The stereoscopic image is often captured using dual cameras arranged side-by-side and optical path switching systems such as two separate solid lenses or biprism/mirrors. The miniaturization of the overall size of current stereoscopic devices down to several millimeters is at a sacrifice of further device size shrinkage. The limited light entry worsens the final image resolution and brightness. It is known that optofluidics offer good re-configurability for imaging systems. Leveraging this technique, we report a reconfigurable optofluidic system whose optical layout can be swapped between a singlet lens with 10 mm in diameter and a pair of binocular lenses with each lens of 3 mm in diameter for switchable two-dimensional (2D) and three-dimensional (3D) imaging. The singlet and the binoculars share the same optical path and the same imaging sensor. The singlet acquires a 3D image with better resolution and brightness, while the binoculars capture stereoscopic image pairs for 3D vision and depth perception. The focusing power tuning capability of the singlet and the binoculars enable image acquisition at varied object planes by adjusting the hydrostatic pressure across the lens membrane. The vari-focal singlet and binoculars thus work interchangeably and complementarily. The device is thus expected to have applications in robotic vision, stereoscopy, laparoendoscopy and miniaturized zoom lens system.
Adaptive elastomer-liquid lens can find a variety of optical applications due to the tunable optical powers without additional lens replacement or displacement. Most current elastomer-liquid lenses use elastomer membrane with a constant thickness. This approach, however, suffers from substantial optical aberration due to the edge clamping effect. In this study, a varied thickness elastomer membrane with customized aspherical profile is designed and developed to encapsulate a plano-convex liquid lens. Such varied thickness membrane is fabricated by double-side replica molding against a deformed elastomer-liquid lens membrane with a constant thickness. Such configuration could alleviate the edge clamping effect. Simulation and experimental results both show that the lens with a varied thickness membrane exhibits improved optical resolutions at both the center and the peripheral regions at the back focal length of 10 mm comparing to the lens with a constant thickness membrane. This study provides an effective solution to suppress the optical aberrations without sacrifice of the optical aperture.
KEYWORDS: Video, Error analysis, Wavelets, Distortion, Error control coding, Video compression, 3D video compression, 3D video streaming, Video coding, Automatic repeat request
In order to provide good QoS for video streaming in error-prone environments, effective error control methods are essential. Current error control methods can be classified into two categories: 1) Transport layer approaches such as FEC and retransmission; 2) Application layer approaches such as error resilience coding and error concealment. By far, most existing research is aimed towards optimizing one of the above approaches to reduce the impact of transmission errors. However, there is usually more than one error control method in a real video streaming system. In this case, how to optimize the system performance becomes more complicated, and is not standardized. This paper presents the research effort to joint-optimize the effects of two error control methods, retransmission and error concealment, in wavelet-based video streaming system. The major difficulty of the joint-optimization is that the two methods are mutually dependent; the system cannot be optimized by improving each error control method independently. To tackle this problem, a new content index, namely "reconstruction distortion", is defined to quantify both the packet content and its importance in error concealment. Based on the defined content index, a content-based retransmission approach is developed to select the best packet-sending scheme to maximize the quality of the received video under the given error concealment method. Experiments results demonstrate the effectiveness of the proposed method.
Burst packet loss imposes significant quality degradation for streaming applications. Interleaving, which helps reduce the probability of losing adjacent packets, is considered an effective method to mitigate burst errors. Most current research on wavelet image/video streaming is focused on how to maximize the interleaving effect in the spatial or spatial-frequency domain. However, in order to achieve the best video quality, optimizing temporal interleaving is very important, especially when error concealment is present in the streaming system because an inappropriate interleaving method may have an adverse effect on error concealment. Optimization of temporal interleaving on wavelet-compressed image/video streaming has not been previously studied. In this paper a novel optimal packet interleaving method is proposed for streaming applications on burst-loss channels. The objective is to achieve the best video quality at the receiver given an error-concealment algorithm and the channel traffic conditions. The proposed method consists of two steps: 1) spatial interleaving is conducted during packetization to disperse damage resulting from packet loss; 2) temporal interleaving is applied during transmission maximize the effect of error concealment at the receiver. In addition, a new concept that addresses the needs of error concealment, namely "temporal neighbor packet distance" is defined in order to facilitate the optimization. A low computational complexity algorithm is developed to satisfy the requirement of real-time transmission. Experimental results show that our proposed method can consistently improve the effects of error concealment.
Kernel-based Feature Extraction (KFE) is an emerging nonlinear discriminant feature extraction technique. In many classification scenarios using KFE allows the dimensionality of raw data to be reduced while class separability is preserved or even improved. KFE offers better performance than alternative linear algorithms because it employs nonlinear discriminating information among the classes. In this paper, we explore the potential application of KFE to radar signatures, as might be used for Automatic Target Recognition (ATR). Radar signatures can be problematic for many traditional ATR algorithms because of their unique characteristics. For example, some unprocessed radar signatures are high dimensional, linearly inseparable, and extremely sensitive to aspect changes. Applying KFE on High Range Resolution (HRR) radar signatures, we observe that KFE is quite effective on HRR data in terms of preserving/improving separability and reducing the dimensionality of the original data. Furthermore, our experiments indicate the number of extracted features that are needed for HRR radar signatures.
Feature Extraction (FE) algorithms have attracted great attention in recent years. In order to improve the performance of FE algorithms, nonlinear kernel transformations (e.g., the kernel trick) and scatter matrix based class separability criteria have been introduced in Kernel-based Feature Extraction (KFE)\cite{}. However, for any L-class problem, at most L-1 nonlinear kernel features can be extracted by KFE, which is not desirable for many applications. To solve this problem, a modified kernel-based feature extraction (MKFE) based on nonparametric scatter matrices was proposed, but with the limitation of only being able to extract multiple features for 2-class problems. In this paper, we present a general MKFE algorithm for multi-class problems. The core of our algorithm is a novel expression of the nonparametric between-class matrix, which is shown to be consistent with the definition of the parametric between-class matrix in the sense of the scatter-matrix-based class separability criteria. Based on this expression of the between-class matrix our algorithm is able to extract multiple kernel features in multi-class problems. To speed up the computation, we also proposed a simplified formula. Experimental results using synthetic data are provided to demonstrate the effectiveness of our proposed algorithm.
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