We introduce an efficient method for recognition and localization of generic objects for robot navigation, which works on real scenes. The generic objects used in our experiments are desks and doors as they are suitable landmarks for navigation. The recognition method uses significant surfaces and accompanying functional evidence for recognition of such objects. Currently, our system works with planar surfaces only and assumes that the objects are in a "standard" pose. The localization and orientation of an object are represented with the most significant surface in an "s-map". Some results for laboratory scenes are given.<>
{"title":"A method for recognition and localization of generic objects for indoor navigation","authors":"Dongsung Kim, R. Nevatia","doi":"10.1109/ACV.1994.341322","DOIUrl":"https://doi.org/10.1109/ACV.1994.341322","url":null,"abstract":"We introduce an efficient method for recognition and localization of generic objects for robot navigation, which works on real scenes. The generic objects used in our experiments are desks and doors as they are suitable landmarks for navigation. The recognition method uses significant surfaces and accompanying functional evidence for recognition of such objects. Currently, our system works with planar surfaces only and assumes that the objects are in a \"standard\" pose. The localization and orientation of an object are represented with the most significant surface in an \"s-map\". Some results for laboratory scenes are given.<<ETX>>","PeriodicalId":437089,"journal":{"name":"Proceedings of 1994 IEEE Workshop on Applications of Computer Vision","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589194","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}
A new method for extracting planar polygonal rooftops in monocular aerial imagery is proposed. Structural features are extracted and hierarchically related using perceptual grouping techniques. Top-down feature verification is used so that features, and links between the features, are verified with local information in the image and weighed in a graph. Cycles in the graph correspond to possible building rooftop hypotheses. Virtual features are hypothesized for the perceptual completion of partially occluded rooftops. Extraction of the "best" grouping features into a building rooftop hypothesis is posed as a graph search problem. The maximally weighted, independent set of cycles in the graph is extracted as the final set of roof boundaries.<>
{"title":"Task driven perceptual organization for extraction of rooftop polygons","authors":"C. Jaynes, F. Stolle, R. Collins","doi":"10.1109/ACV.1994.341303","DOIUrl":"https://doi.org/10.1109/ACV.1994.341303","url":null,"abstract":"A new method for extracting planar polygonal rooftops in monocular aerial imagery is proposed. Structural features are extracted and hierarchically related using perceptual grouping techniques. Top-down feature verification is used so that features, and links between the features, are verified with local information in the image and weighed in a graph. Cycles in the graph correspond to possible building rooftop hypotheses. Virtual features are hypothesized for the perceptual completion of partially occluded rooftops. Extraction of the \"best\" grouping features into a building rooftop hypothesis is posed as a graph search problem. The maximally weighted, independent set of cycles in the graph is extracted as the final set of roof boundaries.<<ETX>>","PeriodicalId":437089,"journal":{"name":"Proceedings of 1994 IEEE Workshop on Applications of Computer Vision","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123681284","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}
Christopher E. Smith, N. Papanikolopoulos, S. Brandt
Flexible operation of a robotic agent in an uncalibrated environment requires the ability to recover unknown or partially known parameters of the workspace through sensing. Of the sensors available to a robotic agent, visual sensors provide information that is richer and more complete than other sensors. In this paper we present robust techniques for the derivation of depth from feature points on a target's surface and for the accurate and high-speed tracking of moving targets. We use these techniques in a system that operates with little or no a priori knowledge of the object-related parameters present in the environment. The system is designed under the controlled active vision framework and robustly determines parameters such as velocity for tracking moving objects and depth maps of objects with unknown depths and surface structure. Such determination of intrinsic environmental parameters is essential for performing higher level tasks such as inspection, exploration, tracking grasping, and collision-free motion planning. For both applications, we use the Minnesota Robotic Visual Tracker (a single visual sensor mounted on the end-effector of a robotic manipulator combined with a real-time vision system) to automatically select feature points on surfaces, to derive an estimate of the environmental parameter in question, and to apply a control vector based upon these estimates to guide the manipulator. The paper concludes with applications of these techniques to transportation problems such as vehicle tracking.<>
{"title":"Application of the controlled active vision framework to robotic and transportation problems","authors":"Christopher E. Smith, N. Papanikolopoulos, S. Brandt","doi":"10.1109/ACV.1994.341311","DOIUrl":"https://doi.org/10.1109/ACV.1994.341311","url":null,"abstract":"Flexible operation of a robotic agent in an uncalibrated environment requires the ability to recover unknown or partially known parameters of the workspace through sensing. Of the sensors available to a robotic agent, visual sensors provide information that is richer and more complete than other sensors. In this paper we present robust techniques for the derivation of depth from feature points on a target's surface and for the accurate and high-speed tracking of moving targets. We use these techniques in a system that operates with little or no a priori knowledge of the object-related parameters present in the environment. The system is designed under the controlled active vision framework and robustly determines parameters such as velocity for tracking moving objects and depth maps of objects with unknown depths and surface structure. Such determination of intrinsic environmental parameters is essential for performing higher level tasks such as inspection, exploration, tracking grasping, and collision-free motion planning. For both applications, we use the Minnesota Robotic Visual Tracker (a single visual sensor mounted on the end-effector of a robotic manipulator combined with a real-time vision system) to automatically select feature points on surfaces, to derive an estimate of the environmental parameter in question, and to apply a control vector based upon these estimates to guide the manipulator. The paper concludes with applications of these techniques to transportation problems such as vehicle tracking.<<ETX>>","PeriodicalId":437089,"journal":{"name":"Proceedings of 1994 IEEE Workshop on Applications of Computer Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129387848","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}
In this paper we present a computational approach for extracting three-dimensional structure of controllable resolution, depth of field, and accuracy, all made available at real-time speeds. This approach utilizes the spatial and the temporal gradients of the streams of images acquired using an actively controlled camera. Depending on the requirements of a particular task, appropriate parameters such as disparity value sought, the inter-frame camera displacement, and number of frames in a stream, are chosen to control the resolution, depth of field, and accuracy. The acquisition and processing of the image stream are done in real-time on a pipeline architecture based processor. Extensive experiments are presented to demonstrate the accuracy, controllability of depth of field and resolution, and ability to perform successfully in a variety of scenes. The system operated with no latency between the image acquisition and processing. The total acquisition and processing time in these experiments is in the range of 0.27 to 1.56 sec. The depth results have an accuracy of 85% to 92%.<>
{"title":"Acquisition of 3D structure of selectable quality from image streams","authors":"A. K. Dalmia, M. Trivedi","doi":"10.1109/ACV.1994.341323","DOIUrl":"https://doi.org/10.1109/ACV.1994.341323","url":null,"abstract":"In this paper we present a computational approach for extracting three-dimensional structure of controllable resolution, depth of field, and accuracy, all made available at real-time speeds. This approach utilizes the spatial and the temporal gradients of the streams of images acquired using an actively controlled camera. Depending on the requirements of a particular task, appropriate parameters such as disparity value sought, the inter-frame camera displacement, and number of frames in a stream, are chosen to control the resolution, depth of field, and accuracy. The acquisition and processing of the image stream are done in real-time on a pipeline architecture based processor. Extensive experiments are presented to demonstrate the accuracy, controllability of depth of field and resolution, and ability to perform successfully in a variety of scenes. The system operated with no latency between the image acquisition and processing. The total acquisition and processing time in these experiments is in the range of 0.27 to 1.56 sec. The depth results have an accuracy of 85% to 92%.<<ETX>>","PeriodicalId":437089,"journal":{"name":"Proceedings of 1994 IEEE Workshop on Applications of Computer Vision","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121602507","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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