Image deconvolution using a Gaussian scale mixtures model to approximate the wavelet sparseness constraint

This paper proposes to use an extended Gaussian Scale Mixtures (GSM) model instead of the conventional ℓ1 norm to approximate the sparseness constraint in the wavelet domain. We combine this new constraint with subband-dependent minimization to formulate an iterative algorithm on two shift-invariant wavelet transforms, the Shannon wavelet transform and dual-tree complex wavelet transform (DTCWT). This extented GSM model introduces spatially varying information into the deconvolution process and thus enables the algorithm to achieve better results with fewer iterations in our experiments. ©2009 IEEE.

Beamforming correction for dipole measurement using two-dimensional microphone arrays

In this paper, a beamforming correction for identifying dipole sources by means of phased microphone array measurements is presented and implemented numerically and experimentally. Conventional beamforming techniques, which are developed for monopole sources, can lead to significant errors when applied to reconstruct dipole sources. A previous correction technique to microphone signals is extended to account for both source location and source power for two-dimensional microphone arrays. The new dipole-beamforming algorithm is developed by modifying the basic source definition used for beamforming. This technique improves the previous signal correction method and yields a beamformer applicable to sources which are suspected to be dipole in nature. Numerical simulations are performed, which validate the capability of this beamformer to recover ideal dipole sources. The beamforming correction is applied to the identification of realistic aeolian-tone dipoles and shows an improvement of array performance on estimating dipole source powers. © 2008 Acoustical Society of America.

A new method for the acquisition of ultrasonic strain image volumes.

This article presents a new method for acquiring three-dimensional (3-D) volumes of ultrasonic axial strain data. The method uses a mechanically-swept probe to sweep out a single volume while applying a continuously varying axial compression. Acquisition of a volume takes 15-20 s. A strain volume is then calculated by comparing frame pairs throughout the sequence. The method uses strain quality estimates to automatically pick out high quality frame pairs, and so does not require careful control of the axial compression. In a series of in vitro and in vivo experiments, we quantify the image quality of the new method and also assess its ease of use. Results are compared with those for the current best alternative, which calculates strain between two complete volumes. The volume pair approach can produce high quality data, but skillful scanning is required to acquire two volumes with appropriate relative strain. In the new method, the automatic quality-weighted selection of image pairs overcomes this difficulty and the method produces superior quality images with a relatively relaxed scanning technique.

Enhanced efficiency of near-UV emitting LEDs for solid-state lighting applications

The performance of a series of near-UV (∼385 nm) emitting LEDs, consisting of high efficiency InGaN/AlInGaN QWs in the active region, was investigated. Significantly reduced roll-over of efficiency at high current density was found compared to InGaN/GaN LEDs emitting at a similar wavelength. The importance of optical cavity effects in flip-chip geometry devices has also been investigated. The light output was enhanced by more than a factor of 2 when the lightemitting region was located at an anti-node position with respect to a high reflectivity current injection mirror. A power of 0.49 mW into a numerical aperture of 0.5 was obtained for a junction area of 50μm in diameter and a current of 30 mA, corresponding to a radiance of 30 W/cm2/str.

Combining interest points and edges for content-based image retrieval

This paper presents a novel approach using combined features to retrieve images containing specific objects, scenes or buildings. The content of an image is characterized by two kinds of features: Harris-Laplace interest points described by the SIFT descriptor and edges described by the edge color histogram. Edges and corners contain the maximal amount of information necessary for image retrieval. The feature detection in this work is an integrated process: edges are detected directly based on the Harris function; Harris interest points are detected at several scales and Harris-Laplace interest points are found using the Laplace function. The combination of edges and interest points brings efficient feature detection and high recognition ratio to the image retrieval system. Experimental results show this system has good performance. © 2005 IEEE.

Image-based localization and pose recovery using scale invariant features

In this paper, we propose a vision based mobile robot localization strategy. Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. The local characteristics of the features we use prove to be robust to occlusion and outliers. In addition, the invariance of the features to viewpoint change makes them suitable landmarks for mobile robot localization. Scale-invariant features detected in the first exploration are indexed into a location database. Indexing and voting allow efficient recognition of global localization. The localization result is verified by epipolar geometry between the representative view in database and the view to be localized, thus the probability of false localization will be decreased. The localization system can recover the pose of the camera mounted on the robot by essential matrix decomposition. Then the position of the robot can be computed easily. Both calibrated and un-calibrated cases are discussed and relative position estimation based on calibrated camera turns out to be the better choice. Experimental results show that our approach is effective and reliable in the case of illumination changes, similarity transformations and extraneous features. © 2004 IEEE.