Search

In image processing and computer vision, image segmentation plays a crucial role. It finds a place in many image applications. Texture and colour features and its combination are among the main features used in image segmentation. This paper presents the literature review for colour-based segmentation techniques and texture-based segmentation techniques. The main objective for this paper is to provide the recent trends of both the color and texture segmentation technique in image segmentation as well as presenting the most popular evaluation measurements used for image segmentation. At the end of this paper, the conclusion for future work and its direction is presented. [ABSTRACT FROM AUTHOR]

Now a day face detection & facial expression recognition is one of the major challenging problemin the field of image processing. It is a challenging issue as human color, pose, expression, and orientation varies with regions. Therefore, to detect face & facial expression we need to accept different models & techniques. Detection of different facial expression based on movement of eyes, position of nose & nose tail, gradient of eye brows, movement of lips etc. Detection of facial expression can be done with the help of any neural network or fuzzy logic. This paper reviews different techniques of face detection & facial expression recognition. [ABSTRACT FROM AUTHOR]

Improving Economic growth of developing countries has promptedvarious vehicular movements in the town roads. Traffic management plays a huge part in regularizing vehicle movements and dodging blockings on roads. The pattern of traffic shall not be similar at all instants during the day and furthermore all the days. Huge traffics are constrained by traffic signals that allows and stops the vehicle movement for a pre-programmed time slots. This conventional method adopting the template have downsides. An intelligent electronic traffic controller can handle the traffic with sense consulting with the requirement at any instant of time. The paper briefs a Digital signal processor (controller) based smart traffic control system with the support of image processing techniques. The frameworkcapture the images from various roads meeting at a junction, recognizes the density of vehicles, analyzes between them, provide priorities, decides the dimensions of time slots for stop and permit and manages the traffic insightfully. Anextraordinarily mounted IR gadget on all the roads with the camera givesdata to the controller about vehicles proceeding onwardcrisis (such as ambulance) and deals with the traffic providing priority to such vehicles. The proposed framework was implemented on a proto mother board with TMS320 DSP processor, and the results are as expected, favorable and encouraging for implementing further developments. [ABSTRACT FROM AUTHOR]

From the past few years spreading of the pests and diseases in plants have been increasing significantly. Paddy plants are the most important crop used in our country for the food, so it is most necessary to detect any disease of them within a short period of time for ensuring a proper and healthy growth of paddy plants. The process of manual disease detection requires labor and a large amount of time. Then utilizing the leaf images of plants for recognizing and classifying of diseases is the more focused research topic in the agriculture field. A survey on recognition and classification of paddy leaf diseases using image processing and machine learning techniques is presented in this paper based on the disease infected leaf images of paddy plants. Firstly the concept of various plant diseases and the standard process of plant disease detection are discussed in this paper. A study and survey on the totally 5 papers of work is carried out in detail by covering a work on leaf diseases of the paddy plants based on the certain criteria. Such criteria are different preprocessing techniques, various diseases/ classes, different segmentation methods, various classifiers and accuracy of employed techniques. The experimental results of these various techniques are compared and evaluated to design a best on detecting and classifying of paddy plant leaf diseases. [ABSTRACT FROM AUTHOR]

In Indian agriculture, the monitoring of the productivity rate of each and every Vegetables/Millets takes an important task. The leaves and stems of the plants are affected by the various diseases such as mildew, anthracnose, leaf-spot, etc. Hence, we are in need to monitor the plants effectively to improve the productivity rate of the Indian agricultural products. So, this paper mainly focused on the pumpkin leaf analysis and detection of the diseases those are presented in the pumpkin leaf images. The entire detection process is held with the help of the Image processing algorithms such as fuzzy based pre-processing, Neural Network based classification and CV based feature extraction. The paper presents the detailed study and analysis of all these three stages. In classifier stage, the feed forward neural network of sizes 10, 8 and 5 have implemented to categorize the affected images from the pumpkin leaf dataset. The parameter cross-entropy is mainly employed for validating the classifier performance. The investigational results illustrate that the proposed classifier efficiently works with minimum amount of cross-entropy rate. [ABSTRACT FROM AUTHOR]

Object detection is a significant task that forms the basis for many image processing and computer vision applications. Automobiles and vehicles are engrained in popular culture as one of the most versatile and convenient means of transportation. While it is a convenient and popular means of transportation, it is also quite dangerous. Every year, several people are killed in car accidents caused by another vehicle or object. A Method for Preventing Mishaps Many incidents can be prevented if drivers avoid exposure and evasion, which are caused by things like loss of control, irresponsible driving, exhausted or drunk drivers, and a lack of concentration on the road. The market currently does not provide a standard safety feature in any car, nor does it provide the use of sensory equipment to identify potentially dangerous objects at a defined distance. The goal of this study is to create intelligent driver monitoring software that identifies tiredness and helps to avoid traffic accidents. A driver and a publicly accessible online tracking program The technology measures him if he blinks his eyes. It is done by measuring the distance between the eyes or by staring at specified spots. Python is the programming language utilized in the development of the software system. It uses the OPENCV and Media Pipe libraries, a Media Pipe Face Mesh technique is used in this paper. The system can calculate the time of nap, and it triggers beep sound if the nap occurs in any time. the system take consider, if the driver haver 30 or more of that value in napping. It will be send alarm message IOT system with the current location of driver to driver family. [ABSTRACT FROM AUTHOR]

Now-a-days, Breast Cancer is a serious cause in world and it raises a death ratio in drastic manner. Most of the women are suffered due to this kind of breast cancer disease, in which several literatures are available regarding this breast cancer identification, but all are strucked over certain level. Lots of researchers in industry providing different digital image processing schemes to identify the Breast Cancer in clear manner to save lives of women but all are strucked with real world prediction scenarios. For Breast Cancer identification mammography is a well-known technique, in which it screens the breast cancer cells in clear manner. However, based on the workload and manual expertise failures, a numerous failures and wrong classification procedures reported as well. So, that a novel computer assisted digital image processing technique and smart tool is required to provide intelligent prediction logics to identify the breast cancer on earlier conditions instead of detecting that in complex stages. In this paper, a novel Deep Learning enabled Image Processing tool is designed, called as Digital Image Processing Scheme enabled SmartKit (DIPSK) to identify the breast cancer disease effectively on earlier stages without any human intervention. The prop osed DIPSB enables the system to acquire the input scanning image and process that with DIP techniques with respect to pre-processing feature extraction and classification. The detailed features are cross-validated with the trained samples, in which the Deep Learning procedure called Improved Convolutional Neural Network (iCNN) is utilized by the SmartKitto provide classification process in accurate manner. The final resulting of this SmartKitis more efficient as compare to the traditional image processing schemes and provides accurate prediction on earlier stages. The resulting section provides the proper proof for the classification accuracy and prediction logics. [ABSTRACT FROM AUTHOR]

Rice is a vital staple crop, and the presence of diseases can significantly impact its production. Consequently, there is an increasing demand to create efficient strategies for recognizing and controlling diseases in rice plants. This paper provides a comprehensive review of the recent advancements in disease identification techniques for rice plants, focusing on the integration of image processing, machine learning, and deep learning methodologies. Despite the progress made, there remains a need for further research to identify and classify additional rice plant diseases beyond the major ones discussed in this paper. By continually exploring new techniques and expanding the scope of disease identification, researchers have the potential to enhance the overall effectiveness of rice disease detection systems. This review serves as a valuable resource for researchers and practitioners in the field, offering insights into the current state of the art and suggesting future directions for advancements in the identification and management of rice plant diseases introduced. [ABSTRACT FROM AUTHOR]

Underwater images often suffer from colour distortion, blurred details, low contrast, and a bluish or greenish tone due to the absorption and scattering of light in the underwater medium. These factors significantly impact visibility in underwater environments. Over the past few decades, numerous researchers have developed image restoration and enhancement methods to improve the quality and enhance the details of underwater images. This research paper provides an overview of various typical methods for underwater image restoration and enhancement which are thoroughly discussed. The evaluation aims to assess the performance and effectiveness of the methods. Finally, the research process of underwater image restoration and enhancement is summarized, and suggestions for future research directions are proposed to guide further advancements in this field. Underwater image processing is an emerging field of research with significant potential for aiding developers in exploring the underwater environment. This paper aims to provide insights into the degradation of underwater images, survey the state-of-the-art intelligence algorithms, for underwater image enhancement and restoration. It demonstrates the performance of various methods for underwater image dehazing and colour restoration and introduces a metric for evaluating the colour accuracy of underwater images. Furthermore, it offersan overview of significant applications of underwater imagery. [ABSTRACT FROM AUTHOR]

The main objective of image compression in the digital era is to reduce the size, of irrelevant and redundant data in digital images. this is able to keep and transfer the image in an efficient manner. In the present digital world, multimedia files have large sizes and consume a lot of space, and are also very difficult to transmit. Image compression plays a prominent role to reduce the size of the images and transmit such compressed data in a permitted time duration. This paper addresses image compression using machine learning methods. Machine learning is one of the subsets in artificial intelligence, in recent days machine learning plays prominent role in image processing. Using machine learning algorithms in image compression provides better-quality images and improves the different performance metrics. This paper attempts a study various machine learning algorithms as well as image compression techniques. [ABSTRACT FROM AUTHOR]

Various object detection models are presented in this paper. Definition of object is well defined in order to understand object detection models. Now-a-days the object detection which is computer vision and image processing related technology is widely used. Applications of object detections are infinite as it can be used all over like objects tracking, surveillance of videos, face recognition, detection of anomaly, counting of people, and the records still continue to many more. This paper compares various object detection algorithms the basic splitting is based on machine learning based and deep learning based. Fundamental objective of the paper focuseson Single stage object detectors (SSD) like You Only Look Once (YOLO) and Two stage object detectors as RCNN, Fast RCNN, etc. This paper's main goal is to highlight such object detection models and their use in various fields. [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]

Texture identification and classification is an ever existing problem in the image processing scenario over the past and the present decades. A statistical design of experiments approach for texture identification and representation has been successfully attempted for a larger grid size of an image. There were four groups of feature vectors (FV) suggested for texturedescriptors. The first FV consisting of off - the main diagonal elements in the interaction groups which are considered to be the feature descriptors in a larger grid under consideration while using the design of experiments based approach. In this paper, the proposed texture descriptor has been demonstrated to work efficiently for the classification of texture images in a supervised manner. The global descriptor (FV) is used for representing the texture content in the image to be classified. The main idea of classification demonstrated in this proposal, between the texture images and the reference image is that the texture from the same group will have zero difference or near zero in the distance measure between the corresponding global descriptors(FV). The integrated absolute difference (IAD) has been used for identifying the closeness between regions and the reference texture through respective FVs. The proposal has been successfully applied for a number of texture images collected from various standard textural albums and the results are reported with the available results. The scheme is being extended for identifying the texture classification with other FVs and in an unsupervised manner also. [ABSTRACT FROM AUTHOR]

Texture segmentation is an ever existing problem in the image processing context. A statistical design of experiments based texture identification and representation has been successfully attempted for a larger grid size of an image. There were four group of feature vectors (FV) suggested for feature descriptors. The first FV consisting of off-the main diagonal elements in the interaction groups which are considered to be the feature descriptors while using the design of experiments based approach. In this paper, the proposed texture descriptor has been demonstrated to work efficiently for the segmentation of texture images into meaningful texture regions based on the textural contents. The global descriptor (FV) is used for representing the texture content of the regions in a textured image to be segmented. The main idea of segmentation employed in this proposal is that the distance between the adjacent textured regions represented by their corresponding feature vectors will be very minimum than that of between two different types of textures. The integrated absolute difference (IAD) has been used for identifying the closeness between regions by their FVs. The proposal has been successfully applied for a number of target texture images to be segmented. The target images are generated by combining texture images collected from various textural albums. The over and under segmentation results are obtained based on the thresholds chosen to confirm the valid difference. All the results are demonstrated. The scheme is being extended for performing the texture segmentation with other FVs and their hybrid version when both untextured and textured regions are present in an image. [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]

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]

Skin diseases are among the most common and widespread diseases in the world, with people developing them as a result of inheritance, ageing, hormones, allergic reactions, sun or toxic chemical exposure, and environmental factors. Despite its prevalence, diagnosis is extremely difficult due to subtle differences in skin tone, hue, and hair presence. Many people ignore the effects of skin disease when it is in its early stages. Skin diseases are currently diagnosed using a biopsy procedure, which is then examined, and medications are administered manually by physicians. To avoid manual inspection and provide accurate results in a timely manner, this paper proposes a skin disease detection method based on image processing, Python, and the Yolov3 tool. The patient can provide an image of the infected skin area as an input to the system. On this image, image processing techniques are used, and feature values are extracted and detected, as well as the disease being analyzed. The proposed system is especially useful in areas where dermatologists are scarce. The paper describes a method for identifying four types of skin disease: acne, melanoma, blisters, and cold sores. The primary goal of this method is to achieve the highest possible level of skin disease prediction accuracy. [ABSTRACT FROM AUTHOR]

The aim of this paper is to carry out work those results in a good friend to farmers in every way. The work primarily recommends a high-yielding crop to the farmer based on regional soil data and the farmer's requirements. The job entails analysing a database of different types of soil from a specific area. To improve the analysis' efficiency, a machine learning method called linear regression is used. The study also implements disease detection in crop leaves using image processing techniques and provides remedy/solutions to farmers with diseased leaves, which promises to be a preventative measure in the future. The study carried out has an effect on various cultivation methods for various crops, assisting farmers as a "SAHA PATI" for this work. This service assists farmers in determining the best method of cultivation based on climatic and geographical factors. The final section of the Paper introduces a programme that provides current market prices for various crops by connecting to a government website that is updated on a regular basis. This service assists farmers in avoiding being duped by business intermediaries. The overall work done with the agriculture-based component turns out to be the best application for the current situation. [ABSTRACT FROM AUTHOR]

Now-a-days people are working recklessly, busy with their hectic schedule and working for long hours in order to live happy. The road accidents today occur due to either drowsiness or fatigue of the driver. There are methods to detect drowsiness of the driver and to prevent accidents. This paper explains the method using Physiological Measurement. The human physiological phenomenon is based on accuracy. It can also be used but is expensive and difficult to implement. This paper proposes a model for detecting the drowsiness of the driver by implementing the technology that provides high security to the driver and people around using OpenCV and image processing by detecting face and eye plots of the driver using the software and alerting them when they are drowsy and fatigue. This system will detect drowsiness of the driver by implementing the technology that provides high security to the driver and people around using OpenCV and image processing by detecting face and eye plots of the driver using the software and alerting them when they are drowsy and fatigue. [ABSTRACT FROM AUTHOR]

Agriculture is essential for everyone to promote sustainable development, the farming, which combines image processing, artificial intelligence, Deep learning, and Internet of Things (IOT). World population incensing every day. Due to the rising demand in the Agriculture industry, the need to collectively improve a plants and growth its field is very useful. In this paper, it is important to maintain the crop during its initial time, and also at period of harvesting. The image processing and artificial networks are used as a different techniques to maintain the detecting the diseases on the leaves and correct time to harvesting. When we take images with help of drones, the images are divided and changed to disease described three things vectors namely the first one is color, one more is texture and morphology. The vectors morphology gives 95% accuracy and its give more compare to other two vector features. This research paper proposed effective and useful algorithms for detection of disease with help implementation of Artificial Neural network algorithms using MATLAB. Detection of leaf or plant diseases with some manual techniques are requires a lot of work by maintaining a huge farm of crops, and it's very early stages it detects different types of symptoms to different diseases on plants, when the displayed on crop leaves. In this research paper survey on various disease classification techniques that can be for plant leaves diseases detection. For this purpose Artificial Intelligence, Neural network algorithms and back propagation techniques for adjustment of training data sets. [ABSTRACT FROM AUTHOR]

Human face recognition is a branch of biometric authentication that has been commonly used in a variety of applications, including video monitoring, human-computer interaction, door control, and network security. This project describes a method for a Student Attendance System that uses Python and Computer Vision to combine with face recognition technology. It recognizes the faces in the picture and compares them to the database's enrolled faces. The attendance register is identified as present when a registered face is found in the acquired picture collections. Since the device is based on an open-source image processing library, it is not hardware or software vendor dependent. Computer vision is a broad field of study that involves extracting information from images. Human activity identification is an example of an area that is currently undergoing extensive study. This model has a wide range of applications in both academic study and real-world applications. A two-model solution using a computer vision and face recognition module is proposed in this paper. The feature vectors for each video are gathered using computer vision, and the video operation is classified using the face recognition module. [ABSTRACT FROM AUTHOR]

To keep up with rapid human population growth, food demand should increase. Quickly spreading illnesses can fundamentally affect plant yields, in any event, slaughtering whole harvests. As a consequence, detecting and preventing disease at an early stage is critical. Traditional approaches depend on lab studies and human experience, both of which are typically costly and in short in supply in most developing countries. As smartphones have become more popular, even in remote regions, researchers have turned to automated image processing to identify plant disease. This paper compares deep learning to conventional machine learning algorithms and explores the most recent developments in this field. AI, crop infections, and deep learning are a portion of the terms that ring a bell when considering this exploration. [ABSTRACT FROM AUTHOR]

Farmers grind away in the fields to take home just an exiguous income. Now, the agriculture industry is already a burdened industry with uncertainties from all directions. One of the major issues the industry is facing is crop loss. In India, every year around 30-40 per cent crops are lost. This has adverse effects on food security of the highly populated nation. For that matter, India is the 16th hungriest country in the world. Thus, there is a need to identify, understand and overcome agronomic losses. Extreme climate events and crop diseases are the two major reasons for crop losses. While climate events are less controllable, plant disease prevention becomes an important area of interest. Detecting plant diseases at early stages using images serves as key to preclusion of severity. In this paper, we have surveyed some of the significant papers on plant disease detection using image processing. [ABSTRACT FROM AUTHOR]

Object localization, computer vision, video reconnaissance frameworks, and other applications all depend on image processing techniques, which are a basic component of today's computer technologies. Image segregation is a crucial step in image handling. The most popular method for breaking up images into several little pieces called fragments is termed image segregation. Image handling makes to work on the image portrayal to break down the images. A vast array of algorithms are created for segmenting images based on the unique characteristics of each pixel. In this paper various algorithms of Segregation can be evaluated, examined lastly drill down the examination for every one of the algorithms. This correlation study is helpful for expanding precision and execution of Segregation strategies in different image handling areas. The objective of the paper is to study on examination inside a wide image and study on Image segregation. [ABSTRACT FROM AUTHOR]

Underwater image suffers from color attenuation, foggy details, fain contrast and bluish or greenish color resulting from light scattering and absorption in the water environment. These factors, in turn, affect the quality of the underwater images. Image processing is a method for improving the visibility of the input image to be clear to the recipient. This involves increasing the density of the image, sharpening the edges, color correction, removing noise, filtering, and so on. In this paper, an overview of the underwater image restoration methods have been introduced which classified into two categories: restoration images in spatial domain and restoration images in frequency domain. Moreover, number of published works on these two fields have been introduced. Finally, Number of conclusions have been pointed from the analysis of the introduced papers. [ABSTRACT FROM AUTHOR]

Curvelet Transform typically offers significantly superior performance in image analysis, multi-resolution and multidi-rectional representation as compared to Wavelet Transform. This paper exploites strong relationship between Wavelet Transform and Curvelet Transform. Also we use mother Wavelet to construct Curvelet Transform as an extension of Wavelet Transform which has broad implications, particularly for the field of signal and image processing. [ABSTRACT FROM AUTHOR]

The crime rate has been rising at an unprecedented rate, and security has become a big concern in ATM machines. Face detection is the most common biometric technique due to its non-invasive nature. It's been used in a variety of fields, including camera auto focus, attendance, crowd monitoring, object tracking, security, system, etc. Face detection systems uses image processing techniques that are being learned to operate reliably in a variety of conditions, including changes in posture, lighting, skin color, occlusion, and face spoofing. The face detection system has become increasingly vulnerable to occlusion. Occlusion refers to the deliberate shielding of one's face with a helmet, sunglasses, scarves, or other items in order to avoid being caught. These issues have a significant impact on the development of image processing techniques and system's performance. In this paper the Haar Cascade Classifier (HCC) scheme is projected for face detection where precision as well as minimal processing time are important factors for ATM. The proposed scheme uses deep learning models such as Convolutional Neural Networks to enhance the reliability in feature extraction plus classification of images. Face biometric access control devices are becoming more common in everyday lives, but they remain vulnerable to spoofing attacks. This paper also proposes face spoofing identification using Local Binary Pattern (LBP) that has useful features for face detection. The proposed spoofing attack detection technique has yielded encouraging results. [ABSTRACT FROM AUTHOR]

The image processing area has become increasingly important in real-time applications in the current world. Such image processing techniques assist in carrying out processes on digitized images in order to deliver superior results. There are a variety of algorithms for data classification, some of which are rule-based and others which are learning-based. Various image analysis techniques and related challenges in the medical field are investigated in this research. This paper presents effective strategies for overcoming the limitations of image analysis approaches, as well as a brief discussion of image pre-processing before focusing on Image Classification and Segmentation. Our research can help readers learn more about many aspects of medical image analysis. This paper will review the most relevant studies on this topic to date and will describe existing image analysis methods for Data Preprocessing, Image Classification, and Image Segmentation. Image analysis will help readers understand how to increase the performance of their models and expand constrained datasets to take use of the possibilities of other data sets. [ABSTRACT FROM AUTHOR]

In the restaurant, the waiters keep the record of customers' orders and then go to the kitchen to prepare the ordered food menu. The cashier calculates the total bill amount. When several customers increase, then the workload of waiters is also increased to avoid misplacing the order and misunderstand the menu. Hence the overall service quality may degrade. Therefore, we shall implement the enhancing technology that replaces the older way of taking orders by pen and paper into advanced technology like touch screen menu display. The IoT method of ordering food has been adopted in our system. This system tends to provide quality dining table service. This system has a Machine Learning (ML) technique-based image processing model embedded with the IoT cloud for on-table touchable menu lists and using fingers. The customer gives the order to the restaurant server for the smart serving. [ABSTRACT FROM AUTHOR]

This paper presents a new machine vision based technique for self-navigation of agricultural mobile robots in the agriculture field. The proposed method for staunch detection of incessant crop rows based on mask region based enhanced self-navigation technique. It utilizes a camera module to self-navigate this mobile robot in a structured agricultural field by following incessant crop rows. First, a camera was fixed in the proper location of the mobile robot to obtain optimized navigation orientation. Second, enhanced image processing techniques were utilized to acquire optimized navigation base lines. Finally, an optimized navigation base line was extracted from the mask region based enhanced self-navigation technique. This self-navigation technique was utilized to perform operations like spraying water, fertilizing, weeding etc. The proposed technique requires 160ms to detect the masked area of crop rows. Experimented results shows, this technique can instantly and preciously find the incessant crop rows even under various light conditions. [ABSTRACT FROM AUTHOR]

Image denoising is one of the most fundamental difficulties in image processing and computer vision, with the objective of recovering the original image by suppressing noise from a noise-contaminated version of the image. Image noise can be created by a variety of internal (sensor) and external (environment) factors that are difficult to prevent in real-world settings. As a result, image denoising is useful in a variety of applications, including image restoration, visual tracking, image registration, image segmentation, and image classification, where recovering the original image content is essential for good performance. While various methods for image denoising have been proposed, the problem of image noise suppression remains unsolved. The purpose of this paper is to present a comprehensive study on vector median filters for removing the adverse impact of impulse noise from color images. Color images are vectored value signals that are nonstationary in nature. As a result, nonlinear filters such as vector median filters outperform linear filters. In the literature, a variety of nonlinear filters have been proposed. [ABSTRACT FROM AUTHOR]

Hyperspectral image processing is a field of investigation for electromagnetic spectrum to obtain spectrum for each pixel inside the image of a scene with the reason of locating, figuring out and detecting the overall analysis of the image. Anomaly detection makes use of such bands that incorporates precise traits carefully associated with goal gadgets. This paper affords a take a look at on the detection of anomalies for Hyper Spectral pictures based on nicely-designed dictionaries: heritage dictionary and capability abnormality lexicon. The detection method used is "Joint sparse representation" (JSR)-based dictionary selection. This paper approaches for the quantitative analysis of the receiver operating characteristics of the different Hyperspectral data sets. For the quantitative evaluation of this data via the implemented method with the various other methods, the ROC curves with point sensible self-assurance intervals had been plotted. Low Rank Sparse Representation method adorns an amazing overall performance at the AVIRIS-1, AVIRIS-2 and HYDICE sets giving a qualitative comparison of 95.25% Experimental analysis pointed out to the matter of fact that detecting anomalies based on potential abnormality and historical past lexicon production is capable of attain advanced consequences in comparison with different winning strategies. [ABSTRACT FROM AUTHOR]

Theo Jansen model is going to be much popular and efficient mechanism in Robotics Era. This mechanism is based upon the leg mechanism. Most of the researchers showing their attention towards this model to make more practical and efficient. This model has more energy efficiency. Theo Jasen mechanism can actually seem like a walking robot. In this paper, we present simulation of mechanism through solid works and implementation in real life with image processing and working of esp32 cam and esp8266 and live transmission through host and MATLAB. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of automated ambulance detection system. It suggests a methodology that makes use of the technologies such as embedded system and image processing. The vehicle detection system captures the images and movements of ambulance and passes the respective signals to the receiver in the Arduino board. Currently, traffic control has become an issue and to add on, the ambulance movements also results in adverse traffic mismanagement. Hence, we propose the detection and control system that mainly focuses in capturing the traces of ambulance and pass the appropriate signals to the embedded system used, that is the Arduino Board and the response is noted in the Emergency Signal Display Board. In addition to this, a display is kept at the traffic police polls to reveal the current status of signal. This can serve to control the violation of traffic rules while an ambulance is on move. [ABSTRACT FROM AUTHOR]

The technological advancements made in the Information Communication and Technologies (ICT) system has impressed the smart classroom management applications. The communication networks play a vital role in connecting the different entities of a smart classroom system. Detecting (or) predicting cheating behavior during online examination at an earlier stage is a never-ending challenging task. This paper is an extensive survey on cheating behavior detection in smart classroom management using object recognition models. The conducted survey covers the significance of cheating behavior detection by means of object recognition approach and the image processing techniques. The survey analysis states that the research challenges such as representation of objects, constancy in scenes, classification of features, searching complexity and the presence of objects and their occlusion needs to be addressed while developing an intelligent cheating behavior detection process. [ABSTRACT FROM AUTHOR]

Identification and classification of plant diseases is a major field of study, as most people in India depend on agriculture for their main source of income and food. This is one of the reasons why the identification of plant diseases plays a significant role in agriculture. It is useful to detect plant disease through any automated technique as it eliminates a significant amount of monitoring work in large crop farms and detects the symptoms of diseases at a very early stage, i.e. when they appear on plant leaves. The framework mainly involves different concepts related to image processing, such as image acquisition, image pre-processing, and extraction of features, database formation, and artificial neural network classification. This paper covers research on various methodologies for the use of neural networks to detect plant leaf diseases. [ABSTRACT FROM AUTHOR]

This paper is purposed to manage the safety of the pedestrian while crossing the Zebra Crossing. The vehicles are supposed to stop behind the standard line as soon as the traffic signal turns red. Nevertheless, people often neglect the rules and regulations, they tend to cross the crosswalk henceforth people crossing the road ought to face consequences. So, this surveillance system will ensure that pedestrians cross the road safely and there is no problem in crossing the traffic junction. This Paper involves several devices which will work together in a single module to ensure the safety of the pedestrians on the road which is very important for the betterment of the local citizens and our paper justify it. The traffic signal area will be monitored by a surveillance camera and PIR sensor all the time, With the help of Image processing and IoT, the proposed system will check if anyone tries to break the traffic rule; an SMS will be sent to the vehicle owner regarding violating the rule and will be fined. This creates a systematic and organized traffic rule management system. [ABSTRACT FROM AUTHOR]

Medical image processing requires image segmentation and image registration as important diagnostic tools. Image segmentation aims to split structures of interest into distinct components. Meanwhile, image registration finds an ideal transformation between given images. Despite their interrelationship, both tasks are often performed separately. As a result, it faces several problems when undertaken independently. Therefore, the joint models between segmentation and registration have advantages over the separate tasks, particularly when dealing with noisy images. This paper reviewed and compared three existing variational joint segmentation and registration (JSR) models based on active contour without edges and linear curvature. The models are tested on 2D mono-modal and multi-modal images with the presence of white Gaussian noise. The Dice coefficient metric and the registration performance measure are used to evaluate the performance of the models. Numerical results showed that the JSR model using the modified sum of the squared difference outperformed the other two JSR models for noisy mono-modal images. However, the JSR model using the modified normalized gradient fields performed better than the JSR model based on the modified mutual information for noisy multi-modal images. [ABSTRACT FROM AUTHOR]

Food production and distribution in the food industry require efficient organization. However, manual grading of agricultural produce faces challenges of subjectivity, inefficiency, and inconsistency. To address these issues, this paper proposes a food identification algorithm that utilizes image processing for agricultural technology to identify the type and grades of food products. The methodology involved capturing images of food produced using an ESP32-CAM camera module connected to an Arduino MEGA 2560 board. The images were processed through algorithms to identify the food produced and grade them based on size. The color of the image was extracted by calculating the Euclidean distance of RGB values in the image. The type of food product was identified using the perimeter and the Euclidean distance of RGB values. Then, the algorithm extracted information about the area from the image and provided the value of the area, which determined the grade of the product. The results and discussions present the test outcomes of the food identification and grading algorithms. The food identification algorithm successfully recognized tomatoes and mangoes from the captured images, achieving high accuracy. The food grading algorithm effectively categorized the produce based on the food product's area into Grade A (Big Food Product) if the food product's area is more than 10,000 pixels, and Grade B (Small Food Product) if it is less than 10,000 pixels. In conclusion, the proposed image processing-based food identification and grading system demonstrated potential in automating the food grading process. It provided reliable results in identifying and categorizing an agricultural produce based on visual attributes. [ABSTRACT FROM AUTHOR]

This study looks into a number of recently developed pre-trained models and assesses how well they perform using the ImageNet Dataset. In the disciplines of image processing, computer vision, and machine learning, image Classification is a well-known issue. In this study, we studied six different models—three transformer-based models and three convolutional neural network-based models. The findings of this research offer insightful information on the perks and drawbacks of using pre-trained models for image classification tasks. The results indicate that while transformer-based models exhibit promising outcomes, convolutional neural network-based models continue to perform better overall and with regard to accuracy. Additionally, the pre-trained models' performance may be greatly enhanced by fine-tuning them on a smaller dataset. This paper provides an assessment of recently developed pre-trained models and their performance on the ImageNet dataset, which adds to the current research in the fields of image processing, computer vision, and machine learning. The knowledge gathered from this work can guide the creation of models for image classification tasks that are more effective and accurate. [ABSTRACT FROM AUTHOR]

The development of aberrant cell proliferation in the lungs is a problematic condition that has the potential to result in death. On the list of diseases that most frequently result in mortality, lung cancer takes first place. The early stages of lung cancer are notoriously difficult to diagnose due to the fact that cancer cells with dimensions less than very small are notoriously difficult to spot by imaging. If the cell abnormalities are discovered in the early stages, it will be possible to begin therapy sooner, which will result in an improved chance of the patient surviving the illness. Several different image processing strategies can be utilized in the diagnostic phase of patient care to help spot signs of disease. In this paper, classification of Lung Cancer from chest X-ray images has been done using optimized Convolutional Neural Network (OCNN) and Ant Lion Optimization (ALO) algorithm. In pre-processing step, the contrast of all images are enhanced using Histogram Equalization (HE) method and the noises are removed from all images using Median Filtering. After the pre-processing step, feature extraction is performed using Gray Level Spatial Dependence (GLSD) to extract the statistical features. The feature vector is then trained and classified using OCNN-ALO algorithm. The ALO algorithm is used to optimize the hyper parameters of CNN layers. It classifies the lung images into normal and lung tumor affected. Performance results have indicated that OCNN-ALO attains the superior performance with 95.15% accuracy, 85.43% sensitivity, 93.4% specificity and 76.43% F1-score. [ABSTRACT FROM AUTHOR]

Advancements in deep learning and computer vision have resulted in a surplus of counterfeit facial content that is incredibly realistic in appearance, which is controlled by artificial intelligence., for example, Deep Fake or Face2Face that control facial personalities or on the other hand articulations. Counterfeit appearances made utilizing Generative Adversarial Networks (GANs) are becoming increasingly sophisticated and difficult to distinguish. The synthetic faces were designed primarily for amusement purposes, but their abuse has created emotional harm. For example, a few celebrities have fallen victim to Deep Fake's false erotic entertainment. Concerns are also growing regarding false political debate recordings generated by Face2Face. Building models that can identify fake faces in the media is essential for maintaining personal, societal, political, and international security. The purpose of this paper is to suggest a novel way to identify these counterfeits on the internet using Convolutional Neural Networks (CNN) and image processing techniques. We first used Contrast Limited Adaptive Histogram Equalization (CLAHE) and Complex Shearlet-based edge detection on the dataset and then used the resultant images obtained to train a proposed CNN to categorize fake and real personalities and separate them. [ABSTRACT FROM AUTHOR]

Among all foods, rice is the one that people from different cultures eat the most of. When rice is of high grade, its price rises sharply. As it is, a physical evaluation method using the unaided eye is used to evaluate the rice variety & grade. Nevertheless, this approach is laborious, time-consuming, depends on human expertise, and poses risks to the investigator's wellbeing. This paper proposes a technique that employs computerized methods of image processing to automatically detect and categorise rice grains, therefore addressing the aforementioned problems. This image processing method is ideal because it does not involve any physical touch while photographing the rice grains. To evaluate rice quality, a CNN is utilised to pre-process, partition, & retrieve characteristics from the capturand SVM algorithmic classifiers. Our comparative analysis shows that the suggested system categorization outperforms the alternative. [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]