15341953
Rapid shape acquisition using color structured light and multi-pass dynamic programming
This paper presents a color structured light technique for recovering object shape from one or more images. The technique works by projecting a pattern of stripes of alternating colors and matching the projected color transitions with observed edges in the image. The correspondence problem is solved using a novel, multi-pass dynamic programming algorithm that eliminates global smoothness assumptions and strict ordering constraints present in previous formulations. The resulting approach is suitable for generating both high-speed scans of moving objects when projecting a single stripe pattern and high-resolution scans of static scenes using a short sequence of time-shifted stripe patterns. In the latter case, space-time analysis is used at each sensor pixel to obtain inter-frame depth localization. Results are demonstrated for a variety of complex scenes.
15973812
Spacetime stereo: shape recovery for dynamic scenes
This paper extends the traditional binocular stereo problem into the spacetime domain, in which a pair of video streams is matched simultaneously instead of matching pairs of images frame by frame. Almost any existing stereo algorithm may be extended in this manner simply by replacing the image matching term with a spacetime term. By utilizing both spatial and temporal appearance variation, this modification reduces ambiguity and increases accuracy. Three major applications for spacetime stereo are proposed in this paper. First, spacetime stereo serves as a general framework for structured light scanning and generates high quality depth maps for static scenes. Second, spacetime stereo is effective for a class of natural scenes, such as waving trees and flowing water, which have repetitive textures and chaotic behaviors and are challenging for existing stereo algorithms. Third, the approach is one of very few existing methods that can robustly reconstruct objects that are moving and deforming over time, achieved by use of oriented spacetime windows in the matching procedure. Promising experimental results in the above three scenarios are demonstrated.
7408744
Projection defocus analysis for scene capture and image display
In order to produce bright images, projectors have large apertures and hence narrow depths of field. In this paper, we present methods for robust scene capture and enhanced image display based on projection defocus analysis. We model a projector's defocus using a linear system. This model is used to develop a novel temporal defocus analysis method to recover depth at each camera pixel by estimating the parameters of its projection defocus kemel in frequency domain. Compared to most depth recovery methods, our approach is more accurate near depth discontinuities. Furthermore, by using a coaxial projector-camera system, we ensure that depth is computed at all camera pixels, without any missing parts. We show that the recovered scene geometry can be used for refocus synthesis and for depth-based image composition. Using the same projector defocus model and estimation technique, we also propose a defocus compensation method that filters a projection image in a spatially-varying, depth-dependent manner to minimize its defocus blur after it is projected onto the scene. This method effectively increases the depth of field of a projector without modifying its optics. Finally, we present an algorithm that exploits projector defocus to reduce the strong pixelation artifacts produced by digital projectors, while preserving the quality of the projected image. We have experimentally verified each of our methods using real scenes.
6538157
Threshold Tuning for Improved Classification Association Rule Mining
One application of Association Rule Mining (ARM) is to identify Classification Association Rules (CARs) that can be used to classify future instances from the same population as the data being mined. Most CARM methods first mine the data for candidate rules, then prune these using coverage analysis of the training data. In this paper we describe a CARM algorithm that avoids the need for coverage analysis, and a technique for tuning its threshold parameters to obtain more accurate classification. We present results to show this approach can achieve better accuracy than comparable alternatives at lower cost.
62394
Support vector machine learning for image retrieval
A novel method of relevance feedback is presented based on support vector machine learning in the content-based image retrieval system. A SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Using the classifier, the system can retrieve more images relevant to the query in the database efficiently. Experiments were carried out on a large-size database of 9918 images. It shows that the interactive learning and retrieval process can find correct images increasingly. It also shows the generalization ability of SVM under the condition of limited training samples.
3761715
A multicommodity flow model for distributed stream processing
The rapid development of computer technology has enabled streaming applications to emerge from many areas of the IT industry. A fundamental problem in such stream processing systems is how to best utilize the available resources so that the overall system performance is optimized. In most real time systems, applications are often running in a decentralized, distributed environment. At any given time, no server has the global information about all the servers in the system. It is thus desirable to have distributed solutions capable of adapting to or absorbing local changes in production rates and in network conditions. This paper addresses the fundamental resource allocation question in the distributed stream processing setting. Specifically, we consider a generic model for stream processing systems. Each stream is required to complete a series of tasks to become finished product. Each server is responsible for a subset of the processing tasks for possibly a subset of the streams. All servers have finite computing resources and all communication links have finite available bandwidth. We consider the resource allocation problem so as to maximize the total throughput of the system. Our problem can be described as a generalization of the traditional multicommodity flow problem [1]. Traditional multicommodity flow problem looks for the best way to use of the link capacities to deliver the maximum throughput for the flows in the whole network. In our problem, in addition to the link bandwidth constraints, we also have processing power constraints for each server. Furthermore, we allow flow shrinkage or expansion so as to model operations such
2595966
Color demosaicking via directional linear minimum mean square-error estimation
Digital cameras sample scenes using a color filter array of mosaic pattern (e.g., the Bayer pattern). The demosaicking of the color samples is critical to the image quality. This paper presents a new color demosaicking technique of optimal directional filtering of the green-red and green-blue difference signals. Under the assumption that the primary difference signals (PDS) between the green and red/blue channels are low pass, the missing green samples are adaptively estimated in both horizontal and vertical directions by the linear minimum mean square-error estimation (LMMSE) technique. These directional estimates are then optimally fused to further improve the green estimates. Finally, guided by the demosaicked full-resolution green channel, the other two color channels are reconstructed from the LMMSE filtered and fused PDS. The experimental results show that the presented color demosaicking technique outperforms the existing methods both in PSNR measure and visual perception.
37845872
The complexity of learning in PLN networks
Abstract In this paper, the complexity of learning in the feedforward PLN network is investigated by using Markov chain theory, when its training samples are incomplete (i.e., a network with hidden nodes). We present a learning algorithm. A formula for computing the average number of steps that the learning algorithm converges is obtained when the PLN network exists a solution. In the probabilistic sense, the completeness of the learning algorithm is proved. Some computer simulations are given to verify the analysis.