Plant breeding programs demand efficient and accurate methods for crop phenotyping, especially fo... more Plant breeding programs demand efficient and accurate methods for crop phenotyping, especially for economically important crops like soybeans. Traditional manual assessment methods are labor-intensive and prone to errors. The emerging field of phenomics leverages advanced technologies, including high-resolution satellite and drone imagery, to monitor crops in a time, cost and labor efficient way. Drones offer localized, high-resolution data but have limitations in coverage and operator skills. In contrast, high-resolution satellite imagery provides broad-scale views of the vegetation with increasing improvements in spatial and temporal resolution. Our study investigates the potential of high-resolution satellite imagery as an alternative to drone imagery for assessing soybean maturity and monitoring the crop condition in a small plot breeding program. We compare the efficiency of these two technologies and we explore the utilization of various vegetation indices (VIs) derived from s...
is a native of India and spent a large part of his life in the United States. His research intere... more is a native of India and spent a large part of his life in the United States. His research interests include safety in distributed systems, centrality in tree graphs, and the social consequences of information technology. Jeremiah Barr is an undergraduate at Mount Mercy College in Cedar Rapids, Iowa. He is studying Computer Science and Mathematics. He is also a part-time business software developer. His fields of interest include Artificial Intelligence. 1
International Journal of Applied Earth Observation and Geoinformation, 2021
Abstract Improved data on crop type and crop area from satellite imagery are invaluable for agron... more Abstract Improved data on crop type and crop area from satellite imagery are invaluable for agronomy managers and are crucial for balancing agricultural expansion and forest degradation. However, large-scale maps of crop type and crop area using satellite imagery are not easily available in some regions, especially Brazil. Reasons for this include limited ground truth data, inadequate spatial and temporal satellite data availability, computational challenges, lack of cropland data and field boundaries. In this paper, we attempted to overcome some of these obstacles by using an ensemble of approaches to generate crop classification maps for Brazil. In order to compensate for the lack of abundant ground truth data in Brazil, we combined extensive field data and satellite input features from the United States with available field data and satellite input features from Brazil to train crop classification model for Brazil. Before applying the crop classification model for Brazil, we classified cropland areas using harmonic functions and delineated field boundaries using a supervised deep learning approach. Cropland masking and field boundary delineation allowed field-level mapping of crop type and crop area. Applying the crop classification model for Brazil in the states of Mato Grosso and Goias gave a true positive accuracy of 88% in the 2017/2018 summer growing season for soybean classification, 95% in the 2018 safrinha growing season for corn classification, and 86% in the 2018/2019 summer growing season for soybean classification. Our crop area estimates also showed a good agreement (correlation of 0.95 and mean absolute error of 0.64) with state-scale statistical data provided by the Companhia Nacional de Abastecimento (CONAB) in both summer and safrinha growing seasons adding further confidence to the results. These results suggest that extensive data from one geography can be used to train machine learning models in conjunction with limited field data from another geography. Accuracy assessments support the portability of crop classification model for Brazil with reasonable accuracy spatially, as tested in the state of Parana, and temporally, to the following year. The approaches and datasets presented in this paper provide building blocks for large-scale crop monitoring benefitting both public and private sectors.
The use of near-IR images for face recognition has been proposed as a means to address illuminati... more The use of near-IR images for face recognition has been proposed as a means to address illumination issues that can hinder standard visible light face matching. However, most existing non-experimental databases contain visible light images. This makes the matching of near-IR face images to visible light face images an interesting and useful chal-lenge. Image pre-processing techniques can potentially be used to help reduce the differences between near-IR and visible light images, with the goal of improving matching accuracy. We evaluate the use of several such techniques in combination with commercial matchers and show that simply extracting the red plane results in a comparable im-provement in accuracy. In addition, we show that many of the pre-processing techniques hinder the ability of existing commercial matchers to extract templates. We also make available a new dataset called Near Infrared Visible Light Database (ND-NIVL) consisting of visible light and near-IR face images with...
IEEE Transactions on Information Forensics and Security, 2014
ABSTRACT Clustering approaches can alleviate the burden of tagging face identities in ad hoc vide... more ABSTRACT Clustering approaches can alleviate the burden of tagging face identities in ad hoc video and image collections. We introduce a novel semisupervised framework for clustering face patterns into identity groups using minimal human interaction. This technique combines concepts from ensemble clustering and active learning to improve clustering accuracy. The framework actively queries the user for a soft link constraint between each pair of neighboring faces that are ambiguously matched according to the ensemble. We demonstrate the efficacy of our approach with the broadest evaluation of active face clustering algorithms to date. Our evaluations focus on data that is appropriate for human-in-the-loop face recognition, including blurry point-and-shoot videos, images of women seen before and after the application of makeup, and photographs of twins. The results indicate that ensemble-based constrained clustering algorithms are generally more robust to noise than alternative approaches. Finally, we show that the proposed clustering algorithm is more accurate and parsimonious than the current state-of-the-art.
International Journal of Pattern Recognition and Artificial Intelligence, 2012
Driven by key law enforcement and commercial applications, research on face recognition from vide... more Driven by key law enforcement and commercial applications, research on face recognition from video sources has intensified in recent years. The ensuing results have demonstrated that videos possess unique properties that allow both humans and automated systems to perform recognition accurately in difficult viewing conditions. However, significant research challenges remain as most video-based applications do not allow for controlled recordings. In this survey, we categorize the research in this area and present a broad and deep review of recently proposed methods for overcoming the difficulties encountered in unconstrained settings. We also draw connections between the ways in which humans and current algorithms recognize faces. An overview of the most popular and difficult publicly available face video databases is provided to complement these discussions. Finally, we cover key research challenges and opportunities that lie ahead for the field as a whole.
2011 IEEE Workshop on Applications of Computer Vision (WACV), 2011
ABSTRACT We introduce the questionable observer detection problem: Given a collection of videos o... more ABSTRACT We introduce the questionable observer detection problem: Given a collection of videos of crowds, determine which individuals appear unusually often across the set of videos. The algorithm proposed here detects these individuals by clustering sequences of face images. To provide robustness to sensor noise, facial expression and resolution variations, blur, and intermittent occlusions, we merge similar face image sequences from the same video and discard outlying face patterns prior to clustering. We present experiments on a challenging video dataset. The results show that the proposed method can surpass the performance of a clustering algorithm based on the VeriLook face recognition software by Neurotechnology both in terms of the detection rate and the false detection frequency.
CiteSeerX - Document Details (Isaac Councill, Lee Giles): We discuss and test empirically the eff... more CiteSeerX - Document Details (Isaac Councill, Lee Giles): We discuss and test empirically the effects of six dimensions along which existing decision tree induction algorithms differ. These are: Node type (univariate vs multivariate), branching factor (two or more), grouping of classes ...
IEEE Winter Conference on Applications of Computer Vision, 2014
ABSTRACT We introduce a method for extracting the social network structure for the persons appear... more ABSTRACT We introduce a method for extracting the social network structure for the persons appearing in a set of video clips. Individuals are unknown, and are not matched against known enrollments. An identity cluster representing an individual is formed by grouping similar-appearing faces from different videos. Each identity cluster is represented by a node in the social network. Two nodes are linked if the faces from their clusters appeared together in one or more video frames. Our approach incorporates a novel active clustering technique to create more accurate identity clusters based on feedback from the user about ambiguously matched faces. The final output consists of one or more network structures that represent the social group(s), and a list of persons who potentially connect multiple social groups. Our results demonstrate the efficacy of the proposed clustering algorithm and network analysis techniques.
Plant breeding programs demand efficient and accurate methods for crop phenotyping, especially fo... more Plant breeding programs demand efficient and accurate methods for crop phenotyping, especially for economically important crops like soybeans. Traditional manual assessment methods are labor-intensive and prone to errors. The emerging field of phenomics leverages advanced technologies, including high-resolution satellite and drone imagery, to monitor crops in a time, cost and labor efficient way. Drones offer localized, high-resolution data but have limitations in coverage and operator skills. In contrast, high-resolution satellite imagery provides broad-scale views of the vegetation with increasing improvements in spatial and temporal resolution. Our study investigates the potential of high-resolution satellite imagery as an alternative to drone imagery for assessing soybean maturity and monitoring the crop condition in a small plot breeding program. We compare the efficiency of these two technologies and we explore the utilization of various vegetation indices (VIs) derived from s...
is a native of India and spent a large part of his life in the United States. His research intere... more is a native of India and spent a large part of his life in the United States. His research interests include safety in distributed systems, centrality in tree graphs, and the social consequences of information technology. Jeremiah Barr is an undergraduate at Mount Mercy College in Cedar Rapids, Iowa. He is studying Computer Science and Mathematics. He is also a part-time business software developer. His fields of interest include Artificial Intelligence. 1
International Journal of Applied Earth Observation and Geoinformation, 2021
Abstract Improved data on crop type and crop area from satellite imagery are invaluable for agron... more Abstract Improved data on crop type and crop area from satellite imagery are invaluable for agronomy managers and are crucial for balancing agricultural expansion and forest degradation. However, large-scale maps of crop type and crop area using satellite imagery are not easily available in some regions, especially Brazil. Reasons for this include limited ground truth data, inadequate spatial and temporal satellite data availability, computational challenges, lack of cropland data and field boundaries. In this paper, we attempted to overcome some of these obstacles by using an ensemble of approaches to generate crop classification maps for Brazil. In order to compensate for the lack of abundant ground truth data in Brazil, we combined extensive field data and satellite input features from the United States with available field data and satellite input features from Brazil to train crop classification model for Brazil. Before applying the crop classification model for Brazil, we classified cropland areas using harmonic functions and delineated field boundaries using a supervised deep learning approach. Cropland masking and field boundary delineation allowed field-level mapping of crop type and crop area. Applying the crop classification model for Brazil in the states of Mato Grosso and Goias gave a true positive accuracy of 88% in the 2017/2018 summer growing season for soybean classification, 95% in the 2018 safrinha growing season for corn classification, and 86% in the 2018/2019 summer growing season for soybean classification. Our crop area estimates also showed a good agreement (correlation of 0.95 and mean absolute error of 0.64) with state-scale statistical data provided by the Companhia Nacional de Abastecimento (CONAB) in both summer and safrinha growing seasons adding further confidence to the results. These results suggest that extensive data from one geography can be used to train machine learning models in conjunction with limited field data from another geography. Accuracy assessments support the portability of crop classification model for Brazil with reasonable accuracy spatially, as tested in the state of Parana, and temporally, to the following year. The approaches and datasets presented in this paper provide building blocks for large-scale crop monitoring benefitting both public and private sectors.
The use of near-IR images for face recognition has been proposed as a means to address illuminati... more The use of near-IR images for face recognition has been proposed as a means to address illumination issues that can hinder standard visible light face matching. However, most existing non-experimental databases contain visible light images. This makes the matching of near-IR face images to visible light face images an interesting and useful chal-lenge. Image pre-processing techniques can potentially be used to help reduce the differences between near-IR and visible light images, with the goal of improving matching accuracy. We evaluate the use of several such techniques in combination with commercial matchers and show that simply extracting the red plane results in a comparable im-provement in accuracy. In addition, we show that many of the pre-processing techniques hinder the ability of existing commercial matchers to extract templates. We also make available a new dataset called Near Infrared Visible Light Database (ND-NIVL) consisting of visible light and near-IR face images with...
IEEE Transactions on Information Forensics and Security, 2014
ABSTRACT Clustering approaches can alleviate the burden of tagging face identities in ad hoc vide... more ABSTRACT Clustering approaches can alleviate the burden of tagging face identities in ad hoc video and image collections. We introduce a novel semisupervised framework for clustering face patterns into identity groups using minimal human interaction. This technique combines concepts from ensemble clustering and active learning to improve clustering accuracy. The framework actively queries the user for a soft link constraint between each pair of neighboring faces that are ambiguously matched according to the ensemble. We demonstrate the efficacy of our approach with the broadest evaluation of active face clustering algorithms to date. Our evaluations focus on data that is appropriate for human-in-the-loop face recognition, including blurry point-and-shoot videos, images of women seen before and after the application of makeup, and photographs of twins. The results indicate that ensemble-based constrained clustering algorithms are generally more robust to noise than alternative approaches. Finally, we show that the proposed clustering algorithm is more accurate and parsimonious than the current state-of-the-art.
International Journal of Pattern Recognition and Artificial Intelligence, 2012
Driven by key law enforcement and commercial applications, research on face recognition from vide... more Driven by key law enforcement and commercial applications, research on face recognition from video sources has intensified in recent years. The ensuing results have demonstrated that videos possess unique properties that allow both humans and automated systems to perform recognition accurately in difficult viewing conditions. However, significant research challenges remain as most video-based applications do not allow for controlled recordings. In this survey, we categorize the research in this area and present a broad and deep review of recently proposed methods for overcoming the difficulties encountered in unconstrained settings. We also draw connections between the ways in which humans and current algorithms recognize faces. An overview of the most popular and difficult publicly available face video databases is provided to complement these discussions. Finally, we cover key research challenges and opportunities that lie ahead for the field as a whole.
2011 IEEE Workshop on Applications of Computer Vision (WACV), 2011
ABSTRACT We introduce the questionable observer detection problem: Given a collection of videos o... more ABSTRACT We introduce the questionable observer detection problem: Given a collection of videos of crowds, determine which individuals appear unusually often across the set of videos. The algorithm proposed here detects these individuals by clustering sequences of face images. To provide robustness to sensor noise, facial expression and resolution variations, blur, and intermittent occlusions, we merge similar face image sequences from the same video and discard outlying face patterns prior to clustering. We present experiments on a challenging video dataset. The results show that the proposed method can surpass the performance of a clustering algorithm based on the VeriLook face recognition software by Neurotechnology both in terms of the detection rate and the false detection frequency.
CiteSeerX - Document Details (Isaac Councill, Lee Giles): We discuss and test empirically the eff... more CiteSeerX - Document Details (Isaac Councill, Lee Giles): We discuss and test empirically the effects of six dimensions along which existing decision tree induction algorithms differ. These are: Node type (univariate vs multivariate), branching factor (two or more), grouping of classes ...
IEEE Winter Conference on Applications of Computer Vision, 2014
ABSTRACT We introduce a method for extracting the social network structure for the persons appear... more ABSTRACT We introduce a method for extracting the social network structure for the persons appearing in a set of video clips. Individuals are unknown, and are not matched against known enrollments. An identity cluster representing an individual is formed by grouping similar-appearing faces from different videos. Each identity cluster is represented by a node in the social network. Two nodes are linked if the faces from their clusters appeared together in one or more video frames. Our approach incorporates a novel active clustering technique to create more accurate identity clusters based on feedback from the user about ambiguously matched faces. The final output consists of one or more network structures that represent the social group(s), and a list of persons who potentially connect multiple social groups. Our results demonstrate the efficacy of the proposed clustering algorithm and network analysis techniques.
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Papers by Jeremiah Barr