@conference {12593,
	title = {Coarse-to-Fine Event Model for Human Activities},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {1},
	year = {2007},
	month = {2007/04//},
	pages = {I-813 -I-816 - I-813 -I-816},
	abstract = {We analyze coarse-to-fine hierarchical representation of human activities in video sequences. It can be used for efficient video browsing and activity recognition. Activities are modeled using a sequence of instantaneous events. Events in activities can be represented in a coarse-to-fine hierarchy in several ways, i.e., there may not be a unique hierarchical structure. We present five criteria and quantitative measures for evaluating their effectiveness. The criteria are minimalism, stability, consistency, accessibility and applicability. It is desirable to develop activity models that rank highly on these criteria at all levels of hierarchy. In this paper, activities are represented as sequence of event probabilities computed using the hidden Markov model framework. Two aspects of hierarchies are analyzed: the effect of reduced frame rate on the accuracy of events detected at a finer scale; and the effect of reduced spatial resolution on activity recognition. Experiments using the UCF indoor human action dataset and the TSA airport tarmac surveillance dataset show encouraging results},
	keywords = {action, activities;spatial, airport, browsing;video, dataset;activity, dataset;UCF, event, framework;human, human, indoor, Markov, model, model;event, models;image, probabilities;hidden, processing;, recognition;coarse-to-fine, reduction;video, representation;image, resolution, resolution;image, sequences;hidden, sequences;stability;video, signal, Surveillance, tarmac, TSA},
	doi = {10.1109/ICASSP.2007.366032},
	author = {Cuntoor, N.P. and Chellapa, Rama}
}
@conference {12729,
	title = {Activity recognition using the dynamics of the configuration of interacting objects},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {2},
	year = {2003},
	month = {2003/06//},
	pages = {II - 633-40 vol.2 - II - 633-40 vol.2},
	abstract = {Monitoring activities using video data is an important surveillance problem. A special scenario is to learn the pattern of normal activities and detect abnormal events from a very low resolution video where the moving objects are small enough to be modeled as point objects in a 2D plane. Instead of tracking each point separately, we propose to model an activity by the polygonal {\textquoteright}shape{\textquoteright} of the configuration of these point masses at any time t, and its deformation over time. We learn the mean shape and the dynamics of the shape change using hand-picked location data (no observation noise) and define an abnormality detection statistic for the simple case of a test sequence with negligible observation noise. For the more practical case where observation (point locations) noise is large and cannot be ignored, we use a particle filter to estimate the probability distribution of the shape given the noisy observations up to the current time. Abnormality detection in this case is formulated as a change detection problem. We propose a detection strategy that can detect both {\textquoteright}drastic{\textquoteright} and {\textquoteright}slow{\textquoteright} abnormalities. Our framework can be directly applied for object location data obtained using any type of sensors - visible, radar, infrared or acoustic.},
	keywords = {2D, abnormal, abnormality, abnormality;, acoustic, activity, analysis;, change;, Computer, configuration, configuration;, data;, DETECTION, detection;, distribution;, drastic, dynamics;, event;, filter;, hand-picked, image, infrared, interacting, learning;, location, low, mean, model;, monitoring;, MOTION, moving, noise;, noisy, object, object;, observation, observation;, particle, pattern, plane;, point, polygonal, probability, probability;, problem;, processing;, radar, recognition;, resolution, sensor;, sensors;, sequence;, SHAPE, shape;, signal, slow, statistic;, strategy;, Surveillance, surveillance;, target, test, tracking;, video, video;, visible, vision;},
	doi = {10.1109/CVPR.2003.1211526},
	author = {Vaswani, N. and RoyChowdhury, A. and Chellapa, Rama}
}
@conference {18289,
	title = {Video transcoding by reducing spatial resolution},
	booktitle = {Image Processing, 2000. Proceedings. 2000 International Conference on},
	volume = {1},
	year = {2000},
	month = {2000///},
	pages = {972 -975 vol.1 - 972 -975 vol.1},
	abstract = {In network delivery of digital video, if the bandwidth required for a video is not available, the video has to be recoded at a reduced bit rate. It is highly desirable that the transcoding is carried out in real time while maintaining reasonable image quality. In this paper, we propose a fast approach to derive from an MPEG stream a new MPEG stream with half the spatial resolution. For the downsized video, we first generate from the original compressed video an improved estimate of the motion vectors. We then propose a compressed domain approach with data hiding to produce DCT residues by an open-loop method. The computational complexity is significantly lower than a number of previous approaches. Simulation suggests that our approach produces reasonable image quality},
	keywords = {approach;computational, coding;, coding;video, complexity;data, compression;data, cosine, DCT, delivery;open-loop, domain, encapsulation;discrete, estimation;network, estimation;transform, hiding;digital, method;spatial, quality;motion, reduction;video, residues;MPEG, resolution, resolution;motion, stream;compressed, transcoding;computational, transforms;image, vectors, video;downsized, video;image},
	doi = {10.1109/ICIP.2000.901123},
	author = {Yin,Peng and M. Wu and Liu,Bede}
}
@article {17964,
	title = {Controlled topology simplification},
	journal = {Visualization and Computer Graphics, IEEE Transactions on},
	volume = {2},
	year = {1996},
	month = {1996/06//},
	pages = {171 - 184},
	abstract = {We present a simple, robust, and practical method for object simplification for applications where gradual elimination of high frequency details is desired. This is accomplished by converting an object into multi resolution volume rasters using a controlled filtering and sampling technique. A multiresolution triangle mesh hierarchy can then be generated by applying the Marching Cubes algorithm. We further propose an adaptive surface generation algorithm to reduce the number of triangles generated by the standard Marching Cubes. Our method simplifies the topology of objects in a controlled fashion. In addition, at each level of detail, multilayered meshes can be used for an efficient antialiased rendering},
	keywords = {(computer, algorithm;adaptive, algorithm;controlled, antialiased, Cubes, Cubes;antialiasing;computational, details;multi, filtering;controlled, Frequency, generation, geometry;rendering, graphics);topology;, hierarchy;object, Marching, mesh, meshes;multiresolution, rasters;multilayered, rendering;high, resolution, simplification;efficient, simplification;sampling, surface, technique;standard, Topology, triangle, volume},
	isbn = {1077-2626},
	doi = {10.1109/2945.506228},
	author = {He,Taosong and Hong,Lichan and Varshney, Amitabh and Wang,S.W.}
}
@article {14189,
	title = {A syntactic approach to scale-space-based corner description},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {16},
	year = {1994},
	month = {1994/07//},
	pages = {748 - 751},
	abstract = {Planar curves are described by information about corners integrated over various levels of resolution. The detection of corners takes place on a digital representation. To compensate for ambiguities arising from sampling problems due to the discreteness, results about the local behavior of curvature extrema in continuous scale-space are employed},
	keywords = {Computer vision, corner detection, curvature extrema, edge detection, IMAGE PROCESSING, image resolution, Image segmentation, Laboratories, Large-scale systems, PARALLEL PROCESSING, pattern recognition, planar curves, resolution, Sampling methods, sampling problems, scale space based corner description, SHAPE, Smoothing methods, syntactic approach},
	isbn = {0162-8828},
	doi = {10.1109/34.297957},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
