Pub Date : 2014-10-01DOI: 10.1109/AIPR.2014.7041914
V. Khryashchev, A. Priorov, A. Ganin
An application for video data analysis based on computer vision and machine learning methods is presented. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 94% accuracy of viewer's gender recognition is achieved. Our age estimation algorithm provides world-quality results for MORTH database, but focused on real-life audience measurement videodata in which faces can be looks more or less similar to RUS-FD private database. In this case we can reach total mean absolute error score less than 7. All the video processing stages are united into a real-time system of audience analysis. The system allows to extract all the possible information about people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters. The promising practical application of such algorithms can be human-computer interaction, surveillance monitoring, video content analysis, targeted advertising, biometrics, and entertainment.
{"title":"Gender and age recognition for video analytics solution","authors":"V. Khryashchev, A. Priorov, A. Ganin","doi":"10.1109/AIPR.2014.7041914","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041914","url":null,"abstract":"An application for video data analysis based on computer vision and machine learning methods is presented. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 94% accuracy of viewer's gender recognition is achieved. Our age estimation algorithm provides world-quality results for MORTH database, but focused on real-life audience measurement videodata in which faces can be looks more or less similar to RUS-FD private database. In this case we can reach total mean absolute error score less than 7. All the video processing stages are united into a real-time system of audience analysis. The system allows to extract all the possible information about people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters. The promising practical application of such algorithms can be human-computer interaction, surveillance monitoring, video content analysis, targeted advertising, biometrics, and entertainment.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132224939","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.2014.7041925
Joseph D. O'Sullivan, Abby Stylianou, Austin Abrams, Robert Pless
Five years ago we reported at AIPR on a nascent project to archive images from every webcam in the world and to develop algorithms to geo-locate, calibrate, and annotate this data. This archive of many outdoor scenes (AMOS) has now grown to include 28000 live outdoor cameras and over 630 million images. This is actively being used in projects ranging from large scale environmental monitoring to characterizing how built environment changes (such as adding bike lanes in DC) affects physical activity patterns over time. But the biggest value in a very long term, widely distributed image dataset is the rich set of before data that can be analyzed to evaluate changes from unexpected or sudden events. To facilitate the analysis of these natural experiments, we build and share a collection of web-tools that support large scale, data driven exploration. In this work we discuss and motivate a visualization tool that uses PCA to find the subspace that characterizes the variations in this scene, This anomaly detection captures both imaging failures such as lens flare and also unusual situations such as street fairs, and we give initial algorithm to clusters anomalies so that they can be quickly evaluated for whether they are of interest.
{"title":"Democratizing the visualization of 500 million webcam images","authors":"Joseph D. O'Sullivan, Abby Stylianou, Austin Abrams, Robert Pless","doi":"10.1109/AIPR.2014.7041925","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041925","url":null,"abstract":"Five years ago we reported at AIPR on a nascent project to archive images from every webcam in the world and to develop algorithms to geo-locate, calibrate, and annotate this data. This archive of many outdoor scenes (AMOS) has now grown to include 28000 live outdoor cameras and over 630 million images. This is actively being used in projects ranging from large scale environmental monitoring to characterizing how built environment changes (such as adding bike lanes in DC) affects physical activity patterns over time. But the biggest value in a very long term, widely distributed image dataset is the rich set of before data that can be analyzed to evaluate changes from unexpected or sudden events. To facilitate the analysis of these natural experiments, we build and share a collection of web-tools that support large scale, data driven exploration. In this work we discuss and motivate a visualization tool that uses PCA to find the subspace that characterizes the variations in this scene, This anomaly detection captures both imaging failures such as lens flare and also unusual situations such as street fairs, and we give initial algorithm to clusters anomalies so that they can be quickly evaluated for whether they are of interest.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132556171","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.2014.7041927
Carrie Pritt
The goal of this work is the development of a low-cost driver assistance system that runs on an ordinary smartphone. It uses computer vision techniques and multiple-resolution template matching to detect speed limit signs and alert the driver if the speed limit is exceeded. It inputs an image of the sign to be detected and creates a set of multiple-resolution templates. It also inputs photographs of the road from the smartphone camera at regular intervals and generates multiple-resolution images from the photographs. In the first step of processing, fast filters restrict the focus of attention to smaller areas of the photographs where signs are likely to be present. In the second step, the system matches the templates against the photographs using fast normalized cross correlation to detect speed limit signs. The multiple resolutions enable this approach to detect signs at different scales. In the third step, the system recognizes the sign by matching a series of annotated speed templates to the image at the position and scale that were determined by the detection step. It compares the speed limit with the actual vehicle speed as computed from the smartphone GPS device and issues warnings to the driver as necessary. The system is implemented as an Android application that runs on an ordinary smartphone as part of a client-server architecture. It processes photos at a rate of 1 Hz with a probability of detection of 0.93 at the 95% confidence level and a false alarm rate of 0.0007, or one false classification every 25 min.
{"title":"Road sign detection on a smartphone for traffic safety","authors":"Carrie Pritt","doi":"10.1109/AIPR.2014.7041927","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041927","url":null,"abstract":"The goal of this work is the development of a low-cost driver assistance system that runs on an ordinary smartphone. It uses computer vision techniques and multiple-resolution template matching to detect speed limit signs and alert the driver if the speed limit is exceeded. It inputs an image of the sign to be detected and creates a set of multiple-resolution templates. It also inputs photographs of the road from the smartphone camera at regular intervals and generates multiple-resolution images from the photographs. In the first step of processing, fast filters restrict the focus of attention to smaller areas of the photographs where signs are likely to be present. In the second step, the system matches the templates against the photographs using fast normalized cross correlation to detect speed limit signs. The multiple resolutions enable this approach to detect signs at different scales. In the third step, the system recognizes the sign by matching a series of annotated speed templates to the image at the position and scale that were determined by the detection step. It compares the speed limit with the actual vehicle speed as computed from the smartphone GPS device and issues warnings to the driver as necessary. The system is implemented as an Android application that runs on an ordinary smartphone as part of a client-server architecture. It processes photos at a rate of 1 Hz with a probability of detection of 0.93 at the 95% confidence level and a false alarm rate of 0.0007, or one false classification every 25 min.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123302992","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.2014.7041903
Christoph Borel-Donohue, D. Rosario, J. Romano
In this paper we describe the end-to-end processing of image Fourier Transform spectrometry data taken at Picatinny Arsenal in New Jersey with the long-wave hyperspectral camera from Telops. The first part of the paper discusses the processing from raw data to calibrated radiance and emissivity data. Data was taken during several months under different weather conditions every 6 minutes from a 213ft high tower of surrogate tank targets for a project sponsored by the Army Research Laboratory in Adelphi, MD. An automatic calibration and analysis program was developed which creates calibrated data files and HTML files. The first processing stage is a flat-fielding. During this step the mean base line is used to find dead pixels (baseline low or at the maximum). Noisy pixels are detected where the standard deviation over the part of the interferogram. A flat-fielded and bad pixel corrected calibration cube using the gain and offset determined by a single blackbody measurement is created. In the second stage each flat-fielded cube is Fourier transformed and a 2-point radiometric calibration is performed. For selected cubes a temperature-emissivity separation algorithm is applied. The second part discusses environmental effects such as diurnal and seasonal atmospheric and temperature changes and the effect of cloud cover on the data. To test the effect of environmental conditions the range-invariant anomaly detection approach is applied to calibrated radiance, brightness temperature and emissivity data.
{"title":"Analysis of diurnal, long-wave hyperspectral measurements of natural background and manmade targets under different weather conditions","authors":"Christoph Borel-Donohue, D. Rosario, J. Romano","doi":"10.1109/AIPR.2014.7041903","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041903","url":null,"abstract":"In this paper we describe the end-to-end processing of image Fourier Transform spectrometry data taken at Picatinny Arsenal in New Jersey with the long-wave hyperspectral camera from Telops. The first part of the paper discusses the processing from raw data to calibrated radiance and emissivity data. Data was taken during several months under different weather conditions every 6 minutes from a 213ft high tower of surrogate tank targets for a project sponsored by the Army Research Laboratory in Adelphi, MD. An automatic calibration and analysis program was developed which creates calibrated data files and HTML files. The first processing stage is a flat-fielding. During this step the mean base line is used to find dead pixels (baseline low or at the maximum). Noisy pixels are detected where the standard deviation over the part of the interferogram. A flat-fielded and bad pixel corrected calibration cube using the gain and offset determined by a single blackbody measurement is created. In the second stage each flat-fielded cube is Fourier transformed and a 2-point radiometric calibration is performed. For selected cubes a temperature-emissivity separation algorithm is applied. The second part discusses environmental effects such as diurnal and seasonal atmospheric and temperature changes and the effect of cloud cover on the data. To test the effect of environmental conditions the range-invariant anomaly detection approach is applied to calibrated radiance, brightness temperature and emissivity data.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123512010","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.2014.7041924
Jing Peng, G. Seetharaman
Parzen Windows classifiers have been applied to a variety of density estimation as well as classification tasks with considerable success. Parzen Windows are known to converge in the asymptotic limit. However, there is a lack of theoretical analysis on their performance with finite samples. In this paper we show a connection between Parzen Windows and the regularized least squares algorithm, which has a well-established foundation in computational learning theory. This connection allows us to provide useful insight into Parzen Windows classifiers and their performance in finite sample settings. Finally, we show empirical results on the performance of Parzen Windows classifiers using a number of real data sets.
{"title":"On Parzen windows classifiers","authors":"Jing Peng, G. Seetharaman","doi":"10.1109/AIPR.2014.7041924","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041924","url":null,"abstract":"Parzen Windows classifiers have been applied to a variety of density estimation as well as classification tasks with considerable success. Parzen Windows are known to converge in the asymptotic limit. However, there is a lack of theoretical analysis on their performance with finite samples. In this paper we show a connection between Parzen Windows and the regularized least squares algorithm, which has a well-established foundation in computational learning theory. This connection allows us to provide useful insight into Parzen Windows classifiers and their performance in finite sample settings. Finally, we show empirical results on the performance of Parzen Windows classifiers using a number of real data sets.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124841976","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.2014.7041935
A. Schaum
In 1950 Abraham Wald proved that every admissible statistical decision rule is either a Bayesian procedure or the limit of a sequence of such procedures. He thus provided a decision-theoretic justification for the use of Bayesian inference, even for non-Bayesian problems. It is often assumed that his result also justified the use of Bayesian priors to solve such problems. However, the principles one should use for defining the values of prior probabilities have been controversial for decades, especially when applied to epistemic unknowns. Now a new approach indirectly assigns values to the quantities usually interpreted as priors by imposing design constraints on a detection algorithm. No assumptions about prior "states of belief are necessary. The result shows how Wald's theorem can accommodate both Bayesian and non-Bayesian problems. The unification is mediated by the fusion of clairvoyant detectors.
{"title":"Bayesian solutions to non-Bayesian detection problems: Unification through fusion","authors":"A. Schaum","doi":"10.1109/AIPR.2014.7041935","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041935","url":null,"abstract":"In 1950 Abraham Wald proved that every admissible statistical decision rule is either a Bayesian procedure or the limit of a sequence of such procedures. He thus provided a decision-theoretic justification for the use of Bayesian inference, even for non-Bayesian problems. It is often assumed that his result also justified the use of Bayesian priors to solve such problems. However, the principles one should use for defining the values of prior probabilities have been controversial for decades, especially when applied to epistemic unknowns. Now a new approach indirectly assigns values to the quantities usually interpreted as priors by imposing design constraints on a detection algorithm. No assumptions about prior \"states of belief are necessary. The result shows how Wald's theorem can accommodate both Bayesian and non-Bayesian problems. The unification is mediated by the fusion of clairvoyant detectors.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126382271","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.2014.7041940
Christoph Borel-Donohue, David J. Bunker, G. Walford
Baseline radiation background is almost never known and constantly changes particularly in urban areas. It is difficult to know what the expected background radiation should be and how a radiological incident may elevate the radiation. Naturally occurring radiation from rocks and building materials often contributes significantly to measured radiation. Buildings and other tall structures also shield radiation and thus need to be taken into account. Models of natural occurring background radiation can be derived from knowledge of geology, building material origins, vegetation, and weather conditions. After a radiological incident, the radiation will be elevated near the event, and some material may be transported by mechanisms such as airborne transport and/or run-off. Locating and characterizing the sources of radiation quickly and efficiently are crucial in the immediate aftermath of a nuclear incident. The distribution of radiation sources will change naturally and also due to clean-up efforts. Finding source strengths and locations during both the initial and clean-up stages is necessary to manage and reduce contaminations. The overall objective of the Rapid Location Of Radiation Sources In Complex Environments Using Optical And Radiation research project is to design and validate gamma ray spectrum estimation algorithms that integrate optical and radiation sensor collections into high resolution, multi-modal site models for use in radiative transport codes. Our initial focus is on modeling the background radiation using hyper-spectral information from visible through the shortwave infrared sensors and thermal imagers. The optical data complements available ancillary data from other sources such as Geographic Information Systems (GIS) layers, e.g. geologic maps, terrain, surface cover type, road network, vegetation (e.g. serpentine vegetation), 3-D building models, known users of radiological sources, etc. In absence of GIS layers, the data from the multi/hyper-spectral imager and height data from LIDAR can be analyzed with special with special software to automatically create GIS layers and radiation survey data to come up with a method to predict background radiation distribution. We believe the estimation and prediction of the natural background will be helpful in finding anomalous point, line and small area sources and minimize the number of false alarms due to natural and known man-made radiation sources such as radiological medical facilities, industrial users of radiological sources.
{"title":"Rapid location of radiation sources in complex environments using optical and radiation sensors","authors":"Christoph Borel-Donohue, David J. Bunker, G. Walford","doi":"10.1109/AIPR.2014.7041940","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041940","url":null,"abstract":"Baseline radiation background is almost never known and constantly changes particularly in urban areas. It is difficult to know what the expected background radiation should be and how a radiological incident may elevate the radiation. Naturally occurring radiation from rocks and building materials often contributes significantly to measured radiation. Buildings and other tall structures also shield radiation and thus need to be taken into account. Models of natural occurring background radiation can be derived from knowledge of geology, building material origins, vegetation, and weather conditions. After a radiological incident, the radiation will be elevated near the event, and some material may be transported by mechanisms such as airborne transport and/or run-off. Locating and characterizing the sources of radiation quickly and efficiently are crucial in the immediate aftermath of a nuclear incident. The distribution of radiation sources will change naturally and also due to clean-up efforts. Finding source strengths and locations during both the initial and clean-up stages is necessary to manage and reduce contaminations. The overall objective of the Rapid Location Of Radiation Sources In Complex Environments Using Optical And Radiation research project is to design and validate gamma ray spectrum estimation algorithms that integrate optical and radiation sensor collections into high resolution, multi-modal site models for use in radiative transport codes. Our initial focus is on modeling the background radiation using hyper-spectral information from visible through the shortwave infrared sensors and thermal imagers. The optical data complements available ancillary data from other sources such as Geographic Information Systems (GIS) layers, e.g. geologic maps, terrain, surface cover type, road network, vegetation (e.g. serpentine vegetation), 3-D building models, known users of radiological sources, etc. In absence of GIS layers, the data from the multi/hyper-spectral imager and height data from LIDAR can be analyzed with special with special software to automatically create GIS layers and radiation survey data to come up with a method to predict background radiation distribution. We believe the estimation and prediction of the natural background will be helpful in finding anomalous point, line and small area sources and minimize the number of false alarms due to natural and known man-made radiation sources such as radiological medical facilities, industrial users of radiological sources.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125351302","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.2014.7041902
Daniel T. Schmitt, Gilbert L. Peterson
During the 1950s and 1960s the United States conducted and filmed over 200 atmospheric nuclear tests establishing the foundations of atmospheric nuclear detonation behavior. Each explosion was documented with about 20 videos from three or four points of view. Synthesizing the videos into a 3D video will improve yield estimates and reduce error factors. The videos were captured at a nominal 2500 frames per second, but range from 2300-3100 frames per second during operation. In order to combine them into one 3D video, individual video frames need to be correlated in time with each other. When the videos were captured a timing system was used that shined light in a video every 5 milliseconds creating a small circle exposed in the frame. This paper investigates several method of extracting the timing from images in the cases when the timing marks are occluded and washed out, as well as when the films are exposed as expected. Results show an improvement over past techniques. For normal videos, occluded videos, and washed out videos, timing is detected with 99.3%, 77.3%, and 88.6% probability with a 2.6%, 11.3%, 5.9% false alarm rate, respectively.
{"title":"Timing mark detection on nuclear detonation video","authors":"Daniel T. Schmitt, Gilbert L. Peterson","doi":"10.1109/AIPR.2014.7041902","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041902","url":null,"abstract":"During the 1950s and 1960s the United States conducted and filmed over 200 atmospheric nuclear tests establishing the foundations of atmospheric nuclear detonation behavior. Each explosion was documented with about 20 videos from three or four points of view. Synthesizing the videos into a 3D video will improve yield estimates and reduce error factors. The videos were captured at a nominal 2500 frames per second, but range from 2300-3100 frames per second during operation. In order to combine them into one 3D video, individual video frames need to be correlated in time with each other. When the videos were captured a timing system was used that shined light in a video every 5 milliseconds creating a small circle exposed in the frame. This paper investigates several method of extracting the timing from images in the cases when the timing marks are occluded and washed out, as well as when the films are exposed as expected. Results show an improvement over past techniques. For normal videos, occluded videos, and washed out videos, timing is detected with 99.3%, 77.3%, and 88.6% probability with a 2.6%, 11.3%, 5.9% false alarm rate, respectively.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124088782","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.2014.7041898
K. Tang, Henry Y. T. Ngan
In visual surveillance, vehicle tracking and identification is very popular and applied in many applications such as traffic incident detection, traffic control and management. Edge detection is the key to the success of vehicle tracking and identification. Edge detection is to identify edge locations or geometrical shape changes in term of pixel value along a boundary of two regions in an image. This paper aims to investigate different edge detection methods and introduce a Cross Filter (CF) method, with a two-phase filtering approach, for vehicle images in a given database. First, four classical edge detectors namely the Canny detector, Prewitt detector, Roberts detector and Sobel detector are tested on the vehicle images. The Canny detected image is found to offer the best performance in Phase 1. In Phase 2, the robust CF, based on a spatial relationship of intensity change on edges, is applied on the Canny detected image as a second filtering process. Visual and numerical comparisons among the classical edge detectors and CF detector are also given. The average DSR of the proposed CF method on 10 vehicle images is 95.57%.
{"title":"Robust vehicle edge detection by cross filter method","authors":"K. Tang, Henry Y. T. Ngan","doi":"10.1109/AIPR.2014.7041898","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041898","url":null,"abstract":"In visual surveillance, vehicle tracking and identification is very popular and applied in many applications such as traffic incident detection, traffic control and management. Edge detection is the key to the success of vehicle tracking and identification. Edge detection is to identify edge locations or geometrical shape changes in term of pixel value along a boundary of two regions in an image. This paper aims to investigate different edge detection methods and introduce a Cross Filter (CF) method, with a two-phase filtering approach, for vehicle images in a given database. First, four classical edge detectors namely the Canny detector, Prewitt detector, Roberts detector and Sobel detector are tested on the vehicle images. The Canny detected image is found to offer the best performance in Phase 1. In Phase 2, the robust CF, based on a spatial relationship of intensity change on edges, is applied on the Canny detected image as a second filtering process. Visual and numerical comparisons among the classical edge detectors and CF detector are also given. The average DSR of the proposed CF method on 10 vehicle images is 95.57%.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115816242","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.2014.7041920
Jacob A. Martin, K. Gross
Polarimetric and hyperspectral imaging are two of the most frequently used remote sensing modalities. While extensive work has been done in both fields independently, relatively little work has been done using both in conjunction with one another. Combining these two common remote sensing techniques, we hope to estimate index of refraction, without a priori knoweledge of local weather conditions or object surface temperature. In general, this is an underdetermined problem, but modeling the spectral behavior of the index of refraction reduces the number of parameters needed to describe the index of refraction, and thus the reflectively. This allows additional scene parameters needed to describe the radiance signature from a target to be simulataneously solved for. The method uses spectrally resolved S0 and S1 radiance measurements, taken using an IFTS with a linear polarizer mounted to the front, to simultaneously solve for a materials index of refraction, surface temperature, and downwelling radiance. Measurements at multiple angles relative to the surface normal can also be taken to provide further constraining information in the fit. Results on both simulated and measured data are presented showing that this technique is largely robust to changes in object temperature.
{"title":"Enhanced material identification using polarimetric hyperspectral imaging","authors":"Jacob A. Martin, K. Gross","doi":"10.1109/AIPR.2014.7041920","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041920","url":null,"abstract":"Polarimetric and hyperspectral imaging are two of the most frequently used remote sensing modalities. While extensive work has been done in both fields independently, relatively little work has been done using both in conjunction with one another. Combining these two common remote sensing techniques, we hope to estimate index of refraction, without a priori knoweledge of local weather conditions or object surface temperature. In general, this is an underdetermined problem, but modeling the spectral behavior of the index of refraction reduces the number of parameters needed to describe the index of refraction, and thus the reflectively. This allows additional scene parameters needed to describe the radiance signature from a target to be simulataneously solved for. The method uses spectrally resolved S0 and S1 radiance measurements, taken using an IFTS with a linear polarizer mounted to the front, to simultaneously solve for a materials index of refraction, surface temperature, and downwelling radiance. Measurements at multiple angles relative to the surface normal can also be taken to provide further constraining information in the fit. Results on both simulated and measured data are presented showing that this technique is largely robust to changes in object temperature.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125480558","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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