Browsing by Author "Biswas, Rubel"

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An Iris detection and recognition system to measure the performance of e-security
(BRAC University, 4/21/2016) Anwar, A.M. Shahed; Biswas, Rubel; Mostakim, Moin
Biometric is a system to identify the individual human by extracting distinguishable features from that particular person. Among many other biometric systems the iris recognition system is most accurate one right now since it has a high recognition rate. This thesis is proposing a system with four major division in the process: Segmentation, Normalization, Feature encoding and Matching. At early stage, Histogram equalization is used on the input image and to detect the objects present in the image, Canny Edge Detection model is employed. Inner circular boundary and center of the pupil in the Iris region is detected by using Hough Transformation. A circle is drawn with the help of Mid-Point Circle drawing algorithm which center is as same as pupil center and hence, outer circular boundary of the iris region can be detected. For the normalization, Daugman’s Rubber Sheet model is used and in the feature encoding process, instead of Gabor filter to extract feature from the iris image, Log-Gabor filter is used in this thesis since it has non-zero DC component advantage over Gabor filter. Last but not the least, Hamming Distance is used to compare two binary iris template for matching purpose.
Automated parking lot management for Bengali license plates using hough transformation and image segmentation
(BRAC University, 2014-08) Mahmud, Abul Ahsan; Dores, Bishal Peter; Nahid Ul Islam; Biswas, Rubel; Alam, Md. Zahangir
This paper is on automated parking lot management system for Bengali language based on vehicle number plate recognition. The need for an efficient parking system stems from increased congestion, motor vehicle pollution. The aim of this research is to develop and implement an automatic parking system that will increase convenience and security of the parking lot with minimum human involvement. This is done with the combination of automated license plate recognition and database manipulation. The image is acquired from a live video feed by comparing frames for significant changes. Then it is passed through several steps including some different types of algorithms and methodologies such as Canny Edge Detection, Connected Component Labeling and Hough Transformation to localize the license plate. Subsequently the license plate is progressed through Character Segmentation and Pattern Recognition to extract the data for manipulating and computing using database to manage the parking lot system efficiently. The system was experimented on 20 different license plates and the algorithms worked correctly in all cases with a very good response time.
Automatic detection of defective rail anchors
(2014-11) Khan, Rubayat Ahmed; Islam, Samiul; Biswas, Rubel
Rail line anchors/fasteners are the metallic components that attach each line with the sleepers. These are essential rail components as absence of these often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. This manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically detect the presence of rail line anchors/fasteners using Shi - Tomasi and Harris - Stephen feature detection algorithms. This approach has confirmed to successfully detect scenarios with both grounded and missing anchors invoked in the experiment, with an accuracy of 83.55%, thus proving its robustness.
Automatic detection of defective rail anchors
(2014-11) Khan, Rubayat Ahmed; Islam, Samiul; Biswas, Rubel
Rail line anchors/fasteners are the metallic components that attach each line with the sleepers. These are essential rail components as absence of these often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. This manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically detect the presence of rail line anchors/fasteners using Shi - Tomasi and Harris - Stephen feature detection algorithms. This approach has confirmed to successfully detect scenarios with both grounded and missing anchors invoked in the experiment, with an accuracy of 83.55%, thus proving its robustness.
Automatic detection of defective rail anchors
(2014-11) Khan, Rubayat Ahmed; Islam, Samiul; Biswas, Rubel
Rail line anchors/fasteners are the metallic components that attach each line with the sleepers. These are essential rail components as absence of these often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. This manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically detect the presence of rail line anchors/fasteners using Shi - Tomasi and Harris - Stephen feature detection algorithms. This approach has confirmed to successfully detect scenarios with both grounded and missing anchors invoked in the experiment, with an accuracy of 83.55%, thus proving its robustness.
Automatic detection of defective rail anchors
(2014-11) Khan, Rubayat Ahmed; Islam, Samiul; Biswas, Rubel
Rail line anchors/fasteners are the metallic components that attach each line with the sleepers. These are essential rail components as absence of these often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. This manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically detect the presence of rail line anchors/fasteners using Shi - Tomasi and Harris - Stephen feature detection algorithms. This approach has confirmed to successfully detect scenarios with both grounded and missing anchors invoked in the experiment, with an accuracy of 83.55%, thus proving its robustness.
Automatic measurement of rail line expansion joint gaps
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2015-03) Islam, Samiul; Khan, Rubayat Ahmed; Biswas, Rubel
Expansion joint gaps are the gaps which are deliberately left between the rail ends to allow for expansion of the rails in hot weather. Over gapping of these end to end gaps often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. Such manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically measure the length of rail line expansion joint gaps using morphological processing. This approach has confirmed to successfully detect scenarios of different condition with an accuracy of 89%, thus proving its robustness.
Automatic measurement of rail line expansion joint gaps
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2015-03) Islam, Samiul; Khan, Rubayat Ahmed; Biswas, Rubel
Expansion joint gaps are the gaps which are deliberately left between the rail ends to allow for expansion of the rails in hot weather. Over gapping of these end to end gaps often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. Such manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically measure the length of rail line expansion joint gaps using morphological processing. This approach has confirmed to successfully detect scenarios of different condition with an accuracy of 89%, thus proving its robustness.
Automatic measurement of rail line expansion joint gaps
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2015-03) Islam, Samiul; Khan, Rubayat Ahmed; Biswas, Rubel
Expansion joint gaps are the gaps which are deliberately left between the rail ends to allow for expansion of the rails in hot weather. Over gapping of these end to end gaps often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. Such manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically measure the length of rail line expansion joint gaps using morphological processing. This approach has confirmed to successfully detect scenarios of different condition with an accuracy of 89%, thus proving its robustness.
Automatic measurement of rail line expansion joint gaps
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2015-03) Islam, Samiul; Khan, Rubayat Ahmed; Biswas, Rubel
Expansion joint gaps are the gaps which are deliberately left between the rail ends to allow for expansion of the rails in hot weather. Over gapping of these end to end gaps often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. Such manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically measure the length of rail line expansion joint gaps using morphological processing. This approach has confirmed to successfully detect scenarios of different condition with an accuracy of 89%, thus proving its robustness.
Automatic slice growing method based 3D reconstruction of liver with its vessels
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2014) Alom, Md. Zahangir; Mostakim, Moin; Biswas, Rubel; Chakrabarty, Amitabha
In the recent years, reconstructing 3D liver and its vessels from abdominal CT volume images becomes an inevitable and necessary research field. In this paper, a method of 3D reconstruction of liver with its vessels has been implemented, which involves volume preprocessing, de-noising, segmentation, contouring, and combination of different modalities. An advanced liver segmentation algorithms have been proposed: The first one is a 2.5D method that utilizes automatic Slice Growing Method (SGM) to segment liver part of each slice of a data set. It takes advantage of curvature control of level set segmentation method to distinguish liver and adjacent organs. It is proved that the result of this proposed method is much better than simple 3D level set method in liver segmentation. In the case of liver vessel segmentation, we have proposed an improved smoothing method dedicate to 3D vascular volume which results from region growing segmentation method. The cooperation of region growing method and proposed smoothing method has been demonstrated the possibility of efficient vessel segmentation with very accurate results. And the results indicate that our method is suitable for anatomical studying and surgical planning.
Automatic slice growing method based 3D reconstruction of liver with its vessels
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2014) Alom, Md. Zahangir; Mostakim, Moin; Biswas, Rubel; Chakrabarty, Amitabha
In the recent years, reconstructing 3D liver and its vessels from abdominal CT volume images becomes an inevitable and necessary research field. In this paper, a method of 3D reconstruction of liver with its vessels has been implemented, which involves volume preprocessing, de-noising, segmentation, contouring, and combination of different modalities. An advanced liver segmentation algorithms have been proposed: The first one is a 2.5D method that utilizes automatic Slice Growing Method (SGM) to segment liver part of each slice of a data set. It takes advantage of curvature control of level set segmentation method to distinguish liver and adjacent organs. It is proved that the result of this proposed method is much better than simple 3D level set method in liver segmentation. In the case of liver vessel segmentation, we have proposed an improved smoothing method dedicate to 3D vascular volume which results from region growing segmentation method. The cooperation of region growing method and proposed smoothing method has been demonstrated the possibility of efficient vessel segmentation with very accurate results. And the results indicate that our method is suitable for anatomical studying and surgical planning.
Automatic slice growing method based 3D reconstruction of liver with its vessels
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2014) Alom, Md. Zahangir; Mostakim, Moin; Biswas, Rubel; Chakrabarty, Amitabha
In the recent years, reconstructing 3D liver and its vessels from abdominal CT volume images becomes an inevitable and necessary research field. In this paper, a method of 3D reconstruction of liver with its vessels has been implemented, which involves volume preprocessing, de-noising, segmentation, contouring, and combination of different modalities. An advanced liver segmentation algorithms have been proposed: The first one is a 2.5D method that utilizes automatic Slice Growing Method (SGM) to segment liver part of each slice of a data set. It takes advantage of curvature control of level set segmentation method to distinguish liver and adjacent organs. It is proved that the result of this proposed method is much better than simple 3D level set method in liver segmentation. In the case of liver vessel segmentation, we have proposed an improved smoothing method dedicate to 3D vascular volume which results from region growing segmentation method. The cooperation of region growing method and proposed smoothing method has been demonstrated the possibility of efficient vessel segmentation with very accurate results. And the results indicate that our method is suitable for anatomical studying and surgical planning.
Automatic slice growing method based 3D reconstruction of liver with its vessels
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2014) Alom, Md. Zahangir; Mostakim, Moin; Biswas, Rubel; Chakrabarty, Amitabha
In the recent years, reconstructing 3D liver and its vessels from abdominal CT volume images becomes an inevitable and necessary research field. In this paper, a method of 3D reconstruction of liver with its vessels has been implemented, which involves volume preprocessing, de-noising, segmentation, contouring, and combination of different modalities. An advanced liver segmentation algorithms have been proposed: The first one is a 2.5D method that utilizes automatic Slice Growing Method (SGM) to segment liver part of each slice of a data set. It takes advantage of curvature control of level set segmentation method to distinguish liver and adjacent organs. It is proved that the result of this proposed method is much better than simple 3D level set method in liver segmentation. In the case of liver vessel segmentation, we have proposed an improved smoothing method dedicate to 3D vascular volume which results from region growing segmentation method. The cooperation of region growing method and proposed smoothing method has been demonstrated the possibility of efficient vessel segmentation with very accurate results. And the results indicate that our method is suitable for anatomical studying and surgical planning.
Automatic slice growing method based 3D reconstruction of liver with its vessels
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2014) Alom, Md. Zahangir; Mostakim, Moin; Biswas, Rubel; Chakrabarty, Amitabha
In the recent years, reconstructing 3D liver and its vessels from abdominal CT volume images becomes an inevitable and necessary research field. In this paper, a method of 3D reconstruction of liver with its vessels has been implemented, which involves volume preprocessing, de-noising, segmentation, contouring, and combination of different modalities. An advanced liver segmentation algorithms have been proposed: The first one is a 2.5D method that utilizes automatic Slice Growing Method (SGM) to segment liver part of each slice of a data set. It takes advantage of curvature control of level set segmentation method to distinguish liver and adjacent organs. It is proved that the result of this proposed method is much better than simple 3D level set method in liver segmentation. In the case of liver vessel segmentation, we have proposed an improved smoothing method dedicate to 3D vascular volume which results from region growing segmentation method. The cooperation of region growing method and proposed smoothing method has been demonstrated the possibility of efficient vessel segmentation with very accurate results. And the results indicate that our method is suitable for anatomical studying and surgical planning.
Detection and classification of speed limit traffic signs
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2014-10) Biswas, Rubel; Fleyeh, Hasan; Mostakim, Moin
This paper presents a novel traffic sign recognition system which can aid in the development of Intelligent Speed Adaptation. This system is based on extracting the speed limit sign from the traffic scene by Circular Hough Transform (CHT) with the aid of colour and non-colour information of the traffic sign. The digits of the speed limit sign are then extracted and classified using SVM classifier which is trained for this purpose. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 270 images which were collected in different light conditions. To check the robustness of this system, it was tested against 210 images which contain 213 speed limit traffic sign and 288 Non- Speed limit signs. It was found that the accuracy of recognition was 98% which indicates clearly the high robustness targeted by this system.
Detection and classification of speed limit traffic signs
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2014-10) Biswas, Rubel; Fleyeh, Hasan; Mostakim, Moin
This paper presents a novel traffic sign recognition system which can aid in the development of Intelligent Speed Adaptation. This system is based on extracting the speed limit sign from the traffic scene by Circular Hough Transform (CHT) with the aid of colour and non-colour information of the traffic sign. The digits of the speed limit sign are then extracted and classified using SVM classifier which is trained for this purpose. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 270 images which were collected in different light conditions. To check the robustness of this system, it was tested against 210 images which contain 213 speed limit traffic sign and 288 Non- Speed limit signs. It was found that the accuracy of recognition was 98% which indicates clearly the high robustness targeted by this system.
Detection and classification of speed limit traffic signs
(© 2014 Institute of Electrical and Electronics Engineers Inc., 2014-10) Biswas, Rubel; Fleyeh, Hasan; Mostakim, Moin
This paper presents a novel traffic sign recognition system which can aid in the development of Intelligent Speed Adaptation. This system is based on extracting the speed limit sign from the traffic scene by Circular Hough Transform (CHT) with the aid of colour and non-colour information of the traffic sign. The digits of the speed limit sign are then extracted and classified using SVM classifier which is trained for this purpose. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 270 images which were collected in different light conditions. To check the robustness of this system, it was tested against 210 images which contain 213 speed limit traffic sign and 288 Non- Speed limit signs. It was found that the accuracy of recognition was 98% which indicates clearly the high robustness targeted by this system.
Detection of Hate Speech on Social Media Using Machine Learning
(Daffodil International University, 2019-12) Biswas, Rubel; Datta, Apurbo
The objective of our research to detect hate speech on social media. Everyday huge amounts of data generated by users of different social media. For this research, we created a data set collecting data from twitter. This data set consists of tweets of different kinds of people of different races and religions. In this work, we followed the machine learning approach and as we know NB and SVM is the most popular algorithm for sentiment analysis and classifying text, so we used Naïve Bayes and Support Vector Machine algorithm in this work. While using NB we find accuracy rate at 94.63% and in SVM the accuracy rate was 92.32%. As the action of a particular event of social media is not only bounded only in the internet it affects the real-life events all well. Again anything spread faster on social media compared to different other media. Many people post many hatred things of social media and it hurts other's feeling and then difficulties arrive and people have to face the further consequences. By detecting hate speech we can control these things and avoid this kind of situation. So our work has value to keep social media free from a few bad things and conflict between people of different believes.
LVQ and HOG based speed limit traffic signs detection and categorization
(© 2014 IEEE Computer Society, 2014) Biswas, Rubel; Tora, Moumita Roy; Bhuiyan, Farazul Haque
The proper identification of the traffic signs can ensure driving safety and can play a very important role in reducing the number of road accidents significantly. This paper represents a uniform way to detect the speed limit traffic signs and to confirm it by recognizing the sign's speed number. In this system, firstly the red color objects are segmented from an image using LVQ. Secondly, detected circular part is extracted from the color segmented image using bounding box and then Histogram Oriented Gradient (HOG) is used to collect the feature of the extracted part of circular object and finally SVM classifier is applied to train the HOG features of each speed no. into their corresponding classes. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 200 images which were collected in different light conditions. To check the robustness of this system, it was tested against 381 images which contain 361 Speed Limit traffic sign and 30 Non- Speed Limit signs. It was found that the accuracy of recognition was 92.75% which indicates clearly the high robustness targeted by this system.