Shaheena Noor - Academia.edu (original) (raw)

Papers by Shaheena Noor

Research paper thumbnail of Framework for Smart E-health Monitoring System

Indian journal of science and technology, Feb 1, 2017

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Research paper thumbnail of Inside-out Vision for Procedure Recognition in Dental Environment

Smart homes and offices are becoming more and more common with the advances in computer vision re... more Smart homes and offices are becoming more and more common with the advances in computer vision research and technology. Identifying the human activities and scenarios are basic components of such systems. This is important not only for the eco-system to work independently, but also to allow robots to be able to assist humans. This is specially true in the more complicated medical setups, e.g. dentistry, where we need subtle cues e.g. eye motion to identify scenarios. We present a hierarchical model in this paper for robustly recognizing scenarios and procedures in a dental setup by using the objects seen in eye gaze trajectories like material and equipment used by the dentist, and symptoms of the patient. We utilize the fact that by identifying the objects viewed during an activity and linking them over time to create more complicated scenarios, the problem of scenario recognition can be hierarchically solved. We performed experiments on a dental dataset and showed that combining multiple parameters results in a better precision and accuracy compared to any of them individually. Our experiments show that the accuracy increased from 45.18% to 94.42% when we used a combination of parameters vs. a single one.

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Research paper thumbnail of Using gaze-directed vision to identify focus of attention in pervasive healthcare systems

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Research paper thumbnail of Probabilistic Hierarchical Model Using First Person Vision for Scenario Recognition

Wireless Personal Communications, Sep 10, 2018

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Research paper thumbnail of First Person Vision for Activity Prediction Using Probabilistic Modeling

Mehran University Research Journal of Engineering and Technology, Oct 1, 2018

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Research paper thumbnail of Using ANN for Multi-View Activity Recognition in Indoor Environment

In this paper, we generated an activity recognition model using an ANN and trained it using Backp... more In this paper, we generated an activity recognition model using an ANN and trained it using Backpropagation learning. We considered a sandwich making scenario and identified the hand-motion-based activities of reaching, sprinkling, spreading and cutting. The contribution of this paper is twofold: First, given the fact that many image processing steps like feature identification are computation intensive and execution time increases sharply as more images are added, we've shown that it is not always useful to add more data. We trained our system using (i) single (front) camera only and (ii) multiple (left, front, right) cameras, and have shown that adding extra cameras decreased the recognition precision from 89.22% to 79.99%. Hence, we've shown that a properly-positioned camera results in a higher precision than multiple, inappropriately-positioned cameras. Second, in the ANN training part, we've shown that adding additional hidden layers/neurons lead to unnecessary complexity which in turn result in longer computational time and lower precision. In our experiments, using a single hidden layer resulted in a precision of 90.77% and the training was completed in less than 1200 cycles. On the other hand, adding or deleting hidden layers not only decreased the precision, but also increased the training time by many folds.

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Research paper thumbnail of Automatic Object Tracking and Segmentation Using Unsupervised SiamMask

IEEE Access, 2021

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Research paper thumbnail of A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

Applied sciences, Feb 28, 2023

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Research paper thumbnail of A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute

Applied sciences, Dec 29, 2022

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Research paper thumbnail of Context-Aware Perception for Cyber-Physical Systems

Springer eBooks, 2014

Being aware of the context is one the important requirements of Cyber-Physical Systems (CPS). Con... more Being aware of the context is one the important requirements of Cyber-Physical Systems (CPS). Context-aware systems have the capability to sense what is happening or changing in their environment and take appropriate actions to adapt to the changes. In this chapter, we present a technique for identifying the focus of attention in a context-aware cyber-physical system. We propose to use first-person vision, obtained through wearable gaze-directed camera that can capture the scene through the wearer’s point-of-view. We use the fact that human cognition is linked to his gaze and typically the object/person of interest holds our gaze. We argue that our technique is robust and works well in the presence of noise and other distracting signals, where the conventional techniques of IR sensors and tagging fail. Moreover, the technique is unobtrusive and does not pollute the environment with unnecessary signals. Our approach is general in that it may be applied to a generic CPS like healthcare, office and industrial scenarios and also in intelligent homes.

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Research paper thumbnail of MapReduce for multi-view object recognition

This paper presents a MapReduce-based implementation of using high-dimensional image streams from... more This paper presents a MapReduce-based implementation of using high-dimensional image streams from inside-out and outside-in views applied to a simplistic SIFT-based feature extraction method to provide a fast and more accurate object recognition algorithm. We have combined multiple camera streams and have shown that using inside-out vision significantly improves the recognition precision. We show an accuracy of 81.25% against 31.25% when we used SIFT using our combined approach against the standard isolated ones. SIFT has a high computation cost and adding more data streams increases the cost even more. Hence, in our work we used MapReduce to parallelize the computation and achieved the same with a speedup of 80. This paper has two major contributions: First, we used inside-out vision as an additional perception source to increase the object recognition precision. Second, we used MapReduce to increase computational speed to achieve increased object recognition precision which would not have been otherwise practically possible.

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Research paper thumbnail of Using context from inside‐out vision for improved activity recognition

Iet Computer Vision, Jan 3, 2018

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Research paper thumbnail of Color image watermarking using spatio-chromatic complex Hadamard transform in sequency domain

World Journal of Engineering, Jul 16, 2021

Purpose Watermarking technique is one of the significant methods in which carrier signal hides di... more Purpose Watermarking technique is one of the significant methods in which carrier signal hides digital information in the form of watermark to prevent the authenticity of the stakeholders by manipulating different coefficients as watermark in time and frequency domain to sustain trade-off in performance parameters. One challenging component among others is to maintain the robustness, to limit perceptibility with embedding information. Transform domain is more popular to achieve the required results in color image watermarking. Variants of complex Hadamard transform (CHT) have been applied for gray image watermarking, and it has been proved that it has better performance than other orthogonal transforms. This paper is aimed at analyzing the performance of spatio-chromatic complex Hadamard transform (Sp-CHT) that is proposed as an application of color image watermarking in sequency domain (SD). Design/methodology/approach In this paper, color image watermarking technique is designed and implemented in SD using spatio-chromatic – conjugate symmetric sequency – ordered CHT. The color of a pixel is represented as complex number a*+jb*, where a* and b* are chromatic components of International Commission on Illumination (CIE) La*b* color space. The embedded watermark is almost transparent to human eye although robust against common signal processing attacks. Findings Based on the results, bit error rate (BER) and peak signal to noise ratio are measured and discussed in comparison of CIE La*b* and hue, saturation and value color model with spatio-chromatic discrete Fourier transform (Sp-DFT), and results are also analyzed with other discrete orthogonal transforms. It is observed from BER that Sp-CHT has 8%–12% better performance than Sp-DFT. Structural similarity index has been measured at different watermark strength and it is observed that presented transform performs better than other transforms. Originality/value This work presents the details and comparative analysis of two orthogonal transforms as color image watermarking application using MATLAB software. A finding from this study demonstrates that the Complex Hadamard transform is the competent candidate that can be replaced with DFT in many signal processing applications.

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Research paper thumbnail of Cluster Estimation in Terrestrial and Underwater Sensor Networks

Wireless Personal Communications, Oct 29, 2020

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Research paper thumbnail of Inside-out Vision for Treatment Identification in Dental Setup using Machine Learning

Research Square (Research Square), Jun 8, 2021

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Research paper thumbnail of Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer

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Research paper thumbnail of Smart Interior Designing Application Using VR

International Conference on Recent Academic Studies

This research project aims to design and develop a 3D interior designing application to provide a... more This research project aims to design and develop a 3D interior designing application to provide a virtual experience to users in which they can visualize a standard home (sample space) and do interior designing. Users can interact with four main interior design modules i.e., Furniture, Tiles, Paints and Customization (Mix and Match). Users will be able to experience it on two different platforms, which are the desktop and the VR version. Its primary purpose is to display interior design products with complete context, unlike stores where small samples are displayed. This will help customers to make a better buying decision when it comes to design and decorate their homes.

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Research paper thumbnail of A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

Applied Sciences

This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on ... more This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on Machine Learning (ML). These individuals, who generally communicate through sign language, are an integral part of our society, and their contribution is vital. They face communication difficulties mainly because others, who generally do not know sign language, are unable to communicate with them. The work presents a solution to this problem through a system enabling the non-deaf and mute (ND-M) to communicate with the D-M individuals without the need to learn sign language. The system is low-cost, reliable, easy to use, and based on a commercial-off-the-shelf (COTS) Leap Motion Device (LMD). The hand gesture data of D-M individuals is acquired using an LMD device and processed using a Convolutional Neural Network (CNN) algorithm. A supervised ML algorithm completes the processing and converts the hand gesture data into speech. A new dataset for the ML-based algorithm is created and pres...

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Research paper thumbnail of Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer

2023 Global Conference on Wireless and Optical Technologies (GCWOT), Jan 24, 2023

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Research paper thumbnail of A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute

Applied Sciences

Deaf and mute people are an integral part of society, and it is particularly important to provide... more Deaf and mute people are an integral part of society, and it is particularly important to provide them with a platform to be able to communicate without the need for any training or learning. These people rely on sign language, but for effective communication, it is expected that others can understand sign language. Learning sign language is a challenge for those with no impairment. Another challenge is to have a system in which hand gestures of different languages are supported. In this manuscript, a system is presented that provides communication between deaf and mute (DnM) and non-deaf and mute (NDnM). The hand gestures of DnM people are acquired and processed using deep learning, and multiple language support is achieved using supervised machine learning. The NDnM people are provided with an audio interface where the hand gestures are converted into speech and generated through the sound card interface of the computer. Speech from NDnM people is acquired using microphone input a...

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Research paper thumbnail of Framework for Smart E-health Monitoring System

Indian journal of science and technology, Feb 1, 2017

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Inside-out Vision for Procedure Recognition in Dental Environment

Smart homes and offices are becoming more and more common with the advances in computer vision re... more Smart homes and offices are becoming more and more common with the advances in computer vision research and technology. Identifying the human activities and scenarios are basic components of such systems. This is important not only for the eco-system to work independently, but also to allow robots to be able to assist humans. This is specially true in the more complicated medical setups, e.g. dentistry, where we need subtle cues e.g. eye motion to identify scenarios. We present a hierarchical model in this paper for robustly recognizing scenarios and procedures in a dental setup by using the objects seen in eye gaze trajectories like material and equipment used by the dentist, and symptoms of the patient. We utilize the fact that by identifying the objects viewed during an activity and linking them over time to create more complicated scenarios, the problem of scenario recognition can be hierarchically solved. We performed experiments on a dental dataset and showed that combining multiple parameters results in a better precision and accuracy compared to any of them individually. Our experiments show that the accuracy increased from 45.18% to 94.42% when we used a combination of parameters vs. a single one.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Using gaze-directed vision to identify focus of attention in pervasive healthcare systems

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Probabilistic Hierarchical Model Using First Person Vision for Scenario Recognition

Wireless Personal Communications, Sep 10, 2018

Bookmarks Related papers MentionsView impact

Research paper thumbnail of First Person Vision for Activity Prediction Using Probabilistic Modeling

Mehran University Research Journal of Engineering and Technology, Oct 1, 2018

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Using ANN for Multi-View Activity Recognition in Indoor Environment

In this paper, we generated an activity recognition model using an ANN and trained it using Backp... more In this paper, we generated an activity recognition model using an ANN and trained it using Backpropagation learning. We considered a sandwich making scenario and identified the hand-motion-based activities of reaching, sprinkling, spreading and cutting. The contribution of this paper is twofold: First, given the fact that many image processing steps like feature identification are computation intensive and execution time increases sharply as more images are added, we've shown that it is not always useful to add more data. We trained our system using (i) single (front) camera only and (ii) multiple (left, front, right) cameras, and have shown that adding extra cameras decreased the recognition precision from 89.22% to 79.99%. Hence, we've shown that a properly-positioned camera results in a higher precision than multiple, inappropriately-positioned cameras. Second, in the ANN training part, we've shown that adding additional hidden layers/neurons lead to unnecessary complexity which in turn result in longer computational time and lower precision. In our experiments, using a single hidden layer resulted in a precision of 90.77% and the training was completed in less than 1200 cycles. On the other hand, adding or deleting hidden layers not only decreased the precision, but also increased the training time by many folds.

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Research paper thumbnail of Automatic Object Tracking and Segmentation Using Unsupervised SiamMask

IEEE Access, 2021

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

Applied sciences, Feb 28, 2023

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute

Applied sciences, Dec 29, 2022

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Context-Aware Perception for Cyber-Physical Systems

Springer eBooks, 2014

Being aware of the context is one the important requirements of Cyber-Physical Systems (CPS). Con... more Being aware of the context is one the important requirements of Cyber-Physical Systems (CPS). Context-aware systems have the capability to sense what is happening or changing in their environment and take appropriate actions to adapt to the changes. In this chapter, we present a technique for identifying the focus of attention in a context-aware cyber-physical system. We propose to use first-person vision, obtained through wearable gaze-directed camera that can capture the scene through the wearer’s point-of-view. We use the fact that human cognition is linked to his gaze and typically the object/person of interest holds our gaze. We argue that our technique is robust and works well in the presence of noise and other distracting signals, where the conventional techniques of IR sensors and tagging fail. Moreover, the technique is unobtrusive and does not pollute the environment with unnecessary signals. Our approach is general in that it may be applied to a generic CPS like healthcare, office and industrial scenarios and also in intelligent homes.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of MapReduce for multi-view object recognition

This paper presents a MapReduce-based implementation of using high-dimensional image streams from... more This paper presents a MapReduce-based implementation of using high-dimensional image streams from inside-out and outside-in views applied to a simplistic SIFT-based feature extraction method to provide a fast and more accurate object recognition algorithm. We have combined multiple camera streams and have shown that using inside-out vision significantly improves the recognition precision. We show an accuracy of 81.25% against 31.25% when we used SIFT using our combined approach against the standard isolated ones. SIFT has a high computation cost and adding more data streams increases the cost even more. Hence, in our work we used MapReduce to parallelize the computation and achieved the same with a speedup of 80. This paper has two major contributions: First, we used inside-out vision as an additional perception source to increase the object recognition precision. Second, we used MapReduce to increase computational speed to achieve increased object recognition precision which would not have been otherwise practically possible.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Using context from inside‐out vision for improved activity recognition

Iet Computer Vision, Jan 3, 2018

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Color image watermarking using spatio-chromatic complex Hadamard transform in sequency domain

World Journal of Engineering, Jul 16, 2021

Purpose Watermarking technique is one of the significant methods in which carrier signal hides di... more Purpose Watermarking technique is one of the significant methods in which carrier signal hides digital information in the form of watermark to prevent the authenticity of the stakeholders by manipulating different coefficients as watermark in time and frequency domain to sustain trade-off in performance parameters. One challenging component among others is to maintain the robustness, to limit perceptibility with embedding information. Transform domain is more popular to achieve the required results in color image watermarking. Variants of complex Hadamard transform (CHT) have been applied for gray image watermarking, and it has been proved that it has better performance than other orthogonal transforms. This paper is aimed at analyzing the performance of spatio-chromatic complex Hadamard transform (Sp-CHT) that is proposed as an application of color image watermarking in sequency domain (SD). Design/methodology/approach In this paper, color image watermarking technique is designed and implemented in SD using spatio-chromatic – conjugate symmetric sequency – ordered CHT. The color of a pixel is represented as complex number a*+jb*, where a* and b* are chromatic components of International Commission on Illumination (CIE) La*b* color space. The embedded watermark is almost transparent to human eye although robust against common signal processing attacks. Findings Based on the results, bit error rate (BER) and peak signal to noise ratio are measured and discussed in comparison of CIE La*b* and hue, saturation and value color model with spatio-chromatic discrete Fourier transform (Sp-DFT), and results are also analyzed with other discrete orthogonal transforms. It is observed from BER that Sp-CHT has 8%–12% better performance than Sp-DFT. Structural similarity index has been measured at different watermark strength and it is observed that presented transform performs better than other transforms. Originality/value This work presents the details and comparative analysis of two orthogonal transforms as color image watermarking application using MATLAB software. A finding from this study demonstrates that the Complex Hadamard transform is the competent candidate that can be replaced with DFT in many signal processing applications.

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Research paper thumbnail of Cluster Estimation in Terrestrial and Underwater Sensor Networks

Wireless Personal Communications, Oct 29, 2020

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Inside-out Vision for Treatment Identification in Dental Setup using Machine Learning

Research Square (Research Square), Jun 8, 2021

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Smart Interior Designing Application Using VR

International Conference on Recent Academic Studies

This research project aims to design and develop a 3D interior designing application to provide a... more This research project aims to design and develop a 3D interior designing application to provide a virtual experience to users in which they can visualize a standard home (sample space) and do interior designing. Users can interact with four main interior design modules i.e., Furniture, Tiles, Paints and Customization (Mix and Match). Users will be able to experience it on two different platforms, which are the desktop and the VR version. Its primary purpose is to display interior design products with complete context, unlike stores where small samples are displayed. This will help customers to make a better buying decision when it comes to design and decorate their homes.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

Applied Sciences

This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on ... more This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on Machine Learning (ML). These individuals, who generally communicate through sign language, are an integral part of our society, and their contribution is vital. They face communication difficulties mainly because others, who generally do not know sign language, are unable to communicate with them. The work presents a solution to this problem through a system enabling the non-deaf and mute (ND-M) to communicate with the D-M individuals without the need to learn sign language. The system is low-cost, reliable, easy to use, and based on a commercial-off-the-shelf (COTS) Leap Motion Device (LMD). The hand gesture data of D-M individuals is acquired using an LMD device and processed using a Convolutional Neural Network (CNN) algorithm. A supervised ML algorithm completes the processing and converts the hand gesture data into speech. A new dataset for the ML-based algorithm is created and pres...

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Research paper thumbnail of Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer

2023 Global Conference on Wireless and Optical Technologies (GCWOT), Jan 24, 2023

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute

Applied Sciences

Deaf and mute people are an integral part of society, and it is particularly important to provide... more Deaf and mute people are an integral part of society, and it is particularly important to provide them with a platform to be able to communicate without the need for any training or learning. These people rely on sign language, but for effective communication, it is expected that others can understand sign language. Learning sign language is a challenge for those with no impairment. Another challenge is to have a system in which hand gestures of different languages are supported. In this manuscript, a system is presented that provides communication between deaf and mute (DnM) and non-deaf and mute (NDnM). The hand gestures of DnM people are acquired and processed using deep learning, and multiple language support is achieved using supervised machine learning. The NDnM people are provided with an audio interface where the hand gestures are converted into speech and generated through the sound card interface of the computer. Speech from NDnM people is acquired using microphone input a...

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