Mumford-Shah Regularizer with Contextual Feedback

We present a simple and robust feature preserving image regularization by letting local region measures modulate the diffusivity. The purpose of this modulation is to disambiguate low level cues in early vision. We interpret the Ambrosio-Tortorelli approximation of the Mumford-Shah model as a system with modulatory feedback and utilize this interpretation to integrate high level information into the regularization process. The method does not require any prior model or learning; the high level information is extracted from local regions and fed back to the regularization step. An important characteristic of the method is that both negative and positive feedback can be simultaneously used without creating oscillations. Experiments performed with both gray and color natural images demonstrate the potential of the method under difficult noise types, non-uniform contrast, existence of multi-scale patterns and textures.

Performance measures for video object segmentation and tracking

We propose measures to evaluate quantitatively the performance of video object segmentation and tracking methods without ground-truth (GT) segmentation maps. The proposed measures are based on spatial differences of color and motion along the boundary of the estimated video object plane and temporal differences between the color histogram of the current object plane and its predecessors. They can be used to localize (spatially and/or temporally) regions where segmentation results are good or bad; and/or they can be combined to yield a single numerical measure to indicate the goodness of the boundary segmentation and tracking results over a sequence. The validity of the proposed performance measures without GT have been demonstrated by canonical correlation analysis with another set of measures with GT on a set of sequences (where GT information is available). Experimental results are presented to evaluate the segmentation maps obtained from various sequences using different segmentation approaches.     

Potential Anomaly Separation and Archeological Site Localization Using Genetically Trained Multi‐level Cellular Neural Networks

In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi‐level cellular neural network (ML‐CNN) is introduced, developed, and applied to real data. ML‐CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas‐Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.     

Liquid–Liquid Diffusion‐Assisted Crystallization: A Fast and Versatile Approach Toward High Quality Mixed Quantum Dot‐Salt Crystals

Here, a new, fast, and versatile method for the incorporation of colloidal quantum dots (QDs) into ionic matrices enabled by liquid–liquid diffusion is demonstrated. QDs bear a huge potential for numerous applications thanks to their unique chemical and physical properties. However, stability and processability are essential for their successful use in these applications. Incorporating QDs into a tight and chemically robust ionic matrix is one possible approach to increase both their stability and processability. With the proposed liquid–liquid diffusion‐assisted crystallization (LLDC), substantially accelerated ionic crystallization of the QDs is shown, reducing the crystallization time needed by one order of magnitude. This fast process allows to incorporate even the less stable colloids including initially oil‐based ligand‐exchanged QDs into salt matrices. Furthermore, in a modified two‐step approach, the seed‐mediated LLDC provides the ability to incorporate oil‐based QDs directly into ionic matrices without a prior phase transfer. Finally, making use of their processability, a proof‐of‐concept white light emitting diode with LLDC‐based mixed QD‐salt films as an excellent color‐conversion layer is demonstrated. These findings suggest that the LLDC offers a robust, adaptable, and rapid technique for obtaining high quality QD‐salts.     

Ultrathin Highly Luminescent Two‐Monolayer Colloidal CdSe Nanoplatelets

Surface effects in atomically flat colloidal CdSe nanoplatelets (NLPs) are significantly and increasingly important with their thickness being reduced to subnanometer level, generating strong surface related deep trap photoluminescence emission alongside the bandedge emission. Herein, colloidal synthesis of highly luminescent two‐monolayer (2ML) CdSe NPLs and a systematic investigation of carrier dynamics in these NPLs exhibiting broad photoluminescence emission covering the visible region with quantum yields reaching 90% in solution and 85% in a polymer matrix is shown. The astonishingly efficient Stokes‐shifted broadband photoluminescence (PL) emission with a lifetime of ≈100 ns and the extremely short PL lifetime of around 0.16 ns at the bandedge signify the participation of radiative midgap surface centers in the recombination process associated with the underpassivated Se sites. Also, a proof‐of‐concept hybrid LED employing 2ML CdSe NPLs is developed as color converters, which exhibits luminous efficacy reaching 300 lm W_opt^?1. The intrinsic absorption of the 2ML CdSe NPLs (≈2.15 × 10⁶ cm^?1) reported in this study is significantly larger than that of CdSe quantum dots (≈2.8 × 10⁵ cm^?1) at their first exciton signifying the presence of giant oscillator strength and hence making them favorable candidates for next‐generation light‐emitting and light‐harvesting applications.     

Resonant frequency of an air gap tuned circular disc microstrip antenna

In this study, the formulation and the computation of the resonant frequency of an air gap tuned circular disc microstrip antenna are simplified, with improved accuracy, by using a new and very simple effective permittivity expression which is valid for thin and thick gaps. Very good agreement between the theoretical and the experimental resonant frequency values is obtained for the various structural parameters and operational modes.     

Efficient multiframe Wiener restoration of blurred and noisy imagesequences

Computationally efficient multiframe Wiener filtering algorithms that account for both intraframe (spatial) and interframe (temporal) correlations are proposed for restoring image sequences that are degraded by both blur and noise. One is a general computationally efficient multiframe filter, the cross-correlated multiframe (CCMF) Wiener filter, which directly utilizes the power and cross power spectra of only NxN matrices, where N is the number of frames used in the restoration. In certain special cases the CCMF lends itself to a closed-form solution that does not involve any matrix inversion. A special case is the motion-compensated multiframe (MCMF) filter, where each frame is assumed to be a globally shifted version of the previous frame. In this case, the interframe correlations can be implicitly accounted for using the estimated motion information. Thus the MCMF filter requires neither explicit estimation of cross correlations among the frames nor matrix inversion. Performance and robustness results are given.     

Classification of Spectral Patterns Obtained from Eustachian Tube Sonometry

Spectral patterns of sound transmission through the Eustachian tube (ET) have been obtained in a series of experiments designed to identify ET dysfunction. Previous studies of ET function using sonometry have relied on heuristic and somewhat arbitrary methods in interpreting the data. In this study, an automated classification algorithm was developed to separate these sonograms into three distinct groups. From a total of 150 sample spectra, 75 were used in the formation of learning sets. The remaining spectra were classified into these three groups using standard Bayesian techniques. Both parametric and nonparametric methods were applied in estimating conditional probability density functions. Results of classification are compared with an independent test of ET function. Agreement between our classifier and the results of the independent test was as good as 97.3 percent. The results of this study indicate that an automated classification procedure can effectively distinguish among the three major types of sonograms obtained from ET sonometry.