pattern spectra have frequently been used in imageanalysis. A drawback is that they are not sensitive to changes in spatial distribution of features. Various methods have been proposed to address this problem. In thi...
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ISBN:
(纸本)9783642036125
pattern spectra have frequently been used in imageanalysis. A drawback is that they are not sensitive to changes in spatial distribution of features. Various methods have been proposed to address this problem. In this paper we compare several of these on both texture classification and image retrieval. Results show that Size Density Spectra are most versatile, and least sensitive to parameter settings.
Topological imageanalysis is a powerful tool for understanding the structure and topology of images, being persistent homology one of its most popular methods. However, persistent homology requires a chain of inclusi...
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Topological imageanalysis is a powerful tool for understanding the structure and topology of images, being persistent homology one of its most popular methods. However, persistent homology requires a chain of inclusions of topological spaces, which can be challenging for digital images. In this article, we explore the use of zigzag persistence, a recent variant of traditional persistence, for digital image processing. To this end, new algorithms are developed to build a simplicial complex associated to a digital image and to compute the relationships between homology classes of a sequence of binary images via zigzag persistence. Additionally, we provide a simple software to use them. We demonstrate its effectiveness by applying it to a real-world problem of analyzing honey bee sperm videos.
In this paper, we present an approach based on clustering analysis and mathematical morphology to extract road information from IKONOS imagery. This road information extraction approach includes several key modules: T...
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ISBN:
(纸本)9780819469502
In this paper, we present an approach based on clustering analysis and mathematical morphology to extract road information from IKONOS imagery. This road information extraction approach includes several key modules: Texture analysis based on the multi-band image to obtain two new features of "MLen/MWid" to improve the road clustering analysis;In order to optimize the primal binary imagery of road object area resulting from clustering Process, a texture analysis defined on binary imagery-"BATS" is presented, which ulteriorly expel the non-road pixels from the road area binary imagery;Furthermore, we carry out the process to extract road centerline network from the binary imagery of road object area based on mathematical morphology, through the process, several other methods, such as connectivity analysis, raster to vector transform, etc, are integrated.
A new algorithm for detecting linear infrastructural objects in aerial photos is presented. It is assumed that these objects pass through the whole image: beginning at one side and finishing at the opposite one. It is...
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This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in four aspec...
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This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in four aspects. First, it designs efficient and well-balanced Markov Chain dynamics to explore the complex solution space and, thus, achieves a nearly global optimal solution independent of initial segmentations. Second, it presents a mathematical principle and a K-adventurers algorithm for computing multiple distinct solutions from the Markov chain sequence and, thus, it incorporates intrinsic ambiguities in image segmentation. Third, it utilizes data-driven (bottom-up) techniques, such as clustering and edge detection, to compute importance proposal probabilities, which drive the Markov chain dynamics and achieve tremendous speedup in comparison to the traditional jump-diffusion methods [12], [11]. Fourth, the DDMCMC paradigm provides a unifying framework in which the role of many existing segmentation algorithms, such as, edge detection, clustering, region growing, split-merge, snake/balloon, and region competition, are revealed as either realizing Markov chain dynamics or computing importance proposal probabilities. Thus, the DDMCMC paradigm combines and generalizes these segmentation methods in a principled way. The DDMCMC paradigm adopts seven parametric and nonparametric image models for intensity and color at various regions. We test the DDMCMC paradigm extensively on both color and gray-level images and some results are reported in this paper.
Hyperspectral imaging provides more information than conventional RGB images. However, its high dimensionality prevents its adaptation to the existing image processing techniques. Defining full-band spectral feature i...
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Hyperspectral imaging provides more information than conventional RGB images. However, its high dimensionality prevents its adaptation to the existing image processing techniques. Defining full-band spectral feature is the first missing step, which is currently dealt with indirectly by band selection or dimension reduction. This article proposes a spectral feature extraction method using the mathematical moments to quantify the shape of the reflectance spectrum from different aspects. A whole family of features is presented by changing the moment attributes. All the features and their combinations are extensively tested in texture analysis of a new hyperspectral image database from textile samples (SpecTex). Two supervised experiments are performed: image patch classification and pixel-wise mosaic image segmentation. The proposed features are compared to four other features: the grayscale intensity, the RGB and CIELab values, and the principal components. Also, three analysismethods are tested: co-occurrence matrix, Gabor filter bank, and local binary pattern. In all cases, the moment features outperformed the opponents. Notably, combining the moment features with complementary attributes remarkably improved the performance. The most discriminative combinations are studied and formulated in this article.
Keypoint detection and the descriptor construction method based on multiscale expansions of Gauss-Laguerre circular harmonic functions is considered. An efficient acceleration procedure is introduced. The procedure is...
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The phase error caused by the speed mismatch issue is researched in the line-scan images capturing 3D profile measurement. The experimental system is constructed by a line-scan CCD camera, an object moving device, a d...
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The phase error caused by the speed mismatch issue is researched in the line-scan images capturing 3D profile measurement. The experimental system is constructed by a line-scan CCD camera, an object moving device, a digital fringe pattern projector, and a personal computer. In the experiment procedure, the detected object is moving relative to the image capturing system by using a motorized translation stage in a stable velocity. The digital fringe pattern is projected onto the detected object, and then the deformed patterns are captured and recorded in the computer. The object surface profile can be calculated by the Fourier transform profilometry. However, the moving speed mismatch error will still exist in most of the engineering application occasion even after an image system calibration. When the moving speed of the detected object is faster than the expected value, the captured image will be compressed in the moving direction of the detected object. In order to overcome this kind of measurement error, an image recovering algorithm is proposed to reconstruct the original compressed image. Thus, the phase values can be extracted much more accurately by the reconstructed images. And then, the phase error distribution caused by the speed mismatch is analyzed by the simulation and experimental methods.
This article surveys deformable models, a promising and vigorously researched computer-assisted medical imageanalysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to...
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ISBN:
(纸本)0818673672
This article surveys deformable models, a promising and vigorously researched computer-assisted medical imageanalysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to imageanalysis that combines geometry, physics, and approximation theory. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size, and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical imageanalysis, including segmentation, shape representation, matching, and motion tracking.
A number of problems of modern quantitative EEG are considered: the non-stationarity of the processed signal, the decreasing accuracy of the received results due to the averaging effect in the spectral analysis method...
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A number of problems of modern quantitative EEG are considered: the non-stationarity of the processed signal, the decreasing accuracy of the received results due to the averaging effect in the spectral analysismethods used in computer EEG, and the impossibility of detecting complexes in analyzed EEG without involving a clinician. A structural (syntactic) approach is suggested, which reduces the negative effect of these problems, and the mathematical apparatus for its realization is developed. The results of application of the structural approach to analysis of a real EEG are given.
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