Contour Filter Research Articles

Deep Edge-Based Fault Detection for Solar Panels.

Solar panels may suffer from faults, which could yield high temperature and significantly degrade their power generation. To detect faults of solar panels in large photovoltaic plants, drones with infrared cameras have been implemented. Drones may capture a huge number of infrared images. It is not realistic to manually analyze such a huge number of infrared images. To solve this problem, we develop a Deep Edge-Based Fault Detection (DEBFD) method, which applies convolutional neural networks (CNNs) for edge detection and object detection according to the captured infrared images. Particularly, a machine learning-based contour filter is designed to eliminate incorrect background contours. Then faults of solar panels are detected. Based on these fault detection results, solar panels can be classified into two classes, i.e., normal and faulty ones (i.e., macro ones). We collected 2060 images in multiple scenes and achieved a high macro F1 score. Our method achieved a frame rate of 28 fps over infrared images of solar panels on an NVIDIA GeForce RTX 2080 Ti GPU.

The mathematical characteristic of the Laplace contour filters used in digital image processing. The third order filters

The mathematical characteristic of the Laplace contour filters used in digital image processing. The third order filters

The mathematical characteristic of the fifth order Laplace contour filters used in digital image processing

The mathematical characteristic of the fifth order Laplace contour filters used in digital image processing

Emotion estimation model for cognitive state analysis of learners in online education using deep learning

AbstractFor Facial Expression Recognition, occlusion and position change that may drastically alter facial expressions are two important challenges (FER). Due to advances in automated FER over the last several decades, it has received less attention in the real world, where occlusion‐ and pose‐invariant aspects of FER are critical. Online education learners' cognitive states may be assessed using this paper's focus on real‐world stance and occlusion robust FER. The human visual system's attention mechanism inspired us to develop a new kind of spatial attention network (SAN‐CNN). Saliency characteristics and spatial importance between adjacent pixels are emphasized in the SAN‐CNN model. Preprocessing an input picture using a median contour filter is used here initially, followed by mask‐based ROI for segmentation. Using CNNs for facial recognition, landmark localization, and head position estimation based on spatial attention networks, it is possible to do emotional categorization. Using the Kaggle public video‐based facial expression datasets, we were able to demonstrate that our proposed approach is more accurate and faster than the usual techniques. In addition, we evaluated the suggested method's performance metrics with those of the already used approaches. FER with occlusion and variant posture performs better than standard approaches using our suggested method, as shown by the results of the experiments.

RESEARCH OF SYNCHRONIZATION CIRCUITS FOR DIGITAL COMMUNICATION SYSTEMS

In a digital communication system, the transmitter and receiver have several generators for modulation and demodulation; increasing and decreasing the sampling frequency; synchronization of symbols and bit streams. The causes of phase and carrier frequency errors are the instability of the frequency of the local generators of the transmitter and receiver; presence of Doppler frequency shift; signal propagation delay from the transmitter to the receiver. Synchronization circuits of modern digital communication systems are built on the basis of phase-locked loop (PLL). The purpose of the work is: research of various characteristics of the digital PLL (locking time; established error; transient behavior) for various types of input action; study of the bit error rate of a coherent digital communication system. The PLL consists of the following components: a phase detector that generates a signal that varies in proportion to the phase difference between the input signal and a locally generated sinusoid; controlled generator that generates an output signal whose phase and frequency depends on the input signal; loop filter, which removes unwanted high-frequency components in the output signal of the phase detector and forms a signal that controls the NCO. During adaptation, the PLL has some transient process that depends, in particular, on three factors: the presence of a zero-phase error is determined by the PLL contour filter; the determined bandwidth of the circuit and the initial deviation between the input and reference frequencies affect the PLL adaptation time; the attenuation coefficient of the PLL affects the adaptation behavior: the speed and magnitude of emissions. The study of the output signal of the linear PLL model for damping factor , and , and we will use an input signal of the step function type, linear voltage changes and hyperbola. type 1, type 2, and type 3 PLLs can adapt to a zero-error step input. If the input signal is a linearly varying voltage, PLL types 2 and 3 can adapt with zero phase error, while type 1 adapts with a residual phase error. For hyperbola input, only type 3 PLL can fully adapt: type 2 adapts with residual error, while type 1 cannot adapt. The damping factor should be in the range of . When changing the input signal frequency from 3.55 MHz to 3.72 MHz, the capture time of the type 2 PLL changes from about 150 μs to about 600 μs, that is, when the frequency increases by 170 kHz, the capture time increases almost four times. The error that has occurred is affected by the choice of the contour filter and the features of the synchronization.

An Intelligent Instrument Reader: Using Computer Vision and Machine Learning to Automate Meter Reading

A novel algorithm using computer vision and machine learning techniques has been developed in this research and applied to automate the reading of analog meters. This approach does not rely on any prior information about the meter being read or any human intervention during the process. High-level features of the meter, including the graduation values and angles, are extracted using a cascade of image contour filters with a series of digit classifiers. The features are refined and used to train regression models that return the reading of the analog meter automatically.

Automatic Quantification of Cardiomyocyte Dimensions and Connexin 43 Lateralization in Fluorescence Images

Cardiomyocytes’ geometry and connexin 43 (CX43) amount and distribution are structural features that play a pivotal role in electrical conduction. Their quantitative assessment is of high interest in the study of arrhythmias, but it is usually hampered by the lack of automatic tools. In this work, we propose a software algorithm (Myocyte Automatic Retrieval and Tissue Analyzer, MARTA) to automatically detect myocytes from fluorescent microscopy images of cardiac tissue, measure their morphological features and evaluate the expression of CX43 and its degree of lateralization. The proposed software is based on the generation of cell masks, contouring of individual cells, enclosing of cells in minimum area rectangles and splitting of these rectangles into end-to-end and middle compartments to estimate CX43 lateral-to-total ratio. Application to human ventricular tissue images shows that mean differences between automatic and manual methods in terms of cardiomyocyte length and width are below 4 m. The percentage of lateral CX43 also agrees between automatic and manual evaluation, with the interquartile range approximately covering from 3% to 30% in both cases. MARTA is not limited by fiber orientation and has an optimized speed by using contour filtering, which makes it run hundreds of times faster than a trained expert. Developed for CX43 studies in the left ventricle, MARTA is a flexible tool applicable to morphometric and lateralization studies of other markers in any heart chamber or even skeletal muscle. This open-access software is available online.

CCTV Surveillance for Unprecedented Violence and Traffic Monitoring

Monitoring of traffic and unprecedented violence has become very much necessary in the urban as well as the rural areas, so the paper attempts to develop a CCTV surveillance for unprecedented violence and traffic monitoring. The proffered method performs the synchronization of the videos and does proper alliance employing the algorithms of motion detection and contour filtering. The steps in motion detection identifies the movement of the objects such as vehicles and unprecedented activities whereas the filtering is used to identify the object itself using its color. The synchronization and the alignment process affords to provide the details of the each objects on the scenario. The proposed algorithm is developed in Java which assists its model using its library that is open source. The validation of the proposed model was carried out using the data set acquired from real time and results were acquired. Moreover the results acquired were compared with the algorithms that were created in the early stages, the comparison proved that the proffered model was capable of obtaining a consecutive quick outcomes of 12.3912 *factor than the existing methods for the resolution of the video used in testing was 240.01x 320.01 with 40 frames per second with cameras of high definition. Further the results acquired were computed to run the application of the embedded CPU and the GPU processors.

An automatic system for bearing surface tiny defect detection based on multi-angle illuminations

Bearings are very common mechanical components that are widely used in all kinds of machines. Currently, bearing surface defect detection is mainly completed by naked-eye observation. This manual detection mode has low reliability and is time consuming (especially for miniature bearings). In this paper, we present an automatic synthetic tiny defect (small defects that are difficult to find at vertical angles) detection system for bearing surfaces with self-developed software and hardware. Under two illumination modes in this system, thresholding segmentation, contour extraction, contour filtering, center location, region zoning and text recognition are successively implemented. Finally, four common defects (gap, stain, shrunken lid and scratch defects) are automatically detected. The experimental results show that this system has a high detection success rate and short time consumption. This method can provide technical support for real engineering applications involving bearing manufacturing and screening.

Image Enhancement Using Active Contour Filtering Methods for a Custom-Developed Real-Time Photoacoustic Tomography System to Accurately Delineate a Target

Image Enhancement Using Active Contour Filtering Methods for a Custom-Developed Real-Time Photoacoustic Tomography System to Accurately Delineate a Target

Unscented Kalman Snake for 3D Vessel Tracking

Purpose In this paper, we propose a robust 3D vessel tracking algorithm by utilizing an active contour model and unscented Kalman filter which are the two representative algorithms on segmentation and tracking. Materials and Methods The proposed algorithm firstly accepts user input to produce an initial estimate of vessel boundary segmentation. On each Computed Tomography Angiography (CTA) slice, the active contour is applied to segment the vessel boundary. After that, the estimation process of the unscented Kalman filter is applied to track the vessel boundary of the current slice to estimate the inter-slice vessel position translation and shape deformation. Finally both active contour and unscented Kalman filter are inter-operated for vessel segmentation of the next slice. Results The arbitrarily shaped blood vessel boundary on each slice is segmented by using the active contour model, and the Kalman filter is employed to track the translation and shape deformation between CTA slices. The proposed algorithm is applied to the 3D visualization of chest CTA images using graphics hardware. Conclusion Through this algorithm, more opportunities, giving quick and brief diagnosis, could be provided for the radiologist before detailed diagnosis using 2D CTA slices, Also, for the surgeon, the algorithm could be used for surgical planning, simulation, navigation and rehearsal, and is expected to be applied to highly valuable applications for more accurate 3D vessel tracking and rendering.

Use of laser scanning confocal microscopy for morphological taxonomy and the potential for digital type specimens (e-types)

The use of laser scanning confocal microscopy (LSCM) for creating taxonomic descriptions of copepods is investigated. A new technique is described, which employs a contour filter to process digital LSCM images, allowing taxonomic information to be quickly and accu- rately distilled into a simple illustration. LSCM allows the imaging of whole specimens, which can be rotated and viewed from any angle—a major benefit over light microscopy. Using this tech- nique, it is suggested that taxonomic descriptions can be rapidly produced in a fraction of the time required to produce similar descriptions using traditional light microscopy and hand drawing techniques. Good staining of specimens is, however, essential to produce accurate descriptions and more research is required in this area. The use of LSCM for morphological taxonomy shows great potential, not only for producing taxonomic descriptions, but also providing a comple - mentary adjunct to traditional type specimens in the form of 3D digital 'e-types' deposited in recognised international databases.

Effects of natural backgrounds on spatial filter responses near object contours

Purpose: The description of an object's contour is often thought to be solely a property of the object's image. However, objects typically appear in complex backgrounds. Thus for any 2D sample region (SR), both the preferred orientation and the magnitude of spatial contrast filters can be significantly perturbed by the background. The degree of perturbation is a function of filter size and the position of the filter relative to the contour. Further, the response of larger filters may be less sensitive to positional uncertainty. Our goal was to measure this perturbation by natural backgrounds. Methods: Images of natural and artificial objects were photographed and hand-segmented from their natural backgrounds. Composite images were made by digitally superimposing and translating the image of an object across various natural image backgrounds. 2D histograms of orientation and magnitude responses were measured from a set of steerable multi-scale filters for SRs across contours and on the background. In order to quantify discriminability, we measured the performance of an ideal observer. The ideal was required to discriminate between specific contour and background SRs, given prior knowledge of the conditional distributions estimated from the histograms. Results: Histograms of SRs on contours varied systematically as a function of displacement from the contour and were distinctly different than those from the background. The performance of the ideal observer was surprisingly good, with an overall correct response rate of .78. When centered on the contour, correct response rate was .82. Larger filter kernels were less sensitive to centering on the contour. Conclusions: Background has a significant effect on contour filter responses. However, these perturbations form predictable patterns, and allow discrimination with reasonable accuracy. Filter size helps to minimize spatial uncertainty.