Region of Interest Extraction in 3D Face Using Local Shape Descriptor

Recently, numerous efforts were focused on 3D face models due to its geometrical information and its reliability against pose estimation and identification problems. The major objective of this work is to reduce the massive amount of information contained the entire 3D face image into a distinctive and informative subset interested regions based 3D face analysis systems. The interested regions are represented by nose and eyes regions of frontal and profile 3D images. These regions are detected based on distance to local plan descriptor only which is copes well with profile views of 3D images. The statistical distribution of distance to local plane descriptor is predicted using Gaussian distribution. The framework of the proposed approach involves two modes: training mode and testing mode. In the training mode, a learning process for local shape descriptor related to the interested regions is carried out. The interested regions (nose and eyes) are extracted automatically in the testing mode. The performance evaluation of the proposed approach has been conducted using 3D images taken from GAVADB 3D face database which consists of both frontal and profile views. The proposed approach achieved high detection rate of interested regions for both frontal and profile views

Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

In Llanas and Lantarón, J. Sci. Comput. 46, 485–518 (2011) we proposed an algorithm (EDAS-d) to approximate the jump discontinuity set of functions defined on subsets of ℝ d . This procedure is based on adaptive splitting of the domain of the function guided by the value of an average integral. The above study was limited to the 1D and 2D versions of the algorithm. In this paper we address the three-dimensional problem. We prove an integral inequality (in the case d=3) which constitutes the basis of EDAS-3. We have performed detailed computational experiments demonstrating effective edge detection in 3D function models with different interface topologies. EDAS-1 and EDAS-2 appealing properties are extensible to the 3D cas

FracDetect: A novel algorithm for 3D fracture detection in digital fractured rocks

Fractures have a governing effect on the physical properties of fractured rocks, such as permeability. Accurate representation of 3D fractures is, therefore, required for precise analysis of digital fractured rocks. However, conventional segmentation methods fail to detect and label the fractures with aperture sizes near or below the resolution of 3D micro-computed tomographic (micro-CT) images, which are visible in the greyscale images, and where greyscale intensity convolution between different phases exists. In addition, conventional methods are highly subjective to user interpretation. Herein, a novel algorithm for the automatic detection of fractures from greyscale 3D micro-CT images is proposed. The algorithm involves a low-level early vision stage, which identifies potential fractures, followed by a high-level interpretative stage, which enforces planar continuity to reject false positives and more reliably extract planar fractures from digital rock images. A manually segmented fractured shale sample was used as the groundtruth, with which the efficacy of the algorithm in 3D fracture detection was validated. Following this, the proposed and conventional methods were applied to detect fractures in digital fractured coal and shale samples. Based on these analyses, the impact of fracture detection accuracy on the analysis of fractured rocks' physical properties was inferred

A combined method of image processing and artificial neural network for the identification of 13 Iranian rice cultivars

Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars

Remuestreo estructurado de contornos de huecos en superficies 3d de objetos de forma libre utilizando bresenham

La etapa de integración dentro del proceso de reconstrucción tridimensional de objetos de forma libre, requiere de la descripción, análisis y corrección de huecos en superficies 3D. Ciertas evaluaciones cuantitativas en este tema implican contar con conjuntos de datos espaciados de forma regular o contenidos en estructuras que garanticen dicha propiedad, por ejemplo voxels, octrees o mallas estructuradas. Lograr lo anterior requiere un proceso de re-muestreo de los puntos que conforman el contorno del hueco en la superficie 3D. En este trabajo se describe un método para obtener conjuntos estructurados de puntos, a partir de los datos de contornos de huecos en objetos de forma libre. El método inicia con el ajuste de una curva NURBS al conjunto inicial de puntos con el fin de asegurar la suavidad del contorno, de lo cual se obtiene un conjunto de puntos ajustados. Finalmente se utiliza el algoritmo de discretización de Bresenham para obtener el conjunto de puntos estructurados. Los resultados obtenidos muestran que el método desarrollado asegura que el conjunto final de puntos estructurados preserven la forma del contorno original con altos niveles de detalle

Application of Nanoparticles for Oil Recovery

The oil industry has, in the last decade, seen successful applications of nanotechnology in completion systems, completion fluids, drilling fluids, and in improvements of well constructions, equipment, and procedures. However, very few full field applications of nanoparticles as an additive to injection fluids for enhanced oil recovery (EOR) have been reported. Many types of chemical enhanced oil recovery methods have been used in fields all over the world for many decades and have resulted in higher recovery, but the projects have very often not been economic. Therefore, the oil industry is searching for a more efficient enhanced oil recovery method. Based on the success of nanotechnology in various areas of the oil industry, nanoparticles have been extensively studied as an additive in injection fluids for EOR. This book includes a selection of research articles on the use of nanoparticles for EOR application. The articles are discussing nanoparticles as additive in waterflooding and surfactant flooding, stability and wettability alteration ability of nanoparticles and nanoparticle stabilized foam for CO2-EOR. The book also includes articles on nanoparticles as an additive in biopolymer flooding and studies on the use of nanocellulose as a method to increase the viscosity of injection water. Mathematical models of the injection of nanoparticle-polymer solutions are also presented

Sketching-based Skeleton Extraction

Articulated character animation can be performed by manually creating and rigging a skeleton into an unfolded 3D mesh model. Such tasks are not trivial, as they require a substantial amount of training and practice. Although methods have been proposed to help automatic extraction of skeleton structure, they may not guarantee that the resulting skeleton can help to produce animations according to user manipulation. We present a sketching-based skeleton extraction method to create a user desired skeleton structure which is used in 3D model animation. This method takes user sketching as an input, and based on the mesh segmentation result of a 3D mesh model, generates a skeleton for articulated character animation. In our system, we assume that a user will properly sketch bones by roughly following the mesh model structure. The user is expected to sketch independently on different regions of a mesh model for creating separate bones. For each sketched stroke, we project it into the mesh model so that it becomes the medial axis of its corresponding mesh model region from the current viewer perspective. We call this projected stroke a “sketched bone”. After pre-processing user sketched bones, we cluster them into groups. This process is critical as user sketching can be done from any orientation of a mesh model. To specify the topology feature for different mesh parts, a user can sketch strokes from different orientations of a mesh model, as there may be duplicate strokes from different orientations for the same mesh part. We need a clustering process to merge similar sketched bones into one bone, which we call a “reference bone”. The clustering process is based on three criteria: orientation, overlapping and locality. Given the reference bones as the input, we adopt a mesh segmentation process to assist our skeleton extraction method. To be specific, we apply the reference bones and the seed triangles to segment the input mesh model into meaningful segments using a multiple-region growing mechanism. The seed triangles, which are collected from the reference bones, are used as the initial seeds in the mesh segmentation process. We have designed a new segmentation metric [1] to form a better segmentation criterion. Then we compute the Level Set Diagrams (LSDs) on each mesh part to extract bones and joints. To construct the final skeleton, we connect bones extracted from all mesh parts together into a single structure. There are three major steps involved: optimizing and smoothing bones, generating joints and forming the skeleton structure. After constructing the skeleton model, we have proposed a new method, which utilizes the Linear Blend Skinning (LBS) technique and the Laplacian mesh deformation technique together to perform skeleton-driven animation. Traditional LBS techniques may have self-intersection problem in regions around segmentation boundaries. Laplacian mesh deformation can preserve the local surface details, which can eliminate the self-intersection problem. In this case, we make use of LBS result as the positional constraint to perform a Laplacian mesh deformation. By using the Laplacian mesh deformation method, we maintain the surface details in segmentation boundary regions. This thesis outlines a novel approach to construct a 3D skeleton model interactively, which can also be used in 3D animation and 3D model matching area. The work is motivated by the observation that either most of the existing automatic skeleton extraction methods lack well-positioned joints specification or the manually generated methods require too much professional training to create a good skeleton structure. We dedicate a novel approach to create 3D model skeleton based on user sketching which specifies articulated skeleton with joints. The experimental results show that our method can produce better skeletons in terms of joint positions and topological structure

Detection of counterfeit coins based on 3D Height-Map Image Analysis

Analyzing 3-D height-map images leads to the discovery of a new set of features that cannot be extracted or even seen in 2-D images. To the best of our knowledge, there was no research in the literature analyzing height-map images to detect counterfeit coins or to classify coins. The main goal of this thesis is to propose a new comprehensive method for analyzing 3D height-map images to detect counterfeit of any type of coins regardless of their country of origin, language, shape, and quality. Therefore, we applied a precise 3-D scanner to produce coin height-map images, since detecting a counterfeit coin using 2D image processing is nearly impossible in some cases, especially when the coin is damaged, corroded or worn out. In this research, we propose some 3-D approaches to model and analyze several large datasets. In our first and second methods, we aimed to solve the degradation problem of shiny coin images due to the scanning process. To solve this problem, first, the characters of the coin images were straightened by a proposed straightening algorithm. The height-map image, then, was decomposed row-wise to a set of 1-D signals, which were analyzed separately and restored by two different proposed methods. These approaches produced remarkable results. We also proposed a 3-D approach to detect and analyze the precipice borders from the coin surface and extract significant features that ignored the degradation problem. To extract the features, we also proposed Binned Borders in Spherical Coordinates (BBSC) to analyze different parts of precipice borders at different polar and azimuthal angles. We also took advantage of stack generalization to classify the coins and add a reject option to increase the reliability of the system. The results illustrate that the proposed method outperforms other counterfeit coin detectors. Since there are traces of deep learning in most recent research related to image processing, it is worthwhile to benefit from deep learning approaches in our study. In another proposed method of this thesis, we applied deep learning algorithms in two steps to detect counterfeit coins. As Generative Adversarial Network is being used for generating fake images in image processing applications, we proposed a novel method based on this network to augment our fake coin class and compensate for the lack of fake coins for training the classifier. We also decomposed the coin height-map image into three types of Steep, Moderate, and Gentle slopes. Therefore, the grayscale height-map image is turned to the proposed SMG height-map channel. Then, we proposed a hybrid CNN-based deep neural network to train and classify these new SMG images. The results illustrated that a deep neural network trained with the proposed SMG images outperforms the system trained by the grayscale images. In this research, the proposed methods were trained and tested with four types of Danish and two types of Chinese coins with encouraging results

Regularity aspects of a higher-order variational approach to the denoising and inpainting of images with TV-type energies

This thesis deals with a certain class of variational problems of higher order that stem from applications in mathematical image processing. The main intention is to study the regularity behavior of minimizers of integral functionals on Sobolev spaces with differentiable energy densities of linear growth approximating the TV-case. Building upon results that were given by Bildhauer, Fuchs, Tietz and Weickert in the first-order case, we treat existence of weakly differentiable, relaxed, dual as well as of classically differentiable solutions under suitable conditions on the model. Our considerations are supplemented with a detailed study of the lower-dimensional cases as well as with a coupling model which offers an alternative approach to the higher-order case.Die vorliegende Arbeit beschäftigt sich mit einer bestimmten Klasse von Variationsproblemen höherer Ordnung, die von Anwendungen in der mathematischen Bildverarbeitung herrühren. Das Hauptaugenmerk liegt dabei auf der Untersuchung des Regularitätsverhaltens der Minimierer von Integral-Funktionalen auf Sobolev-Räumen mit differenzierbaren Energiedichten von linearem Wachstum, die den TV-Fall approximieren. Aufbauend auf Resultaten, die von Bildhauer, Fuchs, Tietz und Weickert im Falle erster Ordnung erbracht wurden, behandeln wir die Existenz von schwach differenzierbaren, relaxierten, dualen sowie von klassisch differenzierbaren Lösungen unter jeweils hinreichenden Voraussetzungen an das Modell. Unsere Betrachtungen werden ergänzt durch eine eingehendere Analyse der niederdimensionalen Fälle sowie eines Kopplungsmodells, das einen alternativen Zugang zum Fall höherer Ordnung bietet