Common Image Processing Techniques Research Articles

Robustness and Collision-Resistance of PhotoDNA

PhotoDNA is a widely utilized hash designed to counteract Child Sexual Abuse Material (CSAM). However, there has been a scarcity of detailed information regarding its performance. In this paper, we present a comprehensive analysis of its robustness and susceptibility to false positives, along with fundamental insights into its structure. Our findings reveal its resilience to common image processing techniques like lossy compression. Conversely, its robustness is limited when confronted with cropping. Additionally, we propose recommendations for enhancing the algorithm or optimizing its application. This work is an extension on our paper [21].

Pengaruh Variasi Window Width Dan Window Level Pada Lung Window Terhadap Kualitas Citra CT Scan Thorax Dengan Klinis Tumor Paru Di Rumah Sakit TK II Pelamonia Makassar

Background: Lung tumors are the growth of abnormal lumps in lung tissue which can be benign or malignant. One diagnostic imaging modality that is more supportive of lung disorders is by using a Computed Tomography Scan examination. Image manipulation can be done to improve the appearance of the image. One of the most common image processing techniques is called windowing. This technique allows the technology to change the contrast of the displayed image by adjusting the window width (Window Width) and window level (Window Level). Method: The type of research used in this research is quantitative research with a quasi-experimental approach, which aims to determine the effect of variations in window width and window level on image quality in CT scans of the Thorax with clinical lung tumors. Using variations of Window Width and Window Level WW 1000 and WL -500, -600, -700, WW 1250, WL -500, -600, -700 and WW 1550, WL -500, -600, -700. The author took CT scan data of the Thorax with clinical lung tumors in 4 samples. Then an assessment was carried out in the form of a check list by respondents, namely 3 radiographers and 1 radiologist. The data is then processed using SPSS. Results & Conclusions: There is an influence of WW 1000 and WL -500, -600, -700, WW 1250 and WL -500, -600, -700 and WW 1550 and WL -500, -600, -700 on image quality, namely artifacts and noise on Thorax CT Scan examination with lung tumor cases, the conclusion was obtained from the results of the Friedman test which showed a p.value value of <0.001 or smaller than 0.05 so that H0 was rejected and Ha was accepted. From the results of the mean rank Friedman test, it is known that the value of image quality is artifacts and noise in Thorax CT scans with lung tumor cases. Variations in window width 1550 and window level 500 are able to produce the best image quality with minimal artifacts and noise. Thorax CT scan examination in lung tumor cases.

Redundant Fault Diagnosis for Photovoltaic Systems Based on an IRT Low-Cost Sensor.

In large solar farms, supervision is an exhaustive task, often carried out manually by field technicians. Over time, automated or semi-automated fault detection and prevention methods in large photovoltaic plants are becoming increasingly common. The same does not apply when talking about small or medium-sized installations, where the cost of supervision at such level would mean total economic infeasibility. Although there are prevention protocols by suppliers, periodic inspections of the facilities by technicians do not ensure that faults such as the appearance of hot-spots are detected in time. That is why, nowadays, the only way of continuous supervision of a small or medium installation is often carried out by unqualified people and in a purely visual way. In this work, the development of a low-cost system prototype is proposed for the supervision of a medium or small photovoltaic installation based on the acquisition and treatment of thermographic images, with the aim of investigating the feasibility of an actual implementation. The work focuses on the system's ability to detect hot-spots in supervised panels and successfully report detected faults. To achieve this goal, a low-cost thermal imaging camera is used for development, applying common image processing techniques, operating with OpenCV and MATLAB R2021b libraries. In this way, it is possible to demonstrate that it is achievable to successfully detect the hottest points of a photovoltaic (PV) installation with a much cheaper camera than the cameras used in today's thermographic inspections, opening up the possibilities of creating a fully developed low-cost thermographic surveillance system.

Towards airborne laser-induced breakdown spectroscopy: A signal recovery method for LIBS instruments subjected to vibrations

Sample or instrument vibrations can scatter laser impacts across the sample surface, which increases the uncertainty of laser-induced breakdown spectroscopy (LIBS) measurements. The common sources of noise associated to mechanical vibration are described and a method for isolating LIBS measurements from artifacts introduced by such fluctuations is presented. The approach circumvents the use of mechanical stabilizers by leveraging simple components common in LIBS systems. A camera was used to capture close-up images of the sample for each laser shot and the laser spot position in the sample surface was measured using common image processing techniques. By associating spectra with spatial coordinates in the sample surface, it was possible to reduce the relative standard deviation of the Cu(I) 427.51 nm signal in a patterned Cu/Al sample from 122.0% to 53.31%, similar to that measured for a pure Cu sample in the same vibration conditions. The spatial resolution of the method was found to depend on the laser spot diameter, the illuminance at the sample, the camera sensitivity and trigger insertion delay, and the speed of the laser beam sweeping the sample surface. The spatial resolution obtained with the setup used was ±0.6 mm at 15 m, i.e., 40 μm per meter of separation between the instrument and the sample with a vibration speed limit of 12 cm·s−1.

Taylor-Series-Based Reconfigurability of Gamma Correction in Hardware Designs

Gamma correction is a common image processing technique that is common in video or still image systems. However, this simple and efficient method is typically expressed using the power law, which gives rise to practical difficulties in designing a reconfigurable hardware implementation. For example, the conventional approach calculates all possible outputs for a pre-determined gamma value, and this information is hardwired into memory components. As a result, reconfigurability is unattainable after deployment. This study proposes using the Taylor series to approximate gamma correction to overcome the aforementioned challenging problem, hence, facilitating the post-deployment reconfigurability of the hardware implementation. In other words, the gamma value is freely adjustable, resulting in the high appropriateness for offloading gamma correction onto its dedicated hardware in system-on-a-chip applications. Finally, the proposed hardware implementation is verified on Zynq UltraScale+ MPSoC ZCU106 Evaluation Kit, and the results demonstrate its superiority against benchmark designs.

New Extensibility and Scripting Tools in the ImageJ Ecosystem.

ImageJ provides a framework for image processing across scientific domains while being fully open source. Over the years ImageJ has been substantially extended to support novel applications in scientific imaging as they emerge, particularly in the area of biological microscopy, with functionality made more accessible via the Fiji distribution of ImageJ. Within this software ecosystem, work has been done to extend the accessibility of ImageJ to utilize scripting, macros, and plugins in a variety of programming scenarios, e.g., from Groovy and Python and in Jupyter notebooks and cloud computing. We provide five protocols that demonstrate the extensibility of ImageJ for various workflows in image processing. We focus first on Fluorescence Lifetime Imaging Microscopy (FLIM) data, since this requires significant processing to provide quantitative insights into the microenvironments of cells. Second, we show how ImageJ can now be utilized for common image processing techniques, specifically image deconvolution and inversion, while highlighting the new, built-in features of ImageJ-particularly its capacity to run completely headless and the Ops matching feature that selects the optimal algorithm for a given function and data input, thereby enabling processing speedup. Collectively, these protocols can be used as a basis for automating biological image processing workflows. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Using PyImageJ for FLIM data processing Alternate Protocol: Groovy FLIMJ in Jupyter Notebooks Basic Protocol 2: Using ImageJ Ops for image deconvolution Support Protocol 1: Using ImageJ Ops matching feature for image inversion Support Protocol 2: Headless ImageJ deconvolution.

Symmetrical filters in convolutional neural networks

Symmetry is present in nature and science. In image processing, kernels for spatial filtering possess some symmetry (e.g. Sobel operators, Gaussian, Laplacian). Convolutional layers in artificial feed-forward neural networks have typically considered the kernel weights without any constraint. We propose to investigate the impact of a symmetry constraint in convolutional layers for image classification tasks, taking our inspiration from the processes involved in the primary visual cortex and common image processing techniques. The goal is to determine if it is necessary to learn each weight of the filters independently, and the extent to which it is possible to enforce symmetrical constraints on the filters throughout the training process of a convolutional neural network by modifying the weight update preformed during the backpropagation algorithm and to evaluate the change in performance. The symmetrical constraint reduces the number of free parameters in the network, and it is able to achieve near identical performance. We address the following cases: x/y-axis symmetry, point reflection, and anti-point reflection. The performance is evaluated on four databases of images representing handwritten digits. The results support the conclusion that while random weights offer more freedom to the model, the symmetry constraint provides a similar level of performance while decreasing substantially the number of free parameters in the model. Such an approach can be valuable in phase-sensitive applications that require a linear phase property throughout the feature extraction process.

Computer-Assisted Aircraft Anti-Icing Fluids Endurance Time Determination

Deicing and anti-icing the aircraft using proper chemical fluids, prior takeoff, are mandatory. A thin layer of ice or snow can compromise the safety, causing lift loss and drag increase. Commercialized deicing and anti-icing fluids all pass a qualification process which is described in Society of Automotive Engineering (SAE) documents. Most of them are endurance time tests under freezing and frozen contaminants, under simulated and natural conditions. They all have in common that the endurance times have to be determined by visual inspection. When a certain proportion of the test plate is covered with contaminants, the endurance time test is called. In the goal of minimizing human error resulting from visual inspection and helping in the interpretation of fluid failure, help-decision computer-assisted algorithms have been developed and tested under different conditions. The algorithms are based on common image processing techniques. The algorithms have been tested under three different icing conditions, water spray endurance test, indoor snow test and light freezing rain tests, and were compared to the times determined by three experimented technicians. A total of 14 tests have been compared. From them, 11 gave a result lower than 5% of the results given by the technicians. In conclusion, the computer-assisted algorithms developed are efficient enough to support the technicians in their failure call. However, further works need to be performed to improve the analysis.

Anisotropic analysis of fibrous and woven materials part 1: Estimation of local orientation

In many material science applications mechanical and thermal properties are often locally anisotropic. As such, a common requirement for high fidelity computational modeling is the knowledge of the local property tensor. In fibrous or woven materials, the properties are known along the direction of the fibers, so it is necessary to know their orientation to correctly align the tensors. In this study, three techniques are investigated for estimating the direction in these microstructures, obtained on Cartesian grids using microtomography as grayscale values: a common image processing technique called structure tensor, a method based on artificial flux, and a novel ray-casting approach. All the methods start by identifying the fibers using a grayscale cutoff. The first technique estimates the fiber direction based on the eigenvector corresponding to the smallest eigenvalue obtained from the grayscale gradients. The second method estimates the orientation as the local steady-state flux vector in a heat transfer simulation with temperature gradients imposed in all three directions. In the third method, rays are created at the center of each solid voxel and travel until the first collision with a material boundary occurs. The local direction vector is estimated based on the travel distance of these rays. The performance of each method is studied by examining their predictions for artificially generated weaves and fibrous materials, whose true local fiber direction is known a priori. The last part of this paper contains fiber orientation simulations for different real world materials scanned with X-ray microtomography, and describes a new workflow for weaves.

Design and Validation of a Holographic Particle Counter.

An in-line holographic particle counter concept is presented and validated where multiple micrometer sized particles are detected in a three dimensional sampling volume, all at once. The proposed Particle Imaging Unit is capable of detecting holograms of particles which sizes are in the lower m- range. The detection and counting principle is based on common image processing techniques using a customized Hough Transform with a result directly relating to the particle number concentration in the recorded sampling volume. The proposed counting unit is mounted ontop of a Condensation Nucleus Magnifier for comparison with a commercial TSI-3775 Condensation Particle Counter (CPC). The concept does not only allow for a precise in-situ determination of low particle number concentrations but also enables easy upscaling to higher particle densities (e.g., ) through its linear expandability and option of cascading. The impact of coincidence at higher particle densities is shown and two coincidence correction approaches are presented where, at last, its analogy to the coincidence correction methods used in state-of-the-art CPCs is identified.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

A Hybrid Color Quantization Algorithm Incorporating a Human Visual Perception Model

Color quantization is a common image processing technique where full color images are to be displayed using a limited palette of colors. The choice of a good palette is crucial as it directly determines the quality of the resulting image. Standard quantization approaches aim to minimize the mean squared error (MSE) between the original and the quantized image, which does not correspond well to how humans perceive the image differences. In this article, we introduce a color quantization algorithm that hybridizes an optimization scheme based with an image quality metric that mimics the human visual system. Rather than minimizing the MSE, its objective is to maximize the image fidelity as evaluated by S‐CIELAB, an image quality metric that has been shown to work well for various image processing tasks. In particular, we employ a variant of simulated annealing with the objective function describing the S‐CIELAB image quality of the quantized image compared with its original. Experimental results based on a set of standard images demonstrate the superiority of our approach in terms of achieved image quality.

Soft computing-based colour quantisation

Soft computing techniques have shown much potential in a variety of computer vision and image analysis tasks. In this paper, an overview of recent soft computing approaches to the colour quantisation problem is presented. Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image. Those selected colours form a colour palette, while the resulting image quality is directly determined by the choice of colours in the palette. The use of generic optimisation techniques such as simulated annealing and soft computing-based clustering algorithms founded on fuzzy and rough set ideas to formulate colour quantisation algorithms is discussed. These methods are capable of deriving good colour palettes and are shown to outperform standard colour quantisation techniques in terms of image quality. Furthermore, a hybrid colour quantisation algorithm which combines a generic optimisation approach with a common clustering algorithm is shown to lead to improved image quality. Finally, it is demonstrated how optimisation-based colour quantisation can be employed in conjunction with a more appropriate measure for image quality.

Mobile Robot Self-Localization System Using Single Webcam Distance Measurement Technology in Indoor Environments

A single-webcam distance measurement technique for indoor robot localization is proposed in this paper. The proposed localization technique uses webcams that are available in an existing surveillance environment. The developed image-based distance measurement system (IBDMS) and parallel lines distance measurement system (PLDMS) have two merits. Firstly, only one webcam is required for estimating the distance. Secondly, the set-up of IBDMS and PLDMS is easy, which only one known-dimension rectangle pattern is needed, i.e., a ground tile. Some common and simple image processing techniques, i.e., background subtraction are used to capture the robot in real time. Thus, for the purposes of indoor robot localization, the proposed method does not need to use expensive high-resolution webcams and complicated pattern recognition methods but just few simple estimating formulas. From the experimental results, the proposed robot localization method is reliable and effective in an indoor environment.

Enhanced Dark Block Extraction Method Performed Automatically to Determine the Number of Clusters in Unlabeled Data Sets

One of the major issues in data cluster analysis is to decide the number of clusters or groups from a set of unlabeled data. In addition, the presentation of cluster should be analyzed to provide the accuracy of clustering objects. This paper propose a new method called Enhanced-Dark Block Extraction (E-DBE), which automatically identifies the number of objects groups in unlabeled datasets. The proposed algorithm relies on the available algorithm for visual assessment of cluster tendency of a dataset, by using several common signal and image processing techniques. The method includes the following steps: 1.Generating an Enhanced Visual Assessment Tendency (E-VAT) image from a dissimilarity matrix which is the input for E-DBE algorithm. 2. Processing image segmentation on E-VAT image to obtain a binary image then performs filter techniques. 3. Performing distance transformation to the filtered binary image and projecting the pixels in the main diagonal alignment of the image to figure a projection signal. 4. Smoothing the outcrop signal, computing its first-order derivative and then detecting major peaks and valleys in the resulting signal to acquire the number of clusters. E-DBE is a parameter-free algorithm to perform cluster analysis. Experiments of the method are presented on several UCI, synthetic and real world datasets.

ENHANCED VAT FOR CLUSTER QUALITY ASSESSMENT IN UNLABELED DATASETS

The increased demand for clustering objects of unlabeled data into similarity group lies in determining a number of clusters. In addition, the performance of the cluster should be analyzed to provide the precise clustering of objects. Available clustering algorithm depends on the number of clusters C to search with threshold. The proposed method of this paper is Enhanced Visual Assessment of Cluster Tendency, which robotically identifies the number of object groups or clusters in unlabeled datasets. The proposed algorithm relies on visual assessment of cluster tendency (VAT) that intermingles Euclidean, Mahalanobis distance measures, and common image processing techniques. Enhanced VAT produces a binary image, which can be visually assessed for the cluster tendency. However, VAT becomes disrupting for huge datasets. Enhanced VAT reduces the amount of computation and performs dissimilarities with different measures of metrics that are used for an effective visual evaluation process. Validation of our algorithm is performed on several UCI datasets and HIV real world datasets.

Efficient registration of optical and IR images for automatic plant water stress assessment

Automatic registration of optical and IR images is a crucial step towards constructing an automated irrigation control system where plant water information is sensed via thermal imaging. The scene of the IR image is assumed to be completely included in the optical image and the alignment between the common scene in the two images may involve translation and rotation by a small angle, though a small scale difference may also be present. This automatic registration of data from two quite different, non-rigid imaging regimes presents several challenges, which cannot be overcome using common image processing techniques. In this paper, a fully automatic image registration algorithm for the alignment of optical and IR image pairs is described, where Pearson's cross-correlation between a pair of images serves as the similarity measure. A computationally efficient algorithm is designed and packaged as a software application. This work provides an intervention free process for extracting plant water stress information which can be fed into an automated irrigation scheduling program. The proposed algorithm is justified by the comparison of its registration performance with that of other potential algorithm techniques using several experimental data collections. Our results demonstrate the effectiveness of the proposed algorithm and efficiency of its application to the registration of IR and optical images.

Automatically Determining the Number of Clusters in Unlabeled Data Sets

Clustering is a popular tool for exploratory data analysis. One of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In this paper we investigate a new method called DBE (dark block extraction) for automatically estimating the number of clusters in unlabeled data sets, which is based on an existing algorithm for visual assessment of cluster tendency (VAT) of a data set, using several common image and signal processing techniques. Basic steps include: 1) generating a VAT image of an input dissimilarity matrix; 2) performing image segmentation on the VAT image to obtain a binary image, followed by directional morphological filtering; 3) applying a distance transform to the filtered binary image and projecting the pixel values onto the main diagonal axis of the image to form a projection signal; 4) smoothing the projection signal, computing its first-order derivative, and then detecting major peaks and valleys in the resulting signal to decide the number of clusters. Our new DBE method is nearly automatic, depending on just one easy-to-set parameter. Several numerical and real-world examples are presented to illustrate the effectiveness of DBE.

흔들리는 영상 분석을 위한 표면 영상 유속계 알고리듬

표면 영상 유속계(SIV, Surface Image Velocimetry)는 영상 처리 기술을 이용하여 수표면의 유속을 측정하는 장비이다. 유속 측정의 정밀도를 높이기 위해서는 화질이 좋고, 왜곡이 적은 영상을 획득하는 것이 매우 중요하다. 트럭에 장착된 기중기를 이용하는 차량탑재형 표면 영상 유속계는 왜곡이 적은 영상을 얻는 좋은 방법이 될 수 있다. 이 때, 기중기의 흔들림 때문에 획득된 영상이 흔들리는 문제가 발생하며, 영상의 흔들림을 보정하여 유속을 측정할 수 있는 영상 처리 알고리듬이 필요하게 된다. 본 연구에서는 영상 분석 기법과 좌표 변환 기법, 유속 산정 기법 등을 조합하여 흔들리는 영상에서 표면 유속을 측정하는 기법을 개발하였다. 즉, 비디오 카메라로 촬영된 연속 영상에서 고정점들의 움직임을 추적하여 카메라의 위치 변동을 파악한다. 영상 분석에서 구한 격자점의 속도에서 고정점의 속도를 빼서, 격자점의 유속장을 산정하였다. 개발된 기법의 검증을 위해서 실험 수로에서 동일한 흐름에 대해 흔들리지 않은 영상과 흔들리는 영상의 두 가지 영상을 만들고, 흔들림이 없는 영상의 처리 결과를 기준으로 삼아, 흔들림이 있는 영상의 처리 결과를 검토하였다. 그 결과, 흔들림이 지나치게 커서 참조점들의 추적이 불가능한 경우를 제외하고는 두 자료의 처리 결과는 거의 동일하였으며, 유속 측정의 오차는 약 6 % 내외로 나타났다. Surface Image Velocimetry (SIV) is an instrument to measure water surface velocity by using image processing techniques. To improve its measuring accuracy, it is essential to get high quality images with low skewness. A truck-mounted SIV system would be a good way to get images, since its crane gives high altitude to the images. However, the images taken with a truck-mounted SIV would be swayed due to the movement of crane and the camera by winds. In that case, to analyze the images, it is necessary to compensate the side sway in the images. The present study is to develop an algorithm to analyze the swayed images by combining common image processing techniques and coordinate transform techniques. The system follows the traces of some selected fixed points and calculates the displacements of the video camera. By subtracting the average velocity of the fixed points from that of grid points, the velocity fields of the flow can be corrected. To evaluate the system's performance, two image sets were used, one image set without side sway and another set with side sway. The comparison of their results showed very close with the error of around 6 %.

In-plane rigid-body vibration mode characterization with a nanometer resolution by stroboscopic imaging of a microstructured pattern

This article introduces an improved approach for the characterization of in-plane rigid-body vibration, based on digital processing of stroboscopic images of the moving part. The method involves a sample preparation step, in order to pattern a periodic microstructure on the vibrating device, for instance, by focused ion beam milling. An image processing method has then been developed to perform the optimum reconstruction of this a priori known object feature. In-plane displacement and rotation are deduced simultaneously with a high resolution (10-2 pixel and 0.5 x 10(-3) rad, respectively). The measurement principle combines phase measurements-that provide the high resolution-with correlation-that unwraps the phase with the proper phase constants. The vibration modes of a tuning fork are used for demonstrating the capabilities of the method. For applications allowing the sample preparation, the proposed methodology is more convenient than common interference methods or image processing techniques for the characterization of the vibration modes, even for amplitudes in the nanometer range.