Improved palmprint segmentation for robust identification and verification
- Brown, Dane L, Bradshaw, Karen L
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/460576 , vital:75966 , xlink:href="https://doi.org/10.1109/SITIS.2019.00013"
- Description: This paper introduces an improved approach to palmprint segmentation. The approach enables both contact and contactless palmprints to be segmented regardless of constraining finger positions or whether fingers are even depicted within the image. It is compared with related systems, as well as more comprehensive identification tests, that show consistent results across other datasets. Experiments include contact and contactless palmprint images. The proposed system achieves highly accurate classification results, and highlights the importance of effective image segmentation. The proposed system is practical as it is effective with small or large amounts of training data.
- Full Text:
- Date Issued: 2019
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/460576 , vital:75966 , xlink:href="https://doi.org/10.1109/SITIS.2019.00013"
- Description: This paper introduces an improved approach to palmprint segmentation. The approach enables both contact and contactless palmprints to be segmented regardless of constraining finger positions or whether fingers are even depicted within the image. It is compared with related systems, as well as more comprehensive identification tests, that show consistent results across other datasets. Experiments include contact and contactless palmprint images. The proposed system achieves highly accurate classification results, and highlights the importance of effective image segmentation. The proposed system is practical as it is effective with small or large amounts of training data.
- Full Text:
- Date Issued: 2019
Multi-angled face segmentation and identification using limited data
- Authors: Brown, Dane L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465711 , vital:76634 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9067899"
- Description: This paper introduces a different approach to face segmentation that aims to improve face recognition when given large pose angles and limited training data. Face segmentation is achieved by extracting landmarks which are manipulated in such a way as to normalize unseen data with a classification model. The approach is compared with related systems, followed by further tests that show consistent results across other datasets. Experiments include frontal and non-frontal training images for classification of various face pose angles. The proposed system is a promising contribution, and especially shows the importance of face segmentation. The results are achieved using minimal training data, such that both accurate and practical face recognition systems can be constructed.
- Full Text:
- Date Issued: 2019
- Authors: Brown, Dane L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465711 , vital:76634 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9067899"
- Description: This paper introduces a different approach to face segmentation that aims to improve face recognition when given large pose angles and limited training data. Face segmentation is achieved by extracting landmarks which are manipulated in such a way as to normalize unseen data with a classification model. The approach is compared with related systems, followed by further tests that show consistent results across other datasets. Experiments include frontal and non-frontal training images for classification of various face pose angles. The proposed system is a promising contribution, and especially shows the importance of face segmentation. The results are achieved using minimal training data, such that both accurate and practical face recognition systems can be constructed.
- Full Text:
- Date Issued: 2019
Plant disease detection and classification for farmers and everyday gardeners
- Poole, Louise C, Brown, Dane L
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465722 , vital:76635 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378684_Plant_Disease_Detection_and_Classification_for_Farmers_and_Everyday_Gardeners/links/5d611905299bf1f70b090b54/Plant-Disease-Detection-and-Classification-for-Farmers-and-Everyday-Gardeners.pdf"
- Description: Identifying and rating diseases by hand is an expensive, time consuming, subjective and unreliable method as compared to what computers can provide. Image processing and machine learning enable automated disease identification. Research has proven that automated disease identification systems can be used as a preventative measure against plant rot and death. This paper narrows down the best techniques to segment images of leaves toward improved classification of diseases found on those leaves. An investigation is conducted on image segmentation and machine learning techniques, including state-of-the-art systems, to determine the most appropriate approach to prevent death and rot in plants. Promising results were observed during testing, and show that a system can be implemented to assist with plant health that is relevant to both home gardeners and farmers.
- Full Text:
- Date Issued: 2019
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465722 , vital:76635 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378684_Plant_Disease_Detection_and_Classification_for_Farmers_and_Everyday_Gardeners/links/5d611905299bf1f70b090b54/Plant-Disease-Detection-and-Classification-for-Farmers-and-Everyday-Gardeners.pdf"
- Description: Identifying and rating diseases by hand is an expensive, time consuming, subjective and unreliable method as compared to what computers can provide. Image processing and machine learning enable automated disease identification. Research has proven that automated disease identification systems can be used as a preventative measure against plant rot and death. This paper narrows down the best techniques to segment images of leaves toward improved classification of diseases found on those leaves. An investigation is conducted on image segmentation and machine learning techniques, including state-of-the-art systems, to determine the most appropriate approach to prevent death and rot in plants. Promising results were observed during testing, and show that a system can be implemented to assist with plant health that is relevant to both home gardeners and farmers.
- Full Text:
- Date Issued: 2019
Poacher detection and wildlife counting system
- Brown, Dane L, Schormann, Daniel
- Authors: Brown, Dane L , Schormann, Daniel
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465733 , vital:76636 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378767_Poacher_Detection_and_Wildlife_Counting_System/links/5d6117c7a6fdccc32ccd2cac/Poacher-Detection-and-Wildlife-Counting-System.pdf"
- Description: The illegal hunting of wildlife is a serious problem, causing a large number of animals to approach extinction or worse. Drones provide a viable option for constant surveillance and multiple instances of using drones for this purpose have been tried. However, existing methods predominantly rely on manual surveillance from camera feeds. This paper shows that using either visible or thermal cameras, with modern image processing and machine learning techniques, enables a system to autonomously detect humans, while tracking animals by their identity number (id). The thermal characteristics of special but inexpensive cameras are used for object detection with centroid tracking, and convolutional neural networks are used to classify humans and wildlife. Classification also enables the counting of wildlife by id, which can help game reserves keep track of wildlife.
- Full Text:
- Date Issued: 2019
- Authors: Brown, Dane L , Schormann, Daniel
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465733 , vital:76636 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378767_Poacher_Detection_and_Wildlife_Counting_System/links/5d6117c7a6fdccc32ccd2cac/Poacher-Detection-and-Wildlife-Counting-System.pdf"
- Description: The illegal hunting of wildlife is a serious problem, causing a large number of animals to approach extinction or worse. Drones provide a viable option for constant surveillance and multiple instances of using drones for this purpose have been tried. However, existing methods predominantly rely on manual surveillance from camera feeds. This paper shows that using either visible or thermal cameras, with modern image processing and machine learning techniques, enables a system to autonomously detect humans, while tracking animals by their identity number (id). The thermal characteristics of special but inexpensive cameras are used for object detection with centroid tracking, and convolutional neural networks are used to classify humans and wildlife. Classification also enables the counting of wildlife by id, which can help game reserves keep track of wildlife.
- Full Text:
- Date Issued: 2019
Virtual Gym Instructor
- Authors: Brown, Dane L , Ndleve, Mixo
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465744 , vital:76637 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378603_Virtual_Gym_Instructor/links/5d6118a892851c619d7268c1/Virtual-Gym-Instructor.pdf"
- Description: The fourth industrial revolution and the continuous development of new technologies have presented a golden platter for sedentary living. Noncommunicable diseases such as, cancers, cardiovascular and respiratory deficiencies, and diabetes have reached epidemic levels as a consequence. A traditional gym instructor screens clients to prescribe exercise programs that can help them lower the risk of noncommunicable lifestyle diseases. However, gym instructors often come at a cost and are not always affordable, available or accessible. This research investigated whether modern computing power can be utilized to develop a system in the form of a cost effective alternative exercise program – Virtual Gym Instructor. The system demonstrated perfect realtime object detection and tracking up to four metres away from the camera and produced results for distances up to eight metres away.
- Full Text:
- Date Issued: 2019
- Authors: Brown, Dane L , Ndleve, Mixo
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465744 , vital:76637 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378603_Virtual_Gym_Instructor/links/5d6118a892851c619d7268c1/Virtual-Gym-Instructor.pdf"
- Description: The fourth industrial revolution and the continuous development of new technologies have presented a golden platter for sedentary living. Noncommunicable diseases such as, cancers, cardiovascular and respiratory deficiencies, and diabetes have reached epidemic levels as a consequence. A traditional gym instructor screens clients to prescribe exercise programs that can help them lower the risk of noncommunicable lifestyle diseases. However, gym instructors often come at a cost and are not always affordable, available or accessible. This research investigated whether modern computing power can be utilized to develop a system in the form of a cost effective alternative exercise program – Virtual Gym Instructor. The system demonstrated perfect realtime object detection and tracking up to four metres away from the camera and produced results for distances up to eight metres away.
- Full Text:
- Date Issued: 2019
- «
- ‹
- 1
- ›
- »