A binocular robot head, BETH, with ten degrees of freedom (DOF) has been developed for research in active vision. To complement BETH, a graphical simulation and visualization environment has also been developed. The objective of building the graphical system is to create a comprehensive design tool to design and study the dynamic behavior of sensory-motor systems and their interactions with the environment. This environment integrates perception and motor actions and features complete kinematic simulation of BETH, it's sensors and it's workspace. We demonstrate the utility of this environment in systematic and extensive experimental development of computational frameworks and algorithms for 3D active vision systems which utilizes multiple passive depth cues (stereo, vergence, and depth from focusing).<>
{"title":"Simulation and visualization of integrated sensory-motor systems","authors":"S. B. Marapane, M. Trivedi","doi":"10.1109/VMV.1994.324990","DOIUrl":"https://doi.org/10.1109/VMV.1994.324990","url":null,"abstract":"A binocular robot head, BETH, with ten degrees of freedom (DOF) has been developed for research in active vision. To complement BETH, a graphical simulation and visualization environment has also been developed. The objective of building the graphical system is to create a comprehensive design tool to design and study the dynamic behavior of sensory-motor systems and their interactions with the environment. This environment integrates perception and motor actions and features complete kinematic simulation of BETH, it's sensors and it's workspace. We demonstrate the utility of this environment in systematic and extensive experimental development of computational frameworks and algorithms for 3D active vision systems which utilizes multiple passive depth cues (stereo, vergence, and depth from focusing).<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127146161","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}
This paper presents a new idea for recognizing human facial expressions from an overall pattern of the face, represented in a potential field activated by edges in the input imagery, rather than from changes in the shape of the facial organs or their geometrical relationships. A two dimensional grid, called Potential Net, of which nodes are moved by the image force of the edges and springs connected to their four neighbors is used as a model of the field. Thus, the nodal displacement vectors in the Net represent the overall pattern. Each facial expression is determined as the means of the nodal displacement vectors yielded by images in each training set. Since the dimension of the space spanned by the nodal displacement vectors is too high, it is mapped into a low dimensional space, called Emotion Space, by applying the KL expansion. Unknown expressions in input images are estimated from their mapping into the Emotion Space.<>
{"title":"Recognition of human facial expressions using 2-dimensional physical model","authors":"Katsuhiro Matsuno, S. Tsuji, Chil-Woo Lee","doi":"10.1109/VMV.1994.324985","DOIUrl":"https://doi.org/10.1109/VMV.1994.324985","url":null,"abstract":"This paper presents a new idea for recognizing human facial expressions from an overall pattern of the face, represented in a potential field activated by edges in the input imagery, rather than from changes in the shape of the facial organs or their geometrical relationships. A two dimensional grid, called Potential Net, of which nodes are moved by the image force of the edges and springs connected to their four neighbors is used as a model of the field. Thus, the nodal displacement vectors in the Net represent the overall pattern. Each facial expression is determined as the means of the nodal displacement vectors yielded by images in each training set. Since the dimension of the space spanned by the nodal displacement vectors is too high, it is mapped into a low dimensional space, called Emotion Space, by applying the KL expansion. Unknown expressions in input images are estimated from their mapping into the Emotion Space.<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125297997","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}
Visualization is the process of converting a set of numbers resulting from numerical simulations or experiments into a graphical image. However, the ultimate goal is to understand the underlying science. A crucial part is to identify, quantify and track important regions and structures (objects of interest). In this realm, the goals of visualization, computer vision and image processing coincide. Namely, the concern is to produce an image, automatically analyze and recognize objects in a field and reconstruct a model. In this paper, we present an overview of some important quantification/visualization operations and demonstrate how these operations incorporate ideas from computer graphics, image processing, computer vision, and mathematical morphology.<>
{"title":"Scientific visualization and computer vision","authors":"D. Silver, N. Zabusky","doi":"10.1109/VMV.1994.324987","DOIUrl":"https://doi.org/10.1109/VMV.1994.324987","url":null,"abstract":"Visualization is the process of converting a set of numbers resulting from numerical simulations or experiments into a graphical image. However, the ultimate goal is to understand the underlying science. A crucial part is to identify, quantify and track important regions and structures (objects of interest). In this realm, the goals of visualization, computer vision and image processing coincide. Namely, the concern is to produce an image, automatically analyze and recognize objects in a field and reconstruct a model. In this paper, we present an overview of some important quantification/visualization operations and demonstrate how these operations incorporate ideas from computer graphics, image processing, computer vision, and mathematical morphology.<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132939216","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}
E. Mesrobian, R. Muntz, J. R. Santos, E. C. Shek, C. Mechoso, J. Farrara, P. Stolorz
A major challenge facing geophysical science today is the unavailability of high-level analysis tools with which to study the massive amount of data produced by sensors or long simulations of climate models. We have developed a prototype information system called QUEST to provide content-based access to massive datasets. QUEST employs workstations as well as teraFLOP computers to analyze geoscience data to produce spatial-temporal features that can be used as high-level indexes. Our first application area is global change climate modeling. In the initial prototype, the first features extracted are cyclones trajectories from the output of multi-year climate simulations produced by a General Circulation Model. We present an algorithm for cyclone extraction and illustrate the use of cyclone indexes to access subsets of GCM data for further analysis and visualization.<>
{"title":"Extracting spatio-temporal patterns from geoscience datasets","authors":"E. Mesrobian, R. Muntz, J. R. Santos, E. C. Shek, C. Mechoso, J. Farrara, P. Stolorz","doi":"10.1109/VMV.1994.324983","DOIUrl":"https://doi.org/10.1109/VMV.1994.324983","url":null,"abstract":"A major challenge facing geophysical science today is the unavailability of high-level analysis tools with which to study the massive amount of data produced by sensors or long simulations of climate models. We have developed a prototype information system called QUEST to provide content-based access to massive datasets. QUEST employs workstations as well as teraFLOP computers to analyze geoscience data to produce spatial-temporal features that can be used as high-level indexes. Our first application area is global change climate modeling. In the initial prototype, the first features extracted are cyclones trajectories from the output of multi-year climate simulations produced by a General Circulation Model. We present an algorithm for cyclone extraction and illustrate the use of cyclone indexes to access subsets of GCM data for further analysis and visualization.<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"333 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114964150","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}
Nonlinear phase portraits are employed to represent the streamlines of scalar flow images generated by particle tracing experiments. The flow fields are decomposed into simple component flows based on the critical point behavior. A Taylor series model is assumed for the velocity components, and the model coefficients are computed by considering both local critical point and global flow field behavior. A merge and split procedure for complex flows is presented, in which patterns of neighboring critical point regions are combined and modeled. The concepts are extended to the compression of vector field data by using orthogonal polynomials derived from the Taylor series model. A critical point scheme and a block transform are presented. They are applied to velocity fields measured in particle image velocimetry experiments and generated by computer simulations.<>
{"title":"Nonlinear models for representation, compression, and visualization of fluid flow images and velocimetry data","authors":"R. M. Ford, R. N. Strickland","doi":"10.1109/VMV.1994.324993","DOIUrl":"https://doi.org/10.1109/VMV.1994.324993","url":null,"abstract":"Nonlinear phase portraits are employed to represent the streamlines of scalar flow images generated by particle tracing experiments. The flow fields are decomposed into simple component flows based on the critical point behavior. A Taylor series model is assumed for the velocity components, and the model coefficients are computed by considering both local critical point and global flow field behavior. A merge and split procedure for complex flows is presented, in which patterns of neighboring critical point regions are combined and modeled. The concepts are extended to the compression of vector field data by using orthogonal polynomials derived from the Taylor series model. A critical point scheme and a block transform are presented. They are applied to velocity fields measured in particle image velocimetry experiments and generated by computer simulations.<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117271790","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}
For future intelligent man-machine systems with vision, it is necessary to visualize the results of shape and motion and analysis of observed objects in the images. As for object recognition, there are at least three steps. The first is to detect edges which correspond to the boundaries of objects (edge detection). The second is to segment each boundary into simple fine or curve segments (image segmentation). The third is to match those features between the data and the model (feature extraction). The paper presents a new method for the second step: boundary segmentation. It can detect not only corners but inflection points on which the sign of the curvature changes and transitional points on which a line and a curve connect smoothly without any delicate threshold. It also calculates the curvature and the normal vector at each point on the boundary with good accuracy. The features extracted by the proposed method are useful for both machine vision and visualization.<>
{"title":"Boundary segmentation by detection of corner, inflection and transition points","authors":"K. Sugimoto, F. Tomita","doi":"10.1109/VMV.1994.324992","DOIUrl":"https://doi.org/10.1109/VMV.1994.324992","url":null,"abstract":"For future intelligent man-machine systems with vision, it is necessary to visualize the results of shape and motion and analysis of observed objects in the images. As for object recognition, there are at least three steps. The first is to detect edges which correspond to the boundaries of objects (edge detection). The second is to segment each boundary into simple fine or curve segments (image segmentation). The third is to match those features between the data and the model (feature extraction). The paper presents a new method for the second step: boundary segmentation. It can detect not only corners but inflection points on which the sign of the curvature changes and transitional points on which a line and a curve connect smoothly without any delicate threshold. It also calculates the curvature and the normal vector at each point on the boundary with good accuracy. The features extracted by the proposed method are useful for both machine vision and visualization.<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126442237","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 key initial stage in the process of translating sensor data into symbolic representations is perceptual organization where edge pixels or voxels are grouped into a set of disjoint segments. For example, edge pixels are linked to form contour lines that can be associated with boundaries of regions; or edge voxels are grouped to form surfaces that often represent boundary surfaces of volumes. This paper studies the perceptual organization problem in both two and three dimensions. An interdisciplinary research work is presented which explores the synergy between perceptual organization in computer vision and streamline and iso-surface techniques in visualization. We show how such visualization techniques can be adapted and unified into a disjoint-union-find framework to solve the perceptual organization problems. The proposed algorithms have been successfully applied to many real-world 2D and 3D images.<>
{"title":"From visualization to perceptual organization","authors":"B. Yeo, S. Liou","doi":"10.1109/VMV.1994.324986","DOIUrl":"https://doi.org/10.1109/VMV.1994.324986","url":null,"abstract":"A key initial stage in the process of translating sensor data into symbolic representations is perceptual organization where edge pixels or voxels are grouped into a set of disjoint segments. For example, edge pixels are linked to form contour lines that can be associated with boundaries of regions; or edge voxels are grouped to form surfaces that often represent boundary surfaces of volumes. This paper studies the perceptual organization problem in both two and three dimensions. An interdisciplinary research work is presented which explores the synergy between perceptual organization in computer vision and streamline and iso-surface techniques in visualization. We show how such visualization techniques can be adapted and unified into a disjoint-union-find framework to solve the perceptual organization problems. The proposed algorithms have been successfully applied to many real-world 2D and 3D images.<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130874927","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}
We present an interactive tool for image object boundary specification for the segmentation of unknown images. It feels like a freehand drawing tool, but behaves according to constraints related to the semantics of image object boundary formation. We find the path as the user traces by interpreting the user data as an approximation to the object boundary. From this, we derive the set of 8-connected paths which may be part of the boundary. To select the best path, we design a cost function in terms of the user data and the image data (e.g. gradient magnitude), and select a minimum cost path using a dynamic programming algorithm. A smooth path is produced that follows the user in low contrast regions and the object boundary otherwise. A method to tune the tool for specific conditions is described. We present quantitative results obtained for a simulated user using random data and qualitative results for a real user tracing in real images.<>
{"title":"Magnetic contour tracing","authors":"C. M. Orange, F. Groen","doi":"10.1109/VMV.1994.324989","DOIUrl":"https://doi.org/10.1109/VMV.1994.324989","url":null,"abstract":"We present an interactive tool for image object boundary specification for the segmentation of unknown images. It feels like a freehand drawing tool, but behaves according to constraints related to the semantics of image object boundary formation. We find the path as the user traces by interpreting the user data as an approximation to the object boundary. From this, we derive the set of 8-connected paths which may be part of the boundary. To select the best path, we design a cost function in terms of the user data and the image data (e.g. gradient magnitude), and select a minimum cost path using a dynamic programming algorithm. A smooth path is produced that follows the user in low contrast regions and the object boundary otherwise. A method to tune the tool for specific conditions is described. We present quantitative results obtained for a simulated user using random data and qualitative results for a real user tracing in real images.<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115681783","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}
The fundamental purpose of scientific visualization is to help scientists extract information from large volumetric datasets. These multi-dimensional datasets may be either derived from observations or generated by simulations. In either case, visualization directly enhances scientific discovery, assists the validation and verification of simulation models, and helps study and predict phenomena. Although the state-of-the-art visualization techniques allow insightful presentations of datasets in various ways, the ability to discern significant features from complex data is lacking. On the other hand, lots of work has been done in the computer vision field, in attempting to automatically detect and recognize features or regions of interest in two-dimensional image data. How to extract features or locate regions of interest in visualizing high-dimensional datasets is an important area of research. We present the work we have done in exploring feature extraction techniques for time-varying three-dimensional volumetric datasets. We used an edge detection method and exploited both temporal and spatial coherences inside features to automatically locate and track the feature movement over time. The results are attractive and show that feature extraction techniques could greatly enhance visualization procedures.<>
{"title":"Exploring feature detection techniques for time-varying volumetric data","authors":"Zhifan Zhu, R. Moorhead","doi":"10.1109/VMV.1994.324988","DOIUrl":"https://doi.org/10.1109/VMV.1994.324988","url":null,"abstract":"The fundamental purpose of scientific visualization is to help scientists extract information from large volumetric datasets. These multi-dimensional datasets may be either derived from observations or generated by simulations. In either case, visualization directly enhances scientific discovery, assists the validation and verification of simulation models, and helps study and predict phenomena. Although the state-of-the-art visualization techniques allow insightful presentations of datasets in various ways, the ability to discern significant features from complex data is lacking. On the other hand, lots of work has been done in the computer vision field, in attempting to automatically detect and recognize features or regions of interest in two-dimensional image data. How to extract features or locate regions of interest in visualizing high-dimensional datasets is an important area of research. We present the work we have done in exploring feature extraction techniques for time-varying three-dimensional volumetric datasets. We used an edge detection method and exploited both temporal and spatial coherences inside features to automatically locate and track the feature movement over time. The results are attractive and show that feature extraction techniques could greatly enhance visualization procedures.<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"13 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116952476","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}
The purpose of the paper is the consideration of the problem of topological ambiguities arising in the Marching Cube algorithm. It also presents the solution of this problem inside the cube. The technique for obtaining the points lying on the surface and for connecting them in the correct sequence inside it is shown. Graph theory methods are used to approximate the isosurface inside the cube.<>
{"title":"Resolving the topological ambiguity in approximating the isosurface of scalar function","authors":"S. Matveyev","doi":"10.1109/VMV.1994.324991","DOIUrl":"https://doi.org/10.1109/VMV.1994.324991","url":null,"abstract":"The purpose of the paper is the consideration of the problem of topological ambiguities arising in the Marching Cube algorithm. It also presents the solution of this problem inside the cube. The technique for obtaining the points lying on the surface and for connecting them in the correct sequence inside it is shown. Graph theory methods are used to approximate the isosurface inside the cube.<<ETX>>","PeriodicalId":380649,"journal":{"name":"Proceedings of Workshop on Visualization and Machine Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130889968","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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