B. Bhanu - Academia.edu (original) (raw)
Papers by B. Bhanu
Image Technology, 1996
Recognition of aircraft in complex, perspective aerial imagery is difficult because of occlusion,... more Recognition of aircraft in complex, perspective aerial imagery is difficult because of occlusion, shadow, cloud cover, haze, seasonal variations, clutter and various forms of image degradation. This chapter describes a system for aircraft recognition that addresses some of these issues. The recognition system uses a hierarchical object model database that includes models represented using advance concepts to geometric entities. It involves three key processes: (a) The qualitative object recognition process is responsible for model-based symbolic feature extraction and generic object recognition; (b) The refocused matching and evaluation process accesses deeper levels of the database hierarchy with input from (a) to refine the extracted features and to perform more specific classification; and (c) the primitive feature extraction process regulates the extracted features based on their saliency and interacts with (a) and (b). Experimental results showing the qualitative recognition of aircraft in perspective, aerial images are presented.
Proceedings of IEEE International Conference on Computer Vision
This paper presents a model-based object recognition approach that uses a hierarchical Gabor wave... more This paper presents a model-based object recognition approach that uses a hierarchical Gabor wavelet representation. The key idea is to use magnitude, phase and frequency measures of Gabor wavelet representation in an innovative flexible matching approach that can provide robust recognition. A Gabor grid, a topology-preserving map, eficiently encodes both signal energy and structural information of an object in a sparse multi-resolution representation. The Gabor grid subsamples the Gabor wavelet decomposition of an object model and is deformed to allow the indexed object model match with the image data. Flexible matching between the model and the image minimizes a cost function based on local similarity and geometric distortion of the Gabor grid. Grid erosion and repairing is performed whenever a collapsed grid, due to object occlusion, is detected. The results on infrared imagery are presented, where objects undergo rotation, translation, scale, occlusion and aspect variations under changing environmental conditions.
In this paper, we present a new model-based approach for building 3-D models of vehicles from col... more In this paper, we present a new model-based approach for building 3-D models of vehicles from color video provided by a traffic surveillance camera. We incrementally build 3D models using a clustering technique. Geometrical relations based on 3D generic vehicle model map 2D features to 3D. The 3D features are then adaptively clustered over the frame sequence to incrementally generate the 3D model of the vehicle. Results are shown for both simulated and real traffic video. They are evaluated by a new structural performance measure underscoring usefulness of incremental learning. 1.
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops
2004 Conference on Computer Vision and Pattern Recognition Workshop
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2013
The demand for robust face recognition in real-world surveillance cameras is increasing due to th... more The demand for robust face recognition in real-world surveillance cameras is increasing due to the needs of practical applications such as security and surveillance. Although face recognition has been studied extensively in the literature, achieving good performance in surveillance videos with unconstrained faces is inherently difficult. During the image acquisition process, the noncooperative subjects appear in arbitrary poses and resolutions in different lighting conditions, together with noise and blurriness of images. In addition, multiple cameras are usually distributed in a camera network and different cameras often capture a subject in different views. In this paper, we aim at tackling this unconstrained face recognition problem and utilizing multiple cameras to improve the recognition accuracy using a probabilistic approach. We propose a dynamic Bayesian network to incorporate the information from different cameras as well as the temporal clues from frames in a video sequence. The proposed method is tested on a public surveillance video dataset with a three-camera setup. We compare our method to different benchmark classifiers with various feature descriptors. The results demonstrate that by modeling the face in a dynamic manner the recognition performance in a multi-camera network is improved over the other classifiers with various feature descriptors and the recognition result is better than using any of the single camera.
2013 Seventh International Conference on Distributed Smart Cameras (ICDSC), 2013
18th International Conference on Pattern Recognition (ICPR'06), 2006
In this paper, we present a novel approach based on gait energy image (GEI) and co-evolutionary g... more In this paper, we present a novel approach based on gait energy image (GEI) and co-evolutionary genetic programming (CGP) for human activity classification. Specifically, Hu's moment and normalized histogram bins are extracted from the original GEIs as input features. CGP is employed to reduce the feature dimensionality and learn the classifiers. The strategy of majority voting is applied to the CGP to improve the overall performance in consideration of the diversification of genetic programming. This learningbased approach improves the classification accuracy by approximately 7 percent in comparison to the traditional classifiers.
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Image Technology, 1996
Recognition of aircraft in complex, perspective aerial imagery is difficult because of occlusion,... more Recognition of aircraft in complex, perspective aerial imagery is difficult because of occlusion, shadow, cloud cover, haze, seasonal variations, clutter and various forms of image degradation. This chapter describes a system for aircraft recognition that addresses some of these issues. The recognition system uses a hierarchical object model database that includes models represented using advance concepts to geometric entities. It involves three key processes: (a) The qualitative object recognition process is responsible for model-based symbolic feature extraction and generic object recognition; (b) The refocused matching and evaluation process accesses deeper levels of the database hierarchy with input from (a) to refine the extracted features and to perform more specific classification; and (c) the primitive feature extraction process regulates the extracted features based on their saliency and interacts with (a) and (b). Experimental results showing the qualitative recognition of aircraft in perspective, aerial images are presented.
2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2010
Publications Chair Patrick J. Flynn, University of Notre Dame, USA ... Mohamed Abdel-Mottaleb, Un... more Publications Chair Patrick J. Flynn, University of Notre Dame, USA ... Mohamed Abdel-Mottaleb, University of Miami, USA George Bebis, University of Nevada – Reno, USA Olga Bellon, Universidade Federal do Parana, Brazil Ross Beveridge, Colorado State University, USA Vijyaykumar Bhagavatula, Carnegie-Mellon University, USA Bir Bhanu, University of California – Riverside, USA Wageeh Boles, Queensland University of Technology, Australia Julien Bringer, Morpho, France Mark Burge, MITRE, USA Patrizio Campisi, Universita degli ...
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009
Symmetry is an important cue for machine perception that involves high-level knowledge of image c... more Symmetry is an important cue for machine perception that involves high-level knowledge of image components. Unlike most of the previous research that only computes symmetry in an image, this paper integrates symmetry with image segmentation to improve the segmentation performance. The symmetry integration is used to optimize both the segmentation and the symmetry of regions simultaneously. Interesting points are initially extracted from an image and they are further refined for detecting symmetry axis. A symmetry affinity matrix is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of segmented regions. Experimental results and comparisons from a wide domain of images indicate a promising improvement by symmetry integrated image segmentation compared to other image segmentation methods that do not exploit symmetry.
Proceedings of IEEE International Conference on Computer Vision
This paper presents a model-based object recognition approach that uses a hierarchical Gabor wave... more This paper presents a model-based object recognition approach that uses a hierarchical Gabor wavelet representation. The key idea is to use magnitude, phase and frequency measures of Gabor wavelet representation in an innovative flexible matching approach that can provide robust recognition. A Gabor grid, a topology-preserving map, eficiently encodes both signal energy and structural information of an object in a sparse multi-resolution representation. The Gabor grid subsamples the Gabor wavelet decomposition of an object model and is deformed to allow the indexed object model match with the image data. Flexible matching between the model and the image minimizes a cost function based on local similarity and geometric distortion of the Gabor grid. Grid erosion and repairing is performed whenever a collapsed grid, due to object occlusion, is detected. The results on infrared imagery are presented, where objects undergo rotation, translation, scale, occlusion and aspect variations under changing environmental conditions.
IEEE Transactions on Affective Computing, 2014
In marketing and advertising research, "zapping" is defined as the action when a viewer stops wat... more In marketing and advertising research, "zapping" is defined as the action when a viewer stops watching a commercial. Researchers analyze users' behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers' zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user's zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers.
Translational Stroke Research, 2012
Objective rapid quantifi cation of injury using computational methods can improve the assessment ... more Objective rapid quantifi cation of injury using computational methods can improve the assessment of the degree of stroke injury, aid in the selection of patients for early or specifi c treatments, and monitor the evolution of injury and recovery. In this chapter, we use neonatal ischemia as a case-study of the application of several computational methods that in fact are generic and applicable across the age and disease spectrum. We provide a summary of current computational approaches used for injury detection, including Gaussian mixture models (GMM), Markov random fi elds (MRFs), normalized graph cut, and K-means clustering. We also describe more recent automated approaches to segment the region(s) of ischemic injury including hierarchical region splitting, support vector machine, a brain symmetry/asymmetry integrated model, and a watershed method that are robust at different developmental stages. We conclude with our assessment of probable future research directions in the fi eld of computational noninvasive stroke analysis such as automated detection of the ischemic core and penumbra, monitoring
2014 IEEE International Conference on Image Processing (ICIP), 2014
18th International Conference on Pattern Recognition (ICPR'06), 2006
Extracting key-frames is the first step for efficient content-based indexing, browsing and retrie... more Extracting key-frames is the first step for efficient content-based indexing, browsing and retrieval of the video data in commercial movies. Most of the existing research deals with "how to extract representative frames?" However the unaddressed question is "how many key-frames are required to represent a video shot properly?" Generally, the user defines this number a priori or some heuristic methods are used. In this paper, we propose a psychological model, which computes this number adaptively and online, from variation of visual features in a video-shot. We incorporate it with an iterative key-frame selection method to automatically select the key-frames. We compare the results of this method with two other well-known approaches, based on a novel effectiveness measure that scores each approach based on its representational power. Movie-clips of varying complexity are used to underscore the success of the proposed model in real-time.
Proceedings - 2011 1st IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2011, 2011
Proceedings - International Symposium on Biomedical Imaging, 2013
In leaves of A. thaliana, there exists an intricate network of epidermal surface layer cells resp... more In leaves of A. thaliana, there exists an intricate network of epidermal surface layer cells responsible for anatomical stability and vigor of flexibility to the entire leaf. Rho GTPases direct this organization of cell polarity, but full understanding of the underlying mechanisms demands further inquiry. We conduct two experiments: (1) a novel procedure is proposed that could be used in other life and plant science studies to quantify microtubule orientation, and (2) shape analysis. We hypothesize ARK2 as a putative interactor in cell polarity maintenance through stabilization of microtubule ordering. We are the first to automate pavement cell phenotype analysis for cell polarity and microtubule orientation. Breakthroughs in the signaling network regulating leaf cell polarity and development will lead science into the frontier of genetically modifying leaves to dramatically increase Earth's plant biomass; impending food shortages in the 21st century will be well served by such research.
Image Technology, 1996
Recognition of aircraft in complex, perspective aerial imagery is difficult because of occlusion,... more Recognition of aircraft in complex, perspective aerial imagery is difficult because of occlusion, shadow, cloud cover, haze, seasonal variations, clutter and various forms of image degradation. This chapter describes a system for aircraft recognition that addresses some of these issues. The recognition system uses a hierarchical object model database that includes models represented using advance concepts to geometric entities. It involves three key processes: (a) The qualitative object recognition process is responsible for model-based symbolic feature extraction and generic object recognition; (b) The refocused matching and evaluation process accesses deeper levels of the database hierarchy with input from (a) to refine the extracted features and to perform more specific classification; and (c) the primitive feature extraction process regulates the extracted features based on their saliency and interacts with (a) and (b). Experimental results showing the qualitative recognition of aircraft in perspective, aerial images are presented.
Proceedings of IEEE International Conference on Computer Vision
This paper presents a model-based object recognition approach that uses a hierarchical Gabor wave... more This paper presents a model-based object recognition approach that uses a hierarchical Gabor wavelet representation. The key idea is to use magnitude, phase and frequency measures of Gabor wavelet representation in an innovative flexible matching approach that can provide robust recognition. A Gabor grid, a topology-preserving map, eficiently encodes both signal energy and structural information of an object in a sparse multi-resolution representation. The Gabor grid subsamples the Gabor wavelet decomposition of an object model and is deformed to allow the indexed object model match with the image data. Flexible matching between the model and the image minimizes a cost function based on local similarity and geometric distortion of the Gabor grid. Grid erosion and repairing is performed whenever a collapsed grid, due to object occlusion, is detected. The results on infrared imagery are presented, where objects undergo rotation, translation, scale, occlusion and aspect variations under changing environmental conditions.
In this paper, we present a new model-based approach for building 3-D models of vehicles from col... more In this paper, we present a new model-based approach for building 3-D models of vehicles from color video provided by a traffic surveillance camera. We incrementally build 3D models using a clustering technique. Geometrical relations based on 3D generic vehicle model map 2D features to 3D. The 3D features are then adaptively clustered over the frame sequence to incrementally generate the 3D model of the vehicle. Results are shown for both simulated and real traffic video. They are evaluated by a new structural performance measure underscoring usefulness of incremental learning. 1.
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops
2004 Conference on Computer Vision and Pattern Recognition Workshop
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2013
The demand for robust face recognition in real-world surveillance cameras is increasing due to th... more The demand for robust face recognition in real-world surveillance cameras is increasing due to the needs of practical applications such as security and surveillance. Although face recognition has been studied extensively in the literature, achieving good performance in surveillance videos with unconstrained faces is inherently difficult. During the image acquisition process, the noncooperative subjects appear in arbitrary poses and resolutions in different lighting conditions, together with noise and blurriness of images. In addition, multiple cameras are usually distributed in a camera network and different cameras often capture a subject in different views. In this paper, we aim at tackling this unconstrained face recognition problem and utilizing multiple cameras to improve the recognition accuracy using a probabilistic approach. We propose a dynamic Bayesian network to incorporate the information from different cameras as well as the temporal clues from frames in a video sequence. The proposed method is tested on a public surveillance video dataset with a three-camera setup. We compare our method to different benchmark classifiers with various feature descriptors. The results demonstrate that by modeling the face in a dynamic manner the recognition performance in a multi-camera network is improved over the other classifiers with various feature descriptors and the recognition result is better than using any of the single camera.
2013 Seventh International Conference on Distributed Smart Cameras (ICDSC), 2013
18th International Conference on Pattern Recognition (ICPR'06), 2006
In this paper, we present a novel approach based on gait energy image (GEI) and co-evolutionary g... more In this paper, we present a novel approach based on gait energy image (GEI) and co-evolutionary genetic programming (CGP) for human activity classification. Specifically, Hu's moment and normalized histogram bins are extracted from the original GEIs as input features. CGP is employed to reduce the feature dimensionality and learn the classifiers. The strategy of majority voting is applied to the CGP to improve the overall performance in consideration of the diversification of genetic programming. This learningbased approach improves the classification accuracy by approximately 7 percent in comparison to the traditional classifiers.
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Image Technology, 1996
Recognition of aircraft in complex, perspective aerial imagery is difficult because of occlusion,... more Recognition of aircraft in complex, perspective aerial imagery is difficult because of occlusion, shadow, cloud cover, haze, seasonal variations, clutter and various forms of image degradation. This chapter describes a system for aircraft recognition that addresses some of these issues. The recognition system uses a hierarchical object model database that includes models represented using advance concepts to geometric entities. It involves three key processes: (a) The qualitative object recognition process is responsible for model-based symbolic feature extraction and generic object recognition; (b) The refocused matching and evaluation process accesses deeper levels of the database hierarchy with input from (a) to refine the extracted features and to perform more specific classification; and (c) the primitive feature extraction process regulates the extracted features based on their saliency and interacts with (a) and (b). Experimental results showing the qualitative recognition of aircraft in perspective, aerial images are presented.
2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2010
Publications Chair Patrick J. Flynn, University of Notre Dame, USA ... Mohamed Abdel-Mottaleb, Un... more Publications Chair Patrick J. Flynn, University of Notre Dame, USA ... Mohamed Abdel-Mottaleb, University of Miami, USA George Bebis, University of Nevada – Reno, USA Olga Bellon, Universidade Federal do Parana, Brazil Ross Beveridge, Colorado State University, USA Vijyaykumar Bhagavatula, Carnegie-Mellon University, USA Bir Bhanu, University of California – Riverside, USA Wageeh Boles, Queensland University of Technology, Australia Julien Bringer, Morpho, France Mark Burge, MITRE, USA Patrizio Campisi, Universita degli ...
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009
Symmetry is an important cue for machine perception that involves high-level knowledge of image c... more Symmetry is an important cue for machine perception that involves high-level knowledge of image components. Unlike most of the previous research that only computes symmetry in an image, this paper integrates symmetry with image segmentation to improve the segmentation performance. The symmetry integration is used to optimize both the segmentation and the symmetry of regions simultaneously. Interesting points are initially extracted from an image and they are further refined for detecting symmetry axis. A symmetry affinity matrix is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of segmented regions. Experimental results and comparisons from a wide domain of images indicate a promising improvement by symmetry integrated image segmentation compared to other image segmentation methods that do not exploit symmetry.
Proceedings of IEEE International Conference on Computer Vision
This paper presents a model-based object recognition approach that uses a hierarchical Gabor wave... more This paper presents a model-based object recognition approach that uses a hierarchical Gabor wavelet representation. The key idea is to use magnitude, phase and frequency measures of Gabor wavelet representation in an innovative flexible matching approach that can provide robust recognition. A Gabor grid, a topology-preserving map, eficiently encodes both signal energy and structural information of an object in a sparse multi-resolution representation. The Gabor grid subsamples the Gabor wavelet decomposition of an object model and is deformed to allow the indexed object model match with the image data. Flexible matching between the model and the image minimizes a cost function based on local similarity and geometric distortion of the Gabor grid. Grid erosion and repairing is performed whenever a collapsed grid, due to object occlusion, is detected. The results on infrared imagery are presented, where objects undergo rotation, translation, scale, occlusion and aspect variations under changing environmental conditions.
IEEE Transactions on Affective Computing, 2014
In marketing and advertising research, "zapping" is defined as the action when a viewer stops wat... more In marketing and advertising research, "zapping" is defined as the action when a viewer stops watching a commercial. Researchers analyze users' behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers' zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user's zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers.
Translational Stroke Research, 2012
Objective rapid quantifi cation of injury using computational methods can improve the assessment ... more Objective rapid quantifi cation of injury using computational methods can improve the assessment of the degree of stroke injury, aid in the selection of patients for early or specifi c treatments, and monitor the evolution of injury and recovery. In this chapter, we use neonatal ischemia as a case-study of the application of several computational methods that in fact are generic and applicable across the age and disease spectrum. We provide a summary of current computational approaches used for injury detection, including Gaussian mixture models (GMM), Markov random fi elds (MRFs), normalized graph cut, and K-means clustering. We also describe more recent automated approaches to segment the region(s) of ischemic injury including hierarchical region splitting, support vector machine, a brain symmetry/asymmetry integrated model, and a watershed method that are robust at different developmental stages. We conclude with our assessment of probable future research directions in the fi eld of computational noninvasive stroke analysis such as automated detection of the ischemic core and penumbra, monitoring
2014 IEEE International Conference on Image Processing (ICIP), 2014
18th International Conference on Pattern Recognition (ICPR'06), 2006
Extracting key-frames is the first step for efficient content-based indexing, browsing and retrie... more Extracting key-frames is the first step for efficient content-based indexing, browsing and retrieval of the video data in commercial movies. Most of the existing research deals with "how to extract representative frames?" However the unaddressed question is "how many key-frames are required to represent a video shot properly?" Generally, the user defines this number a priori or some heuristic methods are used. In this paper, we propose a psychological model, which computes this number adaptively and online, from variation of visual features in a video-shot. We incorporate it with an iterative key-frame selection method to automatically select the key-frames. We compare the results of this method with two other well-known approaches, based on a novel effectiveness measure that scores each approach based on its representational power. Movie-clips of varying complexity are used to underscore the success of the proposed model in real-time.
Proceedings - 2011 1st IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2011, 2011
Proceedings - International Symposium on Biomedical Imaging, 2013
In leaves of A. thaliana, there exists an intricate network of epidermal surface layer cells resp... more In leaves of A. thaliana, there exists an intricate network of epidermal surface layer cells responsible for anatomical stability and vigor of flexibility to the entire leaf. Rho GTPases direct this organization of cell polarity, but full understanding of the underlying mechanisms demands further inquiry. We conduct two experiments: (1) a novel procedure is proposed that could be used in other life and plant science studies to quantify microtubule orientation, and (2) shape analysis. We hypothesize ARK2 as a putative interactor in cell polarity maintenance through stabilization of microtubule ordering. We are the first to automate pavement cell phenotype analysis for cell polarity and microtubule orientation. Breakthroughs in the signaling network regulating leaf cell polarity and development will lead science into the frontier of genetically modifying leaves to dramatically increase Earth's plant biomass; impending food shortages in the 21st century will be well served by such research.