@article {15067,
	title = {Recognizing Human Actions by Learning and Matching Shape-Motion Prototype Trees},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {34},
	year = {2012},
	month = {2012/03//},
	pages = {533 - 547},
	abstract = {A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.},
	keywords = {action prototype, actor location, brute-force computation, CMU action data set, distance measures, dynamic backgrounds, dynamic prototype sequence matching, flexible action matching, frame-to-frame distances, frame-to-prototype correspondence, hierarchical k-means clustering, human action recognition, Image matching, image recognition, Image sequences, joint probability model, joint shape, KTH action data set, large gesture data set, learning, learning (artificial intelligence), look-up table indexing, motion space, moving cameras, pattern clustering, prototype-to-prototype distances, shape-motion prototype-based approach, table lookup, training sequence, UCF sports data set, Video sequences, video signal processing, Weizmann action data set},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2011.147},
	author = {Zhuolin Jiang and Zhe Lin and Davis, Larry S.}
}
@conference {12438,
	title = {The evolution of stochastic grammars for representation and recognition of activities in videos},
	booktitle = {2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {688 - 688},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The speaker is one of the privileged many to have been taught syntactic pattern recognition methods by the Late Prof. K.S. Fu. In this talk, I will discuss the evolution of stochastic image grammars from the early seventies to now with a focus on image and video understanding applications.},
	keywords = {activity recognition, activity representation, Conferences, Educational institutions, Grammar, Image analysis, image recognition, image representation, image understanding, pattern recognition, stochastic image grammars, syntactic pattern recognition methods, video signal processing, video understanding, Videos},
	isbn = {978-1-4673-0062-9},
	doi = {10.1109/ICCVW.2011.6130309},
	author = {Chellapa, Rama}
}
@article {12439,
	title = {Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {33},
	year = {2011},
	month = {2011/11//},
	pages = {2273 - 2286},
	abstract = {In this paper, we examine image and video-based recognition applications where the underlying models have a special structure-the linear subspace structure. We discuss how commonly used parametric models for videos and image sets can be described using the unified framework of Grassmann and Stiefel manifolds. We first show that the parameters of linear dynamic models are finite-dimensional linear subspaces of appropriate dimensions. Unordered image sets as samples from a finite-dimensional linear subspace naturally fall under this framework. We show that an inference over subspaces can be naturally cast as an inference problem on the Grassmann manifold. To perform recognition using subspace-based models, we need tools from the Riemannian geometry of the Grassmann manifold. This involves a study of the geometric properties of the space, appropriate definitions of Riemannian metrics, and definition of geodesics. Further, we derive statistical modeling of inter and intraclass variations that respect the geometry of the space. We apply techniques such as intrinsic and extrinsic statistics to enable maximum-likelihood classification. We also provide algorithms for unsupervised clustering derived from the geometry of the manifold. Finally, we demonstrate the improved performance of these methods in a wide variety of vision applications such as activity recognition, video-based face recognition, object recognition from image sets, and activity-based video clustering.},
	keywords = {activity based video clustering, activity recognition, computational geometry, Computational modeling, Data models, face recognition, feature representation, finite dimensional linear subspaces, geometric properties, Geometry, Grassmann Manifolds, Grassmann., HUMANS, Image and video models, image recognition, linear dynamic models, linear subspace structure, Manifolds, maximum likelihood classification, maximum likelihood estimation, Object recognition, Riemannian geometry, Riemannian metrics, SHAPE, statistical computations, statistical models, Stiefel, Stiefel Manifolds, unsupervised clustering, video based face recognition, video based recognition, video signal processing},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2011.52},
	author = {Turaga,P. and Veeraraghavan,A. and Srivastava, A. and Chellapa, Rama}
}
@conference {12479,
	title = {Moving vistas: Exploiting motion for describing scenes},
	booktitle = {2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2010},
	month = {2010/06/13/18},
	pages = {1911 - 1918},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Scene recognition in an unconstrained setting is an open and challenging problem with wide applications. In this paper, we study the role of scene dynamics for improved representation of scenes. We subsequently propose dynamic attributes which can be augmented with spatial attributes of a scene for semantically meaningful categorization of dynamic scenes. We further explore accurate and generalizable computational models for characterizing the dynamics of unconstrained scenes. The large intra-class variation due to unconstrained settings and the complex underlying physics present challenging problems in modeling scene dynamics. Motivated by these factors, we propose using the theory of chaotic systems to capture dynamics. Due to the lack of a suitable dataset, we compiled a dataset of {\textquoteleft}in-the-wild{\textquoteright} dynamic scenes. Experimental results show that the proposed framework leads to the best classification rate among other well-known dynamic modeling techniques. We also show how these dynamic features provide a means to describe dynamic scenes with motion-attributes, which then leads to meaningful organization of the video data.},
	keywords = {Application software, Automation, Chaos, chaotic system, Computational modeling, Computer vision, dynamic scene categorization, Educational institutions, HUMANS, image recognition, in the wild dynamic scene, Layout, motion attribute, natural scenes, Physics, probability, scene recognition, Snow, video data},
	isbn = {978-1-4244-6984-0},
	doi = {10.1109/CVPR.2010.5539864},
	author = {Shroff, N. and Turaga,P. and Chellapa, Rama}
}
@conference {15922,
	title = {Sparsity inspired selection and recognition of iris images},
	booktitle = {Biometrics: Theory, Applications, and Systems, 2009. BTAS {\textquoteright}09. IEEE 3rd International Conference on},
	year = {2009},
	month = {2009/09//},
	pages = {1 - 6},
	abstract = {Iris images acquired from a partially cooperating subject often suffer from blur, occlusion due to eyelids, and specular reflections. The performance of existing iris recognition systems degrade significantly on these images. Hence it is essential to select good images from the incoming iris video stream, before they are input to the recognition algorithm. In this paper, we propose a sparsity based algorithm for selection of good iris images and their subsequent recognition. Unlike most existing algorithms for iris image selection, our method can handle segmentation errors and a wider range of acquisition artifacts common in iris image capture. We perform selection and recognition in a single step which is more efficient than devising separate specialized algorithms for the two. Recognition from partially cooperating users is a significant step towards deploying iris systems in a wide variety of applications.},
	keywords = {biometrics (access control), image recognition, Image segmentation, iris image recognition, iris image selection, iris video stream, sparsity inspired selection, video signal processing, video streaming},
	doi = {10.1109/BTAS.2009.5339067},
	author = {Pillai,J.K. and Patel, Vishal M. and Chellapa, Rama}
}
@conference {15061,
	title = {A Time Saving Method for Human Detection in Wide Angle Camera Images},
	booktitle = {Machine Learning and Cybernetics, 2006 International Conference on},
	year = {2006},
	month = {2006/08//},
	pages = {4029 - 4034},
	abstract = {A time-saving method for human detection in an indoor environment in wide angle camera images is presented in this paper, without using time-consuming template operations such as edge detection and morphological operations. It excludes other objects in motion to detect humans including cases of the partial occlusion of humans, rotation of the head and variation of skin colors. This algorithm is applicable where the memory space is limited and the time requirements are strict, such as in an embedded system. The experimental results show that it took about 0.35 seconds to process the algorithm after the scene study time},
	keywords = {CAMERAS, Embedded system, human detection, image recognition, Image segmentation, indoor environment, Motion estimation, time-saving method, wide angle camera images},
	doi = {10.1109/ICMLC.2006.258804},
	author = {Zhuolin Jiang and Li,Shao-Fa and Gao,Dong-Fa}
}
@article {12682,
	title = {Visual tracking and recognition using appearance-adaptive models in particle filters},
	journal = {IEEE Transactions on Image Processing},
	volume = {13},
	year = {2004},
	month = {2004/11//},
	pages = {1491 - 1506},
	abstract = {We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and recognition algorithms. Tracking needs modeling interframe motion and appearance changes, whereas recognition needs modeling appearance changes between frames and gallery images. In conventional tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a random walk with fixed noise variance. Also, the number of particles is typically fixed. All these factors make the visual tracker unstable. To stabilize the tracker, we propose the following modifications: an observation model arising from an adaptive appearance model, an adaptive velocity motion model with adaptive noise variance, and an adaptive number of particles. The adaptive-velocity model is derived using a first-order linear predictor based on the appearance difference between the incoming observation and the previous particle configuration. Occlusion analysis is implemented using robust statistics. Experimental results on tracking visual objects in long outdoor and indoor video sequences demonstrate the effectiveness and robustness of our tracking algorithm. We then perform simultaneous tracking and recognition by embedding them in a particle filter. For recognition purposes, we model the appearance changes between frames and gallery images by constructing the intra- and extrapersonal spaces. Accurate recognition is achieved when confronted by pose and view variations.},
	keywords = {adaptive filters, adaptive noise variance, algorithms, appearance-adaptive model, Artificial intelligence, Cluster Analysis, Computer Graphics, Computer simulation, Feedback, Filtering, first-order linear predictor, hidden feature removal, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, image recognition, Information Storage and Retrieval, Kinematics, Laboratories, Male, Models, Biological, Models, Statistical, MOTION, Movement, Noise robustness, Numerical Analysis, Computer-Assisted, occlusion analysis, Particle filters, Particle tracking, Pattern Recognition, Automated, Predictive models, Reproducibility of results, robust statistics, Sensitivity and Specificity, Signal Processing, Computer-Assisted, State estimation, statistical analysis, Subtraction Technique, tracking, Training data, visual recognition, visual tracking},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2004.836152},
	author = {Zhou,Shaohua Kevin and Chellapa, Rama and Moghaddam, B.}
}
@article {18317,
	title = {Recognizing human facial expressions from long image sequences using optical flow},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {18},
	year = {1996},
	month = {1996/06//},
	pages = {636 - 642},
	abstract = {An approach to the analysis and representation of facial dynamics for recognition of facial expressions from image sequences is presented. The algorithms utilize optical flow computation to identify the direction of rigid and nonrigid motions that are caused by human facial expressions. A mid-level symbolic representation motivated by psychological considerations is developed. Recognition of six facial expressions, as well as eye blinking, is demonstrated on a large set of image sequences},
	keywords = {Computer vision, Eyebrows, face recognition, facial dynamics, Facial features, human facial expression recognition, HUMANS, Image motion analysis, image recognition, image representation, Image sequences, Motion analysis, Motion estimation, Optical computing, optical flow, symbolic representation, tracking},
	isbn = {0162-8828},
	doi = {10.1109/34.506414},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {14169,
	title = {Hierarchical curve representation},
	booktitle = {, 11th IAPR International Conference on Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings},
	year = {1992},
	month = {1992/09/30/Aug-3},
	pages = {143 - 146},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Presents a robust method for describing planar curves in multiple resolution using curvature information. The method is developed by taking into account the discrete nature of digital images as well as the discrete aspect of a multiresolution structure (pyramid). The authors deal with the robustness of the technique, which is due to the additional information that is extracted from observing the behavior of corners in the pyramid. Furthermore the resulting algorithm is conceptually simple and easily parallelizable. They develop theoretical results, analyzing the curvature of continuous curves in scale-space, which show the behavior of curvature extrema under varying scale. These results are used to eliminate any ambiguities that might arise from sampling problems due to the discreteness of the representation. Finally, experimental results demonstrate the potential of the method},
	keywords = {Automation, continuous curves, curvature, data mining, digital images, Educational institutions, Feature extraction, hierarchical curve representation, IMAGE PROCESSING, image recognition, image resolution, Image segmentation, multiresolution structure, Object recognition, planar curves, pyramid, Robustness, Sampling methods, Smoothing methods},
	isbn = {0-8186-2920-7},
	doi = {10.1109/ICPR.1992.201947},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
@conference {11990,
	title = {Relative depth from motion using normal flow: an active and purposive solution},
	booktitle = {Proceedings of the IEEE Workshop on Visual Motion, 1991},
	year = {1991},
	month = {1991/10/07/9},
	pages = {196 - 204},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The authors show how an active observer can compute the relative depth of (stationary or moving) objects in the field of view using only the spatiotemporal derivatives of the time varying image intensity function. The solution they propose is purposive in the sense that it solves only the relative depth from motion problem and cannot be used for other problems related to motion; active in the sense that the activity of the observer is essential for the solution of the problem. Results indicate that exact computation of retinal motion does not appear to be a necessary first step for some problems related to visual motion. In addition, optic flow, whose computation is an ill-posed problem, is related to the motion of the scene only under very restrictive assumptions. As a result, the use of optic flow in some quantitative motion analysis studies is questionable},
	keywords = {3D information, Automation, Computer vision, Educational institutions, Image motion analysis, image recognition, Image sequences, Laboratories, Layout, Motion analysis, Motion estimation, Nonlinear optics, normal flow, optic flow, Optical computing, Optical sensors, relative depth, retinal motion, spatiotemporal derivatives, time varying image intensity function, visual motion},
	isbn = {0-8186-2153-2},
	doi = {10.1109/WVM.1991.212807},
	author = {Huang, Liuqing and Aloimonos, J.}
}
