Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041936
Daniel T. Schmitt, Gilbert L. Peterson
Nuclear explosion yield estimation equations based on a 3D model of the explosion volume will have a lower uncertainty than radius based estimation. To accurately collect data for a volume model of atmospheric explosions requires building a 3D representation from 2D images. The majority of 3D reconstruction algorithms use the SIFT (scale-invariant feature transform) feature detection algorithm which works best on feature-rich objects with continuous angular collections. These assumptions are different from the archive of nuclear explosions that have only 3 points of view. This paper reduces 300 dimensions derived from an image based on Fourier analysis and five edge detection algorithms to a manageable number to detect hotspots that may be used to correlate videos of different viewpoints for 3D reconstruction. Furthermore, experiments test whether histogram equalization improves detection of these features using four kernel sizes passed over these features. Dimension reduction using principal components analysis (PCA), forward subset selection, ReliefF, and FCBF (Fast Correlation-Based Filter) are combined with a Mahalanobis distance classifiers to find the best combination of dimensions, kernel size, and filtering to detect the hotspots. Results indicate that hotspots can be detected with hit rates of 90% and false alarms i 1%.
{"title":"Machine learning nuclear detonation features","authors":"Daniel T. Schmitt, Gilbert L. Peterson","doi":"10.1109/AIPR.2014.7041936","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041936","url":null,"abstract":"Nuclear explosion yield estimation equations based on a 3D model of the explosion volume will have a lower uncertainty than radius based estimation. To accurately collect data for a volume model of atmospheric explosions requires building a 3D representation from 2D images. The majority of 3D reconstruction algorithms use the SIFT (scale-invariant feature transform) feature detection algorithm which works best on feature-rich objects with continuous angular collections. These assumptions are different from the archive of nuclear explosions that have only 3 points of view. This paper reduces 300 dimensions derived from an image based on Fourier analysis and five edge detection algorithms to a manageable number to detect hotspots that may be used to correlate videos of different viewpoints for 3D reconstruction. Furthermore, experiments test whether histogram equalization improves detection of these features using four kernel sizes passed over these features. Dimension reduction using principal components analysis (PCA), forward subset selection, ReliefF, and FCBF (Fast Correlation-Based Filter) are combined with a Mahalanobis distance classifiers to find the best combination of dimensions, kernel size, and filtering to detect the hotspots. Results indicate that hotspots can be detected with hit rates of 90% and false alarms i 1%.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132418467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041939
Abby Stylianou, Joseph D. O'Sullivan, Austin Abrams, Robert Pless
The vast amount of public photographic data posted and shared on Facebook, Instragram and other forms of social media offers an unprecedented visual archive of the world. This archive captures events ranging from birthdays, trips, and graduations to lethal conflicts and human rights violations. Because this data is public, it has led to a new genre of journalism, one led by citizens finding, analyzing, and synthesizing data into stories that describe important events. To support this, we have built a set of browser-based tools for the calibration and validation of online images. This paper presents these tools in the context of their use in finding two separate lost burial locations. Often, these locations would have been marked with a headstone or tomb, but for the very poor, the forgotten, or the victims of extremist violence buried in unmarked graves, the geometric cues present in a photograph may contain the most reliable information about the burial location. The tools described in this paper allow individuals without any significant geometry background to utilize those cues to locate these lost graves, or any other outdoor image with sufficient correspondences to the physical world. We highlight the difficulties that arise due to geometric inconsistencies between corresponding points, especially when significant changes have occurred in the physical world since the photo was taken, and visualization features on our browser-based tools that help users address this.
{"title":"Images don't forget: Online photogrammetry to find lost graves","authors":"Abby Stylianou, Joseph D. O'Sullivan, Austin Abrams, Robert Pless","doi":"10.1109/AIPR.2014.7041939","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041939","url":null,"abstract":"The vast amount of public photographic data posted and shared on Facebook, Instragram and other forms of social media offers an unprecedented visual archive of the world. This archive captures events ranging from birthdays, trips, and graduations to lethal conflicts and human rights violations. Because this data is public, it has led to a new genre of journalism, one led by citizens finding, analyzing, and synthesizing data into stories that describe important events. To support this, we have built a set of browser-based tools for the calibration and validation of online images. This paper presents these tools in the context of their use in finding two separate lost burial locations. Often, these locations would have been marked with a headstone or tomb, but for the very poor, the forgotten, or the victims of extremist violence buried in unmarked graves, the geometric cues present in a photograph may contain the most reliable information about the burial location. The tools described in this paper allow individuals without any significant geometry background to utilize those cues to locate these lost graves, or any other outdoor image with sufficient correspondences to the physical world. We highlight the difficulties that arise due to geometric inconsistencies between corresponding points, especially when significant changes have occurred in the physical world since the photo was taken, and visualization features on our browser-based tools that help users address this.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132774023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041916
Kevin Krucki, V. Asari, Christoph Borel-Donohue, David J. Bunker
We propose a human re-identification algorithm for multi-camera surveillance environment where a unique signature of an individual is learned and tracked in a scene. The video feed from each camera is processed using a motion detector to get locations of all individuals. To compute the human signature, we propose a combination of different descriptors on the detected body such as the Local Binary Pattern Histogram (LBPH) for the local texture and a HSV color-space based descriptor for the color representation. For each camera, a signature computed by these descriptors is assigned to the corresponding individual along with their direction in the scene. Knowledge of the persons direction allows us to make separate identifiers for the front, back, and sides. These signatures are then used to identify individuals as they walk across different areas monitored by different cameras. The challenges involved are the variation of illumination conditions and scale across the cameras. We test our algorithm on a dataset captured with 3 Axis cameras arranged in the UD Vision Lab as well as a subset of the SAIVT dataset and provide results which illustrate the consistency of the labels as well as precision/accuracy scores.
{"title":"Human re-identification in multi-camera systems","authors":"Kevin Krucki, V. Asari, Christoph Borel-Donohue, David J. Bunker","doi":"10.1109/AIPR.2014.7041916","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041916","url":null,"abstract":"We propose a human re-identification algorithm for multi-camera surveillance environment where a unique signature of an individual is learned and tracked in a scene. The video feed from each camera is processed using a motion detector to get locations of all individuals. To compute the human signature, we propose a combination of different descriptors on the detected body such as the Local Binary Pattern Histogram (LBPH) for the local texture and a HSV color-space based descriptor for the color representation. For each camera, a signature computed by these descriptors is assigned to the corresponding individual along with their direction in the scene. Knowledge of the persons direction allows us to make separate identifiers for the front, back, and sides. These signatures are then used to identify individuals as they walk across different areas monitored by different cameras. The challenges involved are the variation of illumination conditions and scale across the cameras. We test our algorithm on a dataset captured with 3 Axis cameras arranged in the UD Vision Lab as well as a subset of the SAIVT dataset and provide results which illustrate the consistency of the labels as well as precision/accuracy scores.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131789824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041928
M. D. Pritt
Small unmanned air vehicles (UAVs) provide an economical means of imaging large areas of terrain at far lower cost than satellites. Applications range from precision agriculture to disaster response and power line maintenance. Because small UAVs fly at low altitudes of approximately 100 meters, their cameras have only a limited field of view and must take thousands of photographs to cover a reasonably sized area. To provide a unified view of the area, these photographs must be combined into a seamless photo mosaic. The conventional approach for accomplishing this mosaicking process is called block bundle adjustment, and it works well if there are only a few tens or hundreds of photographs. It runs in O(n3) time, where n is the number of images. When there are thousands of photographs, this method fails because its memory and computational time requirements become prohibitively excessive. We have developed a new technique that replaces bundle adjustment with an iterative algorithm that is very fast and requires little memory. After pairwise image registration, the algorithm projects the resulting tie points to the ground and moves them closer to each other to produce a new set of control points. It fits the image parameters to these control points and repeats the process iteratively to convergence. The algorithm is implemented as an image mosaicking application in Java and runs on a Windows PC. It executes in O(n) time and produces very high resolution mosaics (2 cm per pixel) at the rate of 14 sec per image. This time includes all steps of the mosaicking process from the disk read of the imagery to the disk output of the final mosaic. Experiments show the algorithm to be accurate and reliable for mosaicking thousands of images.
{"title":"Fast orthorectified mosaics of thousands of aerial photographs from small UAVs","authors":"M. D. Pritt","doi":"10.1109/AIPR.2014.7041928","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041928","url":null,"abstract":"Small unmanned air vehicles (UAVs) provide an economical means of imaging large areas of terrain at far lower cost than satellites. Applications range from precision agriculture to disaster response and power line maintenance. Because small UAVs fly at low altitudes of approximately 100 meters, their cameras have only a limited field of view and must take thousands of photographs to cover a reasonably sized area. To provide a unified view of the area, these photographs must be combined into a seamless photo mosaic. The conventional approach for accomplishing this mosaicking process is called block bundle adjustment, and it works well if there are only a few tens or hundreds of photographs. It runs in O(n3) time, where n is the number of images. When there are thousands of photographs, this method fails because its memory and computational time requirements become prohibitively excessive. We have developed a new technique that replaces bundle adjustment with an iterative algorithm that is very fast and requires little memory. After pairwise image registration, the algorithm projects the resulting tie points to the ground and moves them closer to each other to produce a new set of control points. It fits the image parameters to these control points and repeats the process iteratively to convergence. The algorithm is implemented as an image mosaicking application in Java and runs on a Windows PC. It executes in O(n) time and produces very high resolution mosaics (2 cm per pixel) at the rate of 14 sec per image. This time includes all steps of the mosaicking process from the disk read of the imagery to the disk output of the final mosaic. Experiments show the algorithm to be accurate and reliable for mosaicking thousands of images.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129985854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041941
Nina M. Varney, V. Asari
LiDAR data is a set of geo-spatially located points which contain (X, Y, Z) location and intensity data. This paper presents the extraction of a novel set of volume and texture-based features from segmented point clouds. First, the data is segmented into individual object regions using an automatic seeded region growing technique. Then, these object regions are normalized to a N × N × N voxel space, where each voxel contains information about the location and density of points within that voxel. A set of volumetric features are extracted to represent the object region; these features include: 3D form factor, rotation invariant local binary pattern (RILBP), fill, stretch, corrugation, contour, plainness and relative variance. The form factor, fill, and stretch provide a series of meaningful relationships between the volume, surface area, and shape of the object. RILBP provides a textural description from the height variation of the LiDAR data. The corrugation, contour, and plainness are extracted by 3D Eigen analysis of the object volume to describe the details of the object's surface. Relative variance provides an illustration of the distribution of points throughout the object. The new feature set is robust, and scale and rotation invariant for object region classification. The performance of the proposed feature extraction technique has been evaluated on a set of segmented and voxelized point cloud objects in a subset of the aerial LiDAR data from Surrey, British Columbia, which was available through the Open Data Program. The volumetric features, when used as an input to an SVM classifier, correctly classified the object regions with an accuracy of 97.5 %, with a focus on identifying five classes: ground, vegetation, buildings, vehicles, and barriers.
激光雷达数据是一组地理空间定位点，其中包含(X, Y, Z)位置和强度数据。本文提出了一种新的基于体积和纹理的点云特征提取方法。首先，使用自动种子区域生长技术将数据分割为单个目标区域。然后，将这些对象区域归一化为N × N × N体素空间，其中每个体素包含有关该体素内点的位置和密度的信息。提取一组体积特征来表示目标区域;这些特征包括:三维形状因子、旋转不变局部二值模式(RILBP)、填充、拉伸、波纹、轮廓、平面度和相对方差。形状因素、填充和拉伸在物体的体积、表面积和形状之间提供了一系列有意义的关系。RILBP从激光雷达数据的高度变化中提供纹理描述。通过对物体体积进行三维特征分析，提取物体表面的波纹、轮廓和平面，描述物体表面的细节。相对方差提供了整个对象中点分布的说明。该特征集鲁棒性好，且对目标区域分类具有尺度和旋转不变性。在一组来自不列颠哥伦比亚省萨里市的航空激光雷达数据子集的分割和体素化点云对象上，对所提出的特征提取技术的性能进行了评估，该数据可通过开放数据计划获得。当将体积特征用作支持向量机分类器的输入时，正确分类目标区域的准确率为97.5%，重点是识别五类:地面，植被，建筑物，车辆和障碍物。
{"title":"Volumetrie features for object region classification in 3D LiDAR point clouds","authors":"Nina M. Varney, V. Asari","doi":"10.1109/AIPR.2014.7041941","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041941","url":null,"abstract":"LiDAR data is a set of geo-spatially located points which contain (X, Y, Z) location and intensity data. This paper presents the extraction of a novel set of volume and texture-based features from segmented point clouds. First, the data is segmented into individual object regions using an automatic seeded region growing technique. Then, these object regions are normalized to a N × N × N voxel space, where each voxel contains information about the location and density of points within that voxel. A set of volumetric features are extracted to represent the object region; these features include: 3D form factor, rotation invariant local binary pattern (RILBP), fill, stretch, corrugation, contour, plainness and relative variance. The form factor, fill, and stretch provide a series of meaningful relationships between the volume, surface area, and shape of the object. RILBP provides a textural description from the height variation of the LiDAR data. The corrugation, contour, and plainness are extracted by 3D Eigen analysis of the object volume to describe the details of the object's surface. Relative variance provides an illustration of the distribution of points throughout the object. The new feature set is robust, and scale and rotation invariant for object region classification. The performance of the proposed feature extraction technique has been evaluated on a set of segmented and voxelized point cloud objects in a subset of the aerial LiDAR data from Surrey, British Columbia, which was available through the Open Data Program. The volumetric features, when used as an input to an SVM classifier, correctly classified the object regions with an accuracy of 97.5 %, with a focus on identifying five classes: ground, vegetation, buildings, vehicles, and barriers.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125390911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041926
Ashley Prater
Generalized Fourier series with orthogonal polynomial bases have useful applications in several fields, including pattern recognition and image and signal processing. However, computing the generalized Fourier series can be a challenging problem, even for relatively well behaved functions. In this paper, a method for approximating a sparse collection of Fourier-like coefficients is presented that uses a collocation technique combined with an optimization problem inspired by recent results in compressed sensing research. The discussion includes approximation error rates and numerical examples to illustrate the effectiveness of the method. One example displays the accuracy of the generalized Fourier series approximation for several test functions, while the other is an application of the generalized Fourier series approximation to rotation-invariant pattern recognition in images.
{"title":"Sparse generalized Fourier series via collocation-based optimization","authors":"Ashley Prater","doi":"10.1109/AIPR.2014.7041926","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041926","url":null,"abstract":"Generalized Fourier series with orthogonal polynomial bases have useful applications in several fields, including pattern recognition and image and signal processing. However, computing the generalized Fourier series can be a challenging problem, even for relatively well behaved functions. In this paper, a method for approximating a sparse collection of Fourier-like coefficients is presented that uses a collocation technique combined with an optimization problem inspired by recent results in compressed sensing research. The discussion includes approximation error rates and numerical examples to illustrate the effectiveness of the method. One example displays the accuracy of the generalized Fourier series approximation for several test functions, while the other is an application of the generalized Fourier series approximation to rotation-invariant pattern recognition in images.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126543244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041929
Prudhvi K. Gurram, R. Rao
Correlative interferometric image reconstruction is a computational imaging approach for synthesizing images from sensor arrays and relies on estimating source intensity from the cross-correlation across near-field or far-field measurements from multiple sensors of the arrays. Key to using the approach is the exploitation of relationship between the correlation and the source intensity. This relationship is of a Fourier transform type when the sensors are in the far-field of the source and the velocity of wave propagation in the intervening medium is constant. Often the estimation problem is ill-posed resulting in unrealistic reconstructions of images. Positivity constraints, boundary restrictions, ℓ1 regularization, and sparsity constrained optimization have been applied in previous work. This paper considers the noisy case and formulates the estimation problem as least squares minimization with entropy metrics, either minimum or maximum, as regularization terms. Situations involving far-field interferometric imaging of extended sources are considered and results illustrating the advantages of these entropy metrics and their applicability are provided.
{"title":"Entropy metric regularization for computational imaging with sensor arrays","authors":"Prudhvi K. Gurram, R. Rao","doi":"10.1109/AIPR.2014.7041929","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041929","url":null,"abstract":"Correlative interferometric image reconstruction is a computational imaging approach for synthesizing images from sensor arrays and relies on estimating source intensity from the cross-correlation across near-field or far-field measurements from multiple sensors of the arrays. Key to using the approach is the exploitation of relationship between the correlation and the source intensity. This relationship is of a Fourier transform type when the sensors are in the far-field of the source and the velocity of wave propagation in the intervening medium is constant. Often the estimation problem is ill-posed resulting in unrealistic reconstructions of images. Positivity constraints, boundary restrictions, ℓ1 regularization, and sparsity constrained optimization have been applied in previous work. This paper considers the noisy case and formulates the estimation problem as least squares minimization with entropy metrics, either minimum or maximum, as regularization terms. Situations involving far-field interferometric imaging of extended sources are considered and results illustrating the advantages of these entropy metrics and their applicability are provided.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133013665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041911
J. Irvine, J. Kimball, J. Lepanto, J. Regan, Richard J. Wood
Many policy and national security challenges require understanding the social, cultural, and economic characteristics of a country or region. Addressing failing states, insurgencies, terrorist threats, societal change, and support for military operations require a detailed understanding of the local population. Information about the state of the economy, levels of community support and involvement, and attitudes toward government authorities can guide decision makers in developing and implementing policies or operations. However, such information is difficult to gather in remote, inaccessible, or denied areas. Draper's previous work demonstrating the application of remote sensing to specific issues, such as population estimation, agricultural analysis, and environmental monitoring, has been very promising. In recent papers, we extended these concepts to imagery-based prediction models for governance, well-being, and social capital. Social science theory indicates the relationships among physical structures, institutional features, and social structures. Based on these relationships, we developed models for rural Afghanistan and validated the relationships using survey data. In this paper we explore the adaptation of those models to sub-Saharan Africa. Our analysis indicates that, as in Afghanistan, certain attributes of the society are predictable from imagery-derived features. The automated extraction of relevant indicators, however, depends on both spatial and spectral information. Deriving useful measures from only panchromatic imagery poses some methodological challenges and additional research is needed.
{"title":"Imagery-based modeling of social, economic, and governance indicators in sub-Saharan Africa","authors":"J. Irvine, J. Kimball, J. Lepanto, J. Regan, Richard J. Wood","doi":"10.1109/AIPR.2014.7041911","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041911","url":null,"abstract":"Many policy and national security challenges require understanding the social, cultural, and economic characteristics of a country or region. Addressing failing states, insurgencies, terrorist threats, societal change, and support for military operations require a detailed understanding of the local population. Information about the state of the economy, levels of community support and involvement, and attitudes toward government authorities can guide decision makers in developing and implementing policies or operations. However, such information is difficult to gather in remote, inaccessible, or denied areas. Draper's previous work demonstrating the application of remote sensing to specific issues, such as population estimation, agricultural analysis, and environmental monitoring, has been very promising. In recent papers, we extended these concepts to imagery-based prediction models for governance, well-being, and social capital. Social science theory indicates the relationships among physical structures, institutional features, and social structures. Based on these relationships, we developed models for rural Afghanistan and validated the relationships using survey data. In this paper we explore the adaptation of those models to sub-Saharan Africa. Our analysis indicates that, as in Afghanistan, certain attributes of the society are predictable from imagery-derived features. The automated extraction of relevant indicators, however, depends on both spatial and spectral information. Deriving useful measures from only panchromatic imagery poses some methodological challenges and additional research is needed.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129252148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041899
En-Ui Lin, Michel McLaughlin, A. Alshehri
In many medical imaging applications, a clear delineation and segmentation of areas of interest from low resolution images is crucial. It is one of the most difficult and challenging tasks in image processing and directly determines the quality of final result of the image analysis. In preparation for segmentation, we first use preprocessing methods to remove noise and blur and then we use super-resolution to produce a high resolution image. Next, we will use wavelets to decompose the image into different sub-band images. In particular, we will use discrete wavelet transformation (DWT) and its enhanced version double density dual discrete tree wavelet transformations (D3-DWT) as they provide better spatial and spectral localization of image representation and have special importance to image processing applications, especially medical imaging. The multi-scale edge information from the sub-bands is then filtered through an iterative process to produce a map displaying extracted features and edges, which is then used to segment homogenous regions. We have applied our algorithm to challenging applications such as gray matter and white matter segmentations in Magnetic Resonance Imaging (MRI) images.
{"title":"Medical image segmentation using multi-scale and super-resolution method","authors":"En-Ui Lin, Michel McLaughlin, A. Alshehri","doi":"10.1109/AIPR.2014.7041899","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041899","url":null,"abstract":"In many medical imaging applications, a clear delineation and segmentation of areas of interest from low resolution images is crucial. It is one of the most difficult and challenging tasks in image processing and directly determines the quality of final result of the image analysis. In preparation for segmentation, we first use preprocessing methods to remove noise and blur and then we use super-resolution to produce a high resolution image. Next, we will use wavelets to decompose the image into different sub-band images. In particular, we will use discrete wavelet transformation (DWT) and its enhanced version double density dual discrete tree wavelet transformations (D3-DWT) as they provide better spatial and spectral localization of image representation and have special importance to image processing applications, especially medical imaging. The multi-scale edge information from the sub-bands is then filtered through an iterative process to produce a map displaying extracted features and edges, which is then used to segment homogenous regions. We have applied our algorithm to challenging applications such as gray matter and white matter segmentations in Magnetic Resonance Imaging (MRI) images.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121098357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041934
Edward M. Schaefer Unaffiliated
A coarse representation of pictures and images can be created with sound. A series of such audio sounds can be used to represent an animation or a motion picture. In this project, images are divided into a 4×4 array of "sound elements". The position of each sound element is assigned an audio sound, and the contents of each sound element is used to compute an audio intensity. The audio for each sound element is the audio sound for its position played at the computed audio intensity. The result of combining the audios for all sound elements is an audio representing the entire image. Algorithms for creating sounds and intensities will be described. Generating sounds for motion pictures using this technique will be discussed.
{"title":"Representing pictures with sound","authors":"Edward M. Schaefer Unaffiliated","doi":"10.1109/AIPR.2014.7041934","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041934","url":null,"abstract":"A coarse representation of pictures and images can be created with sound. A series of such audio sounds can be used to represent an animation or a motion picture. In this project, images are divided into a 4×4 array of \"sound elements\". The position of each sound element is assigned an audio sound, and the contents of each sound element is used to compute an audio intensity. The audio for each sound element is the audio sound for its position played at the computed audio intensity. The result of combining the audios for all sound elements is an audio representing the entire image. Algorithms for creating sounds and intensities will be described. Generating sounds for motion pictures using this technique will be discussed.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117124270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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