9760560
An edge-guided image interpolation algorithm via directional filtering and data fusion
Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts
16021441
Hybrid inter- and intra-wavelet scale image restoration
This paper exploits both the inter- and intra-scale interdependencies that exist in wavelet coefficients to improve image restoration from noise-corrupted data. Using an over-complete wavelet expansion, we group the wavelet coefficients with the same spatial orientation at several scales. We then apply the linear minimum mean squared-error estimation to smooth noise. This scheme exploits the inter-scale correlation information of wavelet coefficients. To exploit the intra-scale dependencies, we calculate the co-variance matrix of each vector locally using a centered square-shaped window. Experiments show that the proposed hybrid scheme significantly outperforms methods exploiting only the intra- or inter-scale dependencies. The performance of noise removal also depends on wavelet filters. In our experiments a biorthogonal wavelet, which best characterizes the image inter-scale dependencies, achieves the best results.
12034638
A block-based inter-band lossless hyperspectral image compressor
We propose a hyperspectral image compressor called BH which considers its input image as being partitioned into square blocks, each lying entirely within a particular band, and compresses one such block at a time by using the following steps: first predict the block from the corresponding block in the previous band, then select a predesigned code based on the prediction errors, and finally encode the predictor coefficient and errors. Apart from giving good compression rates and being fast, BH can provide random access to spatial locations in the image. We hypothesize that BH works well because it accommodates the rapidly changing image brightness that often occurs in hyperspectral images. We also propose an intra-band compressor called LM which is worse than BH, but whose performance helps explain BH's performance.
10620171
Characterization of palmprints by wavelet signatures via directional context modeling
The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Current palmprint-based systems are more user friendly, more cost effective, and require fewer data signatures than traditional fingerprint-based identification systems. The principal lines and wrinkles captured in a low-resolution palmprint image provide more than enough information to uniquely identify an individual. This paper presents a palmprint identification scheme that characterizes a palmprint using a set of statistical signatures. The palmprint is first transformed into the wavelet domain, and the directional context of each wavelet subband is defined and computed in order to collect the predominant coefficients of its principal lines and wrinkles. A set of statistical signatures, which includes gravity center, density, spatial dispersivity and energy, is then defined to characterize the palmprint with the selected directional context values. A classification and identification scheme based on these signatures is subsequently developed. This scheme exploits the features of principal lines and prominent wrinkles sufficiently and achieves satisfactory results. Compared with the line-segments-matching or interesting-points-matching based palmprint verification schemes, the proposed scheme uses a much smaller amount of data signatures. It also provides a convenient classification strategy and more accurate identification.
548723
Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding
Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.
15953437
A wavelet-based edge detection method by scale multiplication
A wavelet-based multiscale edge detection scheme is presented in this paper. By multiplying the wavelet coefficients at two adjacent scales to magnify significant structures and suppress noise, we determined edges as the local maxima directly in the scale product after an efficient thresholding, instead of first forming the edge maps at several scales and then synthesizing them together, as employed in many multiscale techniques. It is shown that the scale multiplication achieves better results than either of the two scales, especially on the localization performance. Experiments on natural images are compared with the Laplacian of Gaussian and Canny edge detection algorithms.
627898
Fault-tolerant data delivery for multicast overlay networks
Overlay networks represent an emerging technology for rapid deployment of novel network services and applications. However, since public overlay networks are built out of loosely coupled end-hosts, individual nodes are less trustworthy than Internet routers in carrying out the data forwarding function. Here we describe a set of mechanisms designed to detect and repair errors in the data stream. Utilizing the highly redundant connectivity in overlay networks, our design splits each data stream to multiple sub-streams which are delivered over disjoint paths. Each sub-stream carries additional information that enables receivers to detect damaged or lost packets. Furthermore, each node can verify the validity of data by periodically exchanging Bloom filters, the digests of recently received packets, with other nodes in the overlay. We have evaluated our design through both simulations and experiments over a network testbed. The results show that most nodes can effectively detect corrupted data streams even in the presence of multiple tampering nodes.
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.
140083992
Boosting image classification with LDA-based feature combination for digital photograph management
Image classification is of great importance for digital photograph management. In this paper we propose a general statistical learning method based on boosting algorithm to perform image classification for photograph annotation and management. The proposed method employs both features extracted from image content (i.e., color moment and edge direction histogram) and features from the EXIF metadata recorded by digital cameras. To fully utilize potential feature correlations and improve the classification accuracy, feature combination is needed. We incorporate linear discriminant analysis (LDA) algorithm to implement linear combinations between selected features and generate new combined features. The combined features are used along with the original features in boosting algorithm for improving classification performance. To make the proposed learning algorithm more efficient, we present two heuristics for selective feature combinations, which can significantly reduce training computation without losing performance. The proposed image classification method has several advantages: small model size, computational efficiency and improved classification performance based on LDA feature combination.
45439748
Dynamic bandwidth allocation and buffer dimensioning for supporting video-on-demand services in virtual private networks
This paper focuses on the optimization of network bandwidth allocation and buffer dimensioning for transporting pre-stored MPEG video data from the source to the playback destination across virtual private network (VPN). This is one of the most important issues in the support of video-on-demand (VoD) service over VPN. This paper provides a novel scheme in the dynamic allocation of bandwidth to segments of video using the ABR mode. The dynamic bandwidth allocation mechanism is based on a new concept, called playback tunnel obtained from the traffic characteristics of the pre-stored MPEG video trace, to determine the optimum of transmission bandwidth as well as the optimum of buffer capacity to ensure that the playback buffer neither underflows nor overflows. The proposed scheme is tested with real-life MPEG video traces. The obtained results have shown its significant performance improvement in terms of the capacity of playback buffer, the start-up playback delay, network utilization and the network multiplexing gain.
34237375
On-Demand Business Collaboration Enablement with Services Computing
In this talk, I will introduce a model for on-demand business process based collaboration, namely, Extended Business Collaboration (eBC), and its major characteristics: modeling, solution stack, configurable business protocol enabling framework, on-demand services deployment, as well as monitoring and analytical control. Then we present a few major research issues associated with facilitating extended business collaboration, followed by our proposed Annotated Business HyperChain technology leveraging Services computing and semantic annotation model. Services computing is the evolution of Internet computing by leveraging services oriented architecture. Services computing is mainly targeting for a new ground break technology suite – Web services and services oriented Grid/Utility Computing, Business Computing, and Autonomic Computing. A research prototype is illustrated at the end of the talk followed by some observations and discussion of open research issues requiring further exploration.