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Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, roads, vegetation, water, agriculture land, crop types, plants, bare ground, cities, atmosphere conditions. Machine Learning (ML) approaches have been utilized effectively to develop a model for classification, detection, and segmentation tasks. Therefore, Satellite image processing and analysis purpose, ML techniques plays vital role and remotely sensed data become essential while training the model. The aim of this study is to investigate the various of ML techniques in satellite image analysis. However, to predict the various events in advance across the globe, it is necessary to focus more on remote sensed data and data processing techniques for accurate classification. Even though remote sensing quality has been increased and artificial intelligence solutions are equally increased. This paper addressed various types of advanced ML techniques utilized in the classification and assessment of satellite images and used to track the earthquakes, faulting, landslides, floodings, wildfire, and hazards associated with the stated activities. Still there is a gap and interference in the approaches and it is important to fill the gap by thorough review of recent classification approaches. In this connection it is necessary to look in depth to the state-of-the-art ML techniques of satellite image processing. [ABSTRACT FROM AUTHOR]

Agricultural productivity is problematic while the plant attacks several micro-organisms, viruses, and bacterial infections. Earlier disease identification is a unity regarding the challenging solutions for increasing plant production. The signs from these attacks are generally identified in the leaves, fruit, and stems inspection. Nowadays, the need for computational diagnosis of plant diseases increases to gain efficient plant productivity. Here, the process identifies and analyzes the Citrus leaf and Fruits disease literally from the infected images by adopting image-processing procedures to distinguish leaf infections from digital-images. The suggested hybrid algorithm includes pre-processing and RGB HSI conversion. The initial step practices CLAHE segments the features of affected areas applying K-means clustering and statistical GLCM. The SVM classifier has recognized the diseased image and performs those methods in citrus disease detection. Finally, the Fuzzy-based estimation has been convoluted to measure the disease grade severity. [ABSTRACT FROM AUTHOR]

Most people who get sick around the world have some kind of skin condition. Despite its frequency, it is very challenging to identify and requires substantial domain experience. This study explains how to identify various types of these disorders. We use a two-stage strategy that efficiently integrates image processing and deep learning based on molecular diagnostic histological features to reliably detect the disease. During the initial stage of the process, the image of the skin condition is processed in a variety of different ways before "feature extraction" is performed. In the second stage of this process, Machine Learning (ML) algorithms are used to diagnose diseases based on histological characteristics discovered through skin examination. These characteristics can be found by analysing the skin. After being trained and evaluated, the system could correctly name up to 95% of the six diseases. [ABSTRACT FROM AUTHOR]

Anxiety is an element of existence it is a disagreeable condition of expressive stimulation that public understanding in circumstances similar to operational for extended hours in facade of electronic gadgets. Electronic gadgets like laptops, mobiles and computers have grow to be a approach of existence, to a great extent living is worn-out on the Electronic gadgets moreover consequently we are as a result additional exaggerated through the ups and downs that they reason us. One cannot immediately totally circumvent their toil on Electronic gadgets although one knows how to be as a minimum authority his/her handling while being worried regarding him being stressed out at one position of instance. Scrutinize the expressive condition of a human being who is functioning in frontage of a Electronic gadgets for extended period is fundamental for the wellbeing of a human being. In this toil a instantaneous non-intrusive videocassette records are taken by capturing the community, which notice the expressive condition of a person by investigating the facial appearance. We identify a human being feeling in every recorded video capture structure and the conclusion on the strain intensity is completed in chronological times of the videocassette imprisoned. We utilize a method that permits us to educate a representation and investigate differentiations in forecasting these characteristics. The package Theano is a python programming construction which intends at improving both the completing moment in addition to improvement point of the Linear Regression representation that is utilized in the perspective as a DL algorithm. The investigational outcomes demonstrate that the enlarged organization is well on information with the standard representation of every era. [ABSTRACT FROM AUTHOR]

Glaucoma is the 2d main purpose of everlasting blindness worldwide. Early detection of glaucoma can restrict the disorder development. This paper directs the exceptional picture processing strategies for glaucoma detection based totally on CDR. After calculating the effects are in contrast the usage of sensitivity and specificity. Glaucoma is a criticism prompted due to loss of feature of neurons of the eye which leads to loss of eye sight. The photo processing strategies are Multi level thresholding approach, model of Active Contour Method, Region Based Segmentation. It can be calculated by using gazing Intra optical pressure (IOP), view, optical slice appearance, Glaucoma influences the supplemental imaginative and prescient loss by using including Mug to disc ratio. [ABSTRACT FROM AUTHOR]

The increased car density has a significant negative impact on the city's quality of life. As a result, parking in densely populated areas with many cars often results in more driving time in search of free space, which can contribute to pollution. Although there are numerous new smart parking systems, the majority of them are designed for close parking. At the moment, no sensors or technologies are used for open parking spaces, such as those found in commercial complexes. Smart parking sensors and technology can help drivers find parking more easily and efficiently. The inefficiency of parking management for open parking is discussed in this paper. From the literature search it is concluded that currently, an image processing technique was used to solve the problem of open parking space is the best solution. This is because many cars are driving around unnecessarily in search of a free parking space. This can be mitigated by providing drivers with information about available parking spaces in an open parking area. The image processing technique is used to guide the driver in determining which area has a vacant parking slot. To determine the number of available parking spaces, several images of open parking areas were captured and analyzed using image processing techniques. The discovery reveals that as the value of the overall parking space increases, so does the value of marker sizes and font size. The equation of a straight line is derived from the graphs to represent the real-life equation of each graph. [ABSTRACT FROM AUTHOR]

An algorithm for detecting active objects from real-time is presented. Security cameras are common devices that especially use motion sensing technology. Nowadays, cameras with motion detectors are preferred for better protection. Motion detection technology is widely used for security purposes. Here with python programming motion detection software is used that detects human, cat and dog movement in front of the camera. Caffe model is used for image processing based motion sensing that are pretrained. The detected movement is notified to the user via the Telegram chatbot along with detected video. Images and videos can also be retrieved in it. [ABSTRACT FROM AUTHOR]

When the Viola-Jones machine learning algorithm is used to forecast a driver's sleepiness behaviour, it may help save people from nodding off behind the wheel and causing accidents that can leave them seriously injured or dead. To determine the predictive accuracy of the Viola-Jones machine learning system for Blink frequency, the sample size was varied from 100 times. The sensitivity of Viola-Jones is much higher. Viols Jones has a higher blink frequency percentage than HOG (86.14 percent) (75.84 percent). The correlation between Voila-Jones and HOGt was significant (p=0.000). The Viola-Jones model improves the sensitivity % for forecasting driver fatigue. If the algorithm determines that the driver is, in fact, sleepy, an alert will sound. [ABSTRACT FROM AUTHOR]

Positioning within indoor environments is one of the major challenges facing many different applications. It has become necessary to find algorithms and systems capable of locating a specific object within an environment that contains many objects. In this paper, the researchers have made a detailed study on many methods of positioning within different environments. Some methods rely on digital images and others rely on identification systems by radio waves and other methods. Each system has a number of advantages that qualify it to be the best choice in certain applications. On the other hand, there are drawbacks to each system that prevent it from working within certain environments. Clear comparisons are made in this paper showing the different systems with the pros and cons of each one. [ABSTRACT FROM AUTHOR]

Image compression techniques have become a hot topic due to the increasing growth of multimedia applications. This paper presents a ground-breaking full quadtree range block partition-based search-less fractal encoding algorithm that can efficiently encode range blocks or even individual pixels. This method, which may also be applied to high-quality image compression, solves both issues. Experimental results show that the technique outperforms traditional search-based and search-less fractal algorithms at low and high compression rates, thus improving both compression ratio and reconstructed image quality. Another advantage of the approach is its speedy encoding speed because it does not require a search process. When compared to traditional fractal algorithms, this method encrypts data much faster. [ABSTRACT FROM AUTHOR]

Medical imaging has seen substantial improvements not only in image procurement but also in terms of interpretational methodologies. The practice of using technologies such as image processing and artificial intelligence has shown remarkable results in the detection, classification, and localization of human bone fractures. Breakage or cracks in the bone occur when an external force is applied to a bone that is significantly larger than the bone's tolerance, which may lead to dislocation requiring extensive treatment. However, manual examination by experts is a complex and arduous process with a high chance of false detection. Many approaches under Artificial Intelligence and its subsets are proposed to facilitate fracture diagnosis and prevent misinterpretation, including Deep Learning and Convolutional Neural Networks (CNN). This paper is a comprehensive study of the various techniques and models designed using image processing approaches and the advances in machine learning and Deep Learning that have demonstrated improved performance in the precise detection of fractures in human bones. [ABSTRACT FROM AUTHOR]

Xilinx System Generator (XSG) integrates with MATLAB Simulink to build a complex system through Xilinx and Simulink blocks. This paper presents a hardware implementation of various image processing techniques to modify and extract the pixel information from various input images in run time. Complex coding strategies are eradicated with integrated system. The intricacy in structural design is reduced to a huge extent using Xilinx System generator. The reconfigurable attribute of Field Programmable Gate Array (FPGA) bags the massive advantages in image processing like co simulating with selective pixel and verified on FPGA. Image processing algorithms explored in methodical manner by performing diverse operations like edge detection, and color enhancement, complement, contrast stretching etc. The proposed image processing techniques (IPT) intended for various applications. Dissimilar image processing techniques are processed by FPGA and displayed on Video Graphics Array (VGA). Image processing system is designed in XSG, hardware description language (HDL) like VHDL & Verilog code is generated by processing system by system generator token. Design is implemented on Artix-7 and targeted on Basys3 reconfigurable hardware (XC7A35T-1-CPG236C). [ABSTRACT FROM AUTHOR]

Image processing is the process of manipulating images by visualizing, recognizing, sharpening of image features. Digital image processing deals with enhancing quality of image by using image processing techniques. Real time object tracking methods are studied for effective object identification. The algorithms for single and multiple object detection, color detection and face detection of an image are discussed. This article provides a comparative study of various object detection algorithms for video surveillance. The advantages and disadvantages of the system are compared and contrasted. From the results, it is evident that the object tracking algorithms provide high accuracy and precision by capturing the video frame and processing it frame-by-frame. It is used in various applications such as health care, forensics and security systems. [ABSTRACT FROM AUTHOR]

Embedded applications have begun to reap the benefits of embedded vision in recent years. The proliferation of high-performance computers, the maturing of deep learning algorithms, and vision is being added to electronic devices like integrated medical applications due to image processing advances. However, due to the high amount of CPU resources necessary to process even a single image to recognize an object, it is especially challenging to integrate image processing in embedded systems. Deep learning approaches, when deployed and validated on data sets matched to the specific task at hand, may produce better results. We propose and investigate the use of deep learning to improve the image-processing architecture of embedded vision systems in this study. Deep learning algorithm testing on embedded vision yielded encouraging results. [ABSTRACT FROM AUTHOR]

Line following is one of the most efficient and reliable techniques for mobile robots in indoor environments. Line-following robots can autonomously detect and follow designed paths. It is now commonly applied in transportation and assistance. In related research, ensuring success in complex line following tracks (right angles, sharp curves) and complex optical environments has been a challenge. This paper designs a line following vehicle is designed based on a microcontroller (Raspberry Pi), Pi camera, and image processing by OpenCV. The image processing in line following includes the application of RGB, GRAY, and HSV color models. The vehicle movement is controlled by the PID algorithm. Besides, this system incorporates a template matching module. The template matching module uses binarization, polygon approximation, perspective transformation, Gaussian blur, and similarity comparison. The designed vehicle can complete the line following a designed map and successfully matches the correct templates. A discussion on the effect of light reflection is presented in this paper. This effect is improved by an extra algorithm in line following and expending the threshold of binarization. Also, this paper solves the problem that the vehicle may run out of the track in line following. The experiment shows a satisfying result. This study has potential implications for the design of line-following robots. [ABSTRACT FROM AUTHOR]

Cervical cancer is a form of cancer that develops in the cells of the cervix, which links the uterus to the vagina. Cervical cancer can often be prevented by attending the cervical screening, which aims to find and treat changes to cells before they turn into cancer. Pap smear images are a more popular technique for screening cervical cancer. The nucleus and cytoplasm are present in the smear cell images. The spread of cancer is determined by the shape and structure of the nucleus. Thus, nucleus segmentation is an important step in cancer detection. However, overlapping, poor contrast, uneven staining, and other factors make cervical nucleus segmentation difficult. This paper proposes a new segmentation method for the cervical nucleus using digital image processing. In our proposed method, image processing techniques are employed to segment the nucleus. This method mainly compared the performance of using Red Green Blue (RGB) channels and Hue, Saturation, and Value (HSV). The best two channels from each two-colored image representation are compared, where the V channel is the best among H and S channels. On the other hand, G chancel is the best among the G and B channels. Therefore, the best two channels are compared, and the segmentation using the G channel is the best among all, with a sensitivity of 94.3%, specificity of 94.6% and precision of 88.3%. The main impact of this paper will be to assist doctors in diagnosing cervical cancer-based segmentation of the nucleus in Pap smear images. [ABSTRACT FROM AUTHOR]

This paper consists of the comparison on image processing using Minimal spanning tree and thresholding methods. [ABSTRACT FROM AUTHOR]

This work presents an automated and novel system for cricket commentary generation by the introduction of event driven approach and image captioning features. The presented system uses artificial intelligence and machine learning (ML) based methods to investigate real-time match data and generate real-time commentary by Image Captioning technique. For this purpose, deep neural network-based models and digital image processing techniques are used to detect the significant moments in the real-time match and generate commentary based on these real-time match events. The proposed method has been assessed using a dataset of 2 hours live cricket matches of India and England. After processing the match video, it has been observed that the developed model is successfully able to generate high-quality real-time commentary with a significant amount of accuracy. By commissioning leading-edge deep neural network-based model, the developed model determines the fitness to generate subtitles that are not only precise but also contextually appropriate, and efficiently apprehending the essence of the input frames. [ABSTRACT FROM AUTHOR]

Nowadays image processing taking over the internet and plays a major part in human society. We will come across many blurred images in our daily life which may contain information that is very valuable. So, deblurring of an image is the method where we convert low-quality pictures to high-quality pictures. In this process, we deal with noise and crisp to restore the image from the deteriorated image thereby increasing the image quality. This is possible, by applying different types of filters or techniques to theblurred image. The techniques that we worked on for comparison are blind, and non-blind deconvolution methods and the CNN algorithm. In which cnn model gave the better-quality image compared to other methods. We have taken a dataset containing of sharp images from kaggle[9] and generated a dataset of Gaussian blurred images using a Gaussian filter and used this images to build a CNN model, where this CNN model is trained and tested. The low quality picture is given to the CNN model as an input inturn we get the high quality picture as an output. Here the measure psnr ratio is used to determine the quality of the image, where cnn method has shown a higher value compared to other methods. We come across this problem in certain scenarios such as, in criminal perspective, medical field, etc in such cases, this concept is used to solve real-world problems. [ABSTRACT FROM AUTHOR]

Cervical cancer is caused by the growth of abnormal cells in the cervix's lining. Human papillomavirus (HPV), a sexually transmitted infection, plays a role in most cervical cancers. Therefore, cervical cancer can be avoided by having regular screenings and being vaccinated against HPV infection. The term pap-smear refers to human cell samples stained using the Papanicolaou method. The Papanicolaou method is used to detect precancerous cell changes before they become invasive cancer. The smear cell image is composed of a nucleus and cytoplasm. Cancer prevalence is determined by the shape and structure of the nucleus. Therefore, the segmentation of the nucleus is an essential step in detecting cancer. However, overlapping, poor contrast, uneven staining, and other factors make cervical nucleus segmentation difficult. This paper proposes a new segmentation method for the cervical nucleus using digital image processing. Our proposed method used a median filter to remove noise and a non-linear contrast stretching to enhance the Pap smear images. Then, we used Bradley thresholding for the segmented cervical nucleus. The main impact of this paper will assist doctors in diagnosing cervical cancer based on Pap smear images and increase the accuracy percentages compared to the conventional method. [ABSTRACT FROM AUTHOR]

Pothole detection is a challenging task as it involves inhomogeneity in intensity, complex topologies, contrast less, and noise rich background. Still, it's a very important task concerning the maintenance of roads. Potholes are being detected using computer vision which generally involves analysis of a two-dimensional image of the road or modeling of the road surface. Provided both these methods are independently used. The accuracy of this method of pothole detection is far from the top point. These bring the usage of digital image processing which develops the detection of potholes with at most accuracy. Digital Image processing provides strong receptivity, usage of huge actions, and broad benefits. In this paper, we propose a system that supports digital image processing techniques of high efficiency to result in consistent and accurate detection of potholes. This project studies an intelligent digital industrial pothole detection mechanism to increase human safety, detection capability and decrease the factor of risk. Firstly, the sources of images from the road are pre-processed to remove noise and other irrelevant features. Then a training model was developed to train the system to classify between path holes and normal cracks using convolution neural networks and MATLAB. Also, an IoT device is used in this system which updates the detected potholes on the road to the respective web server along with the location coordinates, as a part to notify a complaint to check. This system works on providing a better pothole detection technique and also updating the same to the concerned. [ABSTRACT FROM AUTHOR]

FPGAs are well tailored for a student to experiment and FGPA boards help to work on high-end applications like embedded, computer architecture and software/hardware co-design and can be used to implement almost any DSP algorithm. Practical work promotes experiential learning. Implementation of image processing is done using many boards where we aimed at working on zynq boards and attain efficient results. Spartan3E and basys3 are the boards belonging to FPGA family which we used aiming image processing which usually necessitates multiple outcomes involving simple algorithms and sometimes sophisticated transformations. Disparate algorithms lead to many applications where we focused on edge detection in different fields. Many steps involve in image processing where edge detection is one of them which leads to object recognition and using for trace recognition which is reliable in our daily applications. Finding the boundaries to recognize an object is the key point required for image processing. Since there are limitations while going for hardware implementation we would like to experiment on conventional operators. Images used in our project cover conventional lena image using sobel, canny, robert and prewitt techniques. Daily life applications are also covered in sports zone and tracking of objects by taking images of a badminton player and SRA building. Our results show that canny operator works efficiently and show less latency than other techniques. [ABSTRACT FROM AUTHOR]

Increasing the speed of an analog and mixed mode signal circuit without compromising power consumption is a difficulty in very large scale integrated (VLSI) design. This study work is carried out to construct a 12-bit Pipeline A/D converter (ADC) with 400MS/s sample rate to fulfil the high computational needs. In order to accommodate various applications, the design is concentrated on determining high speed and resolution in pipeline ADC. The pipeline method's key benefits include ease of implementation, increased speed, and ease of layout design. A pipeline ADC architecture is designed using a proposed method that multiplies the D/A converter, comparator, sample and hold circuit (S/H), and operational transconductance amplifier (OTA). A switching capacitor integrator module is used by OTA to convert a differential input voltage into a current. The insertion of a S/H circuit in the pipeline ADC's initial stage eliminates the need for a separate S/H amplifier. The obtained results indicate that the integral and differential non-linear values are, respectively, +0.61/-0.75LSB and +0.48/-0.55LSB. A value of 64.2 dB of Signal to Noise Dynamic Range (SNDR) and 81.8 dB of Spurious Free Dynamic Range (SFDR) is derived through simulation and plotted. The suggested design for the 45nm gpdk library is designed, simulated, and verified using an EDA platform with a cadence tool. [ABSTRACT FROM AUTHOR]

This study investigates the statistical characteristics of liquid film thickness fluctuations during countercurrent flow limitation on a complex geometry containing a horizontal and inclined pipe which are connected by an elbow. The fluctuation data are obtained by image processing technique which was applied towards high-quality images captured by a high-speed video recording. The result indicates that the thickening of the liquid triggers the liquid blockage that initiates flooding at the upper edge of the hydraulic jump. Moreover, the stochastic analysis reveals that the obtained data imply consistence trends across whole water discharge. Furthermore, it also shows a good agreement to the visual observations. Meanwhile, the wavelet analysis can distinguish energy fluctuations under different water discharges. [ABSTRACT FROM AUTHOR]

In recent years, more information has been transferred in electronic form, so the science of cryptography got more attraction. Cryptography is an efficient and effective procedure to ensure that our data is accessed only by the intended receiver. Also, it makes sure that the data is not adulterated. In this paper, we have proposed hybrid cryptography for colour images which could provide a reliable resistance against differential security attacks. We have observed that the use of the blowfish algorithm and chaos logistic maps could ensure improved image security and performance, providing stable results in most conditions. [ABSTRACT FROM AUTHOR]

The scarecrow system described is a revolutionary crop protection approach combining tradition and innovation. It employs advanced features and user-friendly control to effectively deter animals and birds from damaging crops. The system utilizes sound-emitting speakers, disenchanting light beams, and hands with a flapping mechanism to startle and discourage intruders. The integration of time slots, a GSM module, and Raspberry Pi allows for convenient remote control and precise activation of the scarecrow. Additionally, a standalone image processing method with a camera module and Raspberry Pi detects and responds to animal presence, notifying the farmer and initiating deterrent actions. The system offers energy efficiency through time interval technology and utilizes solar-powered batteries for sustainability. Overall, the scarecrow system provides farmers with comprehensive crop protection and peace of mind. [ABSTRACT FROM AUTHOR]

Image processing is a developing area in communication of 2D signals, where in which image fusion gives a clear and important information or data about a particular image. This retrieval of all the information can be done using wavelet transform because the time and frequency domains are both employed in it. This paper deal with the mathematical formulation of the image fusion algorithm starting from image registration up to the resulting fused image. [ABSTRACT FROM AUTHOR]

The diverse motifs of Lampung Batik require an automated identification process using digital image processing technology. This research aims to identify Lampung Batik motifs by utilizing computer technology, particularly in digital image processing, to assist in recognizing these motifs. In this era of digitization, there are various object recognition methods, one of which is image recognition that utilizes GLCM feature extraction to obtain the characteristics of Lampung Batik motifs. After the feature extraction process on Lampung Batik images is completed, this data is used for classification. This study employs the K-Nearest Neighbor and Naïve Bayes classification methods, with a total dataset consisting of 300 images, comprising 150 images of "siger" motifs and 150 images of "sembagi" motifs. In testing with the K-Nearest Neighbor method, the highest accuracy is achieved at K = 3, approximately 77% for the training data and 81% for the testing data. Meanwhile, in testing with the Naïve Bayes method, an accuracy of around 96% is attained for both the training and testing data. [ABSTRACT FROM AUTHOR]

Corona virus disease (COVID-19) was responsible for inevitable chaos in the human life due to its inherent nature of high contagious across the globe. This very reason also hindered the physicians and healthcare support systems to provide in-time medical aid to the suffering patients.The diagnosis of COVID-19 using clinical information is the prime source for detection and treatment. However, computerised tomography (CT) and Chest X-ray (CXR) images have found useful for screening purposes due to the successful usage of image based analysis by physicians which was aided by image processing based algorithms.Automated means of screening, diagnosis and prognostic studies using CT and CXR with the help of comprehensive image processing pipelines could invariably augment the efficiency in the respective tasks. The main objective of this study is to implement multi-class classification of COVID-19, viral-pneumonia (VP) and lung opacity (LO) using threshold-based segmented CXR images further transformed using wavelet transform (WT)and Laws based texture representation. Machine learning (ML) based implementation and comparison was carried using Random Forest (RF) and Logistic regression (LR) techniques with of the WT and Laws features individually as well as with combined feature vectors. The ML based implementation was aimed to classify the CXR image based on the individual or combined features into COVID-19, VP or LO pathology. The validation of the ML techniques was further carried using confusion matrix and other validation measures. The performance of the RF classifier was superior with WT features than Laws texture features on individual category of features than LR technique. Amarginal improvement was observed in the classifier performance when WT and Laws based features were combined. [ABSTRACT FROM AUTHOR]

India is a country with several sorts of currencies and coins. There is a unique designation for every money domination, there are several identification procedures, categorization approaches, currency recognition that might also comprise bogus detection of currency. Using all approaches for digital image treatment can be accomplished. This document follows a new technique known as super-resolution that contributes to the clarity of a currency. I.e. Low - the image of resolution is transformed into an image with high resolution. The platform used here is Matlab. False notes are one of the major challenges in monetary payments. It is becoming a big barrier for a country such as India. Because of improvements in publishing, a person may quickly create counterfeit notes by using the most recent tools for hardware. It's time-consuming and unpleasant to detect bogus notes manually therefore there's a need for automation techniques that can effectively perform money recognition processes. Here we put our planned notion into practice there are two considered ways: By applying hyperspectral image analysis and the second by extracting various aspects Fake and real monetary notes and we may recognize the fake note from the genuine ones by comparing them with each other. [ABSTRACT FROM AUTHOR]

Image encryption has become an important issue and a new field in image processing. The Hyperchaotic image encryption scheme has advantages that make it more robust against attacks, faster encryption speed, and easy to implement. In this study, a Novel cryptographic algorithm (a new hyperchaotic map) can be utilized for color image encryption with differing formats presentation through high levels of security and efficiency. By using Hyperchaotic Image Encryption, Hyper Logistic (HIEHL), a secure image transfer method can develop. Due to the wide range of hyperchaos, our 3D-Logistic map is more ergodic and unpredictable than the Lyapunov exponent. An encryption scheme based on 3D-Logistic maps is developing to protect color images by substituting pixels in multiple directions. A chaotic sequence separates adjacent pixels from their correlations, resulting in a change in pixel value. The scheme has been tested on different color images and is effective against various threats, image encryption (HIEHL), and data loss. [ABSTRACT FROM AUTHOR]

The primary objective of this research project is to utilise a K-neighborhood to locate breast cancer tumours, after which the results will be compared to those obtained through the use of a Novel Gaussian Naive Bayes method for determining the degree to which accuracy is achieved in image processing. The K-neighborhood method is used to ten pictures selected at random from a larger than 300-picture collection. The same goal underlies the process of calculating accuracy values. For the purpose of the research, images of breast cancer were analysed using K-neighborhood machines and a Novel Gaussian Naive Bayes, each with 20 different sample sizes. Following the completion of the statistical analysis, the threshold of significance for establishing the accuracy of the computations was determined to be a p value of 0.05. When it comes to the identification of breast cancer, the mean accuracy of K-neighborhood is 89.3 percent, whereas the best that Gaussian naive bayes can do is 87.5 percent. We get to the conclusion that the K-neighborhood performs much better than the Novel Gaussian Naive Bayes algorithm. [ABSTRACT FROM AUTHOR]

The main is one of the main directions and goal of image reconstruction technology a predetermined quality, as in improving the quality of an image and restoring its blurred or degraded parts. Overlapping and blocking some part of the image, image quality improvement basically, image acquisition is a subjective phenomenon, and image restoration is an objective phenomenon. Restoration, i.e. restoration and restoration of the degraded image using existing knowledge about the degradation phenomenon, is important. Therefore, restoration methods focus on modeling the distortion and applying an inverse process to restore the original image. This study examines linear, space-invariant recovery models applied to various recovery situations. It also covers the basic methods of image reconstruction from shadows or projections and their use in medical computed tomography, which is an effective image processing tool, especially commercial and useful in biotechnology and medicine. This research paper presents the analysis of Gaussian, Rayleigh, Erlang, Exponential, Uniform, Salt-and-Pepper, Periodic, Estimating noise and their main parameters. [ABSTRACT FROM AUTHOR]

This study aims to detect myopia using digital image processing technology, which can be detected by pupil size. The methods of artificial neural networks, namely the Backpropagation and Convolution Neural Network (CNN) methods are used to analyze the digital images of eyes. The different preparation of data input to be fit for these two methods has been conducted. The test results using Backpropagation ANN give the highest accuracy 71.66%. The test results of the CNN model produce an accuracy value of 77.14%. [ABSTRACT FROM AUTHOR]

The solution of the problem of text recognition on raster navigation maps is considered. The implementation of image processing by the method of colorimetric conversion to grayscale based on the Otzu algorithm for text recognition on navigation maps is described. The input data are files containing training data for the neural network (a set of images of individual symbols and a text document containing training data labels) and files of raster images of navigation maps without tilt. The output data is a vector image containing the results of recognition of text labels and a text file containing all recognized labels. The experimental part of the work was carried out for three types of neural networks (three-layer, five-layer and six-layer) trained on three data sets. The results of the study allow us to conclude about the combination of the number of neural network layers used and the number of data types in the training set. [ABSTRACT FROM AUTHOR]

Segregation is a phenomenon of separating small and large fractions in a mixture, resulting in the presence of coarse aggregate and fine aggregate in an uneven mixture. As a result of the non-uniform distribution, the possibility of potholes, raveling, and cracks in the asphalt of the highway is very likely to occur. Therefore, we need to be able to take preventive measures as a form of minimizing the possibility of this phenomenon occurring. Segregation in asphalt is generally detected through manual visual inspection. However, using the assessment method obtained will tend to choose and take a long time. Thus, this research was conducted to provide a new solution to detect segregation areas in a more credible, faster, and economical way. This solution utilizes digital image processing methods that are still rarely used. In the process, this method will be implemented together with the Support Vector Machine method. Then, the variable that will be used as the main focus is the standard deviation. In this study, we will test the classification of segregated and non-segregated areas on the asphalt road environment at the University of Indonesia. [ABSTRACT FROM AUTHOR]

The advancement made in image processing techniques has gained much attraction among the researchers in recent years. Object detection and classification are the recent topics that annoys the researchers for developing new techniques in this field. Conventional object detection models degrade the performance due to complexity in feature selection process. Thus, the rapid development in deep learning is combined with object detectors to resolve the addressed problems. In present research, we introduce detecting of an object and classification model using improved segmentation and classifier. The aim of the study is to detect the weapon in human object and then classified onto its relevant classes for upcoming testing models. The proposed model comprises of five phases where each phase serves as input to its predecessor output. Initially, the thermal images are collected from a public repository and then preprocessed using median filtering which eliminates the irrelevant noises by replacing median values to its neighboring pixels. Further, Adaptive Histogram Equalization (AHE) is used for improving the finest quality of an image. The preprocessed image is further fed into segmentation model. Here, OTSUs, one of the successful models is employed for deriving edges preserved segmented image. By computing SURF, GLCM and matching features on segmented images, the relevant features are extracted. These extracted features are then given to DCNNs which develops a layering stack and creates training and testing model. Based on satisfied requirements of training features, testing images are classified to its relevant classes. The performance assessment of classification accuracy dictates 84% of efficiency than the prior works which is implemented using Fuzzy Segmentation. [ABSTRACT FROM AUTHOR]

With the advent of few software tools, the field of image processing is moving in new direction to solve the problems in real time. Here, we propose an edge detection system using Raspberry Pi to distinguish the edges of the road and use the same for the speed control. The proposed system detects the path, captures the pictures and direct it to raspberry-pi3. The captured image is converted into edge image using edge detection algorithm. The edge detection is based on Open CV software. Experimental results establish that the method is fast in capturing and converting the images for any real time applications. [ABSTRACT FROM AUTHOR]

In today's world, everyday human-human communication is via messages, photos, images etc. The addition of noise can be while capturing or while transferring the image. While capturing the image, there may be blur present in the image or while transferring the image, there may be noise that gets added to the image in between. Image generally gives a lot of information. To extract or make utilization of this information in the image, the image must be clear. Hence the external noise that gets added to the image has to be removed. There are many techniques such as filtering methods to make the image clearer. This review paper focuses on the types of noise in the image and denoising techniques used in the image processing and different signal processing methods used in image denoising and the results obtained using different methods. [ABSTRACT FROM AUTHOR]

Currently, disease recognition is mostly based on the use of modern technology. Image processing system is one of the technologies that can be used in the process of detecting various diseases. The aim of this research is to build a system that can be used to detect COVID-19 infections automatically using image processing. The Covid-19 disease in the human body has a specific area in the body, namely the lungs. Modern medical science is currently so advanced that X-ray machine can provide digital images of the lungs and with the help of image processing, these images can be identified as normal or infected with COVID-19. The classification method used is logistic regression using parameters of contrast, correlation, energy, homogeneity and entropy. The results show that the system built has succeeded in detecting normal lungs and lungs infected with Covid-19 with 94 percent accuracy [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR) is a non-destructive test used as imaging tool for exploration of shallow subsurface such locating the buried infrastructures. Due to the existence of various subsurface material and environmental noise, such as bricks and tree branch, it is a challenging task to interpret the GPR data into a meaningful information. Thus, this project proposes the hyperbolic recognition of buried pipes in GPR images in the presence of scattering objects. In this framework, the GPR images were firstly subjected to image pre-processing. Then, the GPR images were decomposed using Discrete Wavelet Transform (DWT) to analyze the texture analysis of hyperbola signature. Then, the approximation subband of DWT were extracted and used as features to recognize the hyperbolic of buried pipes and scattering objects presence in GPR images. A series of experiment has been conducted on GPR data collection at Agency Nuclear Malaysia. Based on the results obtained, the average recognition rate of extracted approximation subband of DWT features using k-NN classifier is 99.75%, thus shows a promising results in recognizing the buried pipes in the presence of scattering objects. [ABSTRACT FROM AUTHOR]

Gastritis is a disease that occurs in the stomach and can be dangerous if not treated quickly. Gastritis can affect anyone, young or old. Therefore, a system is needed to identify early gastritis identification so that it can help the community to find out early on their gastritis. The identification method used in this study is the Radial Basis Function Neural Network with several image processing techniques. Tongue image is used as input image for image processing. Prior to the initial identification, the image preprocessing process is carried out, namely cropping, resizing, brightness, image feature extraction using color extraction using Hue Saturation Value (HSV). The dataset used in this study are limited to 150 tongue images and resulted in the ability to identify early gastritis with an accuracy of 93.3% for 50 testing images. [ABSTRACT FROM AUTHOR]

All traditional surgical procedures cause a lot of harm to the patient, while the biggest advantage of minimally invasive surgery is that the wound is small, the pain is small, and the recovery is fast. Recently, minimally invasive surgery has been widely adopted in the medical field and has been widely used as an important technique. Because of the high degree of coordination between the hand and the eye, there is a high demand for the operating ability of the physician. The images obtained by endoscopy do not have sufficient depth and are difficult to localize. The key to improving the visualization of such surgeries lies in optimizing image processing methods and feature point extraction algorithms that augment SLAM (Simultaneous Localization and Map Construction) techniques. This paper reviews several methods, such as the Contrast Constrained Adaptive Histogram Equalization (CLAHE) method, which solves the illumination problem by optimizing the grayscale transformation. Other methods such as wavelet transform, homomorphic filtering, and multiscale bilaterally weighted retina are also discussed for their image enhancement capabilities. For feature point extraction, AKAZE-ORB and SuperPoint algorithms combined with k-means algorithms are emphasized for their advancement in resolving uneven illumination and ensuring accurate extraction. [ABSTRACT FROM AUTHOR]