Browsing by Author "Sarker, Md. Ferdousur Rahman"

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Real-time Bangladeshi Currency Detection System for Visually Impaired Person
(2019 International Conference on Bangla Speech and Language Processing, ICBSLP 2019, IEEE, 2020-05-13) Sarker, Md. Ferdousur Rahman; Raju, Md. Israfil Mahmud; Marouf, Ahmed Al; Hafiz, Rubaiya; Hossain, Syed Akhter; Protik, Munim Hossain Khandker
This paper presents a real-time Bangladeshi currency detection system for visually impaired persons. The proposed system exploits the image processing algorithms to facilitate the visually impaired people to prosperously recognize banknotes. The recent banknotes of Bangladesh have blind embossing or blind dots, which could be effective to recognize the value of the bill by touching. As the embossing fades away in the long-term used notes, detecting right value of the banknote using image processing algorithms could be considered as a challenging task. Particularly in Bangladesh, each banknote seems similar using the direct exertion of simplified image processing algorithms. In this paper, a recognition system was implemented that can detect Bangladeshi banknote in different viewpoints and scales. The detection system is also able to detect currency those are rumpled, decrepit or even worn. The detection system includes image preprocessing, image analysis and image recognition. To enhance the determination of currency recognition, the descriptor of an individual input scene is matched with various training images of the same category. After that, by analyzing their matching result it recognizes the currency with higher confidence. For real-time recognition, we have deployed the system into a mobile application.
Real-time Bangladeshi Currency Detection System for Visually Impaired Person
(Daffodil International University, 2019-12) Sarker, Md. Ferdousur Rahman; Raju, Md. Israfil Mahmud
This report presents a real-time Bangladeshi currency detection system for visually impaired person. The proposed system exploits the image processing algorithms to facilitate the visually impaired people to prosperously recognize banknotes. The recent banknotes of Bangladesh have blind embossing or blind dots, which could be effective to recognize the value of the bill by touching. As the embossing fades away in the long-term used notes, detecting right value of the banknote using image processing algorithms could be considered as a challenging task. Particularly in Bangladesh, each banknote seems similar using the direct exertion of simplified image processing algorithms. In this paper, a recognition system was implemented that can detect Bangladeshi banknote in different viewpoints and scales. The detection system is also able to detect currency those are rumpled, decrepit or even worn. The detection system includes image preprocessing, image analysis and image recognition. To enhance the determination of the currency recognition, the descriptor of an individual input scene is matched with various training images of same category. After that by analyzing their matching result it recognizes the currency with higher confidence. For real time recognition, we have deployed the system into a mobile application.
Recognizing Hand-based Actions based on Hip-Joint centered Features using KINECT
(IEEE, 2018-07-19) Marouf, Ahmed Al; Sarker, Md. Ferdousur Rahman; Siddiquee, Shah Md. Tanvir
Microsoft Kinect provides skeletal joints to extract different features which can be applied to identify different actions performed by subjects. As human moves, skeletal joints contribute to the movements and human actions are nothing but different types of movements in specific orders. Hand wave, hand shaking, push, pull, clapping, throw, catch these are some hand based actions which are difficult to recognize properly in an automated system. Hip-joint plays a vital role to determine joint-based features from human skeleton, as it is approximately the middle joint of the whole skeleton. The joint relative distances (JRD) and joint relative angles (JRA) are used as principle features in recent action recognition methodologies. In this paper, we have proposed a new methodology based on hip-joint centered features which are based on basic physiological movements that contributes to the decent accuracy in identifying hand based actions.