Pixel Selection Research Articles

Ultra-Thin Chips With ISFET Array for Continuous Monitoring of Body Fluids Ph.

This paper presents ISFET array based pH-sensing system-on-ultra-thin-chip (SoUTC) designed and fabricated in 350 nm CMOS technology. The SoUTC with the proposed current-mode active-pixel ISFET circuit array is desined to operate at 2 V and consumes 6.28 μW per-pixel. The presented SoUTC exhibits low sensitivity to process, voltage, temperature and strain-induced (PVTS) variations. The silicon area occupancy of each active-pixel is 44.9 × 33.5 µm2 with an ion-sensing area of 576 µm2. The design of presented ISFET device is analysed with finite element modeling in COMSOL Multiphysics using compact model parameters of MOSFET in 350 nm CMOS technology. Owing to thin (∼30 µm) Si-substrate the presented SoUTC can conform to curvilinear surfaces, allowing intimate contact necessary for reliable data for monitoring of analytes in body fluids such as sweat. Further, it can operate either in a rolling shutter fashion or in a pseudo-random pixel selection mode allowing the simultaneous detection of pH from different skin regions. Finally, the circuits have been tested in aqueous Dulbecco's Modified Eagle Medium (DMEM) culture media with 5-9 pH values, which mimics cellular environments, to demonstrate their potential use for continuous monitoring of body-fluids pH.

Modified statistically homogeneous pixel selection for use with a non-local adaptive multilook approach for small SAR datasets

ABSTRACT Multitemporal interferometric synthetic aperture radar (MT-InSAR) technique is an important approach for surface deformation monitoring. Nonlocal adaptive multilooking approach has potential benefits for the filtering and coherence estimation in the data preprocessing steps of MT-InSAR technique. The kernel of nonlocal adaptive multilooking approach lies in the selection of statistically homogeneous pixels (SHPs). Various amplitude-based strategies, such as DespecKS and its variations, have been proposed for selecting SHPs. However, the detection rates of these methods are usually unsatisfactory in the case of small data sets. To overcome this limitation, SHPs are selected based on the adaptive joint data vector, which encompasses both time dimensional samples and spatial information. In this letter, the outliers of the time dimensional samples are removed first based on a revised boxplot method. Additionally, image segmentation and L1 norm are both introduced to adaptively construct joint data vector in the preset window. Experiments on five TSX images are used to verify the reliability and efficiency of the proposed algorithm.

Improving CPT-InSAR Algorithm with Adaptive Coherent Distributed Pixels Selection

The Coherent Pixels Technique Interferometry Synthetic Aperture Radar (CPT-InSAR) method of inverting surface deformation parameters by using high-quality measuring points possesses the flaw inducing sparse measuring points in non-urban areas. In this paper, we propose the Adaptive Coherent Distributed Pixel InSAR (ACDP-InSAR) method, which is an adaptive method used to extract Distributed Scattering Pixel (DSP) based on statistically homogeneous pixel (SHP) cluster tests and improves the phase quality of DSP through phase optimization, which cooperates with Coherent Pixel (CP) for the retrieval of ground surface deformation parameters. For a region with sparse CPs, DSPs and its SHPs are detected by double-layer windows in two steps, i.e., multilook windows and spatial filtering windows, respectively. After counting the pixel number of maximum SHP cluster (MSHPC) in the multilook window based on the Anderson–Darling (AD) test and filtering out unsuitable pixels, the candidate DSPs are selected. For the filtering window, the SHPs of MSHPC’ pixels within the new window, which is different compared with multilook windows, were detected, and the SHPs of DSPs were obtained, which were used for coherent estimation. In phase-linking, the results of Eigen decomposition-based Maximum likelihood estimator of Interferometric phase (EMI) results are used as the initial values of the phase triangle algorithm (PTA) for the purpose of phase estimation (hereafter called as PTA-EMI). The DSPs and estimated phase are then combined with CPs in order to retrievesurface deformation parameters. The method was validated by two cases. The results show that the density of measuring points increased approximately 6–10 times compared with CPT-InSAR, and the quality of the interferometric phase significantly improved after phase optimization. It was demonstrated that the method is effective in increasing measuring point density and improving phase quality, which increases significantly the detectability of the low coherence region. Compared with the Distributed Scatterer InSAR (DS-InSAR) technique, ACDP-InSAR possesses faster processing speed at the cost of resolution loss, which is crucial for Earth surface movement monitoring at large spatial scales.

Surface subsidence monitoring with an improved distributed scatterer interferometric SAR time series method in a filling mining area

Statistically homogeneous pixel (SHP) selection and DS phase optimization are two critical steps in the distributed scatterer interferometric SAR (DS-InSAR) time series method. In this paper, a new algorithm named dynamic hypothesis test of confidence interval (D-HTCI) is proposed, which reduces the wrong selection rate of SHP and increases the number of SHP selections. Using adaptive spatial nonlocal filtering method for DS phase optimization, the phase standard deviation (PSD) and the sum of phase differences (SPD) show that compared with the traditional covariance matrix decomposition method, the optimization quality is improved by 2.1 and 1.8 times, respectively. Combining 24 scenes Sentinel-1A data from September 17, 2017 to July 14, 2018, the method is applied to monitor surface subsidence of the Daizhuang filling mining area (Jining, Shandong, China). The results show that the proposed method has mm-level accuracy for monitoring of surface subsidence in a filling mining area.

Conditional Noise Filter for MRI Images with Revised Theory on Second-order Histograms

Previous research by the author has the theory that histograms of second-order derivatives are capable of determining differences between pixels in MRI images for the purpose of noise reduction without having to refer to ground truth. However, the methodology of the previous research resulted in significant false negatives in determining which pixel is affected by noise. The theory has been revised in this article through the introduction of an additional Laplace curve, leading to comparisons between the histogram profile and two curves instead of just one. The revised theory is that differences between the first curve and the histogram profile and the differences between the second curve and the profile can determine which pixels are to be selected for filtering in order to improve image clarity while minimizing blurring. The revised theory is tested with a modified average filter versus a generic average filter, with PSNR and SSIM for scoring. The results show that for most of the sample MRI images, the theory of pixel selection is more reliable at higher levels of noise but not as reliable at preventing blurring at low levels of noise.

A distributed scatterers InSAR method based on adaptive window with statistically homogeneous pixel selection for mining subsidence monitoring

Statistically homogeneous pixels (SHP) selection is one of the primary steps in time-series InSAR technologies based on distributed scatterers. However, using a fixed window to extract SHP in mining areas may fail to effectively protect and restore the deformation phase in subsequent phase optimization. Therefore, we propose a method based on iterative adaptive window to extract SHP. This approach uses the variation coefficient to judge the intensity of fringes to realize the adaptive window adjustment in and outside the subsidence areas based on different ranges. Moreover, we use eigenvalue decomposition to optimize the phase, and use small baseline subset (SBAS) interferometry to perform time-series modelling and deformation calculations for the selected high-coherence points. The reliability of this method is verified from comparisons with leveling data. Compared with traditional time-series InSAR methods, the proposed method significantly improves the monitoring point density, deformation accuracy and has broad prospects in mining subsidence monitoring.

Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection.

Both Respiratory Flow (RF) and Respiratory Motion (RM) are visible in thermal recordings of infants. Monitoring these two signals usually requires landmark detection for the selection of a region of interest. Other approaches combine respiratory signals coming from both RF and RM, obtaining a Mixed Respiratory (MR) signal. The detection and classification of apneas, particularly common in preterm infants with low birth weight, would benefit from monitoring both RF and RM, or MR, signals. Therefore, we propose in this work an automatic RF pixel detector not based on facial/body landmarks. The method is based on the property of RF pixels in thermal videos, which are in areas with a smooth circular gradient. We defined 5 features combined with the use of a bank of Gabor filters that together allow selection of the RF pixels. The algorithm was tested on thermal recordings of 9 infants amounting to a total of 132 min acquired in a neonatal ward. On average the percentage of correctly identified RF pixels was . Obstructive Apneas (OAs) were simulated as a proof of concept to prove the advantage in monitoring the RF signal compared to the MR signal. The sensitivity in the simulated OA detection improved for the RF signal reaching against the of the MR signal. Overall, the method yielded promising results, although the positioning and number of cameras used could be further optimized for optimal RF visibility.

Novel method for selecting slices of the same cross-sectional view from digital tomosynthesis for monitoring posterior spinal instrumentation

BackgroundA method to evaluate pedicle screw loosening on digital tomosynthesis images is yet to be established owing to lack of methods for selecting slices of the same cross-sectional view. We aimed to develop an objective method for selecting slices of the same cross-sectional view on digital tomosynthesis images. MethodsFirst, an objective method of pixel selection was developed by measuring the size of glass disk and titanium alloy screw on digital tomosynthesis images followed by comparison with the actual sizes. Second, a method for selecting slices of the same cross-sectional view was explored on a bone model with posterior spinal instrumentation using the screw centerline and rod curvature as indicators of the same cross section. The angle between the screw centerline and rod was calculated to verify the accuracy in obtaining the same cross-sectional view. Third, the method for selecting slices of the same cross-sectional view was applied to six patients after posterior lumbar spinal instrumentation. ResultsThe pixel selection method enabled objective determination of a pixel on the peripheral lines of objects with an error as low as 200 μm in distance measurements on titanium alloy and glass. The mean differences of rod-screw angles between two slices were less than 1° and were not statistically significant in the bone model and patient images. ConclusionA method for selecting slices of the same cross-sectional view on digital tomosynthesis images was successfully developed. This method can enable objective and quantitative evaluations of pedicle screw loosening.

PERFORMANCE ASSESSMENT OF CLASSIFICATION ALGORITHMS FOR LANDUSE / LANDCOVER CHANGE USING SENTINEL 2 DATA – A CASE STUDY OF TIRUPPUR

Abstract. In the history of mankind, one of the vibrant geographical phenomena is urbanization. The urbanization process is characterized by the expansion of the city from the core to peripheral areas which includes economic development, social, political forces and population density. Very rapid urbanization in the highly populated country like India, which changes natural land cover into urban land use, which is unavoidable. However, the study region Tiruppur is known as the knitwear capital of India that induces urban development in the region which results in the modification of the natural land cover. For understating the interaction between the natural landscape and human activities, land use and land cover (LULC) is considered as the important indicator. Research on land-use and land cover changes using remote sensing technology has a long history to evident. The advancement in the Remote Sensing and GIS techniques provide the fine resolution of data sets to proceed. Sentinel-2B imagery was chosen for this study for two main reasons one is that compare to Landsat imagery it has a high spatial resolution of 10 m and its radiometry includes three vegetation red edge bands. These two characteristics make the Sentinel-2B data appealing for LULC mapping. Different types of classification algorithms have been used to perform land use and land cover mapping. The study aims to create land use and land cover classification by making a comparison between different algorithms in Tiruppur by using Sentinel-2B satellite imagery. The commonly known classification algorithms, K-means, IsoData, support vector machines (SVMs), and maximum likelihood (ML) classification are adopted for investigation. This is followed by the selection of training pixels from the remaining classes to perform and compare different supervised learning algorithms for the first- and second-level classification in terms of accuracy rates. Accuracy was assessed through metrics derived from an error matrix, but primarily overall accuracy and kappa coefficient was used in allocating algorithm hierarchy. Finally, after the comparison, the highly accurate algorithm was suggested for the mapping of urban areas. The highest overall accuracy and kappa coefficient was produced by support vector machine (SVM) is due to the algorithm’s relatively small number of complex decision boundaries. The results are helpful to understand the performance of the classification algorithm for the future studies.

A LSB Based Image Steganography Using Random Pixel and Bit Selection for High Payload

A LSB Based Image Steganography Using Random Pixel and Bit Selection for High Payload

Application of modify RSA cryptography and randomly LSB steganography on color images of fluid flow in a channel

This paper introduces a combination of three methods including a modified RSA cryptography, hidden text using steganography, and selection of random pixel from image to improve the general security level of the system. In this regard, the image is a carrier of the information transmitted through the media, and the fluid motion image is used to hide the information after it was encoded using modify RSA technique that before hiding it in a random way, because the image of fluid motion possesses the sinusoidal waves and turbulent motion. In this paper, a combination of three techniques is utilized in order to prevent the intruders and attackers from detecting the information and sharing it with others. LSB technique is used for hiding the message in image-based steganography. In addition, pseudo number to choose the random number of pixel when hiding the message after encryption. Four measures including Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), and histogram are used to compare the original image and stego-image. The results demonstrated the outperformance of the proposed method so that neither the attacker nor the intruder could discover the information contained within the image, due to the large value of the PSNR, very small value of the MSE, stability of the SSIM, and matching the histograms.

Non-contact vibration sensor using deep learning and image processing

This paper proposes a non-contact vibration measurement method based on deep learning and image processing. The deep learning method is used to realize the automatic and efficient selection of effective pixels and the optical flow method is used to extract vibration signals to realize non-contact and targetless visual vibration measurement. In this study, a carbon plate board and aluminum C-beam structure were measured and verified under artificial and non-human excitation in a laboratory environment. Additionally, bridge and cable structures in an outdoor environment were selected as measurement targets to verify the reliability of the proposed method. This paper compares the experimental results of Canny and Sobel edge detection algorithms and deep learning methods to verify the efficiency of deep learning. The results demonstrate that our method is robust, even under real-world unfavorable conditions, meaning it can serve as a novel measurement method in the field of vibration measurement.

Actual Evapotranspiration from UAV Images: A Multi-Sensor Data Fusion Approach

Multispectral imaging using Unmanned Aerial Vehicles (UAVs) has changed the pace of precision agriculture. Actual evapotranspiration (ETa) from the very high spatial resolution of UAV images over agricultural fields can help farmers increase their production at the lowest possible cost. ETa estimation using UAVs requires a full package of sensors capturing the visible/infrared and thermal portions of the spectrum. Therefore, this study focused on a multi-sensor data fusion approach for ETa estimation (MSDF-ET) independent of thermal sensors. The method was based on sharpening the Landsat 8 pixels to UAV spatial resolution by considering the relationship between reference ETa fraction (ETrf) and a Vegetation Index (VI). Four Landsat 8 images were processed to calculate ETa of three UAV images over three almond fields. Two flights coincided with the overpasses and one was in between two consecutive Landsat 8 images. ETrf was chosen instead of ETa to interpolate the Landsat 8-derived ETrf images to obtain an ETrf image on the UAV flight. ETrf was defined as the ratio of ETa to grass reference evapotranspiration (ETr), and the VIs tested in this study included the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Land Surface Water Index (LSWI). NDVI performed better under the study conditions. The MSDF-ET-derived ETa showed strong correlations against measured ETa, UAV- and Landsat 8-based METRIC ETa. Also, visual comparison of the MSDF-ET ETa maps was indicative of a promising performance of the method. In sum, the resulting ETa had a higher spatial resolution compared with thermal-based ETa without the need for the Albedo and hot/cold pixels selection procedure. However, wet soils were poorly detected, and in cases of continuous cloudy Landsat pixels the long interval between the images may cause biases in ETa estimation from the MSDF-ET method. Generally, the MSDF-ET method reduces the need for very high resolution thermal information from the ground, and the calculations can be conducted on a moderate-performance computer system because the main image processing is applied on Landsat images with coarser spatial resolutions.

Adaptive center pixel selection strategy in Local Binary Pattern for texture classification

Local Binary Pattern (LBP) is widely used in texture classification because of its powerful capability to extract texture features of a center pixel. However, LBP has three main drawbacks: (1) limited by the low resolution of imaging device, the quality of texture image is degraded, and some real existing pixels with more texture information are unavoidably lost. (2) Center pixel gc is the most important factor to extract correct LBP pattern. However, by far LBP and its variants do not show any solutions to enhance the robustness of center pixel gc. (3) Some LBP patterns include important texture microstructures, but they are ignored by uniform patterns. At the same time, some uniform patterns can be corrupted by noise and misclassified into non-uniform patterns. These LBP patterns therefore all lost their discrimination capability. In order to overcome these three disadvantages, in this paper, we propose a novel adaptive center pixel selection (ACPS) strategy.Inspired by image super-resolution techniques, ACPS firstly applies the interpolation method to recover the lost real existing pixels and generate the center pixel candidates with more texture information. Then, the gradient information is used to obtain the edge image aiming to find the non-uniform patterns at edge points which may contain complicated texture microstructures. After generating the center pixel candidates and edge image, we introduce ACPS strategy into the LBP framework. By adaptively selecting the optimal center pixel from all center pixel candidates, one non-uniform pattern at the edge point can be possibly recovered to the uniform pattern, and regain its discrimination power. It is worth noting that any other LBP variants can also employ the ACPS strategy to more effectively extract its texture features. By observing the experimental results on representative texture databases of Outex, UIUC, CUReT, XU_HR, ALOT and KTHTIPS2b after introducing the ACPS strategy into LBP and its variants of LTP, CLBP, BRINT, CRDP, FbLBP, and CJLBP, the texture classification performances can be significantly improved.

High-Quality Pixel Selection Applied for Natural Scenes in GB-SAR Interferometry

Phase analysis based on high-quality pixel (HQP) is crucial to ensure the measurement accuracy of ground-based SAR (GB-SAR). The amplitude dispersion (ADI) criterion has been widely applied to identify pixels with high amplitude stability, i.e., permanent scatterers (PSs), which typically are point-wise scatterers such as stones or man-made structures. However, the PS number in natural scenes is few and limits the GB-SAR applications. This paper proposes an improved method to take HQP selection applied for natural scenes in GB-SAR interferometry. In order to increase the spatial density of HQP for phase measurement, three types of HQPs including PS, quasi-permanent scatter (QPS), and distributed scatter (DS), are selected with different criteria. The ADI method is firstly utilized to take PS selection. To select those pixels with high phase stability but moderate amplitude stability, the temporal phase coherence (TPC) is defined. Those pixels with moderate ADI values and high TPC are selected as QPSs. Then the feasibility of the DS technique is explored. To validate the feasibility of the proposed method, 2370 GB-SAR images of a natural slope are processed. Experimental results prove that the HQP number could be significantly increased while slightly sacrificing phase quality.

Assessing the Effects of Spatial Scales on Regional Evapotranspiration Estimation by the SEBAL Model and Multiple Satellite Datasets: A Case Study in the Agro-Pastoral Ecotone, Northwestern China

Evapotranspiration (ET) estimation is important for understanding energy exchanges and water cycles. Remote sensing (RS) is the main method used to obtain ET data over large scales. However, owing to surface heterogeneities and different model algorithms, ET estimated from RS products with different spatial resolutions can cause significant uncertainties, whose causes need to be thoroughly analyzed. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) model was selected to explore spatial resolution influences on ET simulations. Three satellite datasets (Landsat Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High-Resolution Radiometer (AVHRR)) were selected to independently estimate ET in SEBAL model to identify the influence of the spatial scale on ET estimation, and analyze the effects and causes of scale aggregation. Results indicated that: (1) the spatial distributions of ET estimated from the three satellite datasets were similar, with the MODIS-based ET having the largest uncertainty; and (2) aggregating input parameters had limited changes in the net radiation and soil heat fluxes. However, errors in the sensible heat and latent heat fluxes were relatively larger, which were caused by changes in the selection of hot and cold pixels and the NDVI and surface albedo parameters during scale aggregation. The scale errors caused by the model mechanisms were larger than those caused by the land use/cover pattern in the SEBAL model. Overall, this study highlights the impact of spatial scale on ET and provides a better understanding of the scale aggregation effect on ET estimation by RS.

Towards Continuous Camera-Based Respiration Monitoring in Infants.

Aiming at continuous unobtrusive respiration monitoring, motion robustness is paramount. However, some types of motion can completely hide the respiration information and the detection of these events is required to avoid incorrect rate estimations. Therefore, this work proposes a motion detector optimized to specifically detect severe motion of infants combined with a respiration rate detection strategy based on automatic pixels selection, which proved to be robust to motion of the infants involving head and limbs. A dataset including both thermal and RGB (Red Green Blue) videos was used amounting to a total of 43 h acquired on 17 infants. The method was successfully applied to both RGB and thermal videos and compared to the chest impedance signal. The Mean Absolute Error (MAE) in segments where some motion is present was and breaths/min higher than the MAE in the ideal moments where the infants were still for testing and validation set, respectively. Overall, the average MAE on the testing and validation set are breaths/min and breaths/min, using and of the included video segments (segments containing events such as interventions were excluded based on a manual annotation), respectively. Moreover, we highlight challenges that need to be overcome for continuous camera-based respiration monitoring. The method can be applied to different camera modalities, does not require skin visibility, and is robust to some motion of the infants.

An improved distributed scatterers extraction algorithm for monitoring tattered ground surface subsidence with DSInSAR: A case study of loess landform in Tongren county

In order to effectively detect the detailed subsidence of tattered ground surface composed of many small fragments with the distributed scatterer interferometric synthetic aperture radar (DSInSAR) technique, a fast and accurate distributed scatterer extraction (FADSE), as an improved distributed scatterers extraction algorithm, is proposed and demonstrated in this paper. The emphasis of FADSE is on the improvement of accuracy of extracted DSs and detection efficiency as well. For the purpose, nonparametric estimation and parametric estimation methods are combined into FADSE to fast identify as many accurate statistically homogeneous pixels (SHP) as possible. Then the thresholds of homogeneous pixel number and coherence coefficient are adjusted to select DSs from SHPs. The validation of FADSE was performed in the case of loess subsidence detection in Tongren county, Qinghai Province of China, using 20 Sentinel-1A SAR images acquired between February 2016 and June 2017. Moreover, FADSE was compared with the Kolmogorov-Smirnov algorithm and Fast Statistically Homogeneous Pixel Selection method. Results show that FADSE is capable of efficiently extracting more DSs that are accurate and the detailed subsidence of tattered ground surface can be accurately detected.

Herriott cell spot imaging increases the performance of tunable laser spectrometers.

With the availability of high-power (milliwatts) single-mode tunable laser sources that operate at room temperature across the infrared (IR) region, tunable laser spectrometers have seen an explosion of growth in applications that include commercial, Earth and planetary science, and medical and industrial sensing. While the laser sources themselves have shown steady improvement, the detection architecture of using a single-element detector at one end of a multipass cell has remained unchanged over the last few decades. We present here an innovative new approach using a detector array coupled to an IR-transmissive mirror to image all or part of the multipass spot pattern of the far mirror and record spectra for each pixel. This novel approach offers improved sensitivity, increased dynamic range, laser power normalization, contaminant subtraction, resilience to misalignment, and reduces the instrument power requirement by avoiding the need for "fringe-wash" heaters. With many tens of pixels representing each spot during the laser spectral scan, intensity and optical fringe amplitude and phase information are recorded. This allows selection and manipulation (e.g., co-addition, subtraction) of the pixel output spectra to minimize optical interference fringes thereby increasing sensitivity. We demonstrate a factor of ∼20 sensitivity improvement over traditional single-element detection. Dynamic range increase of a factor of ∼100 is also demonstrated through spot selection representing different pathlengths. Additionally, subtracting the spectrum of the first spot from that of the higher pass normalizes the laser power and removes the contribution of contaminant gas and fringes in the fore-optics region. These initial results show that this imaging method is particularly advantageous for multi-channel laser spectrometers, and, once the image field is analyzed, pixel selection can be used to minimize data rate and volume collection requirements. This technique could be beneficial to enhanced-cavity detection schemes.

Interactive Estimation of Heterogeneity from Mass Spectrometry Imaging.

In this work, we demonstrate a new approach for interactively assessing hyperspectral data spatial structures for heterogeneity using mass spectrometry imaging. This approach is based on the visualization of the cosine distance as the similarity levels between mass spectra of a chosen region and the rest of the image (sample). The applicability of the method is demonstrated on a set of mass spectrometry images of frontal mouse brain slices. Selection of the reference pixel of the mass spectrometric image and a further view of the corresponding cosine distance map helps to prepare supporting vectors for further analysis, select features, and carry out biological interpretation of different tissues in the mass spectrometry context with or without histological annotation. Visual inspection of the similarity maps reveals the spatial distribution of features in tissue samples, which can serve as the molecular histological annotation of a slide.