Data-flow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of data-flow, termed synchronous data-flow (SDF), offers strong compile-tim...
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Data-flow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of data-flow, termed synchronous data-flow (SDF), offers strong compile-time predictability properties, but has limited expressive power. A new type of hierarchy (Interface-based SDF) has been proposed allowing more expressivity while maintaining its predictability. One of the main problems with this hierarchical SDF model is the lack of trade-off between parallelism and network clustering. This paper presents a systematic method for applying an important class of loop transformation techniques in the context of interface-based SDF semantics. The resulting approach provides novel capabilities for integrating parallelism extraction properties of the targeted loop transformations with the useful modeling, analysis, and code reuse properties provided by SDF.
The gold standard for the localization of epileptic activities in the cerebral cortex is intracranial electrocorticography (ECoG) electrodes placed directly on the brain surface. However, it has limitations in being a...
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The gold standard for the localization of epileptic activities in the cerebral cortex is intracranial electrocorticography (ECoG) electrodes placed directly on the brain surface. However, it has limitations in being able to localize deep brain epileptic sources. As a means to improve the localization of epileptic activities from these subdural electrical recordings, we developed a simple source monitoring system to detect and localize these deep sources. The method is based on an independent component analysis (ICA) algorithm known as joint approximate diagonalization of eigen-matrices (JADE). This method was tested on patients with neocortical epileptic foci focusing on the interictal spikes from ECoG signals. The proposed method localizes the interictal epileptic discharges to both superficial as well as deep regions of the human neocortex and has the potential to improve the localization and surgical outcomes of patients with medically refractory epilepsy undergoing surgical resections.
In this paper, we propose a new color face recognition (FR) method which effectively employs feature selection algorithm in order to find the set of optimal color components (from various color models) for FR purpose....
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In this paper, we propose a new color face recognition (FR) method which effectively employs feature selection algorithm in order to find the set of optimal color components (from various color models) for FR purpose. The proposed FR method is also designed to improve FR accuracy by combining the selected color components at the feature level. The effectiveness of the proposed color FR method has been successfully demonstrated using two public CMU-PIE and Color FERET face databases (DB). In our comparative experiments, traditional grayscale-based FR, previous color-based FR, and popular local binary pattern (LBP) based FR methods were compared with the proposed method. Experimental results show that our color FR method performs better than the aforementioned three different FR approaches. In particular, the proposed method can achieve 7.81% and 18.57% improvement in FR performance on the CMU-PIE and Color FERET DB, respectively, compared to representative color-based FR solutions previously developed.
This paper proposes a novel weighted feature fusion in color face recognition (FR) to automatically annotate faces in personal videos. In the proposed FR method, multiple face images (belonging to the same subject) ar...
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This paper proposes a novel weighted feature fusion in color face recognition (FR) to automatically annotate faces in personal videos. In the proposed FR method, multiple face images (belonging to the same subject) are clustered from a sequence of video frames. To facilitate a complementary effect on improving annotation performance, the grouped faces are combined using the proposed weighted feature fusion. In addition, we make effective use of facial color feature to cope with decrease in annotation performance due to a low-resolution face in personal videos. To evaluate the effectiveness of proposed FR method, more than 40,000 video frames for 10 real-world personal videos are collected from an existing online video sharing website. Experimental results show that the proposed FR method significantly improves annotation performance obtained using conventional grayscale image based FR methods.
We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a mult...
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ISBN:
(纸本)9781424441259
We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of the energy function, a two-step optimization strategy is used. In the first step, shape parameters are optimized based on the analytic derivatives of the energy function. In the second step, shapes of the structures are fine-tuned using a level set method. The proposed method is shown to be superior to some popular methods in the literature using a dataset of 64 patients with mesial temporal lobe epilepsy. In addition, the method can be used for lateralization with accuracy close to that of manual segmentation.
This demonstration shows a spatial-temporal denoising and demosaicking scheme for noisy CFA videos. This scheme can significantly reduce the noise-caused color artifacts and effectively preserve the image edge structu...
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This demonstration shows a spatial-temporal denoising and demosaicking scheme for noisy CFA videos. This scheme can significantly reduce the noise-caused color artifacts and effectively preserve the image edge structures. The experimental results showed that this scheme achieves promising color video reproduction in terms of both PSNR and visual perception.
Since the medical training samples are very limited, it is difficult to construct a statistical shape model with good generalization using few samples. In this paper, we propose a novel statistical shape modeling meth...
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Since the medical training samples are very limited, it is difficult to construct a statistical shape model with good generalization using few samples. In this paper, we propose a novel statistical shape modeling method using 2D PCA. The 3D shape is represented as a matrix by spherical parameterization. The experiments showed that our proposed method can reconstruct statistical shape model with good generalization even using fewer samples.
This paper develops a volume-based technique, called Volume Sphering analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices formed image cube using only one set of training samples obtained...
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This paper develops a volume-based technique, called Volume Sphering analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices.
Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely...
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Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions.
Driving always involves risk. Various means have been proposed to reduce the risk. Critical motion detection of nearby moving vehicles is one of the important means of preventing accidents. In this paper, a computatio...
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