Competitive learning is an important mechanism for data clustering and pattern recognition. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large scale networks...
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Competitive learning is an important mechanism for data clustering and pattern recognition. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large scale networks. In this model, several particles walk in the network and compete with each other to occupy as many nodes as possible, while attempting to reject intruder particles. As a result, each particle will dominate a cluster of the network. Moreover, we propose an efficient method for determining the right number of clusters by using the information generated by the competition process itself, avoiding the calculation of an external evaluating index. In this work, we apply the model to handwritten data clustering. computer simulations reveal that the proposed technique obtains satisfactory cluster detection accuracy.
Motivated by the importance of having real-time feedback in sketch-based modeling tools, we propose a framework for terrain edition capable of generating and displaying complex and high-resolution terrains. Our system...
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Motivated by the importance of having real-time feedback in sketch-based modeling tools, we propose a framework for terrain edition capable of generating and displaying complex and high-resolution terrains. Our system is efficient and fast enough to allow the user to see the terrain morphing at the same time the drawing edition occurs. We have two types of editing interactions: the user can draw strokes creating elevations and crevices, and previous strokes can be interactively moved to different regions of the terrain. One interesting feature of our tool is that terrain primitives can be interactively manipulated similarly to primitives in vector-graphics tools. We achieve real-time performance in both modeling and rendering using a hybrid CPU-GPU coupled solution. We maintain a coarse version of the terrain geometry in the CPU by using a quad tree, while a fine version is produced in the GPU using tessellation shaders.
In the oil industry, clear and unambiguous 3D flow visualization techniques are very important to inspect the results of numerical simulation of black-oil reservoir models. In this paper, we revisit the use of line in...
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In the oil industry, clear and unambiguous 3D flow visualization techniques are very important to inspect the results of numerical simulation of black-oil reservoir models. In this paper, we revisit the use of line integral convolution (LIC) for imaging 3D vector fields on arbitrary surfaces and apply it to reservoir visualization. We use a GPU-based method to image the tangential component using the conventional 2D LIC in projected screen space and propose the use of color to encode the normal component. To attach the flow imaging to the 3D surfaces, avoiding image flickering while manipulating the model, we propose a simple scheme based on randomly generated texture coordinate, avoiding the use of a solid 3D texture noise. For animation, we adjust the use of filters to ensure that the animation speed varies in accordance to the field magnitude. We also explore the visualization of multiphase (oil, gas, and water) flow.
Most games and other interactive virtual environments focus on rendering natural phenomena in the most believable manner by using accurate visuals and physics. However, not much effort has been put into accounting for...
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Most games and other interactive virtual environments focus on rendering natural phenomena in the most believable manner by using accurate visuals and physics. However, not much effort has been put into accounting for the physics of sound. The simulation of the real behavior of sound through an environment, when considering the speed of sound, reflection, and absorption, is computationally expensive and is usually left aside. In this work, an algorithm that calculates sound wave propagation using a finite difference method is used and extended to present a novel approach to sound rendering. This approach reaches the objective more quickly, and the sound generated has no perceptible loss of accuracy. The approach is designed to be implemented in GPU architectures and eventually enable real-time results.
Screening mammography has been established worldwide for early detection of breast cancer, one of the main causes of death among women in occidental countries. In this paper, we aim at moving towards computer-aided di...
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Screening mammography has been established worldwide for early detection of breast cancer, one of the main causes of death among women in occidental countries. In this paper, we aim at moving towards computer-aided diagnostics of screening mammography. Tissue and lesion are classified using the methodology of content-based image retrieval. In addition, we aim at comprehensive evaluation and have established a large database of annotated reference images (ground truth), which has been merged and unified from different sources publicly available to research. In total, 10,509 mammographic images have been collected from the different sources. From this, 3,375 images are provided with one and 430 radiographs with more than one chain code annotations. This data supports experiments with up to 12 classes, and 233 images per class if a equal distribution is required. Using a two-dimensional principal component analysis with four eigenvalues and a support vector machine with Gaussian kernel for feature extraction and image retrieval, respectively, the precision of computer-aided diagnosis is above 80%. It therefore may be used as second opinion in screening mammography.
We present a comprehensive study of two important subspace clustering algorithms and their contribution to enhance results for the difficult task of matching images of the same object using different devices at differ...
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We present a comprehensive study of two important subspace clustering algorithms and their contribution to enhance results for the difficult task of matching images of the same object using different devices at different conditions. Our experiments were performed on two distinct databases containing urban scenes which were tested using state-of-the-art matching algorithms. Our start point was the hypothesis that low discriminant local point descriptors lead to misclassification, which can be reduced employing clustering techniques as filters. A significantly amelioration of the results obtained for the two tested databases was achieved, which indicates that subspace clustering techniques have much to contribute at this research area. Another point is whether the occurrence of obstacles like trees and shadows are responsible for misclassification of images.
Video encoding techniques play an important role in data reduction. Fractal compression has received considerable attention in the past decades. While early methods presented prohibitively large encoding times, recent...
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Video encoding techniques play an important role in data reduction. Fractal compression has received considerable attention in the past decades. While early methods presented prohibitively large encoding times, recent search less fractal encoders reduced this problem. A fast 3D purely fractal video encoder based on a flexible adaptive spatial subdivision data structure is proposed in this work. The method completely avoids any kind of search for a matching domain block and is capable of performing fast compression and decompression with high visual fidelity. Experimental results show that the developed approach outperforms the state-of-the-art x264 video encoder at very low bit rates in high motion video sequences in both structural dissimilarity measure and encoding time.
This work presents a fast method for motion estimation by using low discrepancy sequences. The basic idea in this proposal is one based on the reduction of the computational effort involved in the matching of a given ...
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This work presents a fast method for motion estimation by using low discrepancy sequences. The basic idea in this proposal is one based on the reduction of the computational effort involved in the matching of a given block with the reference block. Instead of using a metric that employs all corresponding pixels in both blocks, it uses one that selects a subset of pixels for which the coordinates are determined by the so called low discrepancy sequences. The proposed method is applied to the New Three Step Search (NTSS), typically used as a technique for motion estimation in video applications. The results show a reduction in computational complexity of about 80% with regard to the NTSS, with little degradation in the recovered frame. The block matching of this present method can couple nicely with virtually any motion estimation technique, requiring very little change.
We propose a memory-efficient method that computes persistent homology for 3D gray-scale images. The basic idea is to compute the persistence of the induced Morse-Smale complex. Since in practice this complex is much ...
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We propose a memory-efficient method that computes persistent homology for 3D gray-scale images. The basic idea is to compute the persistence of the induced Morse-Smale complex. Since in practice this complex is much smaller than the input data, significantly less memory is required for the subsequent computations. We propose a novel algorithm that efficiently extracts the Morse-Smale complex based on algorithms from discrete Morse theory. The proposed algorithm is thereby optimal with a computational complexity of O(n2). The persistence is then computed using the Morse-Smale complex by applying an existing algorithm with a good practical running time. We demonstrate that our method allows for the computation of persistent homology for large data on commodity hardware.
Research on human speech production is highly dependent upon information about the position and movements of the speech articulators. Dynamic magnetic resonance imaging (MRI) has been the main tool to support this pro...
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Research on human speech production is highly dependent upon information about the position and movements of the speech articulators. Dynamic magnetic resonance imaging (MRI) has been the main tool to support this process. With this technique, image sequences can be acquired in the act of speech, which allows identifying shapes of the vocal tract in real time. However, the spatial and temporal resolution requirements are not known a priori and are expected to vary according to the speech task. Several available approaches enhance resolution by either changing the acquisition process of current devices, or by trading the acquisition devices themselves by more powerful ones. Both solutions involve additional hardware costs. In this paper, we propose an evolution of an approach to enhance spatio-temporal resolution of MRI image sequences of the vocal tract using only digital imageprocessing techniques. On one hand, temporal resolution is increased by generating intermediate images according to the movement present in an observed sequence. On the other hand, spatial resolution is increased by applying a novel approach to super-resolution image reconstruction based on the Wiener filter. To evaluate the proposed approach, we processed a set of five simulated low resolution images in a sequence. Compared to available methods, results provide evidence of the effectiveness of the proposed method.
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