2018-2019 IEEE Python Image Processing Projects

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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 score-level fusion of recently popular convolutional neural network (CNN) features and appearance-based descriptor features is presented. This method utilises a score-level fusion of two different approaches; one uses CNN which can automatically extract features, learn and classify them; and the other one uses kernel Fisher analysis

(KFA) for its feature extraction phase. 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), and additionally, appropriate score-level combination of CNN and simple features can achieve even higher accuracy than applying CNN alone. The authors showed that the score-level fusion of CNN extracted features and appearance-based KFA method have a positive effect on classification accuracy. The proposed method achieves 95.31% classification rate on animal faces which is significantly better than the other state-of-the-art methods.


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.


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).


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.


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.


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.



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.


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.


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.



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.


IEEE Python Image Processing Projects

Project CODE
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

Image 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
2018-2019 IEEE Python Image Processing Projects
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