Journal of Image Processing and Artificial Intelligence

Journal Information
EISSN : 2581-3803
Total articles ≅ 21

Latest articles in this journal

A. Dash, Devasis Pradhan, Hla Myo Tun, Zaw Min Naing
Journal of Image Processing and Artificial Intelligence, Volume 8, pp 14-20;

AI alludes to a developing group of computational procedures connecting with PC frameworks equipped for performing undertakings that would somehow require human insight. Models incorporate the analysis of sicknesses, settling complex numerical conditions, and dissecting electronic circuits. With the end goal of this note, we follow the definition and depiction of essential, high level, and independent computerized reasoning set forward in past EM Compass Notes. AI is the science and designing of making machines shrewd, particularly insightful PC programs. This likewise implies that AI isn't one sort of machine or robot, but a progression of approaches, techniques, and innovations that show savvy conduct by dissecting their surroundings and making moves — with some level of independence — to accomplish explicit goals.
Asharani. R, Naveen Kumar. R
Journal of Image Processing and Artificial Intelligence, Volume 8, pp 1-6;

The progressive image segmentation is one of the necessary stages in image acquisition and recognition for an effective identification of brain tumor in advanced medical equipment’s, any image segmentation algorithms working effectively in distinguishing impaired and malignant information from tomographic images through various classification techniques. There is an ambiguity in segmentation for effective regeneration of disseminated information during investigation and extraction of features like shape, volume, and motions of organs from medical images is essential. Current research in medical imaging is aimed at developing automated image recognition and diagnostic systems, which require efficient image segmentation and quantification tools. This paper made an effort to realize the Time-frequency method of image segmentation and reviewing the findings of existing Medical segmentation techniques for medical images.
Rajesh I S, Bharathi Malakreddy A, Bharati M. Reshmi, Ms. Nazneen Kiresur
Journal of Image Processing and Artificial Intelligence, Volume 8, pp 31-34;

Medical imaging is the practice of visualizing internal human body parts in order to make diagnoses. The discipline of medicine now relies heavily on medical imaging as a tool. Digital image processing and pattern recognition techniques in medical imaging aid in the detection of eye disorders. Changes in retinal Blood Vessels (BVs) are used in retinal image analysis to help diagnose retinal vascular illnesses such Diabetic Retinopathy (DR), Macular Edema, and Glaucoma. The retinal blood vessels are given a brief introduction in this review paper, along with a detailed description of the methods/techniques utilised to segment the retinal blood vessels. This paper also discusses the difficulties in segmenting BVs.
Ms. Veeramallu Satya Sahithi, Iyyanki V Murali Krishna, M V S S Giridhar
Journal of Image Processing and Artificial Intelligence, Volume 8, pp 15-23;

Support Vector Machine is a non- parametric statistical learning theory-based machine learning technique that classifies the data by maximising the margin between two classes by constructing a hyperplane between them. Although SVM’s are well known for binary classification problems, for multiclass classification problems- they use certain kernels, to turn the linear boundaries into non-linear boundaries in a higher dimensional feature space. In this context, the type of kernel used in non-linear SVMs have a profound effect on final performance of SVM classifier. The current study focuses on analysing the sensitivity of various linear and non-linear SVM kernels and parameters used in them for classifying 10 different land use/cover features from remotely sensed space borne hyperspectral image. Forty five different models were tested to analyse the performance of each kernel with varying penalty values for classification of land use/land cover classes. Analysis of the classified maps was made based on the final overall accuracy, error/penalty parameter and degree of polynomial. Experimental results show that SVM with RBF kernel outperformed the other kernels with an overall accuracy of 90.63%, with an error penalty of 100 and a gamma value of 0.006 followed by polynomial kernel of degree 2.
Gopinathan M, SoundarraKumar R, Mohideen A, Kalaiselvi A
Journal of Image Processing and Artificial Intelligence, Volume 8, pp 1-8;

Over the last two decades, lane recognition and tracking has been a hot topic in research, particularly in driver aid applications. It is also an essential method for imaginative, predictive, primarily based fully autonomous vehicle devices. The main thing is to avoid human error, increase safety, reduce road accidents, and save lives. Road lane detection, is a technique for detecting lanes and boundaries. This article explains how to detect and track lanes on the road in an efficient manner. The grayscale conversions are conducted after the camera has been calibrated. The image's edges are subsequently followed by the Canny edge detection method. After that, a region of interest(ROI) is selected. The shape of a triangle that appears in a given location is used to create ROIs. Unwanted are hidden by ROIs. The Hough transform method is then used to identify the accessible track labels. The method has high speed, high accuracy, good dependability, and outstanding robustness, according to the findings of the experiments.
Ms. S. Parameshwari, S. Gopinath, R. Uma Maheshwari, Ms. G. Kowsalya
Journal of Image Processing and Artificial Intelligence, Volume 8, pp 18-23;

We identify the plant disease diagnosis using plant disease datasets. In this dataset, plant disease is recognised by means of visual region. Cluster distribution is used to indicate the level of patch with which from each image the weights of divided patches are calculated. Then finally extract the patch features from the trained network and apply the modified deep learning network which is used to bring a panoramic representation.
Amit Raikar, Sharanabasappa Tadkal
Journal of Image Processing and Artificial Intelligence, Volume 8, pp 10-17;

The current methods for taking attendance are inconvenient and time-consuming. Manual recording allows for easy manipulation of attendance records. A smart and validated attendance system is needed to tackle this problem. In smart attendance systems, face identification, fingerprints, Genetic material, retina, eye retina, gesture recognition, and other biometrics are often used. Humans can be identified by their faces, which have distinguishing traits. Face recognition is one of the most useful image processing applications, and it plays an important part in the technical world. The recognition of a human face is a hot topic for verification reasons, particularly in the context of school attendance. A face detection and recognition attendance system uses face biostatistics known for high monitoring and other computing technologies to identify students. The purpose of creating this system is to replace the traditional technique of documenting attendance by calling names and storing pen-and-paper records with a digital version. An innovative solution for a face recognition-based automatic attendance system is presented in this study. The findings are tabulated in an excel file, and students' attendance is processed depending on the date from the excel file, and relevant conclusions are drawn from this.
Beena Ullala Mata B N, Rishika I. S, Nikita Jain, Kaliprasad C S, Niranjan K R
Journal of Image Processing and Artificial Intelligence, Volume 7;

Utilizing exclusively picture handling procedures, this examination proposes an original strategy for distinguishing the presence of pneumonia mists in chest X-rays (CXR). Collected the several analogue chest CXRs from patients with normal and Pneumonia-infected lungs. Indigenous algorithms have been developed for cropping and for extraction of the lung region from the images. To detect pneumonia clouds first conducted the preprocessing of the image then used the image segmentation techniques like Otsu thresholding K-means clustering and global thresholding and then contour detection algorithm was applied which helped to detect lung boundary, the area’s ratio is used to classify the normal lung from pneumonia affected lung.
T. Ramalingam, R. Umamaheswari, R. C. Karpagalakshmi, K. Chandramohan, M. S. Sabari
Journal of Image Processing and Artificial Intelligence, Volume 7;

Agrarian efficiency is tall on which economy exceedingly depends. Typically, the as it were for cause malady discovery potted plant imperative part during horticulture park, as have to one's name illness in plants are very normal. In case legitimate care is not grip in this zone, its justification genuine impacts on potted plant through which item quality, amount or efficiency is pretentious. For occurrence a malady called small malady could be an unsafe illness establish in pine trees in Joined together condition. Location of plant infection by way of a few self-activating advantageous because it diminishes an expansive production of checking in enormous ranches of riding crop, conjointly it recognizes the side effects of illnesses they show up on plant clears out. This venture presents a calculation for image break-up strategy which is apply for framed spot and classification of plant leaf maladies. It too frames study on diverse maladies categorizes methods that can be utilized for plant leaf malady discovery. Image break-up which is an imperative part for malady discovery in plant leaf malady, is done by use inbred reckon.
C. Saraswathy, S. Sarumathi
Journal of Image Processing and Artificial Intelligence, Volume 7;

Theft is a major cause of violence around the world. Several valuable things are stolen as a result of security issues in the workplace, bank, and home. In previous years, a variety of techniques were used to reduce the risks. Two of these methods are burglar alarms and CCTV recording. However, these methods are inaccurate and disorganized due to a lack of human attention in such processes. Since such a system requires human maintenance to control the data collected by the camera, it presents a greater problem for shop or home owners. The that rate of crime causes people to struggle both financially and emotionally. As a result, there is a need to prevent theft and develop a security system. It has to be simple to use, free of false alarms, human interference-free, and cost-effective. The primary goal of this paper is to serve as both a concise overview and a reference by providing primary knowledge of various techniques used as well as various research opportunities in this field. A variety of methods for identifying the thief using artificial intelligence based on the face and behaviour recognition are demonstrated in this survey.
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