Prewitt Filter Research Articles

MoOx Synaptic Memristor with Programmable Multilevel Conductance for Reliable Neuromorphic Hardware.

Memristor holds great potential for enabling next-generation neuromorphic computing hardware. Controlling the interfacial characteristics of the device is critical for seamlessly integrating and replicating the synaptic dynamic behaviors; however, it is commonly overlooked. Herein, we report the straightforward oxidation of a Mo electrode in air to design MoOx memristors that exhibit nonvolatile ultrafast switching (0.6-0.8 mV/decade, <1 mV/decade) with a high on/off ratio (>104), a long durability (>104 s), a low power consumption (17.9 μW), excellent device-to-device uniformity, ingeniously synaptic behavior, and finely programmable multilevel analog switching. The analyzed physical mechanism of the observed resistive switching behavior might be the conductive filaments formed by the oxygen vacancies. Intriguingly, upon organization into memristor-based crossbar arrays, in addition to simulated multipattern memorization, edge detection on random images can be implemented well by parallel processing of pixels using a 3 × 3 × 2 array of Prewitt filter groups. These are vital functions for neural system hardware in efficient in-memory computing neural systems with massive parallelism beyond a von Neumann architecture.

Covid-19 Detection by Machine Learning Using Chest Radiographs

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has highlighted the importance of early detection of infections, especially when RT-PCR testing equipment is scarce. This study introduces a machine learning algorithm using CT scan imaging for rapid COVID-19 identification. The algorithm, designed as a computer-aided detection model, analyzed 536 CT images (32x32 pixels) categorized into COVID-19 infected and non-infected groups. The model preprocesses images using the Prewitt filter and discrete cosine transform, then extracts features through various statistical methods and the histogram of oriented gradients (HOG). Out of 32 analyzed features, 29 showed high significance (p-value &lt; 0.05), effectively distinguishing normal and abnormal cases. These features were classified using support vector machine (SVM) and k-nearest neighbor (KNN) methods. Performance metrics like sensitivity, specificity, and accuracy were used to evaluate the classifiers. The results of metrics showed that the classifiers of KNN-1, KNN-3, KNN-5, and SVM-Linear could distinguish between normal and abnormal images perfectly (100%) when it was applied to the proposed model on the tested ROIs images. Also, the SVM-RBF had less performance than other classifiers with 98.38% of accuracy but was still at a high-performance level. These results indicate that physicians can utilize the proposed model as an assisted tool for detecting COVID-19.

Crack detection in historic structures using an improved Prewitt filter: Case study of the Historic Si-o-se-pol Bridge in Iran

In contrast to new and modern structures, historical structures have cultural significance, and it is essential to make an effort to preserve them. For this purpose, fast and accurate damage detection of historical structures plays a crucial role. Thus, the current study introduces a novel and effective tool for detecting cracks in historic structures. A new and modified version of the Prewitt filter is proposed to detect cracks in the Si-o-se-pol Bridge, a historic bridge in Iran. Results of crack detection using vertical and horizontal Prewitt filters are compared to those obtained from the proposed improved filter. The findings show that the improved Prewitt filter has significantly better performance compared to the vertical and horizontal Prewitt filters.

Designing a Smart Agri-Crop Framework on Cotton Production using ABO Optimized Vision Transformer Model

Due to its widespread cultivation and large yields by most farmers, cotton is another vital cash crop. However, a number of illnesses lower the quantity and quality of cotton harvests, which causes a large loss in output. Early diagnosis detection of these illnesses is essential. This study employs a thorough methodology to solve the crucial job of cotton leaf disease identification by utilising the "Cotton-Leaf-Infection" dataset. Preprocessing is the first step, in which noise is removed from the dataset using a Prewitt filter, which improves the signal-to-noise ratio. Next, a state-of-the-art process for image classification errands called Vision Transformer (ViT) model is used to carry out the disease categorization. Additionally, the study presents the African Buffalo Optimisation (ABO) method, which optimises weight during the classification procedure. The African buffalo's cooperative behaviour served as the model's inspiration for the ABO algorithm, which is remarkably effective at optimising the model's parameters. By integrating ABO, the problems caused by the dynamic character of real-world agricultural datasets are addressed and improved model resilience and generalisation are facilitated. The suggested ViT-based categorization model shows remarkable effectiveness, with a remarkable 99.3% accuracy rate. This performance is higher than current models.

Phantom study of layered sensor module for photon-counting BMD detector.

In this study, we perform bone mineral density (BMD) calculation by designing a layered sensor module (LSM) that divides high- and low-energy spectra from a single shot of X-rays. Gamma-ray evaluation supports this mechanism; low-energy gamma rays are absorbed in the front detector, whereas high-energy gamma rays are absorbed in the rear detector. In this phantom study, LSM divides a single shot of X-ray into two spectra with different distributions of energy, thereby affording X-ray images with different properties, such as contrast and gray scale. The region of interest (ROI) is classified by the Prewitt operator to sort the pixels for BMD calculation or Rs value. The calculated final value is 1.2051g/cm2 with a standard deviation (SD) of 0.3690g/cm2, as obtained from our previous study. An improved SD results from the layered structure with two channels for signal processing, the introduction of Rs value, and the use of Prewitt filter to sort reliable data. Overall, this study displays the feasibility of LSM for BMD calculation with a small error, thereby enabling the diagnosis of osteoporosis with novel mechanism.

Edge Detection Based Autofocus Algorithm to Detect Accurate Camera Working Distance

In the last 10 years, industrial image processing has been used in many areas such as automotive, medicine and aviation. Especially for sensitive measurement applications, camera control systems are preferred due to their speed and quality advantages. In this study, a method is proposed to determine the optimum camera position at the image acquisition phase for industrial cameras. In the proposed approach, images are captured while the camera and telecentric lens, which are connected to the servo motor, are moving up and down. The edge informations of the captured images are extracted in the x and y directions and the amplitude value is calculated. Then, meaningful and meaningless edge information at each motor position is found with a median-based approach. Finally, the sharpness of the images are arranged by statistical analysis and the optimum focal point for the camera is found. Sobel, Robert and Prewitt Filters were applied to determine the best method for extracting edge information. Measurements were performed on the workpieces using camera positions where the focus was optimum for each method. According to the results, it has been observed that the camera position, which produces the closest result to the real values, is obtained from the focus determined by the Sobel Filter. The proposed method is leaded to researchers to provide automatic focus for camera control systems.

Improving Image Quality Assessment Based on the Combination of the Power Spectrum of Fingerprint Images and Prewitt Filter

The assessment of fingerprint image quality is critical for most fingerprint applications. It has an impact on the performance and compatibility of fingerprint recognition, authentication, and built-in cryptosystems. This paper developed an improved fingerprint image quality assessment derived from the image power spectrum approach and combined it with the Prewitt filter and an improved weighting method. The conventional image power spectrum approach and our proposed approach were implemented for accuracy and reliability tests using good, faulty, and blurred fingerprint images. The experimental results showed the proposed algorithm accurately identified the sharpness of fingerprint images and improved the average difference in FIQMs to 61% between three different levels of blurred fingerprints compared with that achieved by a conventional algorithm.

Efficient mode decision scheme based on edge detection with Gaussian pulse for Intra-prediction in H.264/AVC

Advanced Video Coding (H.264/AVC) is one of the most important video coding standards which was developed by the ITU-T Video Coding Experts Group (VCEG) together with the ISO/IEC Moving Picture Experts Group (MPEG). Due to the complex procedures needed to find the optimal mode in intra prediction mode stage of H.264/AVC, an efficient mode decision is suggested in this paper. The main objectives of the proposed work are improving the compression efficiency of video coding and minimizing the degradation of video quality. The proposed algorithm is based on the gradient method for edge detection using the Prewitt filter to determine the texture direction of a block to be predicted in the luminance component of a video frame. To decide either implemented the DC mode or the edge-detection based gradient method to predict block under consideration, a predefined threshold value is used for the homogeneity test of each macro-block. To reduce the Bit Rate of the compressed video, Gaussian pulse modulation is also suggested to the Discrete Cosine Transformation (DCT) components of each macro-block during the coding process. The proposed algorithm is carried out on a luminance component of a set of different YUV video sequences with different resolutions and different quantization parameters using MATLAB. The suggested algorithm is evaluated using different metrics which are Peak Signal to Noise Ratio (PSNR) and bit rate measured by Bjontegaard Delta method. The simulation results show the ability of the proposed algorithm in enhancing the BDPSNR averagely by 1.14 dB and reducing the BDBR averagely by 11.34 %for QCIF sequences compared to the existing state-of-the-art fast intra-prediction mode decision algorithms.

Significant of multi-level pre-processing steps and its proper sequence in SegCaps skin lesion segmentation of dermoscopic images

In computer-assisted diagnosis of dermoscopy images, skin lesion segmentation is a critical but difficult process. Many segmentation methods fail to extract correct lesion boundaries due to the lack of pre-processing steps or a very few number of pre-processing steps that aren't in the right order. This paper demonstrates the significant of Multi-level pre-processing steps and its proper sequence before SegCaps segmentation of Lesion images. In the proposed work author have planned four pre-processing steps before segmentation of skin lesion images in the sequence of first is a 2nd Order grey edge algorithm is used to achieve color consistency;. Second process consists of two sub-processes in the hair removal procedure. To get started, we use the Prewitt filter, which is adaptable to the hair pixel level. The following procedure is the Repairing Operation, which is carried out using the anisotropic Fast Marching Method. Third process is a noise filtering with the Improved Switching Bilateral Filter (ISBF), which uses the Sorted Quadrant Median Vector (SQMV) to determine best median value and the last process is The Directed Edge Enhancer algorithm is used to perform edge enhancement. The proposed model is tested Over MatlabR2017b, using the HAM10000 dataset. The proposed method's output is assessed by comparing segmentation effects with and without pre-processing, as well as visual appearances of pre-processed images from input images. From the results it has been concludes that when pre-processing steps are used in the correct order and proper sequence, segmentation accuracy is very high, as compared to segmentation without pre-processing steps.

Inertial Sensor Data to Image Encoding for Human Action Recognition

Convolutional Neural Networks (CNNs) are successful deep learning models in the field of computer vision. To get the maximum advantage of CNN model for Human Action Recognition (HAR) using inertial sensor data, in this paper, we use four types of spatial domain methods for transforming inertial sensor data to activity images, which are then utilized in a novel fusion framework. These four types of activity images are Signal Images (SI), Gramian Angular Field (GAF) Images, Markov Transition Field (MTF) Images and Recurrence Plot (RP) Images. Furthermore, for creating a multimodal fusion framework and to exploit activity images, we made each type of activity images multimodal by convolving with two spatial domain filters: Prewitt filter and High-boost filter. ResNet-18, a CNN model, is used to learn deep features from multi-modalities. Learned features are extracted from the last pooling layer of each ResNet and then fused by canonical correlation based fusion (CCF) for improving the accuracy of human action recognition. These highly informative features are served as input to a multi-class Support Vector Machine (SVM). Experimental results on three publicly available inertial datasets show the superiority of the proposed method over the current state-of-the-art.

Validation Approach of the Tectonic Lineament Extraction Enhancement Using Sentinel 2A Images and Modified 3 &#215; 3 Bidirectional Prewitt Filters. Case Study: Grombalia, Tunisia

In this study, filters are used to extract directional tectonic lineaments and, thus, to reveal the presence of faults or fractures in the satellite images. The extraction process consists first in eliminating other different types of lineaments (e.g. the lithological limit, ridgelines, hydrographic network, roads, etc.). Besides, a comparative and quantitative approach is applied to show that when the directional Prewitt filter has a zero setting of the opposite of the assumed direction, as is the case of the N-S direction, the East-West direction is also set to zero. This research work, based on a bidirectional filter (N-S; E-W), shows satisfactory results especially concerning the quantitative fluctuation of lineament directions by interval. This fluctuation is in perfect agreement with the lineaments provided by the digitizing of the different tectonic accidents directions extracted in a GIS environment from the geological map of Grombalia. In this research work, a quantitative approach was used to evaluate the result of the lineament extraction methodology based on one direction analysis and by an interval of directions. Indeed, the N45 lineament direction, well documented in the geological study of the region, was more clearly distinguished by applying the Prewitt filter than by using the Sobel filter. The result was validated by comparison with the results obtained by the digitization of the tectonic accidents mapped on the geological map.

Diagnosis of brain tumours by MRI binarisation with variable fuzzy level

Brain tumour detection is still a challenging problem both in medical and image processing. In this study, two scenarios are applied to diagnose human brain tumours, classical image processing (CIP) and a new algorithm called binary image with variable fuzzy level (BIVFL). Magnetic resonance imaging (MRI) process from the BraTs 2019 database. Edge detection and prognosis area cropping are parts of the high-computational CIP algorithm. For the CIP, the best filter is the combination of the horizontal and vertical modes of the Prewitt filter. In the BIVFL, by changing the fuzzy level for binarisation images two clusters are filled with tumour and no tumour areas, and the tumour area is extracted in various accuracies. Sensitivity, specificity, precision, and accuracy of the BIVFL are varied according to the fuzzy level, and the best value of sensitivity is 0.9, but the value of three other parameters is 1 for an interval of fuzzy levels. The BIVFL is a simple and fast tumour area extraction algorithm, and also it is easy to implement with digital signal processors. Histogram and power signal-to-noise ratio of the BIVFL is remarkable.

3D Reconstruction of Power Lines Using UAV Images to Monitor Corridor Clearance

Regular power line inspections are essential to ensure the reliability of electricity supply. The inspections of overground power submission lines include corridor clearance monitoring and fault identification. The power lines corridor is a three-dimensional space around power cables defined by a set distance. Any obstacles breaching this space should be detected, as they potentially threaten the safety of the infrastructure. Corridor clearance monitoring is usually performed either by a labor-intensive total station survey (TS), terrestrial laser scanning (TLS), or expensive airborne laser scanning (ALS) from a plane or a helicopter. This paper proposes a method that uses unmanned aerial vehicle (UAV) images to monitor corridor clearance. To maintain the adequate accuracy of the relative position of wires in regard to surrounding obstacles, the same data were used both to reconstruct a point cloud representation of a digital surface model (DSM) and a 3D power line. The proposed algorithm detects power lines in a series of images using decorrelation stretch for initial image processing, the modified Prewitt filter for edge enhancement, random sample consensus (RANSAC) with additional parameters for line fitting, and epipolar geometry for 3D reconstruction. DSM points intruding into the corridor are then detected by calculating the spatial distance between a reconstructed power line and the DSM point cloud representation. Problematic objects are localized by segmenting points into voxels and then subsequent clusterization. The processing results were compared to the results of two verification methods—TS and TLS. The comparison results show that the proposed method can be used to survey power lines with an accuracy consistent with that of classical measurements.

Automatic recognition of Indian vehicles license plates using machine learning approaches

A proposed method for automation of Indian vehicles number plate conversion is presented in this paper. Many vehicles are plying on Indian roads with varying in size and structure and hence there are a number of variations in number plates too. Although there are strict norms to be followed for number plate designs, however, it can be seen many deviations in actual number plates of many vehicles. It can be also observed that the number plates are written in many Indian languages, but the prescribed norm is Hindu-Arabic numerals with Latin letters which is prescribed by the licensing plate issuing authority. The number plate designs are varied according to types of vehicles, like, personal, commercial, public transport, etc. In order to design an automatic vehicle number plate, considerations of these factors are taken into account. The steps in the design include segmentation and character extraction from number plates and recognition of segmented characters in order to extract the complete information from number plates. The work in this paper is focused on Hindu-Arabic numerals with Latin letters. For feature extraction, Prewitt filter technique is used to extraction of characters from the vehicle number plates and connected component analysis is used for segmenting characters. Three different classifiers, namely, k-Nearest Neighbor, Artificial Neural Network and Decision Tree, are used in the experiments. Recognition accuracy up to 98.10% is achieved and is a promising factor for future research in this direction.

Three-dimensional memristor circuits as complex neural networks

Constructing a computing circuit in three dimensions (3D) is a necessary step to enable the massive connections and efficient communications required in complex neural networks. 3D circuits based on conventional complementary metal–oxide–semiconductor transistors are, however, difficult to build because of challenges involved in growing or stacking multilayer single-crystalline silicon channels. Here we report a 3D circuit composed of eight layers of monolithically integrated memristive devices. The vertically aligned input and output electrodes in our 3D structure make it possible to directly map and implement complex neural networks. As a proof-of-concept demonstration, we programmed parallelly operated kernels into the 3D array, implemented a convolutional neural network and achieved software-comparable accuracy in recognizing handwritten digits from the Modified National Institute of Standard and Technology database. We also demonstrated the edge detection of moving objects in videos by applying groups of Prewitt filters in the 3D array to process pixels in parallel. A three-dimensional circuit composed of eight layers of monolithically integrated memristive devices is built and used to implement complex neural networks, demonstrating accurate MNIST classification and effective edge detection in videos.

Human Action Recognition Using Deep Multilevel Multimodal (${M}^{2}$ ) Fusion of Depth and Inertial Sensors

Multimodal fusion frameworks for Human Action Recognition (HAR) using depth and inertial sensor data have been proposed over the years. In most of the existing works, fusion is performed at a single level (feature level or decision level), missing the opportunity to fuse rich mid-level features necessary for better classification. To address this shortcoming, in this paper, we propose three novel deep multilevel multimodal (M2) fusion frameworks to capitalize on different fusion strategies at various stages and to leverage the superiority of multilevel fusion. At input, we transform the depth data into depth images called sequential front view images (SFIs) and inertial sensor data into signal images. Each input modality, depth and inertial, is further made multimodal by taking convolution with the Prewitt filter. Creating “modality within modality” enables further complementary and discriminative feature extraction through Convolutional Neural Networks (CNNs). CNNs are trained on input images of each modality to learn low-level, high-level and complex features. Learned features are extracted and fused at different stages of the proposed frameworks to combine discriminative and complementary information. These highly informative features are served as input to a multi-class Support Vector Machine (SVM). We evaluate the proposed frameworks on three publicly available multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTD-MHAD Kinect V2. Experimental results show the supremacy of the proposed fusion frameworks over existing methods.

Statistical features analysis and discrimination of maize seeds utilizing machine vision approach

The purpose of this study is the statistical analysis and discrimination of maize seed using a machine vision (MV) approach. The foundation of the digital image dataset holds six maize seed varieties named as Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88. The digital image dataset acquired via a digital imaging laboratory. For preprocessing, we crop the image into a size of 600×600 pixels, and convert it into a gray level image format. After that, line and edge detection are performed by using a Prewitt filter, and five non-overlapping areas of interest (AOIs) size of (200×200), and (250×250) are drawn. A total of 56 statistical features, containing texture features, histogram features, and spectral features, is extracted from each AOI. The 11 optimized statistical features have been selected by deploying “Correlation-based Feature Selection” (CFS) with the Greedy algorithm. For the discrimination analysis, four MV classifiers named as “Support Vector Machine” (SVM), “Logistic” (Lg), “Bagging” (B), and “LogitBoost” (LB) have been deployed on optimized statistical features dataset. After analysis, the SVM classifier has shown a promising accuracy of 99.93% on AOIs size (250×250). The obtained accuracy by SVM classifier on six maize seed varieties, namely Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88, were 99.9%, 99.8%, 100%, 100%, 99.9%, and 99.8%, respectively.

Type-2 fuzzy control for line following using line detection images

This work presents a comparative analysis of Type-1 and Type-2 fuzzy controllers to drive an omnidirectional mobile robot in line-following tasks using line detection images. Image processing uses a Prewitt filter for edge detection and determines the error from the line location. The control systems are tested using four different paths from the Robotino® SIM simulator. Also, two different strategies in the design and implementation of the controllers are presented. In the first one, a PD controller scheme is extended by using a fuzzy system to have adaptive parameters P and D, additionally, Type-2 Fuzzy sets are used to give robustness to the controller. In the second case, a Fuzzy controller is designed to compute in a direct way the control variables and it is extended to Type-2 Fuzzy controller. Finally, experimental results and comparative analysis are presented for the five control schemes by comparing the running time and the standard deviation to measure the robustness of the control systems.

Seismic imaging of mélanges; Pieniny Klippen Belt case study

The authors present results of the first high-resolution deep seismic reflection survey in the Pieniny Klippen Belt (PKB) in Poland. This survey sheds new light on the matter of olistostromes and the mélange character of the PKB. The sedimentary mass-transport deposits represented by olistoliths and olistostromes manifest themselves by different petrophysical parameters of rocks (velocity, density and resistivity) and seismic attributes. Seismic attributes are very effective in the interpretation of the geology of complex mélanges. The authors used selected attributes: low-pass filter, energy, energy gradient, dip-steered median filter, Prewitt filter, Laplacian edge enhancing filter and square root of the energy gradient. These attributes emphasize changes of the seismic image inside mélange zones. The distinguished olistoliths are now inside imbricated thrust structures and they are tectonically rearranged. Polygenetic mélanges in the PKB originated as a result of sedimentary and tectonic processes. The PKB in the investigated area forms several north-vergent thrust sheets belonging to the Złatne and Hulina nappes. Both nappes contain large chaotic, non-reflective olistoliths as well as the smaller mainly high-reflective olistoliths. Olistoliths are arranged parallel to the flysch layering and thrusts. The results presented confirm the postulated two olistostrome belts within the PKB structure. Thematic collection: This article is part of the Polygenetic mélanges collection available at: https://www.lyellcollection.org/cc/polygenetic-melanges

Edges identification based on the derivative filters and fractal dimension

The purpose of this paper is to offer a comparative study on the fractal dimension (D) used to differentiate edges in brain images processed using first-order derivative filters (Prewitt, Roberts) and second-order derivative filters (Laplacian and Laplacian of Gaussian). PDw (proton density) and&nbsp;T2w (T2-weighted type) brain images of healthy patients and patients diagnosed with metastatic bronchogenic carcinoma (MBC) are used. Experimental results showed that second-order derivative filters clearly separate healthy controls from diseased patients while the first-order derivative filters create false edges that affect the fractal dimension (D) values. The Kullback-Leibler divergence (DKL) determines that the probability distribution of the "real" fractal measurements, specific to healthy patients is different from the probability distribution of the "arbitrary" fractal dimensions, specific to patients with MBC. The highest value to the distance DKL is for Prewitt filter.&nbsp; The value of distance DKL is close to zero for Laplacian, LoG and Roberts filters.