In order to extract accurate 3D models from uncalibrated image data it is necessary to upgrade the generated projective reconstructions to a metric space, a process known as auto calibration. The key challenge associa...
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In order to extract accurate 3D models from uncalibrated image data it is necessary to upgrade the generated projective reconstructions to a metric space, a process known as auto calibration. The key challenge associated with auto calibration is the nonlinear optimization of a cost function based on extracting camera intrinsics from a potential upgrading transform, and evaluating fitness with respect to prior knowledge of physical cameras. The nonlinearity of the problem leads, in general, to poor convergence and a failure of the calibration process. This paper presents a novel auto calibration pipeline that seeks to develop a more robust approach to the nonlinear optimization. After testing a variety of methods, none of which yielded satisfactory solutions, we have developed a strategy combining the best aspects of two methods representing the current state of the art. The former method preconditions the projective space by ensuring it is quasi-affine with respect to camera centers, allowing a naive initialization in the new space, and uses a fitness measure resistant to focal length collapse. The latter method initializes using the best results of an exhaustive search over reasonable values of focal length. Our novel approach, presented here, uses the exhaustive search initialization of the latter combined with the improved fitness measure of the former, producing results that outperform both of its predecessors.
In this paper, we propose a new shape based segmentation and registration of the vertebral bodies (VBs) in clinical computed tomography (CT) images. The VB and surrounding organs have very close gray level information...
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In this paper, we propose a new shape based segmentation and registration of the vertebral bodies (VBs) in clinical computed tomography (CT) images. The VB and surrounding organs have very close gray level information and there are no strong edges in some CT images. To overcome these challenges, image appearance and shape information of VBs are used. There are three phases of our experiments: i) the detection of the VB region using the Matched filter, ii) initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, iii) registration of the shape priors and initially segmented region to obtain the final segmentation. Preliminary results show that our proposed algorithm gives very encouraging results and can solve many segmentation and registration problems.
In this paper, we present a new dynamic and probabilistic shape based segmentation method using statistical and variational approaches. We use two models in this paper: i) intensity and ii) shape. In the first phase, ...
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In this paper, we present a new dynamic and probabilistic shape based segmentation method using statistical and variational approaches. We use two models in this paper: i) intensity and ii) shape. In the first phase, the intensity based segmentation is done using a basic statistical level set method. In the second phase, to which we contribute, the shape model is constructed using the implicit representation of the training shapes. The resulting probability density function is used to embed the shape model into the image domain with a new energy minimization solution. Our method' s invariance to parameter initialization is evaluated through validation, and various synthetic and clinical shape registration examples are implemented. Experiments show that our proposed algorithm enhances the conventional global registration results, overcomes segmentation challenges, and is robust under various noise levels, severe occlusions, and missing parts.
We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior. Training shapes are grouped to form a prob...
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We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior. Training shapes are grouped to form a probabilistic model. The shape model is embedded into the image domain taking in consideration the evolution of a contour represented by a level set function. The evolution of the front gathers information from the image intensities and shape prior. The segmentation approach is applied in segmenting the vertebral bodies in CT images. Our results shows that the technique is accurate and robust compared with the other alternative in the literature.
Lung nodules from low dose CT (LDCT) scans may be used for early detection of lung cancer. However, these nodules vary in size, shape, texture, location, and may suffer from occlusion within the tissue. This paper pre...
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Lung nodules from low dose CT (LDCT) scans may be used for early detection of lung cancer. However, these nodules vary in size, shape, texture, location, and may suffer from occlusion within the tissue. This paper presents an approach for segmentation of lung nodules detected by a prior step. First, regions around the detected nodules are segmented; using automatic seed point placement levels sets. The outline of the nodule region is further improved using the curvature characteristics of the segmentation boundary. We illustrate the effectiveness of this method for automatic segmentation of the Juxta-pleural nodules.
Human action recognition can be performed using multi-scale salient features which encode the local events in the video. Existing feature extraction methods use non-causal spatio-temporal filtering, and hence, they ar...
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An ongoing challenge in automatic sea-ice monitoring using synthetic aperture radar (SAR) is the automatic segmentation of SAR sea-ice images based on the underlying ice type. Given the intractability of obtaining gro...
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An ongoing challenge in automatic sea-ice monitoring using synthetic aperture radar (SAR) is the automatic segmentation of SAR sea-ice images based on the underlying ice type. Given the intractability of obtaining ground-truth segmentation data from polar regions, the evaluation of automatic SAR sea-ice image segmentation algorithms is generally limited to tests using real SAR imagery based on pseudo-ground truth data (e.g., manual segmentations) and simple synthetic tests using basic shape primitives. As such, it is difficult to evaluate automatic segmentation algorithms in a systematic and reliable manner using realistic scenarios. To tackle this issue, a novel image synthesis system named IceSynth is presented, which is capable of generating a variety of synthetic sea-ice images that are representative of real SAR sea-ice imagery. In IceSynth, SAR sea-ice textures for each ice type are synthesized via stochastic sampling based on non-parametric local conditional texture probability distribution estimates. A stochastic sampling approach based on non-parametric local class probability distribution estimates is used to generate large-scale sea-ice structures of various ice types based on ice classification priors extracted from real SAR sea-ice imagery. Experimental results show that IceSynth is capable of generating realistic-looking SAR sea-ice images that are well-suited for performing objective evaluation of SAR sea-ice image segmentation algorithms.
As the volume of digital video captured and stored continues to increase, research efforts have focused on content management systems for video indexing and retrieval applications. A first step in generic video proces...
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As the volume of digital video captured and stored continues to increase, research efforts have focused on content management systems for video indexing and retrieval applications. A first step in generic video processing is shot boundary detection. This paper addresses a novel algorithm for abrupt shot (cut/pause) detection-especially on frames with similar statistics-based on the wavelet transform and content entropy. The algorithm has been successfully tested on some video categories including sport and live videos. Its quantitative performance has been compared to other known methods including pixel, histogram, frequency domain and statistics difference. In each test, the proposed wavelet method outperforms the others.
The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment SAR sea-ice imagery based on the under...
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The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment SAR sea-ice imagery based on the underlying ice type, which is a challenging task to perform automatically due to various imaging and environmental conditions. A novel stochastic ensemble consensus approach to sea-ice segmentation (SEC) is presented to tackle this challenging task. In SEC, each pixel in the SAR sea-ice image is assigned an initial sub-class based on its tonal characteristics. Ensembles of random samples are generated from a random field representing the SAR sea-ice imagery. The generated ensembles are then used to re-estimate the sub-class of the pixels using a weighted median consensus strategy. Based on the probability distribution of the sub-classes, an expectation maximization (EM) approach is utilized to estimate the final class likelihoods using a Gaussian mixture model (GMM). Finally, maximum likelihood (ML) classification is performed to estimate the final class of each pixel within the SAR sea-ice imagery based on the estimated GMM and the assigned sub-classes. SEC was tested using a variety of operational RADARSAT-1 and RADARSAT-2 SAR sea-ice imagery provided by the Canadian Ice Service (CIS) and was shown to produce successfully segmentation results that were superior to approaches based on K-means clustering, Gamma mixture models, and Markov Random Field (MRF) models for sea-ice segmentation.
A novel stochastic Retinex method based on adaptive Monte Carlo estimation is presented for the purpose of illumination and reflectance separation and color image enhancement. A spatially-adaptive sampling scheme is e...
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A novel stochastic Retinex method based on adaptive Monte Carlo estimation is presented for the purpose of illumination and reflectance separation and color image enhancement. A spatially-adaptive sampling scheme is employed to generate a set of random samples from the image field. A Monte Carlo estimate of the illumination is computed based on the Pearson Type VII error statistics of the drawn samples. The proposed method takes advantage of both local and global contrast information to provide better separation of reflectance and illumination by reducing the effects of strong shadows and other sharp illumination changes on the estimation process, improving the preservation of the original photographic tone, and avoiding the amplification of noise in dark regions. Experimental results using monochromatic face images under different illumination conditions and low-contrast chromatic images show the effectiveness of the proposed method for illumination and reflectance separation and color image enhancement when compared to existing Retinex and color enhancement techniques.
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