2021- 2022 IEEE Python Image Processing Projects

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Abstract

In recent years, due to the booming development of online social networks, fake news for   various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This paper addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators and subjects. This paper introduces a novel gated graph neural network, namely FAKEDETECTOR. Based on a set of explicit and latent features extracted from the textual information, FAKEDETECTOR builds a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously.

 

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Abstract

Object detection, which aims to automatically mark the coordinates of objects of interest in pictures or videos, isan extension of image classification. In recent years, it has been widely used in intelligent traffic management, intelligent monitoring systems, military object detection, and surgical linstrument positioning in medical navigation surgery, etc.COVID-19, a novel coronavirus outbreak at the end of 2019,poses a serious threat to public health. Many countries require everyone to wear a mask in public to prevent the spread of coronavirus

 

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Abstract- In developing or poor countries, it is not the easy job to discard the Tuberculosis (TB) outbreak by the persistent social inequalities in health. The less number of local health care professionals like doctors and the weak healthcare apparatus found in poor expedients settings.The modern computer enlargement strategies has corrected the recognition of TB testificanduming.  In this paper, It offer a paperback plan of action using Convolutional Neural Network (CNN) to handle with um-balanced; less-category X-ray portrayals (data sets), by using CNN plan of action, our plan of action boost the efficiency and correctness for stratifying multiple TB demonstration by a large margin It  traverse the effectiveness and efficiency of shamble with cross validation in instructing the network and discover the amazing effect in medical portrayal classification.This plan of actions and conclusions manifest a promising path for more accurate and quicker Tuberculosis healthcare facilities recognition.

 

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Abstract : Global health has been seriously threatened due to the rapid spread of the Coronavirus disease. In some cases, patients with high risk require early detection. Considering the less RT-PCR sensitivity as a screening tool, medical imaging techniques like computed tomography (CT) provide great advantages when compared. To reduce the fatality CT or X-ray image diagnosis plays an important role. To lessen the burden of radiologists in this global health crisis use of Computer-aided diagnosis is crucial. As a reason, automated image segmentation is also of great benefit for clinical resolution assistance in quantitative research and health monitoring.

 

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Abstract—Thyroid disease is one of the most common diseases among the female mass in Bangladesh. Hypothyroid is a common variation of thyroid disease. It is clearly visible that hypothyroid disease is mostly seen in female patients. Most people are not aware of that disease as a result of which, it is rapidly turning into a critical disease. It is very much important to detect it in the primary stage so that doctors can provide better medication to keep itself turning into a serious matter. Predicting disease in machine learning is a difficult task. Machine learning plays an important role in predicting diseases. Again distinct feature selection techniques have facilitated this process prediction and assumption of diseases. There are two types of thyroid diseases namely 1. Hyperthyroid and 2.Hypothyroid. Here, in this paper, we have attempted to predict hypothyroid in the primary stage. To do so, we have mainly used three feature selection techniques along with diverse classification techniques. Feature selection techniques used by us are Recursive Feature Selection(RFE), Univariate Feature Selection(UFS) and Principal Component Analysis(PCA) along with classification algorithms named Support Vector Machine(SVM), Decision Tree(DT), Random Forest(RF), Logistic Regression(LR) and Naive Bayes(NB).   Thus it’s deduced from our research that RFE helps each classifier to attain better accuracy than all the other feature selection methods used.

 

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Abstract—With the increasing demand of social life in today’s world one famous platform known to be as Twitter plays important role for every citizen to connect socially, whether in the form of tweeting a tweet for other person or exploring various fields in the running world. But this platform, now a days gets infected by some spammers, with the intention of increasing traffic in their spam web sites they connect their URL with the informative tweet where there is no relation between the content present in URL and the tweet message, these are called spammed tweets. This paper provides a unique approach to detect whether the tweet mentioned by the user is spam or not spam using the encoder decoder technique used with vectorizer converter on the mentioned tweets and on its linked URL, which leads to the prediction of similarity score between them.

 

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Abstract— Cardiovascular diseases (CVD) like stroke and heart attack have a higher risk to cause the life of human beings in heart diseases category. The challenge to predict cardiovascular diseases is high risky by using the hospitalized patients Intensive Care Unit (ICU) data. The recent technologies play a vital role in the medical industry to predict various health causes using various algorithms. Machine Learning (ML) technologies are one among them to predict the diseases using quality dataset and data analysis model. In this paper, we propose a model to find the similarity by using Hierarchical Random Forest Formation with Nonlinear Regression Model (HRFFNRM). By using this model, which produces 90.3% accurate prediction in cardiovascular diseases.

 

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Abstract— Sending and receiving e-mails have continued to take the lead being the easiest and fastest way of ecommunication despite the presence of other forms of ecommunication such as social networking. The rise in onlinetransactions through email has globally contributed to the increasing rate of spam emails relatively which has been a major problem in the field of computing. In this note, there are many machine learning techniques available for detecting these unwanted spams. Each algorithm only utilizes limited features and properties for classification. Therefore, identifying the best algorithm is an important task as their strengths need to be weighed against their limitations. In this paper we explored different machine learning techniques relevant to the spam detection and discussed the contributions provided by researchers for controlling the spamming problem using machine learning classifiers by conducting a comparative study of the selected machine learning algorithms such as: Naive Bayes, Clustering techniques, Random Forest, Decision Tree and Support Vector Machine (SVM).

 

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Abstract — Primary sector yield is the division of GDP on which the Indian Economy depends vastly, agriculture being the major contributor. This is one of the reasons that securing this income is of the utmost importance since diseases can affect the yield. Automating the process of detecting plant diseases can help in identifying the diseases in early stages and reduce their effects on the yield. In this paper a comparison between five widely known and new pre-trained Convolutional Neural Network (CNN) architectures viz. GoogLeNet, ResNet, ShuffleNet, ResNeXt and Wide ResNet is made along with their ensemble with different weights with respect to the detection of three paddy leaf diseases viz Blast, Bacterial Leaf Blight (BLB) and Brownspot. The models were compared on performance parameters like loss and accuracy of trained model.

 

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ABSTRACT- COVID-19 outbreak has put the whole world in an unprecedented difcult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19’s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Articial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Articial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specic inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

 

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ABSTRACT—Object detection, which aims to automatically mark the coordinates of objects of interest in pictures or videos, is an extension of image classification. In recent years, it has been widely used in intelligent traffic management, intelligent monitoring systems, military object detection, and surgical instrument positioning in medical navigation surgery, etc. COVID-19, a novel coronavirus outbreak at the end of 2019, poses a serious threat to public health. Many countries require everyone to wear a mask in public to prevent the spread of coronavirus. To effectively prevent the spread of the coronavirus, we present an object detection method based on single-shot detector (SSD), which focuses on accurate and real-time face masks detection in the supermarket. We make contributions in the following three aspects: 1) presenting a lightweight backbone network for feature extraction, which based on SSD and spatial separable convolution, aiming to improve the detection speed and meet the requirements of real-time detection; 2) proposing a Feature Enhancement Module (FEM) to strengthen the deep features learned from CNN models, aiming to enhance the feature representation of the small objects; 3) constructing COVID-19- Mask, a large-scale dataset to detect whether shoppers are wearing masks, by collecting images in two supermarkets. The experiment results illustrate the high detection precision and realtime performance of the proposed algorithm.

 

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Abstract— Depression is a major depressive disorder. It is a psychological problem that hampers a person’s daily life and decreases productivity. It is becoming a severe problem in our world. People of any age can be depressed. Despite having a flourishing tech industry in Bangladesh, cases of depression among tech employees are highly seen, which eventually leads to an unwanted situation in an employee’s professional career and personal life. Depressed employees neither can concentrate on work nor are they able to be productive. This kind of incident is becoming common in the tech industry. For this reason, we are facing problem to achieve our desired goal in the tech industry. People can be depressed for various reasons. Many risk factors that contribute to depression include family problems, work pressure, lack of physical movement, drug abuse, etc. This research aims to predict the depression risk of a tech employee and determine the root cause of depression so that it becomes easier to treat depression at an early stage. To predict depression risk, the authors have used the Adaboosted decision tree. Using this Adaptive boosting technique on the standard decision tree approach, the authors have achieved better accuracy compared to the standard decision tree approach. In adaptive boosting, errors of previous models are corrected. The features of the dataset that were used to train and test the machine learning model was determined by a psychiatrist by rigorous analysis. The features were selected considering risk factors that contribute to depression. The data used for this research was collected under the direct supervision of a psychiatrist and technology expert. The Author’s primary purpose of this research is to predict depression at a preventive stage so that necessary measures can be taken to treat depression among tech employees and to avoid unwanted situations.

 

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Abstract: A real-world animal biometric system that detects and describes animal life in image and video data is an emerging subject in machine vision. These systems develop computer vision approaches for the classification of animals. A novel method for animal face classification based on one of the popular convolutional neural network (CNN) features. We are using CNN which can automatically extract features, learn and classify them. The proposed method may also be used in other areas of image classification and object recognition. The experimental results show that automatic feature extraction in CNN is better than other simple feature extraction techniques (both local- and appearance-based features. The authors showed that the proposed technique have a positive effect on classification accuracy.

 

                                     

 

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Abstract: The leaves of a plant provides the most important information or data which provides us to know which type of plant it is and which type of disease is infected on that leaf. The plants play an important role in the biological field. In this project we have describe the development of an Android application that gives users or farmers the capability to identify the plant leaf diseases based on the photographs of plant leaves taken from an android application. Detecting diseases on leaf of plant at early stages gives strength to overcome it and treat it appropriately by providing the details to the farmer that which prevention action should be taken.

                       

 

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Abstract: In medical diagnostic application, early defect detection is a crucial task as it provides critical insight into diagnosis. Medical imaging technique is actively developing field inengineering. Magnetic Resonance imaging (MRI) is one those reliable imaging techniques on which medical diagnostic is based upon. Manual inspection of those images is a tedious job as the amount of data and minute details are hard to recognize by the human. For this automating those techniques are very crucial. In this paper, we are proposing a method which can be utilized to make tumor detection easier. The MRI deals with the complicated problem of brain tumor detection. Due to its complexity and variance getting better accuracy is a challenge. Using Adaboost machine learning algorithm we can improve over accuracy issue. The proposed system consists of three parts such as Preprocessing, Feature extraction and Classification. Preprocessing has removed noise in the raw data, for feature extraction we used GLCM (Gray Level Co- occurrence Matrix) and for classification boosting technique used (Adaboost).

                       

 

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Abstract: Deep learning has brought a series of breakthroughs in image processing. Specifically, there are significant improvements in the application of food image classification using deep learning techniques. However, very little work has been studied for the classification of food ingredients. Therefore, this paper proposes a new framework, called DeepFood which not only extracts rich and effective features from a dataset of food ingredient images using deep learning but also improves the average accuracy of multi-class classification by applying advanced machine learning techniques. First, a set of transfer learning algorithms based on Convolutional Neural Networks (CNNs) are leveraged for deep feature extraction. Then, a multi-class classification algorithm is exploited based on the performance of the classifiers on each deep feature set. The DeepFood framework is evaluated on a multi-class dataset that includes 41 classes of food ingredients and 100 images for each class. Experimental results illustrate the effectiveness of the DeepFood framework for multi-class classification of food ingredients. This model that integrates ResNet deep feature sets, Information Gain (IG) feature selection, and the SMO classifier has shown its supremacy for food-ingredients recognition compared to several existing work in this area.

                       

 

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Abstract: We consider the use of deep Convolutional Neural Networks (CNN) with transfer learning for the image classification and detection problems posed within the context of X-ray baggage security imagery. The use of the CNN approach requires large amounts of data to facilitate a complex end-to-end feature extraction and classification process. Within the context of Xray security screening, limited availability of object of interest data examples can thus pose a problem. To overcome this issue, we employ a transfer learning paradigm such that a pre-trained CNN, primarily trained for generalized image classification tasks where sufficient training data exists, can be optimized explicitly as a later secondary process towards this application domain. To provide a consistent feature-space comparison between this approach and traditional feature space representations, we also train Support Vector Machine (SVM) classifier on CNN features.We empirically show that fine-tuned CNN features yield superior performance to conventional hand-crafted features on object classification tasks within this context. Overall we achieve 0.994 accuracy based on AlexNet features trained with Support Vector Machine (SVM) classifier. In addition to classification, we also explore the applicability of multiple CNN driven detection paradigms such as sliding window based CNN (SW-CNN), Faster RCNN (F-RCNN), Region-based Fully Convolutional Networks (R-FCN) and YOLOv2. We train numerous networks tackling both single and multiple detections over SW-CNN/F-RCNN/RFCN/ YOLOv2 variants. YOLOv2, Faster-RCNN, and R-FCN provide superior results to the more traditional SW-CNN approaches. With the use of YOLOv2, using input images of size 544_544, we achieve 0.885 mean average precision (mAP) for a six-class object detection problem. The same approach with an input of size 416_416 yields 0.974 mAP for the two-class firearm detection problem and requires approximately 100ms per image. Overall we illustrate the comparative performance of these techniques and show that object localization strategies cope well with cluttered X-ray security imagery where classification techniques fail.

                       

 

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Abstract: facial expression analysis and recognition have been researched since the 17’th century. The foundational studies on facial expressions, which have formed the basis of today’s research, can be traced back to few centuries ago. Precisely, a detailed note on the various expressions and movements of head muscles was given in 1649 by John Bulwer(1). Another important milestone in the study of facial expressions and human emotions, is the work done by the psychologist Paul Ekman(2) and his colleagues. This important work have been done in the 1970s and has a significant importance and large influence on the development of modern day automatic facial expression recognizers. This work lead to adapting and developing the comprehensive Facial Action Coding System(FACS), which has since then become the de-facto standard for facial expression recognition. Over the last decades, automatic facial expressions analysis has become an active research area that finds potential applications in fields such as Human-Computer Interfaces (HCI), Image Retrieval, Security and Human Emotion Analysis. Facial

expressions are extremely important in any human interaction, and additional to emotions, it also reflects on other mental activities, social interaction and physiological signals. In this paper, we proposes an Artificial Neural Network (ANN) of two hidden layers, based on multiple Radial Bases Functions

Networks (RBFN’s) to recognize facial expressions. The ANN, is trained on features extracted from images by applying a multiscale and multi-orientation Gabor filters. We have considered the cases of subject independent/dependent facial expression recognition using The JAFFE and the CK+ benchmarks to evaluate the proposed model.

 

                       

 

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Abstract: In 1899, Galton first captured ink-on-paper fingerprints of a single child from birth until the age of 4.5 years, manually compared the prints, and concluded that “the print of a child at the age of 2.5 years would serve to identify hi ever after”. Since then, ink-on-paper fingerprinting and manual comparison methods have been superseded by digital capture and automatic fingerprint comparison techniques, but only a few feasibility studies on child fingerprint recognition have been conducted. Here, we present the first systematic and rigorous longitudinal study that addresses the following questions: (i) Do fingerprints of young children possess the salient features required to uniquely recognize a child? (ii) If so, at what age can a child’s fingerprints be captured with sufficient fidelity for recognition? (iii) Can a child’s fingerprints be used to reliably recognize the child as he ages? For our study, we collected fingerprints of 309 children (0-5 years old) four different times over a one year period. We show, for the first time, that fingerprints acquired from a child as young as 6 hours old exhibit distinguishing features necessary for recognition, and that state-of-the-art fingerprint technology achieves high recognition accuracy (98.9% true accept rate at 0.1% false accept rate) for children older than 6 months. Additionally, we use mixed-effects statistical models to study the persistence of child fingerprint recognition accuracy and show that the recognition accuracy is not significantly affected over the one year time lapse in our data. Given rapidly growing requirements to recognize children for vaccination tracking, delivery of supplementary food, and national identification documents, our study demonstrates that fingerprint recognition of young children (6 months and older) is a viable solution based on available capture and recognition technology.

                       

 

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Abstract: Malaria is a very serious infectious disease caused by a peripheral blood parasite of the genus Plasmodium. Conventional microscopy, which is currently “the gold standard” for malaria diagnosis has occasionally proved inefficient since it is time consuming and results are difficult to reproduce. As it poses a serious global health problem, automation of the evaluation process is of high importance. In this work, an accurate, rapid and affordable model of malaria diagnosis using stained thin blood smear images was developed. The method made use of the intensity features of Plasmodium parasites and erythrocytes.Images of infected and non-infected erythrocytes were acquired, pre-processed, relevant features extracted from them and eventually diagnosis was made based on the features extracted from the images. A set of features based on intensity have been proposed, and the performance of these features on the red blood cell samples from the created database have been evaluated using an artificial neural network (ANN) classifier. The results have shown that these features could be successfully used for malaria detection.

                       

 

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Abstract: This paper proposes a skin disease detection method based on image processing techniques. This method is mobile based and hence very accessible even in remote areas and it is completely noninvasive to patient’s skin. The patient provides an image of the infected area of the skin as an input to the prototype. Image processing techniques are performed on this image and the detected disease is displayed at the output. The proposed system is highly beneficial in rural areas where access to dermatologists is limited.

                       

 

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Abstract: Video OCR is a technique that can greatly help to locate the topics of interest in video via the automatic extraction and reading of captions and annotations. Text in video can provide key indexing information. Recognizing such text for search application is critical. Major difficult problem for character recognition for videos is degraded and deformated characters, low resolution characters or very complex background. To tackle the problem preprocessing on text image plays vital role. Most of the OCR engines are working on the binary image so to find a better binarization procedure for image to get a desired result is important. Accurate binarization process minimizes the error rate of video OCR.

                       

 

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Abstract: Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention. Following the recent remarkable results achieved for image classification and object detection utilizing Convolutional Neural Networks (CNNs), we empirically study material classification of everyday objects employing these techniques. More specifically, we conduct a rigorous evaluation of how state-of-the art CNN architectures compare on a common ground over widely used material databases. Experimental results on three challenging material databases show that the best performing CNN architectures can achieve  mean average precision when classifying materials.

 

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Abstract : The high-resolution devices for image capturing and the high professional requirement for users, are very complex to extract features of the fruit fly image for classification. Therefore, a bi-linear CNN model based on the mid-level and high-level feature fusion (FB-CNN) is proposed for classifying the fruit fly image. At the first step, the images of fruit fly are blurred by the Gaussian algorithm, and then the features of the fruit fly images are extracted automatically by using CNN. Afterward, the mid- and high-level features are selected to represent the local and global features, respectively. Then, they are jointly represented. When finished, the FB-CNN model was constructed to complete the task of image classification of the fruit fly. Finally, experiments data show that the FB-CNN model can effectively classify four kinds of fruit fly images. The accuracy, precision, recall, and F1 score in testing dataset are 95.00%, respectively.

Abstract : Gender prediction accuracy increases as CNN architecture evolves. This paper proposes voting schemes to utilize the already developed CNN models to further improve gender prediction accuracy. Majority voting usually requires odd numbered models while proposed softmax based  voting can utilize any number of models to improve accuracy. With experiments, it is shown that the voting of CNN models leads to further improvement of gender prediction accuracy and that softmax-based voters always show better gender prediction accuracy than majority voters though they consist of the same CNN models.

Abstract : In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this paper, Echo State Network (ESN), a recurrent neural network with a great success in time series prediction and classification, is optimized with different neural plasticity rules for classification of emotions based on electroencephalogram (EEG) time series. Actually, the neural plasticity rules are a kind of unsupervised learning adapted for the reservoir, i.e. the hidden layer of ESN. More specifically, an investigation of Oja’s rule, BCM rule and gaussian intrinsic plasticity rule was carried out in the context of EEG-based emotion recognition. The study, also, includes a comparison of the offline and online training of the ESN. When testing on the well-known affective benchmark ”DEAP dataset” which contains EEG signals from 32 subjects, we find that pretraining ESN with gaussian intrinsic plasticity enhanced the classification accuracy and outperformed the results achieved with an ESN pretrained with synaptic plasticity. Four classification problems were conducted in which the system complexity is increased and the discrimination is more challenging, i.e. inter-subject emotion discrimination. Our proposed method achieves higher performance over the state of the art methods.

Abstract : Reversible data hiding (RDH) in color image is an important topic of data hiding. This paper presents an efficient RDH algorithm for color image via double-layer embedding. The key contribution is the proposed double-layer embedding technique based on histogram shifting (HS). This technique exploits image interpolation to generate prediction error matrices for HS in the first-layer embedding and uses local pixel similarity to calculate difference matrices for HS in the second-layer embedding. It inherits reversibility from HS and makes high embedding capacity due to the use of double layers in data embedding. In addition, interchannel correlation is incorporated into the first-layer embedding and the second-layer embedding for generating histograms with high peaks, so as to improve embedding capacity. Experiments with open standard datasets are done to validate performance of the proposed RDH algorithm. Comparison results show that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in terms of embedding capacity and visual quality.

IEEE Python Image Processing Projects

Project CODE
TITLES
BASEPAPER
SYNOPSIS
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1.  IEEE 2018:Eye Recognition with Mixed Convolutional and Residual Network(MiCoRe-Net) Title Title Title
2.  IEEE 2018:Latent Fingerprint Value Prediction: Crowd-based Learning Title Title Title
3.  IEEE 2018:Developing LSB Method Using Mask in Colored Images Title Title Title
4.  IEEE 2018:Efficient Quantum Information Hiding for Remote Medical Image Sharing Title Title Title
5.  IEEE 2018:An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images Title Title Title
6.  IEEE 2018:Human Identification from Freestyle Walks using Posture-Based Gait Feature Title Title Title
7.  IEEE 2018:Visual Secret Sharing Schemes Encrypting Multiple Images Title Title Title
8.  IEEE 2018:Computer Assisted Segmentation of Palmprint Images for Biometric Research Title Title Title
9.  IEEE 2018:Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures Title Title Title
10.  IEEE 2018:Image Classification using Manifold Learning Based Non-Linear Dimensionality Reduction Title Title Title
11.  IEEE 2018:Conceptual view of the IRIS recognition systems in the biometric world using image processing techniques Title Title Title
12.  IEEE 2018:Animal classification using facial images with score-level fusion Title Title Title
13.  IEEE 2018:Design of biometric recognition software based on image processing Title Title Title
14.  IEEE 2018:Smile Detection in the Wild Based on Transfer Learning Title Title Title
15.  IEEE 2018:PassBYOP: Bring Your Own Picture for Securing Graphical Passwords Title Title Title
16.  IEEE 2018:Detection of Malaria Parasites Using Digital Image Processing Title Title Title

IEEE Python Image Processing Projects

ieee python image processing projectsImage processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two dimensional signals while applying already set signal processing methods to them. IEEE Python Image Processing Projects Click here.

It is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too.IEEE Python Image Processing Projects Click here.
IEEE Python Image Processing Projects
IEEE Python Image Processing Projects
IEEE Python Image Processing Projects