High color saturation and angle-insensitive ultrathin color filter based on effective medium theory

An ultrathin angle-insensitive color filter enabling high color saturation and a wide color gamut is proposed by relying on magnesium hydride-hydrogenated amorphous silicon[MgH2-a-Si:H]lossy dielectric layer.Based on effective medium theory,the MgH2-a-Si:H layer with an ultrathin thickness can be equivalent to a quasi-homogeneous dielectric layer wit an effective complex refractive index,which can be tuned by altering the thickness of MgH2to obtain the targeted value o the imaginary part,corresponding to the realization of high color saturation.It is verified that the proposed color filte offers highly enhanced color saturation in conjunction with a wide color gamut by introducing a few-nanometer thic MgH2layer.As the MgH2-a-Si:H layer retains the advantages of high refractive index and tiny thickness,the proposed colo filter exhibits large angular tolerance up to±60°.In addition,MgH2with an unstable property can interconvert with Mg unde a dehydrogenation/hydrogenation reaction,which empowers the proposed color filter with dynamically tunable outpu color.The proposed scheme shows great promise in color printing and ultracompact display devices with high color sat uration,wide gamut,large angular tolerance,and dynamic tunability.

A NEW METHOD OF MOVING OBJECT DETECTION AND SHADOW REMOVING

This paper presents an adaptive method of objects and shadows detection in video streams. Models of background are firstly set up and adaptively updated in Hue Saturation Intensity (HSI) color space to detect motion regions. Then, detection errors are dealt with by motion continuity and velocity consistency. Finally, cast shadows are removed by the generic properties of luminance, chrominance and gradient density. Experimental results and their evaluation are presented to verify the effectiveness of this new method.

Exploiting global and local features for image retrieval

Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval.However,there are some problems in both of them:1)the methods defining directly texture in color space put more emphasis on color than texture feature;2)the methods extract several features respectively and combine them into a vector,in which bad features may lead to worse performance after combining directly good and bad features.To address the problems above,a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision.The bag-of-visual words(BoW)models and color intensity-based local difference patterns(CILDP)are exploited to capture local and global features of an image.The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method.The performance of our proposed framework in terms of average precision on Corel-1K database is86.26%,and it improves the average precision by approximately6.68%and12.53%over CILDP and BoW,respectively.Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.

Indoor and outdoor people detection and shadow suppression by exploiting HSV color information

An adaptive background model based on max-imum statistical probability and a shadow suppression scheme for indoor and outdoor people detection by exploiting hue saturation value(HSV)color information is proposed.To obtain the initial background scene,the frequency of R,G,and B component values for each pixel at the same position in the learning sequence are respec-tively calculated;the R,G,and B component values with the biggest ratios are incorporated to model the initial background.The background maintenance,or the so-called background re-initiation,is also proposed to adapt to scene changes such as illumination changes and scene geometry changes.Moving cast shadows generally exhibit a challenge for accurate moving target detection.Based on the observation that a shadow cast on a background region lowers its brightness but does not change its chro-maticity significantly,we address this problem in the ar-ticle by exploiting HSV color information.In addition,quantitative metrics is introduced to evaluate the algo-rithm on a benchmark suite of indoor and outdoor video sequences.The experimental results are given to show the performance of the algorithm.

An important step towards commercialization of quantum-dot light-emitting diode displays

Colloidal quantum dots(QDs)are a unique class of emissive materials with size-tunable emission wavelengths,saturated emission colors,near-unity luminance efficiency,inherent photo-and thermal-stability,and excellent solution processability.Display based on quantum-dot light-emitting diodes(QLED)may combine the superior properties of QDs,the benefits of solution-based fabrication techniques,and the advantages of self-emission devices,which promises an unprecedented generation of cost-effective,large-area,energysaving,wide-color-gamut,ultra-thin and flexible displays.

Novel metal-dielectric metameric optical filters for optical security devices

Optical security devices play an essential role in the fight against counterfeiting. In this paper, we study and design a pair of metal-dielectric optical filters based on metameric effect, which offer a hidden image effect by the color shift at a specific angle of observation. Compared with all-dielectric multilayer system, the metal-dielectric multilayer structure has larger color shift with varying incident angle, higher color saturation and fewer layers. Finally, the stacks with 5 layers and 7 layers are achieved, and the color difference index is only 0.71, which shows good metameric matching effect. Simultaneously, the sensitivity of filters to deposition errors is analyzed when the thickness deviation is ±2%, and the results show that the two filters have good manufacturability.