Image Strips Research Articles

Quantifying colorimetric tests using a smartphone app based on machine learning classifiers

A smartphone application based on machine learning classifier algorithms was developed for quantifying peroxide content on colorimetric test strips. The strip images were taken from five different Android based smartphones under seven different illumination conditions to train binary and multi-class classifiers and to extract the learning model. A custom app, “ChemTrainer”, was designed to capture, crop, and process the active region of the strip, and then to communicate with a remote server that contains the learning model through a Cloud hosted service. The application was able to detect the color change in peroxide strips with over 90% success rate for primary colors with inter-phone repeatability under versatile illumination. The utilization of a grey-world color constancy image processing algorithm positively affected the classification accuracy for binary classifiers. The developed app with a Cloud based learning model paves the way for better colorimetric detection for paper-based chemical assays.

Precise Sensor Orientation of High-Resolution Satellite Imagery With the Strip Constraint

To achieve precise sensor orientation of high- resolution satellite imagery (HRSI), ground control points (GCPs) or height models are necessary to remove biases in orientation parameters. However, measuring GCPs is costly, laborious, and time consuming. We cannot even acquire well-defined GCPs in some areas. In this paper, a strip constraint model is established according to the geometric invariance that the biases of image points remain the same in dividing a strip image into standard images. Based on the rational function model and the strip constraint model, a feasible sensor orientation approach for HRSI with the strip constraint is presented. Through the use of the strip constraint, the bias compensation parameters of each standard image in the strip can be solved simultaneously with sparse GCPs. This approach remains effective even when the intermediate standard images in the strip are unavailable. Experimental results of the three ZiYuan-3 data sets show that two GCPs in the first image and two GCPs in the last image are sufficient for the sensor orientation of all the standard images in the strip. An orientation accuracy that is better than 1.1 pixels can be achieved in each standard image. Moreover, the inconsistent errors of tie points between adjacent standard images can also be reduced to less than 0.1 pixel. This result can guarantee that the generated complete digital orthophoto map of the whole strip is geometrically seamless.

Inversion of Side Scan Sonar Motion and Posture in Seabed Geomorphology

Abstract Side scan sonar measurement platform, affected by underwater environment and its own motion precision, inevitably has posture and motion disturbance, which greatly affects accuracy of geomorphic image formation. It is difficult to sensitively and accurately capture these underwater disturbances by relying on auxiliary navigation devices. In this paper, we propose a method to invert motion and posture information of the measurement platform by using the matching relation between the strip images. The inversion algorithm is the key link in the image mosaic frame of side scan sonar, and the acquired motion posture information can effectively improve seabed topography and plotting accuracy and stability. In this paper, we first analyze influence of platform motion and posture on side scan sonar mapping, and establish the correlation model between motion, posture information and strip image matching information. Then, based on the model, a reverse neural network is established. Based on input, output of neural network, design of and test data set, a motion posture inversion mechanism based on strip image matching information is established. Accuracy and validity of the algorithm are verified by the experimental results.

PUSHBROOM HYPERSPECTRAL IMAGING FROM AN UNMANNED AIRCRAFT SYSTEM (UAS) – GEOMETRIC PROCESSINGWORKFLOW AND ACCURACY ASSESSMENT

Abstract. In this study, we assess two push broom hyperspectral sensors as carried by small (10–15 kg) multi-rotor Unmanned Aircraft Systems (UAS). We used a Headwall Photonics micro-Hyperspec push broom sensor with 324 spectral bands (4–5 nm FWHM) and a Headwall Photonics nano-Hyperspec sensor with 270 spectral bands (6 nm FWHM) both in the VNIR spectral range (400–1000 nm). A gimbal was used to stabilise the sensors in relation to the aircraft flight dynamics, and for the micro-Hyperspec a tightly coupled dual frequency Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU), and Machine Vision Camera (MVC) were used for attitude and position determination. For the nano-Hyperspec, a navigation grade GNSS system and IMU provided position and attitude data. This study presents the geometric results of one flight over a grass oval on which a dense Ground Control Point (GCP) network was deployed. The aim being to ascertain the geometric accuracy achievable with the system. Using the PARGE software package (ReSe – Remote Sensing Applications) we ortho-rectify the push broom hyperspectral image strips and then quantify the accuracy of the ortho-rectification by using the GCPs as check points. The orientation (roll, pitch, and yaw) of the sensor is measured by the IMU. Alternatively imagery from a MVC running at 15 Hz, with accurate camera position data can be processed with Structure from Motion (SfM) software to obtain an estimated camera orientation. In this study, we look at which of these data sources will yield a flight strip with the highest geometric accuracy.

A computer vision-based system for automatic detection of misarranged warp yarns in yarn-dyed fabric. Part I: continuous segmentation of warp yarns

In textile and garment industries, misarranged warp yarns of yarn-dyed fabrics disorganize the layout of fabrics and lead to poor product quality. This series of studies aims to develop a computer vision-based system for automatic detection of misarranged color warp yarns in terms of high efficiency and good accuracy. Four main parts are included in this series of studies: warp yarn segmentation, fabric image stitching, warp regional segmentation, and yarn layout proofing. This paper proposes a continuous segmentation method of warp yarns to detect the misarranged color warp yarns for yarn-dyed fabrics automatically, which is the foundation of the developed computer vision-based system. The proposed framework consists of two main components: warp yarn segmentation and fabric image stitching. Firstly, the sequence images of a fabric stripe are captured using a designed offline image acquisition platform. Secondly, the warp yarns in the sequence images are segmented by a sub-image projection-based method successively. Thirdly, the sequence images are stitched by a yarn-template matching method based on their warp segmentation results. Finally, the continuous segmentation result of warp yarns is saved for the further processing of warp regional segmentation and color warp layout proofing. The proposed method has been evaluated on 720 fabric images of five fabric examples with plain and 2/2 twill, and experimental results show that the proposed method can realize the continuous segmentation of warp yarns in yarn-dyed fabrics with the yarn segmentation accuracy of 97.43% and image stitching accuracy of 99.53%.

Single Infrared Image Stripe Noise Removal Using Deep Convolutional Networks

In this paper, we present a deep learning method for single infrared image stripe noise removal. Our method is denoted as a deep convolutional neural network (CNN) that takes the noisy image as the input and outputs the clean image. The deep CNN consists of two components: 1) image denoising, substantially removing the stripe noise but losing details, 2) image denoising and super resolution, completely eliminating the residual stripe noise and restore details. Our deep CNN exhibits excellent image denoising and detail preserving performance. Meanwhile it achieves fast speed for real-time image processing. Experiments study the relationship between model parameter settings and model performance.

OPTIMIZING THE DISTRIBUTION OF TIE POINTS FOR THE BUNDLE ADJUSTMENT OF HRSC IMAGE MOSAICS

Abstract. For a systematic mapping of the Martian surface, the Mars Express orbiter is equipped with a multi-line scanner: Since the beginning of 2004 the High Resolution Stereo Camera (HRSC) regularly acquires long image strips. By now more than 4,000 strips covering nearly the whole planet are available. Due to the nine channels, each with different viewing direction, and partly with different optical filters, each strip provides 3D and color information and allows the generation of digital terrain models (DTMs) and orthophotos. To map larger regions, neighboring HRSC strips can be combined to build DTM and orthophoto mosaics. The global mapping scheme Mars Chart 30 is used to define the extent of these mosaics. In order to avoid unreasonably large data volumes, each MC-30 tile is divided into two parts, combining about 90 strips each. To ensure a seamless fit of these strips, several radiometric and geometric corrections are applied in the photogrammetric process. A simultaneous bundle adjustment of all strips as a block is carried out to estimate their precise exterior orientation. Because size, position, resolution and image quality of the strips in these blocks are heterogeneous, also the quality and distribution of the tie points vary. In absence of ground control points, heights of a global terrain model are used as reference information, and for this task a regular distribution of these tie points is preferable. Besides, their total number should be limited because of computational reasons. In this paper, we present an algorithm, which optimizes the distribution of tie points under these constraints. A large number of tie points used as input is reduced without affecting the geometric stability of the block by preserving connections between strips. This stability is achieved by using a regular grid in object space and discarding, for each grid cell, points which are redundant for the block adjustment. The set of tie points, filtered by the algorithm, shows a more homogenous distribution and is considerably smaller. Used for the block adjustment, it yields results of equal quality, with significantly shorter computation time. In this work, we present experiments with MC-30 half-tile blocks, which confirm our idea for reaching a stable and faster bundle adjustment. The described method is used for the systematic processing of HRSC data.

Image Mosaicking Approach for a Double-Camera System in the GaoFen2 Optical Remote Sensing Satellite Based on the Big Virtual Camera.

The linear array push broom imaging mode is widely used for high resolution optical satellites (HROS). Using double-cameras attached by a high-rigidity support along with push broom imaging is one method to enlarge the field of view while ensuring high resolution. High accuracy image mosaicking is the key factor of the geometrical quality of complete stitched satellite imagery. This paper proposes a high accuracy image mosaicking approach based on the big virtual camera (BVC) in the double-camera system on the GaoFen2 optical remote sensing satellite (GF2). A big virtual camera can be built according to the rigorous imaging model of a single camera; then, each single image strip obtained by each TDI-CCD detector can be re-projected to the virtual detector of the big virtual camera coordinate system using forward-projection and backward-projection to obtain the corresponding single virtual image. After an on-orbit calibration and relative orientation, the complete final virtual image can be obtained by stitching the single virtual images together based on their coordinate information on the big virtual detector image plane. The paper subtly uses the concept of the big virtual camera to obtain a stitched image and the corresponding high accuracy rational function model (RFM) for concurrent post processing. Experiments verified that the proposed method can achieve seamless mosaicking while maintaining the geometric accuracy.

Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to directly estimate the clear images from the stripe images without considering the intrinsic properties of stripe noise, which causes the image structure destroyed. In this paper, we propose a new destriping method from the perspective of image decomposition, which takes the intrinsic properties of stripe noise and image characteristics into full consideration. The proposed method integrates the unidirectional total variation (TV) regularization, group sparsity regularization, and TV regularization together in an image decomposition framework. The first two terms are utilized to exploit the stripe noise properties by implementing statistical analysis, and the TV regularization is adopted to explore the spatial piecewise smooth structure of stripe-free image. Moreover, an efficient alternating minimization scheme is designed to solve the proposed model. Extensive experiments on simulated and real data demonstrate that our method outperforms several existing state-of-the-art destriping methods in terms of both quantitative and qualitative assessments.

An accurate method for the extraction of line structured light stripe

In order to improve the accuracy in the On-line measurement of rail profile with a line structured light based on machine vision, the accurate extraction of a structured light stripe is a necessary and key step. An accurate extraction method is proposed for the structural light stripe in practical applications. The structured light stripe is separated and extracted accurately based on the geometric characteristics of the structure light stripe in the binary image. And the noise in the captured image is removed with region segmentation method. The above method was tested in laboratory conditions. Experiment results show that the developed method can effectively solve the problem of the accurate extraction of structured light stripe in real time.

Group sparsity based regularization model for remote sensing image stripe noise removal

Stripe noise degradation is a common phenomenon in remote sensing image, which largely affects the visual quality and brings great difficulty for subsequent processing. In contrast to existing stripe noise removal (destriping) models in which the reconstruction is performed to directly estimate the clean image from the striped one, the proposed model achieves the destriping by estimating the stripe component firstly. Since the stripe component possesses column sparse structure, the group sparsity is employed in this study. In addition, difference-based constraints are used to describe the direction information of the stripes. Then, we build a novel convex optimization model which consists of a unidirectional total variation term, a group sparsity term and a gradient domain fidelity term solved by an efficient alternating direction method of multiplier. Compared with the state-of-the-art methods, experiment results on simulated and real data are reported to demonstrate the effectiveness of the proposed method.

The application of three-dimensional MRI for posterior sclera reinforcement of patients with high myopia

Objective To observe the application and function of three-dimensional magnetic resonance imaging (3-D MRI) for posterior sclera reinforcement (PSR) of patients with high myopia. Methods Twenty eyes of 19 cases with high myopia underwent PSR. All cases took 3-D MRI before and after surgery. Before surgery, the arc length from upper corneal limbus through the peak of the scleral staphyloma to under corneal limbus, horizontal and vertical arc length of the staphyloma, and arc length from temporal border of the staphyloma to optical nerve were measured. In postoperative PSR, the image of strip was reconstructed. Results The three dimensional measured value related to the staphyloma was obtained by 3-D MRI. PSR strip showed clear. Among all cases, the strips just reinforcement in the staphyloma were 14 eyes. Six eyes occurred deviation from the staphyloma, including 5 eyes deviated to temporal side of the staphyloma and 1 eye closed to the optic nerve. After the second times of the PSR, the position of the strip was moderate. Conclusion The application of 3-D MRI contributes to preoperative assessment and postoperative evaluation, it has the certain value in clinical application. Key words: Myopia, high; Staphyloma, scleral, posterior; Posterior sclera reinforcement; MRI, three-dimensional

Laser stripe extraction method in industrial environments utilizing self-adaptive convolution technique.

A line-structured laser scanner is widely applied for 3D reconstruction in industrial environments with ubiquitous various luminance, complicated background, diverse objects, and instable lasers. These elements will show up as noise in the obtained laser stripe images. Therefore, the basic and key point for a line-structured laser scanner is to accurately extract the laser stripe from noise. This paper proposes an effective laser stripe extraction procedure with two steps. First, a novel laser stripe center extraction method based on the geometry information and correlation in the laser stripe, is designed to significantly eliminate noise and accurately extract the laser stripe centers. In addition, the robustness, speed, and accuracy of this method are respectively analyzed in detail. Second, piecewise fitting is adopted to acquire a smooth and continuous laser stripe centerline. In order to select the optimal fitting method, the characteristics of two spline methods, Akima spline and cubic Hermite spline, are deeply analyzed and compared. Finally, an experiment is carried out by using a rough metal step and a line-structured laser scanning system. The experiment results demonstrate that the proposed self-adaptive convolution-mass method can significantly eliminate noise in industrial environments. In addition, the cubic Hermite spline is a better choice for 3D reconstruction, rather than the Akima spline.

Image pre-processing method for near-wall PIV measurements over moving curved interfaces

PIV measurements near a moving interface are always difficult. This paper presents a PIV image pre-processing method that returns high spatial resolution velocity profiles near the interface. Instead of re-shaping or re-orientating the interrogation windows, interface tracking and an image transformation are used to stretch the particle image strips near a curved interface into rectangles. Then the adaptive structured interrogation windows can be arranged at specified distances from the interface. Synthetic particles are also added into the solid region to minimize interfacial effects and to restrict particles on both sides of the interface. Since a high spatial resolution is only required in high velocity gradient region, adaptive meshing and stretching of the image strips in the normal direction is used to improve the cross-correlation signal-to-noise ratio (SN) by reducing the velocity difference and the particle image distortion within the interrogation window. A two dimensional Gaussian fit is used to compensate for the effects of stretching particle images. The working hypothesis is that fluid motion near the interface is ‘quasi-tangential flow’, which is reasonable in most fluid-structure interaction scenarios. The method was validated against the window deformation iterative multi-grid scheme (WIDIM) using synthetic image pairs with different velocity profiles. The method was tested for boundary layer measurements of a supersonic turbulent boundary layer on a flat plate, near a rotating blade and near a flexible flapping flag. This image pre-processing method provides higher spatial resolution than conventional WIDIM and good robustness for measuring velocity profiles near moving interfaces.

Smartphone-based colorimetric detection via machine learning.

We report the application of machine learning to smartphone-based colorimetric detection of pH values. The strip images were used as the training set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms that were able to successfully classify the distinct pH values. The difference in the obtained image formats was found not to significantly affect the performance of the proposed machine learning approach. Moreover, the influence of the illumination conditions on the perceived color of pH strips was investigated and further experiments were conducted to study the effect of color change on the learning model. Non-integer pH levels are identified as their nearest integer pH values, whereas the test results for integer pH levels using JPEG, RAW and RAW-corrected image formats captured under different lighting conditions lead to perfect classification accuracy, sensitivity and specificity, which proves that colorimetric detection using machine learning based systems is able to adapt to various experimental conditions and is a great candidate for smartphone-based sensing in paper-based colorimetric assays.

Strip wrinkling detection based on feature extraction and sparse representation

Strip wrinkling is a kind of adverse phenomenon occurring during strip steel production. It is necessary to detect strip wrinkling promptly for production lines, most of which rely on manual survey for detection until now. To provide a better condition for wrinkling detection, this paper introduces a feature-based image processing method. Feature extraction, dictionary learning and sparse representation are included in this method. Draped surface of wrinkling strip is suitable for SIFT technique extracting features of wrinkles. The extracted features will be collected by dictionary learning. For every original strip image, detecting wrinkles may be realised by assessing which dictionary its features incline to. At this stage, the assessment is implemented by sparse representation. The proposed method has been tested in a strip steel production line and the performance showed its applicability.

Detecting Curved Edges in Noisy Images in Sublinear Time

Detecting edges in noisy images is a fundamental task in image processing. Motivated, in part, by various real-time applications that involve large and noisy images, in this paper we consider the problem of detecting long curved edges under extreme computational constraints, that allow processing of only a fraction of all image pixels. We present a sublinear algorithm for this task, which runs in two stages: (1) a multiscale scheme to detect curved edges inside a few image strips; and (2) a tracking procedure to estimate their extent beyond these strips. We theoretically analyze the runtime and detection performance of our algorithm and empirically illustrate its competitive results on both simulated and real images.

Ghost microstructure evolution and identification in the coarse grain heat affected zone of 2.25Cr-1Mo-V-Ti steel using tint etching

Microstructural characteristics of the CGHAZ (coarse grained heat affected zone) made of the 2.25Cr-1Mo-V-Ti material for the thermal power plant boiler tube were discussed using the technique of tint etching. To conduct the micro structural characterization, the sample on which CGHAZ was produced by using a high temperature thermal cycle simulator, Gleeble 3500 equipment was used for comparative analyses using the existing Nital etching (ASTM E407-74) and the alkaline etching (ASTM E40785). The latter was used to observe a specific phase. For the microstructure on which the alkaline etching was experimented, the shape of a black strip (Ghost microstructure) in a few microns was observed, which was not observed from the Nital etching. It was found from the phase identifications based EPMA, EBSD and TEM experiments that the image of the black strip in a few microns represented the alpha phase in which C, Cr and Mo became segregated. In addition, it was verified that austenite and M23C6 phase were present around the segregated zone. Based on such results, the mechanism by which the image of the black strip in a few microns was formed at the CGHAZ. In this study, we have investigated the mechanism of the appeared black strip in the CGHAZ.

3D Model Reconstruction System Development Based on Laser-Vision Technology

We propose a three-dimensional (3D) scanning system based on laser-vision technique and rotary mechanism combination for automatic 3D model reconstruction. The proposed scanning system consists of a laser projector, camera, and turntable. For laser-camera calibration, a new and simple method is applied. The 3D point cloud data of the surface of the scanned object are fully collected by integrating the extracted laser profiles from the laser stripe images corresponding to the rotary angles of the rotary mechanism. The obscured laser profile problem is solved by an additional camera at another viewpoint. From the collected 3D point cloud data, the 3D model of the scanned object is reconstructed based on the facet-representation method. The reconstructed 3D models showed the effectiveness and applicability of the proposed 3D scanning system in many 3D model-based applications.

Development of a Calibration Strip for Immunochromatographic Assay Detection Systems.

With many benefits and applications, immunochromatographic (ICG) assay detection systems have been reported on a great deal. However, the existing research mainly focuses on increasing the dynamic detection range or application fields. Calibration of the detection system, which has a great influence on the detection accuracy, has not been addressed properly. In this context, this work develops a calibration strip for ICG assay photoelectric detection systems. An image of the test strip is captured by an image acquisition device, followed by performing a fuzzy c-means (FCM) clustering algorithm and maximin-distance algorithm for image segmentation. Additionally, experiments are conducted to find the best characteristic quantity. By analyzing the linear coefficient, an average value of hue (H) at 14 min is chosen as the characteristic quantity and the empirical formula between H and optical density (OD) value is established. Therefore, H, saturation (S), and value (V) are calculated by a number of selected OD values. Then, H, S, and V values are transferred to the RGB color space and a high-resolution printer is used to print the strip images on cellulose nitrate membranes. Finally, verification of the printed calibration strips is conducted by analyzing the linear correlation between OD and the spectral reflectance, which shows a good linear correlation (R2 = 98.78%).