For effective mobile robots we need a concise yet adequately descriptive mechanism for representing their surroundings. Traditionally 2D occupancy grids have proven effective for task such as SLAM, path planning and obstacle avoidance. Applying this to 3D maps requires consideration due to the large memory requirements of the resulting dense arrays. Approaches to address this, such as octrees and occupied voxel lists, take advantage of the relative sparsity of occupied voxels. We enhance the occupied voxel list representation by filtering out those voxels that are on planar sections of the environment to leave edge-like voxels. To do this we apply a structure tensor operation to the voxel map followed by a classification of the eigen values to remove voxels that are part of flat regions such as floors, walls and ceilings. This leaves the voxels tracing the edges of the environment producing a wire-frame like model. Fewer edge voxels require less memory and enable faster alignment. We compare the performance of scan-to-map matching of extracted edge voxels with that of the corresponding full 3D scans. We show that alignment accuracy is preserved when using edge voxels, while achieving a five times speedup and reduced memory requirements, compared to matching with all occupied voxels. It is posited that these edge voxel maps could also be useful for appearance based localisation.
{"title":"Extracting Edge Voxels from 3D Volumetric Maps to Reduce Map Size and Accelerate Mapping Alignment","authors":"J. Ryde, J. Delmerico","doi":"10.1109/CRV.2012.50","DOIUrl":"https://doi.org/10.1109/CRV.2012.50","url":null,"abstract":"For effective mobile robots we need a concise yet adequately descriptive mechanism for representing their surroundings. Traditionally 2D occupancy grids have proven effective for task such as SLAM, path planning and obstacle avoidance. Applying this to 3D maps requires consideration due to the large memory requirements of the resulting dense arrays. Approaches to address this, such as octrees and occupied voxel lists, take advantage of the relative sparsity of occupied voxels. We enhance the occupied voxel list representation by filtering out those voxels that are on planar sections of the environment to leave edge-like voxels. To do this we apply a structure tensor operation to the voxel map followed by a classification of the eigen values to remove voxels that are part of flat regions such as floors, walls and ceilings. This leaves the voxels tracing the edges of the environment producing a wire-frame like model. Fewer edge voxels require less memory and enable faster alignment. We compare the performance of scan-to-map matching of extracted edge voxels with that of the corresponding full 3D scans. We show that alignment accuracy is preserved when using edge voxels, while achieving a five times speedup and reduced memory requirements, compared to matching with all occupied voxels. It is posited that these edge voxel maps could also be useful for appearance based localisation.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125668834","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}
Eye movement is a rich modality that can provide us with a window into a person's mind. In a typical human-human interaction, we can get information about the behavioral state of the others by examining their eye movements. For instance, when a poker player looks into the eyes of his opponent, he looks for any indication of bluffing by verifying the dynamics of the eye movements. However, the information extracted from the eyes is not the only source of information we get in a human-human interaction and other modalities, such as speech or gesture, help us infer the behavioral state of the others. Most of the time this fusion of information refines our decisions and helps us better infer people's cognitive and behavioral activity based on their actions. In this paper, we develop a probabilistic framework to fuse different sources of information to infer the ongoing task in a visual search activity given the viewer's eye movement data. We propose to use a dynamic programming method called token passing in an eye-typing application to reveal what the subject is typing during a search process by observing his direction of gaze during the execution of the task. Token passing is a computationally simple technique that allows us to fuse higher order constraints in the inference process and build models dynamically so we can have unlimited number of hypotheses. In the experiments we examine the effect of higher order information, in the form of a lexicon dictionary, on the task recognition accuracy.
{"title":"Information Fusion in Visual-Task Inference","authors":"Amin Haji Abolhassani, James J. Clark","doi":"10.1109/CRV.2012.14","DOIUrl":"https://doi.org/10.1109/CRV.2012.14","url":null,"abstract":"Eye movement is a rich modality that can provide us with a window into a person's mind. In a typical human-human interaction, we can get information about the behavioral state of the others by examining their eye movements. For instance, when a poker player looks into the eyes of his opponent, he looks for any indication of bluffing by verifying the dynamics of the eye movements. However, the information extracted from the eyes is not the only source of information we get in a human-human interaction and other modalities, such as speech or gesture, help us infer the behavioral state of the others. Most of the time this fusion of information refines our decisions and helps us better infer people's cognitive and behavioral activity based on their actions. In this paper, we develop a probabilistic framework to fuse different sources of information to infer the ongoing task in a visual search activity given the viewer's eye movement data. We propose to use a dynamic programming method called token passing in an eye-typing application to reveal what the subject is typing during a search process by observing his direction of gaze during the execution of the task. Token passing is a computationally simple technique that allows us to fuse higher order constraints in the inference process and build models dynamically so we can have unlimited number of hypotheses. In the experiments we examine the effect of higher order information, in the form of a lexicon dictionary, on the task recognition accuracy.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129326359","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}
Navigating unstructured environments requires reliable perception that generates an appropriate world representation. This representation must encompass all types of impediments to traversal, whether they be insurmountable obstacles, or mobility inhibitors such as soft soil. Traditionally, traversability and obstacle avoidance have represented separate capabilities with individual rangefinders dedicated to each task. This paper presents a statistical technique that, through the analysis of the underlying 21/2 D terrain map, determines the probability of an obstacle. This integrated approach eliminates the need for multiple data sources and is applicable to range data from various sources, including laser rangefinders and stereo vision. The proposed obstacle detection technique has been tested in simulated environments and under real world conditions, and these experiments revealed that it accurately identifies obstacles.
{"title":"Probabilistic Obstacle Detection Using 2 1/2 D Terrain Maps","authors":"G. Broten, David Mackay, J. Collier","doi":"10.1109/CRV.2012.10","DOIUrl":"https://doi.org/10.1109/CRV.2012.10","url":null,"abstract":"Navigating unstructured environments requires reliable perception that generates an appropriate world representation. This representation must encompass all types of impediments to traversal, whether they be insurmountable obstacles, or mobility inhibitors such as soft soil. Traditionally, traversability and obstacle avoidance have represented separate capabilities with individual rangefinders dedicated to each task. This paper presents a statistical technique that, through the analysis of the underlying 21/2 D terrain map, determines the probability of an obstacle. This integrated approach eliminates the need for multiple data sources and is applicable to range data from various sources, including laser rangefinders and stereo vision. The proposed obstacle detection technique has been tested in simulated environments and under real world conditions, and these experiments revealed that it accurately identifies obstacles.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114067989","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 novel action matching method based on a hierarchical codebook of local spatio-temporal video volumes (STVs). Given a single example of an activity as a query video, the proposed method finds similar videos to the query in a video dataset. It is based on the bag of video words (BOV) representation and does not require prior knowledge about actions, background subtraction, motion estimation or tracking. It is also robust to spatial and temporal scale changes, as well as some deformations. The hierarchical algorithm yields a compact subset of salient code words of STVs for the query video, and then the likelihood of similarity between the query video and all STVs in the target video is measured using a probabilistic inference mechanism. This hierarchy is achieved by initially constructing a codebook of STVs, while considering the uncertainty in the codebook construction, which is always ignored in current versions of the BOV approach. At the second level of the hierarchy, a large contextual region containing many STVs (Ensemble of STVs) is considered in order to construct a probabilistic model of STVs and their spatio-temporal compositions. At the third level of the hierarchy a codebook is formed for the ensembles of STVs based on their contextual similarities. The latter are the proposed labels (code words) for the actions being exhibited in the video. Finally, at the highest level of the hierarchy, the salient labels for the actions are selected by analyzing the high level code words assigned to each image pixel as a function of time. The algorithm was applied to three available video datasets for action recognition with different complexities (KTH, Weizmann, and MSR II) and the results were superior to other approaches, especially in the cases of a single training example and cross-dataset action recognition.
{"title":"A Multi-Scale Hierarchical Codebook Method for Human Action Recognition in Videos Using a Single Example","authors":"M. J. Roshtkhari, M. Levine","doi":"10.1109/CRV.2012.32","DOIUrl":"https://doi.org/10.1109/CRV.2012.32","url":null,"abstract":"This paper presents a novel action matching method based on a hierarchical codebook of local spatio-temporal video volumes (STVs). Given a single example of an activity as a query video, the proposed method finds similar videos to the query in a video dataset. It is based on the bag of video words (BOV) representation and does not require prior knowledge about actions, background subtraction, motion estimation or tracking. It is also robust to spatial and temporal scale changes, as well as some deformations. The hierarchical algorithm yields a compact subset of salient code words of STVs for the query video, and then the likelihood of similarity between the query video and all STVs in the target video is measured using a probabilistic inference mechanism. This hierarchy is achieved by initially constructing a codebook of STVs, while considering the uncertainty in the codebook construction, which is always ignored in current versions of the BOV approach. At the second level of the hierarchy, a large contextual region containing many STVs (Ensemble of STVs) is considered in order to construct a probabilistic model of STVs and their spatio-temporal compositions. At the third level of the hierarchy a codebook is formed for the ensembles of STVs based on their contextual similarities. The latter are the proposed labels (code words) for the actions being exhibited in the video. Finally, at the highest level of the hierarchy, the salient labels for the actions are selected by analyzing the high level code words assigned to each image pixel as a function of time. The algorithm was applied to three available video datasets for action recognition with different complexities (KTH, Weizmann, and MSR II) and the results were superior to other approaches, especially in the cases of a single training example and cross-dataset action recognition.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128040488","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}
One of the major goals of computer vision and machine intelligence is the development of flexible and efficient methods for shape representation. This paper presents an approach for shape retrieval based on sparse representation of scale-invariant heat kernel. We use the Laplace-Beltrami eigen functions to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales, combined with the normalized eigen values of the Lap lace-Beltrami operator. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the collaborative representation-based classification with regularized least square algorithm. We compare our approach to two well-known approaches on two different data sets: the nonrigid world data set and the SHREC 2011. The results have indeed confirmed the improved performance of the proposed approach, yet reducing the time and space complicity of the shape retrieval problem.
{"title":"Heat Kernels for Non-Rigid Shape Retrieval: Sparse Representation and Efficient Classification","authors":"M. Abdelrahman, M. El-Melegy, A. Farag","doi":"10.1109/CRV.2012.28","DOIUrl":"https://doi.org/10.1109/CRV.2012.28","url":null,"abstract":"One of the major goals of computer vision and machine intelligence is the development of flexible and efficient methods for shape representation. This paper presents an approach for shape retrieval based on sparse representation of scale-invariant heat kernel. We use the Laplace-Beltrami eigen functions to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales, combined with the normalized eigen values of the Lap lace-Beltrami operator. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the collaborative representation-based classification with regularized least square algorithm. We compare our approach to two well-known approaches on two different data sets: the nonrigid world data set and the SHREC 2011. The results have indeed confirmed the improved performance of the proposed approach, yet reducing the time and space complicity of the shape retrieval problem.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131159516","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}
Local spatio-temporal salient features are used for a sparse and compact representation of video contents in many computer vision tasks such as human action recognition. To localize these features (i.e., key point detection), existing methods perform either symmetric or asymmetric multi-resolution temporal filtering and use a structural or a motion saliency criteria. In a common discriminative framework for action classification, different saliency criteria of the structured-based detectors and different temporal filters of the motion-based detectors are compared. We have two main observations. (1) The motion-based detectors localize features which are more effective than those of structured-based detectors. (2) The salient motion features detected using an asymmetric temporal filtering performbetter than all other sparse salient detectors and dense sampling. Based on these two observations, we recommend the use of asymmetric motion features for effective sparse video content representation and action recognition.
{"title":"Evaluation of Local Spatio-temporal Salient Feature Detectors for Human Action Recognition","authors":"A. Shabani, David A Clausi, J. Zelek","doi":"10.1109/CRV.2012.69","DOIUrl":"https://doi.org/10.1109/CRV.2012.69","url":null,"abstract":"Local spatio-temporal salient features are used for a sparse and compact representation of video contents in many computer vision tasks such as human action recognition. To localize these features (i.e., key point detection), existing methods perform either symmetric or asymmetric multi-resolution temporal filtering and use a structural or a motion saliency criteria. In a common discriminative framework for action classification, different saliency criteria of the structured-based detectors and different temporal filters of the motion-based detectors are compared. We have two main observations. (1) The motion-based detectors localize features which are more effective than those of structured-based detectors. (2) The salient motion features detected using an asymmetric temporal filtering performbetter than all other sparse salient detectors and dense sampling. Based on these two observations, we recommend the use of asymmetric motion features for effective sparse video content representation and action recognition.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115727600","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}
Much research has been conducted so far to find a perfect structured light coding system. Among them, spatial neighborhood techniques which use a single pattern have become more popular as they can be used for dynamic scene capturing. But the difficulties of decoding the pattern when it loses few pattern symbols still remain as a problem. As a solution for this problem we introduce a new strategy which encodes two patterns into a single pattern image. In our particular experiment, we show that our decoding method can decode the pattern even it has some lost symbols.
{"title":"Combination of Color and Binary Pattern Codification for an Error Correcting M-array Technique","authors":"Udaya Wijenayake, Sung-In Choi, Soon-Yong Park","doi":"10.1109/CRV.2012.26","DOIUrl":"https://doi.org/10.1109/CRV.2012.26","url":null,"abstract":"Much research has been conducted so far to find a perfect structured light coding system. Among them, spatial neighborhood techniques which use a single pattern have become more popular as they can be used for dynamic scene capturing. But the difficulties of decoding the pattern when it loses few pattern symbols still remain as a problem. As a solution for this problem we introduce a new strategy which encodes two patterns into a single pattern image. In our particular experiment, we show that our decoding method can decode the pattern even it has some lost symbols.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122425977","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 investigates the use of a single image of a smooth Lambertian surface to calibrate and remove some image nonlinearities due to the imaging device. To the best of our knowledge, this has not been addressed before in the literature. We show that this is possible, both theoretically and practically, taking advantage of some local shading measures that vary nonlinearly as a function of luminance and geometric nonlinearities (e.g., gamma correction and lens distortion). This can work as a basis for developing a simple method to estimate these nonlinearities from a single image. Several experiments are reported to validate the proposed method.
{"title":"What Can an Image of a Smooth Lambertian Surface Tell About Camera Nonlinearity?","authors":"M. El-Melegy, A. Farag","doi":"10.1109/CRV.2012.51","DOIUrl":"https://doi.org/10.1109/CRV.2012.51","url":null,"abstract":"This paper investigates the use of a single image of a smooth Lambertian surface to calibrate and remove some image nonlinearities due to the imaging device. To the best of our knowledge, this has not been addressed before in the literature. We show that this is possible, both theoretically and practically, taking advantage of some local shading measures that vary nonlinearly as a function of luminance and geometric nonlinearities (e.g., gamma correction and lens distortion). This can work as a basis for developing a simple method to estimate these nonlinearities from a single image. Several experiments are reported to validate the proposed method.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121990246","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 a method to register kidneys from Computed Tomography (CT) scans with and without contrast enhancement. The method builds a patient-specific kidney shape model from the contrast enhanced image, and then matches it against automatically segmented candidate surfaces extracted from the pre-contrast image to find the alignment. Only the object of interest is used to drive the alignment, providing results that are robust to near-rigid relative motions of the kidney with respect to the surrounding tissues. Shape-based features are used, as opposed to intensity-based ones, and consequently the resulting registration is invariant to the inherent contrast variations. The contributions of this work are: a surface grouping and segmentation algorithm driven by smooth curvature constraints, and a framework to register image volumes under contrast variation, relative motion and local deformation with minimal user intervention. Encouraging experimental results with real patient images, all with various kinds and sizes of kidney lesions, validate the approach.
{"title":"Shape-Based Registration of Kidneys Across Differently Contrasted CT Scans","authors":"F. Flores-Mangas, A. Jepson, M. Haider","doi":"10.1109/CRV.2012.39","DOIUrl":"https://doi.org/10.1109/CRV.2012.39","url":null,"abstract":"We present a method to register kidneys from Computed Tomography (CT) scans with and without contrast enhancement. The method builds a patient-specific kidney shape model from the contrast enhanced image, and then matches it against automatically segmented candidate surfaces extracted from the pre-contrast image to find the alignment. Only the object of interest is used to drive the alignment, providing results that are robust to near-rigid relative motions of the kidney with respect to the surrounding tissues. Shape-based features are used, as opposed to intensity-based ones, and consequently the resulting registration is invariant to the inherent contrast variations. The contributions of this work are: a surface grouping and segmentation algorithm driven by smooth curvature constraints, and a framework to register image volumes under contrast variation, relative motion and local deformation with minimal user intervention. Encouraging experimental results with real patient images, all with various kinds and sizes of kidney lesions, validate the approach.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132079411","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 paper tackles the problem of designing intuitive graphical interfaces for selecting navigational targets for an autonomous robot. Our work focuses on the design and validation of such a flexible interface for an intelligent wheelchair navigating in a large indoor environment. We begin by describing the robot platform and interface design. We then present results from a user study in which participants were required to select navigational targets using a variety of input and filtering methods. We considered two types of input modalities (point-and-click and single-switch), to investigate the effect of constraints on the input mode. We take a particular look at the use of filtering methods to reduce the amount of information presented onscreen and thereby accelerate selection of the correct option.
{"title":"Design and Evaluation of a Flexible Interface for Spatial Navigation","authors":"Emily Tsang, S. W. Ong, Joelle Pineau","doi":"10.1109/CRV.2012.53","DOIUrl":"https://doi.org/10.1109/CRV.2012.53","url":null,"abstract":"The paper tackles the problem of designing intuitive graphical interfaces for selecting navigational targets for an autonomous robot. Our work focuses on the design and validation of such a flexible interface for an intelligent wheelchair navigating in a large indoor environment. We begin by describing the robot platform and interface design. We then present results from a user study in which participants were required to select navigational targets using a variety of input and filtering methods. We considered two types of input modalities (point-and-click and single-switch), to investigate the effect of constraints on the input mode. We take a particular look at the use of filtering methods to reduce the amount of information presented onscreen and thereby accelerate selection of the correct option.","PeriodicalId":372951,"journal":{"name":"2012 Ninth Conference on Computer and Robot Vision","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128425294","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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