Emon Kumar Dey

Work place: Institute of Infromation Technology, University of Dhaka, Dhaka-1000, Bangladesh

E-mail: emonkd@iit.du.ac.bd


Research Interests: Image Processing, Pattern Recognition, Natural Language Processing, Computational Learning Theory


Emon Kumar Dey is currently an assistant professor in Institute of Information Technology (IIT), University of Dhaka. He received his M.S degree from the Department of Computer Science and Engineering, University of Dhaka, Bangladesh in 2011. His research area include pattern recognition, machine learning, image processing, LiDAR data processing, 3D building modelling etc

Author Articles
Remote Sensing Image Scene Classification

By Md. Arafat Hussain Emon Kumar Dey

DOI: https://doi.org/10.5815/ijem.2018.04.02, Pub. Date: 8 Jul. 2018

Remote sensing image scene classification has gained remarkable attention because of its versatile use in different applications like geospatial object detection, natural hazards detection, geographic image retrieval, environment monitoring and etc. We have used the strength of convolutional neural network in scene image classification and proposed a new CNN to classify the images. Pre-trained VGG16 and ResNet50 are used to reduce overfitting and the training time in this paper. We have experimented on a recently proposed NWPU-RESISC45 dataset which is the largest dataset of remote sensing scene images. This paper found a significant improvement of accuracy by applying the proposed CNN and also the approaches have applied.

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A Gender Recognition Approach with an Embedded Preprocessing

By Md. Mostafijur Rahman Shanto Rahman Emon Kumar Dey Mohammad Shoyaib

DOI: https://doi.org/10.5815/ijitcs.2015.07.03, Pub. Date: 8 Jun. 2015

Gender recognition from facial images has become an empirical aspect in present world. It is one of the main problems of computer vision and researches have been conducting on it. Though several techniques have been proposed, most of the techniques focused on facial images in controlled situation. But the problem arises when the classification is performed in uncontrolled conditions like high rate of noise, lack of illumination, etc. To overcome these problems, we propose a new gender recognition framework which first preprocess and enhances the input images using Adaptive Gama Correction with Weighting Distribution. We used Labeled Faces in the Wild (LFW) database for our experimental purpose which contains real life images of uncontrolled condition. For measuring the performance of our proposed method, we have used confusion matrix, precision, recall, F-measure, True Positive Rate (TPR), and False Positive Rate (FPR). In every case, our proposed framework performs superior over other existing state-of-the-art techniques.

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