DrNoura Semary | Menoufiya University (original) (raw)

Papers by DrNoura Semary

2007 National Radio Science Conference, 2007

2007 International Conference on Computer Engineering & Systems, 2007

2014 9th International Conference on Computer Engineering & Systems (ICCES), 2014

The early detection of breast cancer makes many women survive. In this paper, a CAD system classi... more The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation
and classification. For the former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%.

Tarek Gaber, Gehad Zahran, Ahmed Anter, Soliman Mona, Mona Abdelbaset Sadek Ali, Noura Semary, Ha... more Tarek Gaber, Gehad Zahran, Ahmed Anter, Soliman Mona, Mona Abdelbaset Sadek Ali, Noura Semary, Hassanien Aboul Alla, Snasel Vaclav. Thermogram breast cancer detection approach based on Neutrosophic sets and fuzzy c-means algorithm. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15), Milano, Italy August 25-29, 2015

One of the most important problems facing visual impaired people is money recognition especially ... more One of the most important problems facing visual impaired people is money recognition especially for paper currency. In this paper we present a simple system currency recognition system applied on Egyptian banknote. Our proposed system is based on simple image processing utilities that insure performing the process as fast and robust as possible. The basic techniques utilized in our proposed system include image foreground segmentation, histogram enhancement, region of interest (ROI) extraction and finally template matching based on the cross-correlation between the captured image and our data set. The experimental results demonstrate that the proposed method can recognize Egyptian paper money with high quality reaches 89% and short time.

Due to the several forms of different Arabic font types, Arabic character recognition is still a ... more Due to the several forms of different Arabic font types,
Arabic character recognition is still a challenge. Most literature
works consider only one font per text what results in low
recognition accuracy. This paper tends to enhance the accuracy of
AOCR (Arabic Optical Character Recognition) by considering an
automatic Optical Font Recognition (OFR) stage before going
ahead with the traditional OCR stages. This has been achieved
using SIFT (Scale Invariant Feature Transform) descriptors.
First, a comparative study of four most recent algorithms of
primitive OCR has been performed to evaluate the different
features and classifiers utilized in their systems. Accordingly, a
combining of statistical features have been proposed as well as
selecting Random Forest Tree classifier for classification
stage. The combination of the features are used to train the
classifiers. As a result, each recognized text font is directed to a
specific classifier tree. The proposed system was tested on a
generated Primitive Arabic Characters Noise Free dataset (PAC-NF) containing 30000 samples. Experimental results achieved a promising character recognition accuracy of 99.8-100%.

Augmented reality (AR) is the combination of a real scene viewed by the user and a virtual scene ... more Augmented reality (AR) is the combination of a real scene viewed by the user and a virtual scene generated by the computer that augments the scene with additional information. The user of an AR application should feel that the augmented object is a part of the real world. One of the factors that greatly affect this condition is the tracking technique used. In this paper, an augmented reality application is adopted with markerless tracking as a classification task. ORB algorithm is used for feature detection and the FREAK algorithm is used for feature description. The classifiers used for the tracking task are KNN, Random Forest, Extremely Randomized Trees, SVM and Bayes classifier. The performance of each classifier used is evaluated in terms of speed and efficiency. It has been observed that KNN outperforms other classifiers including Random Forest with different number of trees.

Communications in Computer and Information Science, 2014

Local Detectors and Descriptors for Object Class Recognition, 2015

Faten A. Khalifa, Noura A. Semary, Hatem M. El-Sayed, Mohiy M. Hadhoud Local Detectors and Descri... more Faten A. Khalifa, Noura A. Semary, Hatem M. El-Sayed, Mohiy M. Hadhoud Local Detectors and Descriptors for Object Class Recognition, International Journal of Intelligent Systems and Applications(IJISA), vol. 7 (10) 2015 PP.12-18
http://www.mecs-press.org/ijisa/ijisa-v7-n10/IJISA-V7-N10-2.pdf

37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15), Aug 25, 2015

The International Conference on Communication, Management and Information Technology (ICCMIT 2015), Apr 20, 2015

Ahmed Samy, Mohamed Hamdy, and Noura Semary , Fast Forced Handover Technique for Load Balancing i... more Ahmed Samy, Mohamed Hamdy, and Noura Semary , Fast Forced Handover Technique for Load Balancing in Mobile WiMAX for Real-time Applications , The International Conference on Communication, Management and Information Technology (ICCMIT 2015), Prague, Czech Republic, 20-22 April 2015, Elsevier

the 2015 Industry Academia Collaboration ( IAC'15), Apr 6, 2015

IEEE Visual Communications and Image Processing Conference, VCIP, Dec 7, 2015

Image colorization is a new image processing topic to recolor gray images to look as like the ori... more Image colorization is a new image processing topic to recolor gray images to look as like the original color images as possible. Different methods have appeared in the literature to solve this problem, the way that leads to thinking about decolorization, eliminating the colors of color images to just small color keys, aid in the colorization process. Due to this idea, decolorization is considered as a color image encoding mechanism. In this chapter, the authors propose a new decolorization system depends on extracting the color seeds (Representative Pixels [RP]) using morphology operations. Different decolorization methods are studied and compared to the system results using different quality metrics.