Browsing by Subject "Computer simulation."

Now showing 1 - 17 of 17

Open Access
Detection of tree trunks as visual landmarks in outdoor environments
(2010) Yıldız, Tuğba
One of the basic problems to be addressed for a robot navigating in an outdoor environment is the tracking of its position and state. A fundamental first step in using algorithms for solving this problem, such as various visual Simultaneous Localization and Mapping (SLAM) strategies, is the extraction and identification of suitable stationary “landmarks” in the environment. This is particularly challenging in the outdoors geometrically consistent features such as lines are not frequent. In this thesis, we focus on using trees as persistent visual landmark features in outdoor settings. Existing work to this end only uses intensity information in images and does not work well in low-contrast settings. In contrast, we propose a novel method to incorporate both color and intensity information as well as regional attributes in an image towards robust of detection of tree trunks. We describe both extensions to the well-known edge-flow method as well as complementary Gabor-based edge detection methods to extract dominant edges in the vertical direction. The final stages of our algorithm then group these vertical edges into potential tree trunks using the integration of perceptual organization and all available image features. We characterize the detection performance of our algorithm for two different datasets, one homogeneous dataset with different images of the same tree types and a heterogeneous dataset with images taken from a much more diverse set of trees under more dramatic variations in illumination, viewpoint and background conditions. Our experiments show that our algorithm correctly finds up to 90% of trees with a false-positive rate lower than 15% in both datasets. These results establish that the integration of all available color, intensity and structure information results in a high performance tree trunk detection system that is suitable for use within a SLAM framework that outperforms other methods that only use image intensity information.
Open Access
Histopathological image classification using salient point patterns
(2011) Çığır, Celal
Over the last decade, computer aided diagnosis (CAD) systems have gained great importance to help pathologists improve the interpretation of histopathological tissue images for cancer detection. These systems offer valuable opportunities to reduce and eliminate the inter- and intra-observer variations in diagnosis, which is very common in the current practice of histopathological examination. Many studies have been dedicated to develop such systems for cancer diagnosis and grading, especially based on textural and structural tissue image analysis. Although the recent textural and structural approaches yield promising results for different types of tissues, they are still unable to make use of the potential biological information carried by different tissue components. However, these tissue components help better represent a tissue, and hence, they help better quantify the tissue changes caused by cancer. This thesis introduces a new textural approach, called Salient Point Patterns (SPP), for the utilization of tissue components in order to represent colon biopsy images. This textural approach first defines a set of salient points that correspond to nuclear, stromal, and luminal components of a colon tissue. Then, it extracts some features around these salient points to quantify the images. Finally, it classifies the tissue samples by using the extracted features. Working with 3236 colon biopsy samples that are taken from 258 different patients, our experiments demonstrate that Salient Point Patterns approach improves the classification accuracy, compared to its counterparts, which do not make use of tissue components in defining their texture descriptors. These experiments also show that different set of features can be used within the SPP approach for better representation of a tissue image.
Open Access
Image information mining using spatial relationship constraints
(2012) Karakuş, Fatih
There is a huge amount of data which is collected from the Earth observation satellites and they are continuously sending data to Earth receiving stations day by day. Therefore, mining of those data becomes more important for effective processing of collected multi-spectral images. The most popular approaches for this problem use the meta-data of the images such as geographical coordinates etc. However, these approaches do not offer a good solution for determining what those images contain. Some researches make a big step from the meta-data based approaches in this area by moving the focus of the study to content based approaches such as utilizing the region information of the sensed images. In this thesis, we propose a novel, generic and extendable image information mining system that uses spatial relationship constraints. In this system, we use not only the region content, but also relationships of those regions. First, we extract the region information of the images and then extract pairwise relationship information of those regions such as left, right, above, below, near, far and distance etc. This feature extraction process is defined as a generic process which is independent from how the region segmentation is obtained. In addition to these, since new features and new approaches are continuously being developed by the image information mining researchers, extendability feature of the our system plays a big role while we are designing our system. In this thesis, we also propose a novel feature vector structure in which a feature vector consists of several sub-feature vectors. In the proposed feature vector structure, each sub-feature vector can be exclusively selected to be used for search process and they can have different distance metrics to be used in comparisons between the same sub-feature vector of the other feature vector structures. Therefore, the system gives ability to users to choose which information about the region and its pairwise relationship with other regions to be used when they perform a search on the system. The proposed system is illustrated by using region based retrieval scenarios on very high spatial resolution satellite images.
Open Access
Improving the performance of similarity joins using graphics processing unit
(2012) Korkmaz, Zeynep
The similarity join is an important operation in data mining and it is used in many applications from varying domains. A similarity join operator takes one or two sets of data points and outputs pairs of points whose distances in the data space is within a certain threshold value, ". The baseline nested loop approach computes the distances between all pairs of objects. When considering large set of objects which yield too long query time for nested loop paradigm, accelerating such operator becomes more important. The computing capability of recent GPUs with the help of a general purpose parallel computing architecture (CUDA) has attracted many researches. With this motivation, we propose two similarity join algorithms for Graphics Processing Unit (GPU). To exploit the advantages of general purpose GPU computing, we rst propose an improved nested loop join algorithm (GPU-INLJ) for the speci c environment of GPU. Also we present a partitioning-based join algorithm (KMEANS-JOIN) that guarantees each partition can be joined independently without missing any join pair. Our experiments demonstrate massive performance gains and the suitability of our algorithms for large datasets.
Open Access
A key-pose based representation for human action recognition
(2011) Kurt, Mehmet Can
This thesis utilizes a key-pose based representation to recognize human actions in videos. We believe that the pose of the human figure is a powerful source for describing the nature of the ongoing action in a frame. Each action can be represented by a unique set of frames that include all the possible spatial configurations of the human body parts throughout the time the action is performed. Such set of frames for each action referred as “key poses” uniquely distinguishes that action from the rest. For extracting “key poses”, we define a similarity value between the poses in a pair of frames by using the lines forming the human figure along with a shape matching method. By the help of a clustering algorithm, we group the similar frames of each action into a number of clusters and use the centroids as “key poses” for that action. Moreover, in order to utilize the motion information present in the action, we include simple line displacement vectors for each frame in the “key poses” selection process. Experiments on Weizmann and KTH datasets show the effectiveness of our key-pose based approach in representing and recognizing human actions.
Open Access
A line based pose representation for human action recognition
(2011) Baysal, Sermetcan
In this thesis, we utilize a line based pose representation to recognize human actions in videos. We represent the pose in each frame by employing a collection of line-pairs, so that limb and joint movements are better described and the geometrical relationships among the lines forming the human figure is captured. We contribute to the literature by proposing a new method that matches line-pairs of two poses to compute the similarity between them. Moreover, to encapsulate the global motion information of a pose sequence, we introduce line-flow histograms, which are extracted by matching line segments in consecutive frames. Experimental results on Weizmann and KTH datasets, emphasize the power of our pose representation; and show the effectiveness of using pose ordering and line-flow histograms together in grasping the nature of an action and distinguishing one from the others. Finally, we demonstrate the applicability of our approach to multi-camera systems on the IXMAS dataset.
Open Access
Multilevel cluster ensembling for histopathological image segmentation
(2011) Şimşek, Ahmet Çağrı
In cancer diagnosis and grading, histopathological examination of tissues by pathologists is accepted as the gold standard. However, this procedure has observer variability and leads to subjectivity in diagnosis. In order to overcome such problems, computational methods which use quantitative measures are proposed. These methods extract mathematical features from tissue images assuming they are composed of homogeneous regions and classify images. This assumption is not always true and segmentation of images before classification is necessary. There are methods to segment images but most of them are proposed for generic images and work on the pixel-level. Recently few algorithms incorporated medical background knowledge into segmentation. Their high level feature definitions are very promising. However, in the segmentation step, they use region growing approaches which are not very stable and may lead to local optima. In this thesis, we present an efficient and stable method for the segmentation of histopathological images which produces high quality results. We use existing high level feature definitions to segment tissue images. Our segmentation method significantly improves the segmentation accuracy and stability, compared to existing methods which use the same feature definition. We tackle image segmentation problem as a clustering problem. To improve the quality and the stability of the clustering results, we combine different clustering solutions. This approach is also known as cluster ensembles. We formulate the clustering problem as a graph partitioning problem. In order to obtain diverse and high quality clustering results quickly, we made modifications and improvements on the well-known multilevel graph partitioning scheme. Our method clusters medically meaningful components in tissue images into regions and obtains the final segmentation. Experiments showed that our multilevel cluster ensembling approach performed significantly better than existing segmentation algorithms used for generic and tissue images. Although most of the images used in experiments, contain noise and artifacts, the proposed algorithm produced high quality results.
Open Access
Multiple view human activity recognition
(2012) Pehlivan, Selen
This thesis explores the human activity recognition problem when multiple views are available. We follow two main directions: we first present a system that performs volume matching using constructed 3D volumes from calibrated cameras, then we present a flexible system based on frame matching directly using multiple views. We examine the multiple view systems compared to single view systems, and measure the performance improvements in recognition using more views by various experiments. Initial part of the thesis introduces compact representations for volumetric data gained through reconstruction. The video frames recorded by many cameras with significant overlap are fused by reconstruction, and the reconstructed volumes are used as substitutes of action poses. We propose new pose descriptors over these three dimensional volumes. Our first descriptor is based on the histogram of oriented cylinders in various sizes and orientations. We then propose another descriptor which is view-independent, and which does not require pose alignment. We show the importance of discriminative pose representations within simpler activity classification schemes. Activity recognition framework based on volume matching presents promising results compared to the state-of-the-art. Volume reconstruction is one natural approach for multi camera data fusion, but there can be few cameras with overlapping views. In the second part of the thesis, we introduce an architecture that is adaptable to various number of cameras and features. The system collects and fuses activity judgments from cameras using a voting scheme. The architecture requires no camera calibration. Performance generally improves when there are more cameras and more features; training and test cameras do not need to overlap; camera drop in or drop out is handled easily with little penalty. Experiments support the performance penalties, and advantages for using multiple views versus single view.
Open Access
Object-oriented testure analysis and unsupervised segmentation for histopathological images
(2012) Tosun, Akif Burak
The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. The segmentation algorithms in literature commonly use pixel-level color/texture descriptors that they define on image pixels for quantizing a tissue. On the other hand, it is usually harder to express domain specific knowledge about tissues, such as the spatial organization of tissue components, using only the pixel-level descriptors. This may become even harder for tissue images, which typically consist of a considerable amount of variation and noise at their pixel-level, such as similar color distribution of different tissue components, distortion in cell alignments, and color contrast caused by too much stain in a particular region. The previous segmentation algorithms are more susceptible to these problems as they work on pixel-level descriptors. In order to successfully address these issues, in this thesis, we introduce three new texture descriptors, namely ObjSEG, GraphRLM, and ObjCooc textures, and implement algorithms that use these descriptors for segmenting histopathological tissue images. We extract these texture descriptors on tissue components that are approximately represented by circular objects. Since these objectoriented texture descriptors are defined on the tissue components, and hence domain specific knowledge, they represent the spatial organization of the components better than their previous counterparts. Thus, our algorithms based on these descriptors give more effective and robust segmentation results. Furthermore, since the descriptors are not directly defined on image pixels, they are effective to alleviate the pixel-level problems. In our experiments, we tested our algorithms that use the proposed objectoriented descriptors on a dataset of 200 colon tissue images. Our experiments demonstrated that our new object-oriented feature descriptors led to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with its previous counterparts, the experimental results also showed that our proposed algorithms are more effective in segmenting histopathological images.
Open Access
Particle based modeling and simulation of natural phenomena
(2010) Bayraktar, Serkan
This thesis is about modeling and simulation of fluids and cloth-like deformable objects by the physically-based simulation paradigm. Simulated objects are modeled with particles and their interaction with each other and the environment is defined by particle-to-particle forces. We propose several improvements over the existing particle simulation techniques. Neighbor search algorithms are crucial for the performance efficiency and robustness of a particle system. We present a sorting-based neighbor search method which operates on a uniform grid, and can be parallelizable. We improve upon the existing fluid surface generation methods so that our method captures surface details better since we consider the relative position of fluid particles to the fluid surface. We investigate several alternatives of particle interaction schema (i.e. Smoothed Particle Hydrodynamics, the Discrete Element Method, and Lennard-Jones potential) for the purpose of defining fluid-fluid, fluid-cloth, fluid-boundary interaction forces. We also propose a practical way to simulate knitwear and its interaction with fluids. We employ capillary pressure–based forces to simulate the absorption of fluid particles by knitwear. We also propose a method to simulate the flow of miscible fluids. Our particle simulation system is implement to exploit parallel computing capabilities of the commodity computers. Specifically, we implemented the proposed methods on multicore CPUs and programmable graphics boards. The experiments show that our method is computationally efficient and produces realistic results.
Open Access
Physical simulation of wood combustion by using particle system
(2010) Gürcüoğlu, Gizem
In computer graphics, the most challenging problem is modeling natural phenomena such as water, re, smoke etc. The reason behind this challenge is the structural complexity, as the simulation of natural phenomena depends on some physical equations that are di cult to implement and model. In complex physically based simulations, it is required to keep track of several properties of the object that participates in the simulation. These properties can change and their alteration may a ect other physical and thermal properties of object. As one of natural phenomena, burning wood has various properties such as combustion reaction, heat transfer, heat distribution, fuel consumption and object shape in which change in one during the duration of simulation alters the e ects of some other properties. There have been several models for animating and modeling re phenomena. The problem with most of the existing studies related to re modeling is that decomposition of the burning solid is not mentioned, instead solids are treated only as fuel source. In this thesis, we represent a physically based simulation of a particle based method for decomposition of burning wood and combustion process. In our work, besides being a fuel source, physical and thermal a ects of combustion process over wood has been observed. A particle based system has been modelled in order to simulate the decomposition of a wood object depending on internal and external properties and their interactions and the motion of the spreading re according to combustion process.
Open Access
Real-time simulation and visualization of deformations on heightfields
(2010) Yalçın, M. Adil
The applications of computer graphics raise new expectations, such as realistic rendering, real-time dynamic scenes and physically correct simulations. The aim of this thesis is to investigate these problems on the height eld structure, an extended 2D model that can be processed e ciently by data-parallel architectures. This thesis presents methods for simulation of deformations on height eld as caused by triangular objects, physical simulation of objects interacting with height eld and advanced visualization of deformations. The height eld is stored in two di erent resolutions to support fast rendering and precise physical simulations as required. The methods are implemented as part of a large-scale height- eld management system, which applies additional level of detail and culling optimizations for the proposed methods and data structures. The solutions provide real-time interaction and recent graphics hardware (GPU) capabilities are utilized to achieve real-time results. All the methods described in this thesis are demonstrated by a sample application and performance characteristics and results are presented to support the conclusions.
Open Access
Real-time smoke simulation
(2010) Algan, Eren
Realistic simulation of uid-like behaviour is an important and challenging problem in computer graphics. Huge and increasing amount of animations has made this phenomena even more important. Although many scientists provided solutions regarding this issue, recently, the need of fast and easy implemented uid simulations has directed researches to focus on quick and stable solutions. This thesis presents an unconditionally stable, easy implemented real-time smoke simulation, solving Navier-Stokes equations with Lagrangian and implicit methods. The study focuses on the comprehension of uid dynamics as much as the solution, by providing background information about Navier-Stokes equations, how they are derived and used. While the proposed solution is applied only to create a simulation for smoke like behaviour, it is highly adaptive for other uids as well. One important aspect of the simulation is being suitable for 2 and 3 dimensions, giving the exibility to the animator to choose in between.
Open Access
Resampling-based Markovian modeling for automated cancer diagnosis
(2011) Özdemir, Erdem
Correct diagnosis and grading of cancer is very crucial for planning an effective treatment. However, cancer diagnosis on biopsy images involves visual interpretation of a pathologist, which is highly subjective. This subjectivity may, however, lead to selecting suboptimal treatment plans. In order to circumvent this problem, it has been proposed to use automatic diagnosis and grading systems that help decrease the subjectivity levels by providing quantitative measures. However, one major challenge for designing these systems is the existence of high variance observed in the biopsy images due to the nature of biopsies. Thus, for successful classifications of unseen images, these systems should be trained with a large number of labeled images. However, most of the training sets in this domain have limited size of labeled data since it is quite difficult to collect and label histopathological images. In this thesis, we successfully address this issue by presenting a new resampling framework. This framework relies on increasing the generalization capacity of a classifier by augmenting the size and variation in the training set. To this end, we generate multiple sequences from an image, each of which corresponds to a perturbed sample of the image. Each perturbed sample characterizes different parts of the image, and hence, they are slightly different from each other. The use of these perturbed samples for representing the image increases the size and variability of the training set. These samples are modeled with Markov processes which are used to classify unseen image. Working with histopathological tissue images, our experiments demonstrate that the proposed framework is more effective for both larger and smaller training sets compared against other approaches. Additionally, they show that the use of perturbed samples is effective in a voting scheme which boosts the performance of the classifier.
Open Access
SIMLIB : a class library for object-oriented simulation
(1993) Işıklı, Oğuz
Simulation is one of the most widely used techniques in decision making. Mathematical modeling of a real world system is a major task of the simulation analyst. The selection of a computer language for implementing the model is also important. Recent research in this area has focused on the compatibility between simulation implementations and the object-oriented paradigm. It is the purpose of this thesis to explore the use of an object-oriented approach for the implementation of discrete event simulation applications. We present a class library which provides the skeletal elements of a simulation. The advantages and the disadvantages of the approach are discussed with the help of three prototype implementations: the single-queue/single-server system, the production-line system, and the elevator system.
Open Access
A simulation program for efficient analysis of linear circuits
(1996) Sungur, Mustafa
circuit simulation program using generalized asymptotic waveform evaluation technique is introduced. The program analyzes circuits with lumped a.nd distributed components. It computes the moments ci.t a few Irecjuency points and extracts the coefficients of an approximating rational by employing one of t,he two different methods. One of the examined methods is proposed to compare the accuracy of results and the execution times with conventional simulators and sevei’cil examples are demonstrated, indicating that our sirnulcv tor provides a. speed improvement without a significant loss of accuracy.
Open Access
A three-dimensional nonlinear finite element method implementation toward surgery simulation
(2011) Gülümser, Emir
Finite Element Method (FEM) is a widely used numerical technique for finding approximate solutions to the complex problems of engineering and mathematical physics that cannot be solved with analytical methods. In most of the applications that require simulation to be fast, linear FEM is widely used. Linear FEM works with a high degree of accuracy with small deformations. However, linear FEM fails in accuracy when large deformations are used. Therefore, nonlinear FEM is the suitable method for crucial applications like surgical simulators. In this thesis, we propose a new formulation and finite element solution to the nonlinear 3D elasticity theory. Nonlinear stiffness matrices are constructed by using the Green-Lagrange strains (large deformation), which are derived directly from the infinitesimal strains (small deformation) by adding the nonlinear terms that are discarded in infinitesimal strain theory. The proposed solution is a more comprehensible nonlinear FEM for those who have knowledge about linear FEM since the proposed method directly derived from the infinitesimal strains. We implemented both linear and nonlinear FEM by using same material properties with the same tetrahedral elements to examine the advantages of nonlinear FEM over the linear FEM. In our experiments, it is shown that nonlinear FEM gives more accurate results when compared to linear FEM when rotations and high external forces are involved. Moreover, the proposed nonlinear solution achieved significant speed-ups for the calculation of stiffness matrices and for the solution of a system as a whole.