This paper presents two algorithms for estimating depth from integral images, which capture a scene by using multiple lenses, offering anaglyph depictions. The first algorithm involves the 3-D integral imaging grid fo...
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This paper presents two algorithms for estimating depth from integral images, which capture a scene by using multiple lenses, offering anaglyph depictions. The first algorithm involves the 3-D integral imaging grid formed by casting rays inversely through the lenses used to capture the integral image. In this formulation, depth estimation is equivalent to finding correspondences on the ray-crossing points. The second algorithm follows the depth-through disparity approach. In this case, a stereo-like minimization problem is formulated which is handled by the graph cuts method. The novelty of the proposed paper lies in constraining the optimization procedures with the “anchor points”. This results in enhanced estimation accuracy, while eliminating the optimization complexity. Anchor points is a set of reliable reference points, detected by applying a robust local image descriptor to viewpoint images, called self-similarity descriptor. The performance of both algorithms is evaluated on a synthetic integral image database in comparison with another state-of-the-art algorithm.
In recent decades, Laser-based spectroscopy (LAS) has been used in a wide range of research and application fields due to developments in laser technology and infrared spectroscopy. A particular application of interes...
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In recent decades, Laser-based spectroscopy (LAS) has been used in a wide range of research and application fields due to developments in laser technology and infrared spectroscopy. A particular application of interest is mid-IR laser-based gas detection systems for health and environment assessment. In this paper, we use our statistical analysis model for a generic mid-IR pulsed-laser gas detection system to predict trace gas detection and concentration estimation performance, and their sensitivity to system parameters. Based on PNNL data and the Beer-Lambert law, we use the three main spectral peaks of a trace gas, as the basis for gas detection, and use the relationship between gas transmittance β, molar absorptivity ε, concentration, and the sample-mean measurement, x N , from the photodetector, as the basis for concentration estimation using a standard confidence interval method.
Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for th...
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Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for the identification and quantification of food consumed at a meal. In this paper we describe a new approach to food identification using several features based on local and global measures and a “voting” based late decision fusion classifier to identify the food items. Experimental results on a wide variety of food items are presented.
In this paper, we present the results of a study on the social focus of attention as a time function derived from the multisource multimodal signals, recorded by different personal capturing devices during social even...
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In this paper, we present the results of a study on the social focus of attention as a time function derived from the multisource multimodal signals, recorded by different personal capturing devices during social events. The core of the approach is based on fission and fusion of multichannel audio, video and social modalities to derive the social focus of attention. The results achieved to date on 16+ hours of real-life data prove the feasibility of the approach.
In this paper, we address the problem of multiple simultaneous sources localization by means of Blind Source Separation (BSS)-based algorithms. Considering BSS demixing filters as some blind null beamformer and produc...
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In this paper, we address the problem of multiple simultaneous sources localization by means of Blind Source Separation (BSS)-based algorithms. Considering BSS demixing filters as some blind null beamformer and producing an acoustical map from them, source localization can then be achieved by identifying the local minima of this acoustical map. To improve the performance of this method in reverberant environments, we have proposed to replace the demixing filter with one corresponds to only the direct path. This is done by keeping only the largest coefficient in each demixing filter and neglecting the other coefficients. Furthermore, the proposed method reduces the computational complexity. Our experiments demonstrate the efficiency of the proposed method in the localization of multiple simultaneous sound sources in reverberant environments.
This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung no...
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This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured by the normalized cross-correlation (NCC). Geometric feature descriptors (e.g., SIFT, LBP and SURF) are applied to the output of the detection step, in order to extract features from the nodule candidates, for further enhancement of output and possible reduction of false positives. Results on the clinical ELCAP database showed that the descriptors provide 2% enhancements in the specificity of the detected nodule above the NCC results when used in a k-NN classifier. Thus quantitative measures of enhancements of the performance of CAD models based on LDCT are now possible and are entirely model-based. Most importantly, our approach is applicable for classification of nodules into categories and pathologies.
The recent emergence of user generated content requires new content creation tools that will be both easy to learn and easy to use. These new tools should enable the user to construct new high-quality content with min...
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The recent emergence of user generated content requires new content creation tools that will be both easy to learn and easy to use. These new tools should enable the user to construct new high-quality content with minimum effort;it is essential to allow existing multimedia content to be reused as building blocks when creating new content. In this work we present a new tool for automatically constructing virtual worlds with minimum user intervention. Users can create these worlds by drawing a simple sketch, or by using interactively segmented 2D objects from larger images. The system receives as a query the sketch or the segmented image, and uses it to find similar 3D models that are stored in a Content Centric Network. The user selects a suitable model from the retrieved models, and the system uses it to automatically construct a virtual 3D world.
In this study, we focus on the development of energy efficient and achievable load balancing mechanisms for wireless sensor networks. Due to resource constraint and tremendous amount of sensors, one possible way of ac...
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In a traditional anti-windup design, the anti-windup mechanism is set to be activated as soon as the control signal saturates the actuator. A recent innovation of delaying the activation of the anti-windup mechanism, ...
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In this paper, we compare registration results obtained using different diffusion maps extracted from diffusion tensor imaging (DTI). Fractional Anisotropy (FA) and Ellipsoidal Area Ratio (EAR) are two diffusion maps ...
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In this paper, we compare registration results obtained using different diffusion maps extracted from diffusion tensor imaging (DTI). Fractional Anisotropy (FA) and Ellipsoidal Area Ratio (EAR) are two diffusion maps (indices) that may be used for image registration. First, we use FA maps to find deformation matrix and register diffusion weighted images. Then, we use EAR maps and finally we use both of FA and EAR maps to register diffusion weighted images. The difference between FA values before deformation and after registration using the FA alone or EAR alone has a median of 0.57 and using both of them has a median of 0.29. Therefore, the results of registration using both of the FA and EAR indices are superior to those obtained using only one of them alone.
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