Pixel Camera Research Articles

Prototyping hexagonal light concentrators using high-reflectance specular films for the Large-Sized Telescopes of the Cherenkov Telescope Array

We have developed a prototype hexagonal light concentrator for the Large-Sized Telescopes of the Cherenkov Telescope Array. To maximize the photodetection efficiency of the focal-plane camera pixels for atmospheric Cherenkov photons and to lower the energy threshold, a specular film with a very high reflectance of 92–99% has been developed to cover the inner surfaces of the light concentrators. The prototype has a relative anode sensitivity (which can be roughly regarded as collection efficiency) of about 95 to 105% at the most important angles of incidence. The design, simulation, production procedure, and performance measurements of the light-concentrator prototype are reported.

Lensless Photoluminescence Hyperspectral Camera Employing Random Speckle Patterns

We propose and demonstrate a spectrally-resolved photoluminescence imaging setup based on the so-called single pixel camera – a technique of compressive sensing, which enables imaging by using a single-pixel photodetector. The method relies on encoding an image by a series of random patterns. In our approach, the image encoding was maintained via laser speckle patterns generated by an excitation laser beam scattered on a diffusor. By using a spectrometer as the single-pixel detector we attained a realization of a spectrally-resolved photoluminescence camera with unmatched simplicity. We present reconstructed hyperspectral images of several model scenes. We also discuss parameters affecting the imaging quality, such as the correlation degree of speckle patterns, pattern fineness, and number of datapoints. Finally, we compare the presented technique to hyperspectral imaging using sample scanning. The presented method enables photoluminescence imaging for a broad range of coherent excitation sources and detection spectral areas.

Super-Resolution Imaging Through Scattering Medium Based on Parallel Compressed Sensing

Recent studies show that compressed sensing (CS) can recover sparse signal with much fewer measurements than traditional Nyquist theorem. From another point of view, it provides a new idea for super-resolution imaging, like the emergence of single pixel camera. However, traditional methods implemented measurement matrix by digital mirror device (DMD) or spatial light modulator, which is a serial imaging process and makes the method inefficient. In this paper, we propose a super resolution imaging system based on parallel compressed sensing. The proposed method first measures the transmission matrix of the scattering sheet and then recover high resolution objects by “two-step phase shift” technology and CS reconstruction algorithm. Unlike traditional methods, the proposed method realizes parallel measurement matrix by a simple scattering sheet. Parallel means that charge-coupled device camera can obtain enough measurements at once instead of changing the patterns on the DMD repeatedly. Simulations and experimental results show the effectiveness of the proposed method.

Single-shot 3D motion picture camera with a dense point cloud.

We discuss physical and information theoretical limits of optical 3D metrology. Based on these principal considerations we introduce a novel single-shot 3D movie camera that almost reaches these limits. The camera is designed for the 3D acquisition of macroscopic live scenes. Like a hologram, each movie-frame encompasses the full 3D information about the object surface and the observation perspective can be varied while watching the 3D movie. The camera combines single-shot ability with a point cloud density close to the theoretical limit. No space-bandwidth is wasted by pattern codification. With 1-megapixel sensors, the 3D camera delivers nearly 300,000 independent 3D points within each frame. The 3D data display a lateral resolution and a depth precision only limited by physics. The approach is based on multi-line triangulation. The requisite low-cost technology is simple. Only two properly positioned synchronized cameras solve the profound ambiguity problem omnipresent in 3D metrology.

Identify and Classify Normal and Defects of Prunus_armeniaca Using Imaging Techniques

The Prunus_armeniaca fruit is classified manually in wholesale markets, supermarkets and food processing plants on a normal or defects basis. The aim of this research is to replace the manual sorting techniques using computer vision techniques and applications by proposing techniques for identify and recognitions patterns through the use of 150 fruits of Prunus_armeniaca, 10 for the testing stage in fresh and 10 for testing stage in case of defects. The fruits Prunus_armeniaca collected from growing trees in the large fields of Salah al-Din province\Iraq. The system designed for classification based on the color image taken inside a black box used camera pixel resolution of (13 mega) with a constant intensity of light. . Used K-mean in phase segmentations and only computed 13 features derive statistics from GLCM .classification phase used SVM classify fruit into two class, either (normal or defects) .Results the system success rate reach 100%.The work done using MATLAB R2016a.

Fusion of Multispectral Imagery and Spectrometer Data in UAV Remote Sensing

Abstract: High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unmanned aerial vehicles (UAVs) are emerging as lower cost and more flexible means to acquire very high resolution imagery. Miniaturized hyperspectral sensors have been developed for UAVs, but the sensors, associated hardware, and data processing software are still cost prohibitive for use by individual farmers or small remote sensing firms. This study simulated hyperspectral image data by fusing multispectral camera imagery and spectrometer data. We mounted a multispectral camera and spectrometer, both being low cost and low weight, on a standard UAV and developed procedures for their precise data alignment, followed by fusion of the spectrometer data with the image data to produce estimated spectra for all the multispectral camera image pixels. To align the data collected from the two sensors in both the time and space domains, a post-acquisition correlation-based global optimization method was used. Data fusion, to estimate hyperspectral reflectance, was implemented using several methods for comparison. Flight data from two crop sites, one being tomatoes, and the other corn and soybeans, were used to evaluate the alignment procedure and the data fusion results. The data alignment procedure resulted in a peak R2 between the spectrometer and camera data of 0.95 and 0.72, respectively, for the two test sites. The corresponding multispectral camera data for these space and time offsets were taken as the best match to a given spectrometer reading, and used in modelling to estimate hyperspectral imagery from the multispectral camera pixel data. Of the fusion approaches evaluated, principal component analysis (PCA) based models and Bayesian imputation reached a similar accuracy, and outperformed simple spline interpolation. Mean absolute error (MAE) between predicted and observed spectra was 17% relative to the mean of the observed spectra, and root mean squared error (RMSE) was 0.028. This approach to deriving estimated hyperspectral image data can be applied in a simple fashion at very low cost for crop assessment and monitoring within individual fields.

Introduction to a new emphatic model for predicting patients and understanding mining techniques

Data mining is the act of inspecting substantial prior databases to create new data, choosing and exploring large volumes of information. This procedure has turned into an inexorably forceful activity in every aspect of health care and research. A lot of scattered data about health is accessible. This paper actualizes an imaginative thought to recognize ailments influenced in humans and gives the results in terms of accuracy. Hybrid algorithm approach of simple logistic and IB1 is used. The image set of the sick humans are caught with the assistance of high pixel cameras or cell phones like android, iPhone or remote PDA. These images are then nourished for application for distinguishing human ailments and recommend solutions. Result in terms of accuracy shows improvement by15% than existing algorithms.

Aliasing, coherence, and resolution in a lensless holographic microscope.

We have shown that the maximum achievable resolution of an in-line lensless holographic microscope is limited by aliasing and, for collimated illumination, cannot exceed the camera pixel size. This limit can be achieved only when the optimal conditions on the spatial and temporal coherence state of the illumination are satisfied. The expressions defining the configuration, delivering maximum resolution with given spatial and temporal coherence of the illumination, are obtained. The validity of these conditions is confirmed experimentally.

CIFAR10-DVS: An Event-Stream Dataset for Object Classification.

Neuromorphic vision research requires high-quality and appropriately challenging event-stream datasets to support continuous improvement of algorithms and methods. However, creating event-stream datasets is a time-consuming task, which needs to be recorded using the neuromorphic cameras. Currently, there are limited event-stream datasets available. In this work, by utilizing the popular computer vision dataset CIFAR-10, we converted 10,000 frame-based images into 10,000 event streams using a dynamic vision sensor (DVS), providing an event-stream dataset of intermediate difficulty in 10 different classes, named as “CIFAR10-DVS.” The conversion of event-stream dataset was implemented by a repeated closed-loop smooth (RCLS) movement of frame-based images. Unlike the conversion of frame-based images by moving the camera, the image movement is more realistic in respect of its practical applications. The repeated closed-loop image movement generates rich local intensity changes in continuous time which are quantized by each pixel of the DVS camera to generate events. Furthermore, a performance benchmark in event-driven object classification is provided based on state-of-the-art classification algorithms. This work provides a large event-stream dataset and an initial benchmark for comparison, which may boost algorithm developments in even-driven pattern recognition and object classification.

An Iterative Distortion Compensation Algorithm for Camera Calibration Based on Phase Target.

Camera distortion is a critical factor affecting the accuracy of camera calibration. A conventional calibration approach cannot satisfy the requirement of a measurement system demanding high calibration accuracy due to the inaccurate distortion compensation. This paper presents a novel camera calibration method with an iterative distortion compensation algorithm. The initial parameters of the camera are calibrated by full-field camera pixels and the corresponding points on a phase target. An iterative algorithm is proposed to compensate for the distortion. A 2D fitting and interpolation method is also developed to enhance the accuracy of the phase target. Compared to the conventional calibration method, the proposed method does not rely on a distortion mathematical model, and is stable and effective in terms of complex distortion conditions. Both the simulation work and experimental results show that the proposed calibration method is more than 100% more accurate than the conventional calibration method.

Digital micromirror device camera with per-pixel coded exposure for high dynamic range imaging.

In this paper, we overcome the limited dynamic range of the conventional digital camera, and propose a method of realizing high dynamic range imaging (HDRI) from a novel programmable imaging system called a digital micromirror device (DMD) camera. The unique feature of the proposed new method is that the spatial and temporal information of incident light in our DMD camera can be flexibly modulated, and it enables the camera pixels always to have reasonable exposure intensity by DMD pixel-level modulation. More importantly, it allows different light intensity control algorithms used in our programmable imaging system to achieve HDRI. We implement the optical system prototype, analyze the theory of per-pixel coded exposure for HDRI, and put forward an adaptive light intensity control algorithm to effectively modulate the different light intensity to recover high dynamic range images. Via experiments, we demonstrate the effectiveness of our method and implement the HDRI on different objects.

Super Resolution Imaging Based on a Dynamic Single Pixel Camera

In this paper, we present a super resolution imaging method based on a dynamic single pixel camera. First, the proposed method obtains subhigh-resolution (HR) images with subpixel shifts by the dynamic single pixel camera. To achieve this purpose, the patterns on the digital micromirror device are subpixel shifted instead of mechanical scanning. Then, the final HR image can be reconstructed by sub-HR images and wiener filter. In order to avoid the subpixel shifts error that caused by the instability of the system, a subpixel registration algorithm is adopted. Besides, to improve the signal-noise-ratio (SNR) performance of the measurements, a simple and effective method based on the correlation of the signal and the randomness of the noise is adopted. Compared with traditional single pixel imaging system, the proposed method has a remarkable performance at spatial resolution and SNR. The feasibility of the proposed method is demonstrated by simulations and experiments.

Super-resolved 3-D imaging of live cells’ organelles from bright-field photon transmission micrographs

Current biological and medical research is aimed at obtaining a detailed spatiotemporal map of a live cell’s interior to describe and predict cell’s physiological state. We present here an algorithm for complete 3-D modelling of cellular structures from a z-stack of images obtained using label-free wide-field bright-field light-transmitted microscopy. The method visualizes 3-D objects with a volume equivalent to the area of a camera pixel multiplied by the z-height. The computation is based on finding pixels of unchanged intensities between two consecutive images of an object spread function. These pixels represent strongly light-diffracting, light-absorbing, or light-emitting objects. To accomplish this, variables derived from Rényi entropy are used to suppress camera noise. Using this algorithm, the detection limit of objects is only limited by the technical specifications of the microscope setup–we achieve the detection of objects of the size of one camera pixel. This method allows us to obtain 3-D reconstructions of cells from bright-field microscopy images that are comparable in quality to those from electron microscopy images.

Ratiometric Dissolved Oxygen Sensors Based on Ruthenium Complex Doped with Silver Nanoparticles.

A ratiometric optical sensor has been developed with electrospinning processing method for dissolved oxygen measurement. The sensing film is fabricated by using silver nano-particles (Ag NPs) doped with tris(4,7-diphenyl-1,10-phenanthroline) ruthenium(II) dichloride complex (Ru(DPP)3Cl2) encapsulated in plasticized polymethyl methacrylate (PMMA). An insensitive 3-(2-benzothiazolyl)-7-(diethy lamino)-(6CI,7CI) (Coumarin6) is adopted as reference. The ratio of oxygenation is calculated at each image pixel of a 3CCD camera to quantify the oxygen concentration in aqueous environment. Compared to Ag-free film, the response time of Ag-containing films were improved from 1.5 s to 1.0 s upon switching from deoxygenated to air saturation and from 65 s to 45 s from air saturation to fully deoxygenated. The response times of the Ag-free film obtained by knifing was 2.0 s upon switching from deoxygenated to air saturation and 104 s from air saturation to fully deoxygenated. Results of the evaluation of accuracy, limit of detection, stability, and photostability are presented. An experiment measuring the spatiotemporal variation of oxygen distribution within the photosynthesis and respiration of Chlorella vulgaris is demonstrated. It is shown that the nanofiber-based optical sensor film could serve as a promising method for rapid oxygen monitoring in aqueous applications.

Quantifying Filopodia in Cultured Astrocytes by an Algorithm.

Astrocytes in vivo extend thin processes termed peripheral astrocyte processes (PAPs), in particular around synapses where they can mediate glia-neuronal communication. The relation of PAPs to synapses is not based on coincidence, but it is not clear which stimuli and mechanisms lead to their formation and are active during process extension/ retraction in response to neuronal activity. Also, the molecular basis of the extremely fine PAP morphology (often 50 to 100nm) is not understood. These open questions can be best investigated under in vitro conditions studying glial filopodia. We have previously analyzed filopodial mechanisms (Lavialle et al. PNAS 108:12915) applying an automated method for filopodia morphometry, which is now described in greater detail. The Filopodia Specific Shape Factor (FSSF) developed integrates number and length of filopodia. It quantifies filopodia independent of overall astrocytic shape or size, which can be intricate in itself. The algorithm supplied here permits automated image processing and measurements using ImageJ. Cells have to be sampled in higher numbers to obtain significant results. We validate the FSSF, and characterize the systematic influence of thresholding and camera pixel grid on measurements. We provide exemplary results of substance-induced filopodia dynamics (glutamate, mGluR agonists, EGF), and show that filopodia formation is highly sensitive to medium pH (CO2) and duration of cell culture. Although the FSSF was developed to study astrocyte filopodia with focus on the perisynaptic glial sheath, we expect that this parameter can also be applied to neuronal growth cones, non-neural cell types, or cell lines.

An optimisation design of adaptive illumination for a multi-reflective 3D scene

An illumination optimisation technique applied to multi-reflective 3-D machine vision based on a projector-camera system is introduced, in which the projector plays a key role to compensate for surface reflectance at each pixel to be inversely proportional to the brightness of the pixel under ambient light. The adaptive illumination technology was achieved by iterations emphasising different illumination intensities according to different surface orientations and requiring an accurate correspondence between the projector pixels and the camera pixels. In order to establish the most effective correspondence to prepare for subsequent adaptive illumination, 4 kinds of grating patterns, including sinusoidal, rectangular, triangular, and dual-frequency sinusoidal grating patterns, were projected and compared. The iterations were halted when an optimally lit scene was obtained; the further experiments under weak and strong light searched for the best method of illumination optimisation and confirmed the reliability of the adaptive illumination. The proposed optimisation design could run in real time and became a viable solution for industry.

Rate of Strabismus Detection on Digital Photographs Increases by Using Off-center Near Target.

To increase the detection rate of strabismus on digital photographs, with the ultimate aim of developing a new automated strabismus detection algorithm. In this prospective case series, the authors acquired digital face photographs of 409 children with manifest or latent strabismus, using a 14-million-pixel camera with CCD image sensor. Of the last 52 enrolled, 34 image sets were selected for this study: 29 with manifest and 5 with latent strabismus. Images were taken at a distance of 40 to 70 cm in primary position, with the camera lens as the fixation target and in slight off-center fixation, and using a novel target of small light-emitting diodes mounted onto the camera case. The location of the corneal light reflection was manually calculated in relation to the center of the pupil in both eyes and ocular deviation as the difference in corneal light reflection location between the two eyes. In orthotropia, the expected deviation is zero. In children with phorias, the mean corneal light reflection location difference between the eyes was -0.10 ± 0.14 mm in primary position and -2.02 ± 0.39 mm in off-center fixation. Using a threshold of ±0.5 mm on either side of zero for central and of 2 mm for off-center fixation, sensitivity to detect strabismus increased from 65.6% in central to 79.3% in off-center fixation, respectively. The calculation of specificity will require inclusion of a population of individuals without strabismus. Off-center fixation onto a near target ensures that participants are actively looking at the target and may increase accommodative effort, thereby increasing the detection rate of strabismus. [J Pediatr Ophthalmol Strabismus. 2017;54(2):90-96.].

The Meaning and Measure of Vertical Resolution in Optical Surface Topography Measurement

Vertical resolution is the most widely quoted and most frequently misunderstood performance specification for equipment that measures surface topography. Here I propose to use internationally standardized terms and definitions for measurement noise and surface topography repeatability as more meaningful quantifiers for measurement performance. A specific example is an interference microscope operating with a 100 Hz, 1 k × 1 k pixel camera, and a sinusoidal phase modulation to convert intensity data to a height map. The measurement noise is found experimentally to be 0.072 nm for a 1 s data acquisition using a surface topography repeatability test, which determines the random height-equivalent noise level for an individual pixel in the areal surface topography map. Under ideal conditions, the measured noise is equivalent to the instrument noise that may be published in a performance specification in place of the more common, but poorly defined, vertical resolution specification.

像素刻划偏振相机的高精度像素级偏振非均匀性矫正

像素刻划偏振相机的高精度像素级偏振非均匀性矫正

Evaluation of Sample Taxicab Security Cameras

Background: Taxicab drivers have high homicide rates compared to all worker occupations. To help taxi fleets select effective taxicab security cameras, this project tested eight sample taxicab security cameras for determining their photographic quality which correlated to the effectiveness of in-taxicab facial identification. Methods: Five photographic quality metric thresholds: 1) resolution, 2) highlight dynamic range, 3) shadow dynamic range, 4) lens distortion, and 5) shutter speed, were employed to evaluate the photographic quality of the sample cameras. Waterproof tests and fire-resistive tests on recording memory cards were conducted to determine the memory card survivability in water and simulated fire. Results: The Full-HD (1920 × 1080 pixels), HD (1280 × 720 pixels) and dual-lens VGA (2 × 640 × 480 pixels with wide-angle and telephoto lenses) cameras performed well in resolution tests in daylight conditions. The resolution of a single-lens VGA (640 × 480 pixels) camera did not meet the resolution minimum requirements. All of the recording memory cards passed the five-meter/72-hour waterproof test. A fire resistant chamber made with one fire insulation material could protect a single memory card at 538&degC/1000°F for a five-minute simulated fire test. Conclusions: Single-lens VGA-resolution (640 × 480 pixels) cameras are not suggested for use as security cameras in taxicabs with two or more rows of seats. The recording memory cards can survive 5-meter/72-hour waterproof tests. The memory card chamber built with an existing heat insulation material can protect an individual memory card during 538&degC (1000°F)/5-minute fire resistance oven-test.