Pub Date : 2003-10-01DOI: 10.1109/AIPR.2003.1284249
P. Perconti, J. Hilger, M. Loew
The US Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate (NVESD) has a dynamic applied research program in sensor fusion for a wide variety of defense & defense related applications. This paper highlights efforts under the NVESD Sensor Fusion Testbed (SFTB) in the area of detection of moving vehicles with a network of image and acoustic sensors. A sensor data collection was designed and conducted using a variety of vehicles. Data from this collection included signature data of the vehicles as well as moving scenarios. Sensor fusion for detection and classification is performed at both the sensor level and the feature level, providing a basis for making tradeoffs between performance desired and resources required. Several classifier types are examined (parametric, nonparametric, learning). The combination of their decisions is used to make the final decision.
{"title":"Vehicle detection approaches using the NVESD Sensor Fusion Testbed","authors":"P. Perconti, J. Hilger, M. Loew","doi":"10.1109/AIPR.2003.1284249","DOIUrl":"https://doi.org/10.1109/AIPR.2003.1284249","url":null,"abstract":"The US Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate (NVESD) has a dynamic applied research program in sensor fusion for a wide variety of defense & defense related applications. This paper highlights efforts under the NVESD Sensor Fusion Testbed (SFTB) in the area of detection of moving vehicles with a network of image and acoustic sensors. A sensor data collection was designed and conducted using a variety of vehicles. Data from this collection included signature data of the vehicles as well as moving scenarios. Sensor fusion for detection and classification is performed at both the sensor level and the feature level, providing a basis for making tradeoffs between performance desired and resources required. Several classifier types are examined (parametric, nonparametric, learning). The combination of their decisions is used to make the final decision.","PeriodicalId":176987,"journal":{"name":"32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132681101","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 : 2003-10-01DOI: 10.1109/AIPR.2003.1284262
Ming-Jung Seow, Deepthi Valaparla, V. Asari
This paper presents a novel neural network based technique for face detection that eliminates limitations pertaining to the skin color variations among people. We propose to model the skin color in the three dimensional RGB space which is a color cube consisting of all the possible color combinations. Skin samples in images with varying lighting conditions, from the Old Dominion University skin database, are used for obtaining a skin color distribution. The primary color components of each plane of the color cube are fed to a three-layered network, trained using the backpropagation algorithm with the skin samples, to extract the skin regions from the planes and interpolate them so as to provide an optimum decision boundary and hence the positive skin samples for the skin classifier. The use of the color cube eliminates the difficulties of finding the non-skin part of training samples since the interpolated data is consider skin and rest of the color cube is consider non-skin. Subsequent face detection is aided by the color, geometry and motion information analyses of each frame in a video sequence. The performance of the new face detection technique has been tested with real-time data of size 320/spl times/240 frames from video sequences captured by a surveillance camera. It is observed that the network can differentiate skin and non-skin effectively while minimizing false detections to a large extent when compared with the existing techniques. In addition, it is seen that the network is capable of performing face detection in complex lighting and background environments.
{"title":"Neural network based skin color model for face detection","authors":"Ming-Jung Seow, Deepthi Valaparla, V. Asari","doi":"10.1109/AIPR.2003.1284262","DOIUrl":"https://doi.org/10.1109/AIPR.2003.1284262","url":null,"abstract":"This paper presents a novel neural network based technique for face detection that eliminates limitations pertaining to the skin color variations among people. We propose to model the skin color in the three dimensional RGB space which is a color cube consisting of all the possible color combinations. Skin samples in images with varying lighting conditions, from the Old Dominion University skin database, are used for obtaining a skin color distribution. The primary color components of each plane of the color cube are fed to a three-layered network, trained using the backpropagation algorithm with the skin samples, to extract the skin regions from the planes and interpolate them so as to provide an optimum decision boundary and hence the positive skin samples for the skin classifier. The use of the color cube eliminates the difficulties of finding the non-skin part of training samples since the interpolated data is consider skin and rest of the color cube is consider non-skin. Subsequent face detection is aided by the color, geometry and motion information analyses of each frame in a video sequence. The performance of the new face detection technique has been tested with real-time data of size 320/spl times/240 frames from video sequences captured by a surveillance camera. It is observed that the network can differentiate skin and non-skin effectively while minimizing false detections to a large extent when compared with the existing techniques. In addition, it is seen that the network is capable of performing face detection in complex lighting and background environments.","PeriodicalId":176987,"journal":{"name":"32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128977940","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 : 2003-10-01DOI: 10.1109/AIPR.2003.1284263
Rajkiran Gottumukkal, V. Asari
We present a face detection system capable of detection of faces in real time from a streaming color video. Currently this system is able to detect faces as long as both the eyes are visible in the image plane. Extracting skin color regions from a color image is the first step in this system. Skin color detection is used to segment regions of the image that correspond to face regions based on pixel color. Under normal illumination conditions, skin color takes small regions of the color space. By using this information, we can classify each pixel of the image as skin region or non-skin region. By scanning the skin regions, regions that do not have shape of a face are removed. Principle Component Analysis (PCA) is used to classify if a particular skin region is a face or a non-face. The PCA algorithm is trained for frontal view faces only. The system is tested with images captured by a surveillance camera in real time.
{"title":"Real time face detection from color video stream based on PCA method","authors":"Rajkiran Gottumukkal, V. Asari","doi":"10.1109/AIPR.2003.1284263","DOIUrl":"https://doi.org/10.1109/AIPR.2003.1284263","url":null,"abstract":"We present a face detection system capable of detection of faces in real time from a streaming color video. Currently this system is able to detect faces as long as both the eyes are visible in the image plane. Extracting skin color regions from a color image is the first step in this system. Skin color detection is used to segment regions of the image that correspond to face regions based on pixel color. Under normal illumination conditions, skin color takes small regions of the color space. By using this information, we can classify each pixel of the image as skin region or non-skin region. By scanning the skin regions, regions that do not have shape of a face are removed. Principle Component Analysis (PCA) is used to classify if a particular skin region is a face or a non-face. The PCA algorithm is trained for frontal view faces only. The system is tested with images captured by a surveillance camera in real time.","PeriodicalId":176987,"journal":{"name":"32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122372546","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 : 2003-10-01DOI: 10.1109/AIPR.2003.1284242
D. Fay, R. Ivey, N. Bomberger, A. Waxman
We have continued development of a system for multisensor image fusion and interactive mining based on neural models of color vision processing, learning and pattern recognition. We pioneered this work while at MIT Lincoln Laboratory, initially for color fused night vision (low-light visible and uncooled thermal imagery) and later extended it to multispectral IR and 3D ladder. We also developed a proof-of-concept system for EO, IR, SAR fusion and mining. Over the last year we have generalized this approach and developed a user-friendly system integrated into a COTS exploitation environment known as ERDAS Imagine. In this paper, we have summarized the approach and the neural networks used, and demonstrate fusion and interactive mining (i.e., target learning and search) of low-light visible/SWIR/MWIR/LWIR night imagery, and IKONOS multispectral and high-resolution panchromatic imagery. In addition, we had demonstrated how target learning and search can be enabled over extended operating conditions by allowing training over multiple scenes. This has been illustrated for the detection of small boats in coastal waters using fused visible/MWIR/LWIR imagery.
我们继续开发了一个基于色彩视觉处理、学习和模式识别的神经模型的多传感器图像融合和交互式挖掘系统。我们在麻省理工学院林肯实验室开创了这项工作，最初用于彩色融合夜视(低光可见光和非冷却热图像)，后来扩展到多光谱红外和3D阶梯。我们还开发了一个用于EO, IR, SAR融合和采矿的概念验证系统。在过去的一年里，我们推广了这种方法，并开发了一个用户友好的系统，集成到一个被称为ERDAS Imagine的COTS开发环境中。本文总结了低光可见光/SWIR/MWIR/LWIR夜间图像和IKONOS多光谱高分辨率全色图像的融合和交互挖掘(即目标学习和搜索)方法和所使用的神经网络。此外，我们还演示了如何通过允许在多个场景上进行训练来在扩展的操作条件下启用目标学习和搜索。这已用于使用融合可见光/中波红外/低波红外图像检测沿海水域的小船。
{"title":"Multisensor & spectral image fusion & mining: from neural systems to applications","authors":"D. Fay, R. Ivey, N. Bomberger, A. Waxman","doi":"10.1109/AIPR.2003.1284242","DOIUrl":"https://doi.org/10.1109/AIPR.2003.1284242","url":null,"abstract":"We have continued development of a system for multisensor image fusion and interactive mining based on neural models of color vision processing, learning and pattern recognition. We pioneered this work while at MIT Lincoln Laboratory, initially for color fused night vision (low-light visible and uncooled thermal imagery) and later extended it to multispectral IR and 3D ladder. We also developed a proof-of-concept system for EO, IR, SAR fusion and mining. Over the last year we have generalized this approach and developed a user-friendly system integrated into a COTS exploitation environment known as ERDAS Imagine. In this paper, we have summarized the approach and the neural networks used, and demonstrate fusion and interactive mining (i.e., target learning and search) of low-light visible/SWIR/MWIR/LWIR night imagery, and IKONOS multispectral and high-resolution panchromatic imagery. In addition, we had demonstrated how target learning and search can be enabled over extended operating conditions by allowing training over multiple scenes. This has been illustrated for the detection of small boats in coastal waters using fused visible/MWIR/LWIR imagery.","PeriodicalId":176987,"journal":{"name":"32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129216954","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 : 2003-10-01DOI: 10.1109/AIPR.2003.1284247
Anthony Downs, R. Madhavan, T. Hong
In the research reported in this paper, we propose to overcome the unavailability of Global Positioning System (GPS) using combined information obtained from a scanning LADAR rangefinder on an Unmanned Ground Vehicle (UGV) and a LADAR mounted on an Unmanned Aerial Vehicle (UAV) that flies over the terrain being traversed. The approach to estimate and update the position of the UGV involves registering range data from the two LADARs using a combination of a feature-based registration method and a modified version of the well-known Iterative Closest Point (ICP) algorithm. Registration of range data thus guarantees an estimate of the vehicle's position even when only one of the vehicles has GPS information. Additionally, such registration over time (i.e., from sample to sample), enables position information to be maintained even when both vehicles can no longer maintain GPS contact. The approach has been validated by conducting systematic experiments on complex real-world data.
{"title":"Registration of range data from unmanned aerial and ground vehicles","authors":"Anthony Downs, R. Madhavan, T. Hong","doi":"10.1109/AIPR.2003.1284247","DOIUrl":"https://doi.org/10.1109/AIPR.2003.1284247","url":null,"abstract":"In the research reported in this paper, we propose to overcome the unavailability of Global Positioning System (GPS) using combined information obtained from a scanning LADAR rangefinder on an Unmanned Ground Vehicle (UGV) and a LADAR mounted on an Unmanned Aerial Vehicle (UAV) that flies over the terrain being traversed. The approach to estimate and update the position of the UGV involves registering range data from the two LADARs using a combination of a feature-based registration method and a modified version of the well-known Iterative Closest Point (ICP) algorithm. Registration of range data thus guarantees an estimate of the vehicle's position even when only one of the vehicles has GPS information. Additionally, such registration over time (i.e., from sample to sample), enables position information to be maintained even when both vehicles can no longer maintain GPS contact. The approach has been validated by conducting systematic experiments on complex real-world data.","PeriodicalId":176987,"journal":{"name":"32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115885994","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 : 2003-10-01DOI: 10.1109/AIPR.2003.1284285
J. Doi, M. Yamanaka
A new and practical method of reliable and real-time authentication is proposed. Finger geometry and feature extraction of the palmar flexion creases are integrated in a few numbers of discrete points for faster and robust processing. A video image of either palm, palm placed freely facing toward a near infrared video camera in front of a low-reflective board, is acquired. Fingers are brought together without any constraints. Discrete feature point involves intersection points of the three digital (finger) flexion creases on the four finger skeletal lines and intersection points of the major palmar flexion creases on the extended finger skeletal lines, and orientations of the creases at the points. These metrics define the feature vectors for matching. Matching results are perfect for 50 subjects so far. This point wise processing, extracting enough feature from non contacting video image, requiring no time-consumptive palm print image analysis, and requiring less than one second processing time, will contribute to a real-time and reliable authentication.
{"title":"Personal authentication using feature points on finger and palmar creases","authors":"J. Doi, M. Yamanaka","doi":"10.1109/AIPR.2003.1284285","DOIUrl":"https://doi.org/10.1109/AIPR.2003.1284285","url":null,"abstract":"A new and practical method of reliable and real-time authentication is proposed. Finger geometry and feature extraction of the palmar flexion creases are integrated in a few numbers of discrete points for faster and robust processing. A video image of either palm, palm placed freely facing toward a near infrared video camera in front of a low-reflective board, is acquired. Fingers are brought together without any constraints. Discrete feature point involves intersection points of the three digital (finger) flexion creases on the four finger skeletal lines and intersection points of the major palmar flexion creases on the extended finger skeletal lines, and orientations of the creases at the points. These metrics define the feature vectors for matching. Matching results are perfect for 50 subjects so far. This point wise processing, extracting enough feature from non contacting video image, requiring no time-consumptive palm print image analysis, and requiring less than one second processing time, will contribute to a real-time and reliable authentication.","PeriodicalId":176987,"journal":{"name":"32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings.","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129138207","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 : 1900-01-01DOI: 10.1109/AIPR.2003.1284238
The following topics are dealt with: military applications; remote sensing; medical applications; data fusion using neural networks; visual learning in humans and machines; homeland security.
{"title":"Proceedings. 32nd Applied Imagery Pattern Recognition Workshop","authors":"","doi":"10.1109/AIPR.2003.1284238","DOIUrl":"https://doi.org/10.1109/AIPR.2003.1284238","url":null,"abstract":"The following topics are dealt with: military applications; remote sensing; medical applications; data fusion using neural networks; visual learning in humans and machines; homeland security.","PeriodicalId":176987,"journal":{"name":"32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings.","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123243927","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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