@conference {12446,
	title = {Illumination robust dictionary-based face recognition},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {777 - 780},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we present a face recognition method based on simultaneous sparse approximations under varying illumination. Our method consists of two main stages. In the first stage, a dictionary is learned for each face class based on given training examples which minimizes the representation error with a sparseness constraint. In the second stage, a test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. Furthermore, to handle changes in lighting conditions, we use a relighting approach based on a non-stationary stochastic filter to generate multiple images of the same person with different lighting. As a result, our algorithm has the ability to recognize human faces with good accuracy even when only a single or a very few images are provided for training. The effectiveness of the proposed method is demonstrated on publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms.},
	keywords = {albedo, approximation theory, classification, competitive face recognition algorithms, Databases, Dictionaries, Face, face recognition, face recognition method, filtering theory, human face recognition, illumination robust dictionary-based face recognition, illumination variation, image representation, learned dictionary, learning (artificial intelligence), lighting, lighting conditions, multiple images, nonstationary stochastic filter, publicly available databases, relighting, relighting approach, representation error, residual vectors, Robustness, simultaneous sparse approximations, simultaneous sparse signal representation, sparseness constraint, Training, varying illumination, vectors},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6116670},
	author = {Patel, Vishal M. and Tao Wu and Biswas,S. and Phillips,P.J. and Chellapa, Rama}
}
@conference {14229,
	title = {Measuring 1st order stretchwith a single filter},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008},
	year = {2008},
	month = {2008/04/31/March},
	pages = {909 - 912},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We analytically develop a filter that is able to measure the linear stretch of the transformation around a point, and present results of applying it to real signals. We show that this method is a real-time alternative solution for measuring local signal transformations. Experimentally, this method can accurately measure stretch, however, it is sensitive to shift.},
	keywords = {Cepstral analysis, Educational institutions, filter, filtering theory, Fourier transforms, Frequency domain analysis, Frequency estimation, Gabor filters, Image analysis, IMAGE PROCESSING, linear stretch measurement, local signal transformation measurement, Nonlinear filters, Phase estimation, Signal analysis, Speech processing},
	isbn = {978-1-4244-1483-3},
	doi = {10.1109/ICASSP.2008.4517758},
	author = {Bitsakos,K. and Domke, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {15060,
	title = {A New Approach of Dynamic Background Modeling for Surveillance Information},
	booktitle = {Computer Science and Software Engineering, 2008 International Conference on},
	volume = {1},
	year = {2008},
	month = {2008/12//},
	pages = {850 - 855},
	abstract = {This paper presents a new approach of best background modeling for surveillance information. The approach makes orthogonal non-separable wavelet transformation of information frames used for background modeling, extracts the approximate information to reconstruct information frames, filters out the disturbance, shadow and noise from the reconstructed frames, constructs basic background with the method of binary mask images, uses multi-frame combination of non-uniform noise to filter noise in basic background, applies mutual information to detect the situation of adjacent changes. If the background has a gradual change, weighted superposition of multi background modeling images with time will be applied to update the background. If the background has a major or sudden change, the background will remodel from this frame.},
	keywords = {approximate information extraction, binary mask images, disturbance filtering, dynamic background modeling, Feature extraction, filtering theory, Image reconstruction, information frame reconstruction, NOISE, noise filtering, orthogonal nonseparable wavelet transformation, Surveillance, surveillance information, Wavelet transforms},
	doi = {10.1109/CSSE.2008.601},
	author = {Gao,Dongfa and Zhuolin Jiang and Ye,Ming}
}
@conference {11985,
	title = {Multiple View Image Reconstruction: A Harmonic Approach},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07},
	year = {2007},
	month = {2007/06/17/22},
	pages = {1 - 8},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a new constraint connecting the signals in multiple views of a surface. The constraint arises from a harmonic analysis of the geometry of the imaging process and it gives rise to a new technique for multiple view image reconstruction. Given several views of a surface from different positions, fundamentally different information is present in each image, owing to the fact that cameras measure the incoming light only after the application of a low-pass filter. Our analysis shows how the geometry of the imaging is connected to this filtering. This leads to a technique for constructing a single output image containing all the information present in the input images.},
	keywords = {CAMERAS, filtering theory, Geometry, Harmonic analysis, Image reconstruction, imaging process geometry, Information filtering, Joining processes, Low pass filters, low-pass filter, low-pass filters, multiple view image reconstruction, Position measurement, Power harmonic filters, surface reconstruction},
	isbn = {1-4244-1180-7},
	doi = {10.1109/CVPR.2007.383285},
	author = {Domke, J. and Aloimonos, J.}
}
@article {19663,
	title = {MCMC-based particle filtering for tracking a variable number of interacting targets},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/11//},
	pages = {1805 - 1819},
	abstract = {We describe a particle filter that effectively deals with interacting targets, targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.},
	keywords = {algorithms, Animals, Artificial intelligence, Computer simulation, Computer vision, Filtering, filtering theory, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, Index Terms- Particle filters, Information Storage and Retrieval, Insects, interacting targets, Markov chain Monte Carlo sampling step, Markov chain Monte Carlo., Markov chains, Markov processes, Markov random field motion, Markov random fields, Models, Biological, Models, Statistical, Monte Carlo Method, Monte Carlo methods, MOTION, Movement, multitarget filter, multitarget tracking, particle filtering, Particle filters, Particle tracking, Pattern Recognition, Automated, Sampling methods, Subtraction Technique, target tracking, Video Recording},
	isbn = {0162-8828},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {19665,
	title = {Multitarget tracking with split and merged measurements},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {605 - 610 vol. 1},
	abstract = {In many multitarget tracking applications in computer vision, a detection algorithm provides locations of potential targets. Subsequently, the measurements are associated with previously estimated target trajectories in a data association step. The output of the detector is often imperfect and the detection data may include multiple, split measurements from a single target or a single merged measurement from several targets. To address this problem, we introduce a multiple hypothesis tracker for interacting targets that generate split and merged measurements. The tracker is based on an efficient Markov chain Monte Carlo (MCMC) based auxiliary variable particle filter. The particle filter is Rao-Blackwellized such that the continuous target state parameters are estimated analytically, and an MCMC sampler generates samples from the large discrete space of data associations. In addition, we include experimental results in a scenario where we track several interacting targets that generate these split and merged measurements.},
	keywords = {Application software, Computer vision, Detection algorithms, Detectors, filtering theory, Markov chain Monte Carlo based auxiliary variable particle filter, Markov processes, merged measurements, Monte Carlo methods, multiple hypothesis tracker, multitarget tracking, parameter estimation, Particle filters, Particle tracking, Rao-Blackwellized filter, split measurements, target tracking, Trajectory},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {19659,
	title = {Efficient particle filter-based tracking of multiple interacting targets using an MRF-based motion model},
	booktitle = {2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings},
	volume = {1},
	year = {2003},
	month = {2003/10//},
	pages = {254 - 259 vol.1},
	abstract = {We describe a multiple hypothesis particle filter for tracking targets that are influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost.},
	keywords = {collision avoidance, computational cost, Computational efficiency, Educational institutions, exponential complexity, Filtering, filtering theory, Insects, joint particle tracker, Markov processes, Markov random field motion, Markov random fields, multiple interacting targets, particle filter-based tracking, Particle filters, Particle tracking, Radar tracking, social insect tracking application, target tracking, Trajectory},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {18468,
	title = {Multimodal 3-D tracking and event detection via the particle filter},
	booktitle = {IEEE Workshop on Detection and Recognition of Events in Video, 2001. Proceedings},
	year = {2001},
	month = {2001///},
	pages = {20 - 27},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Determining the occurrence of an event is fundamental to developing systems that can observe and react to them. Often, this determination is based on collecting video and/or audio data and determining the state or location of a tracked object. We use Bayesian inference and the particle filter for tracking moving objects, using both video data obtained from multiple cameras and audio data obtained using arrays of microphones. The algorithms developed are applied to determining events arising in two fields of application. In the first, the behavior of a flying echo locating bat as it approaches a moving prey is studied, and the events of search, approach and capture are detected. In a second application we describe detection of turn-taking in a conversation between possibly moving participants recorded using a smart video conferencing setup},
	keywords = {algorithms, APPROACH, audio data collection, audio signal processing, Bayesian inference, Bayesian methods, belief networks, CAMERAS, capture, conversation, echo, Educational institutions, Event detection, event occurrence, filtering theory, flying echo locating bat behaviour, Image motion analysis, inference mechanisms, Laboratories, microphone arrays, moving object tracking, moving participants, moving prey, multimodal 3D tracking, multiple cameras, Object detection, particle filter, Particle filters, Particle tracking, Robustness, search, smart video conferencing setup, target tracking, Teleconferencing, tracking filters, turn-taking detection, video data collection, video signal processing},
	isbn = {0-7695-1293-3},
	doi = {10.1109/EVENT.2001.938862},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S.}
}
