literature. Additionally it is shown that under moderate and strong aberrating conditions, MV-PC is a preferable option to ESMV. On the second part, we propose a new, locally-adaptive, phase aberration correction method (LAPAC) able to improve both DAS and MV beamformers that integrates aberration correction for each point in the image domain into the formulation of the MV beamformer. The new method is tested using fullwave simulations of models of human abdominal wall, experiments with tissue aberrators, and in vivo carotid images. The LAPAC method is compared with conventional phase aberration correction with delay-and-sum beamforming (DAS-PC) and MV-PC. The proposed method showed between 1-4 dB higher contrast than DAS-PC and MV-PC in all cases, and LAPAC-MV showed better performance than LAPAC-DAS. We conclude that LAPAC may be a viable option to enhance ultrasound image quality of both DAS and MV in the presence of clinically-relevant aberrating conditions.Ítem Texto completo enlazado 3D updating of solid models based on local geometrical meshes applied to the reconstruction of ancient monumental structures(Pontificia Universidad Católica del Perú, 2014-10-14) Zvietcovich Zegarra, José Fernando; Castañeda Aphan, Benjamín; Perucchio, RenatoWe introduce a novel methodology for locally updating an existing 3D solid model of a complex monumental structure with the geometric information provided by a 3D mesh (point cloud) extracted from the digital survey of a specific sector of a monument. Solid models are fundamental for engineering analysis and conservation of monumental structures of the cultural heritage. Finite elements analysis (FEA), the most versatile and commonly used tool for the numerical simulation of the static and dynamic response of large structures, requires 3D solids which accurately represent the outside as well as the inside geometry and topology of the domain to be analyzed. However, the structural changes introduced during the lifetime of the monument and the damage caused by anthropogenic and natural factors contribute to producing complex geometrical configurations that may not be generated with the desired accuracy in standard CAD solid modeling software. On the other hand, the development of digital techniques for surveying historical buildings and cultural monuments, such as laser scanning and photogrammetric reconstruction, has made possible the creation of accurate 3D mesh models describing the geometry of those structures for multiple applications in heritage documentation, preservation, and archaeological interpretations. The proposed methodology consists of a series of procedures which utilize image processing, computer vision, and computational geometry algorithms operating on entities defined in the Solid Modeling space and the Mesh space. The operand solid model is defined as the existing solid model to be updated. The 3D mesh model containing new surface information is first aligned to the operand solid model via 3D registration and, subsequently, segmented and converted to a provisional solid model incorporating the features to be added or subtracted. Finally, provisional and operand models are combined and data is transferred through regularized Boolean operations performed in a standard CAD environment. We test the procedure on the Main Platform of the Huaca de la Luna, Trujillo, Peru, one of the most important massive earthen structures of the Moche civilization. Solid models are defined in AutoCAD while 3D meshes are recorded with a Faro Focus laser scanner. The results indicate that the proposed methodology is effective at transferring complex geometrical and topological features from the mesh to the solid modeling space. The methodology preserves, as much as possible, the initial accuracy of meshes on the geometry of the resultant solid model which would be highly difficult and time consuming using manual approaches.Ítem Texto completo enlazado Multi-scale image inpainting with label selection based on local statistics(Pontificia Universidad Católica del Perú, 2014-09-09) Paredes Zevallos, Daniel Leoncio; Rodríguez Valderrama, Paúl AntonioWe proposed a novel inpainting method where we use a multi-scale approach to speed up the well-known Markov Random Field (MRF) based inpainting method. MRF based inpainting methods are slow when compared with other exemplar-based methods, because its computational complexity is O(jLj2) (L feasible solutions’ labels). Our multi-scale approach seeks to reduces the number of the L (feasible) labels by an appropiate selection of the labels using the information of the previous (low resolution) scale. For the initial label selection we use local statistics; moreover, to compensate the loss of information in low resolution levels we use features related to the original image gradient. Our computational results show that our approach is competitive, in terms reconstruction quality, when compare to the original MRF based inpainting, as well as other exemplarbased inpaiting algorithms, while being at least one order of magnitude faster than the original MRF based inpainting and competitive with exemplar-based inpaiting.Ítem Texto completo enlazado Computationally inexpensive parallel parking supervisor based on video processing(Pontificia Universidad Católica del Perú, 2013-12-05) Espejo Pérez, Caterina María; Rodríguez Valderrama, Paúl AntonioParallel parking, in general, is a moderate difficulty maneuver. Moreover, for inexperienced drivers, it can be a stressful situation that can lead to errors such as stay far from the sidewalk or damage another vehicle resulting in traffic tickets that range from simple parking violation to crash-related violations. In this work, we propose a computationally effective approach to perform a collisionfree parallel parking. The method will calculate the minimum parking space needed and then the efficient path for the parallel parking. This method is computationally inexpensive in comparison with the current state of the art. Moreover, it could be used by any car because the parameters needed to perform all computations are taken from the specifications of real cars. Preliminary results of this work were summarized in [1] that was presented at the 15th International IEEE Conference on Intelligent Transportation Systems. The simulation and experimental data show the effectiveness of the method. This effectiveness is specified when the path followed by the driver and the path calculated with the method are compared. The image capture of the vehicle is used to get the path made by the driver for the parallel parking. Furthermore, road surface marks were determined (in a parking lot) as a visual aid for the drivers in order to perform the parallel parking maneuver. After analyzing the paths, it is noted that the vehicles that properly followed the marks, parked correctly.Ítem Texto completo enlazado Automatic regularization parameter selection for the total variation mixed noise image restoration framework(Pontificia Universidad Católica del Perú, 2013-03-27) Rojas Gómez, Renán Alfredo; Rodríguez Valderrama, Paúl AntonioImage restoration consists in recovering a high quality image estimate based only on observations. This is considered an ill-posed inverse problem, which implies non-unique unstable solutions. Regularization methods allow the introduction of constraints in such problems and assure a stable and unique solution. One of these methods is Total Variation, which has been broadly applied in signal processing tasks such as image denoising, image deconvolution, and image inpainting for multiple noise scenarios. Total Variation features a regularization parameter which defines the solution regularization impact, a crucial step towards its high quality level. Therefore, an optimal selection of the regularization parameter is required. Furthermore, while the classic Total Variation applies its constraint to the entire image, there are multiple scenarios in which this approach is not the most adequate. Defining different regularization levels to different image elements benefits such cases. In this work, an optimal regularization parameter selection framework for Total Variation image restoration is proposed. It covers two noise scenarios: Impulse noise and Impulse over Gaussian Additive noise. A broad study of the state of the art, which covers noise estimation algorithms, risk estimation methods, and Total Variation numerical solutions, is included. In order to approach the optimal parameter estimation problem, several adaptations are proposed in order to create a local-fashioned regularization which requires no a-priori information about the noise level. Quality and performance results, which include the work covered in two recently published articles, show the effectivity of the proposed regularization parameter selection and a great improvement over the global regularization framework, which attains a high quality reconstruction comparable with the state of the art algorithms.