Search Results (12)

Using a stainless shadow mask combined with a magnetron-ion-assisted deposition (IAD) sputtering system, we investigate the surface morphologies and optical properties of microfilms. Optimal color-filter (CF) coating microfilms with niobium pent-oxide (Nb₂O₅)/silicon dioxide (SiO₂) multilayers on a hard polycarbonate (HPC) substrate, grown at 85 °C and 50 SCCM oxygen flow, can obtain a fairly uniform thickness (with an average roughness of 0.083 and 0.106 nm respectively for Nb₂O₅ and SiO₂ films) through all positions. On a flexible HPC substrate with the Nb₂O₅/SiO₂ microfilms, meanwhile, the peak transmittances measured in the visible range are 95.70% and 91.47%, respectively, for coatings with and without a shadow mask for this new-tech system. For the optimal CF application with a shadow mask, transmittance on each 100 nm band-pass wavelength is enhanced by 4.04% absolute (blue), 2.96% absolute (green), and 2.12% absolute (red). Moreover, the developed new-tech system not only enhances the quality of the films by achieving smoother and uniform surfaces but also reduces deposition time, thereby improving overall process efficiency. For the with-shadow-mask condition, there is little shift at 50% transmittance (T50%), and high transmittance (~97%) is maintained after high-temperature (200 °C) baking for 12 h. These results are well above the commercial CF standard (larger than 90%) and demonstrate reliability and good durability for flexible optical applications. Full article

Chlorpyrifos (CHL), profenofos (PRO) and cypermethrin (CYP) are widely used in combination to increase crop yields. However, these three pesticides can cause serious harm to human health and do not easily degrade. In this study, a novel visible paper sensor has been prepared successfully and different colorimetric reactions were utilized to detect the three pesticides simultaneously. The sensor was constructed by grafting a zwitterionic polymer onto a cellulose filter (CF) and placing it on a glass surface modified with PDMS. The branch shape was designed to form multiple detection areas, which were modified with specific pesticides and corresponding chromogenic reagents. The as-prepared colorimetric platform exhibited high sensitivity, a short detection time, a good linear response and a low detection limit (LOD) for the three pesticides (chlorpyrifos: y = 46.801 − 1.939x, R² = 0.983, LOD = 0.235 mg/L; profenofos: y = 40.068 + 42.5x, R² = 0.988, LOD = 4.891 mg/L; cypermethrin: y = 51.993 + 1.474x, R² = 0.993, LOD = 4.053 mg/L). The comparison of the results obtained by the proposed paper sensor and those obtained by spectrophotometry further revealed the stability and reliability of the paper sensor. In particular, the color intensity of the interaction between the pesticides and coloring agents could be directly observed by the human eye. The consistency of the colorimetric/optical assay was proven in real target pesticide samples. Thus, this sensing strategy provides a portable, cost-effective, accurate and visualized paper platform, which could be suitable for application in the fruit and vegetable industry for monitoring CHL, PRO and CYP in parallel. Full article

Bayer color filter array (CFA) images are captured by a single-chip image sensor covered with a Bayer CFA pattern which has been widely used in modern digital cameras. In the past two decades, many compression methods have been proposed to compress Bayer CFA images. These compression methods can be roughly divided into the compression-first-based (CF-based) scheme and the demosaicing-first-based (DF-based) scheme. However, in the literature, no review article for the two compression schemes and their compression performance is reported. In this article, the related CF-based and DF-based compression works are reviewed first. Then, the testing Bayer CFA images created from the Kodak, IMAX, screen content images, videos, and classical image datasets are compressed on the Joint Photographic Experts Group-2000 (JPEG-2000) and the newly released Versatile Video Coding (VVC) platform VTM-16.2. In terms of the commonly used objective quality, perceptual quality metrics, the perceptual effect, and the quality–bitrate tradeoff metric, the compression performance comparison of the CF-based compression methods, in particular the reversible color transform-based compression methods and the DF-based compression methods, is reported and discussed. Full article

Recently due to the good balance between performance and tracking speed, the discriminative correlation filter (DCF) has become a popular and excellent tracking method in short-term tracking. Computing the correlation of a response map can be efficiently performed in the Fourier domain by the discrete Fourier transform (DFT) of the input, where the DFT of an image has symmetry in the Fourier domain. However, most of the correlation filter (CF)-based trackers cannot deal with the tracking results and lack the effective mechanism to adjust the tracked errors during the tracking process, thus usually perform poorly in long-term tracking. In this paper, we propose a long-term tracking framework, which includes a tracking-by-detection part and redetection part. The tracking-by-detection part is built on a DCF framework, by integrating with a multifeature fusion model, which can effectively improve the discriminant ability of the correlation filter for some challenging situations, such as occlusion and color change. The redetection part can search the tracked object in a larger region and refine the tracking results after the tracking has failed. Benefited by the proposed redetection strategy, the tracking results are re-evaluated and refined, if it is necessary, in each frame. Moreover, the reliable estimation module in the redetection part can effectively identify whether the tracking results are correct and determine whether the redetector needs to open. The proposed redetection part utilizes a saliency detection algorithm, which is fast and valid for object detection in a limited region. These two parts can be integrated into DCF-based tracking methods to improve the long-term tracking performance and robustness. Extensive experiments on OTB2015 and VOT2016 benchmarks show that our proposed long-term tracking method has a proven effectiveness and high efficiency compared with various tracking methods. Full article

This paper describes the non-contact optical detection of debris material that adheres to the substrates of color filters (CFs) and thin-film transistors (TFTs) by area charge-coupled devices (CCDs) and laser sensors. One of the optical detections is a side-view illumination by an area CCD that emits a coherency light to detect debris on the CF. In contrast to the height of the debris material, the image is acquired by transforming the geometric shape from a square to a circle. As a result, the side-view illumination from the area CCD identified the height of the debris adhered to the black matrix (BM) as well as the red, green, and blue of a CF with 95, 97, 98, and 99% accuracy compared to the golden sample. The uncertainty analysis was at 5% for the BM, 3% for the red, 2% for the green, and 1% for the blue. The other optical detection, a laser optical interception with a horizontal alignment, inspected the material foreign to the TFT. At the same time, laser sensors intercepted the debris on the TFT at a voltage of 3.5 V, which the five sets of laser optics make scanning the sample. Consequently, the scanning rate reached over 98% accuracy, and the uncertainty analysis was within 5%. Thus, both non-contact optical methods can detect debris at a 50 μm height or lower. The experiment presents a successful design for the efficient prevention of a valuable component malfunction. Full article

This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data. Full article

In this study, the optimal structure for obtaining high green color purity was investigated by modeling quantum dot (QD)–organic light-emitting diodes (OLED). It was found that even if the green quantum dot (G-QD) density in the G-QD layer was 30%, the full width at half maximum (FWHM) in the green wavelength band could be minimized to achieve a sharp emission spectrum, but it was difficult to completely block the blue light leakage with the G-QD layer alone. This blue light leakage problem was solved by stacking a green color filter (G-CF) layer on top of the G-QD layer. When G-CF thickness 5 μm was stacked, blue light leakage was blocked completely, and the FWHM of the emission spectrum in the green wavelength band was minimized, resulting in high green color purity. It is expected that the overall color gamut of QD-OLED can be improved by optimizing the device that shows such excellent green color purity. Full article

Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots. Full article

Color filter (CF) and touch panel (or touch sensor (TS)) are important components for optoelectronic materials and component manufacturing. Due to the cut-throat world of market in the manufacturing, the processes of color filters are similar to touch sensors. The case invested in the production of touch panels in 2009. After a long period of quality improvement, the problem of contamination pollution still accounts for ~30% of the total variation. In addition to the external problem, there is also the fail of communication caused by dirt or peeling. Therefore, the case was established to improve the dirt defect by setting up Lean Six Sigma project, and the project goal was to reduce the proportion to 0.18%. After three months of improvement and three months of continuous observation, the abnormal proportion of pollution decreased from 0.35% of the overall average defect loss to 0.13% (the improvement rate reached 63%). It is estimated that the entire product can generate 3 million (USD) of performance for the case in one year. It is also possible to increase the customer’s satisfaction and the company’s technical competitiveness by improving yield and achieving the continuous improvement of objectives. Full article

Aiming at the problem of poor robustness and the low effectiveness of target tracking in complex scenes by using single color features, an object-tracking algorithm based on dual color feature fusion via dimension reduction is proposed, according to the Correlation Filter (CF)-based tracking framework. First, Color Name (CN) feature and Color Histogram (CH) feature extraction are respectively performed on the input image, and then the template and the candidate region are correlated by the CF-based methods, and the CH response and CN response of the target region are obtained, respectively. A self-adaptive feature fusion strategy is proposed to linearly fuse the CH response and the CN response to obtain a dual color feature response with global color distribution information and main color information. Finally, the position of the target is estimated, based on the fused response map, with the maximum of the fused response map corresponding to the estimated target position. The proposed method is based on fusion in the framework of the Staple algorithm, and dimension reduction by Principal Component Analysis (PCA) on the scale; the complexity of the algorithm is reduced, and the tracking performance is further improved. Experimental results on quantitative and qualitative evaluations on challenging benchmark sequences show that the proposed algorithm has better tracking accuracy and robustness than other state-of-the-art tracking algorithms in complex scenarios. Full article

Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods. Full article

In this study, indium-tin-oxide (ITO) nanoparticles were simply recovered from the thin film transistor-liquid crystal display (TFT-LCD) panel scraps by means of lift-off method. This can be done by dissolving color filter (CF) layer which is located between ITO layer and glass substrate. In this way the ITO layer was easily lifted off the glass substrate of the panel scrap without panel crushing. Over 90% of the ITO on the TFT-LCD panel was recovered by using this method. After separating, the ITO was obtained as particle form and their characteristics were investigated. The recovered product appeared as aggregates of particles less than 100 nm in size. The weight ratio of In/Sn is very close to 91/9. XRD analysis showed that the ITO nanoparticles have well crystallized structures with (222) preferred orientation even after recovery. The method described in this paper could be applied to the industrial recovery business for large size LCD scraps from TV easily without crushing the glass substrate. Full article