@article {13030,
	title = {Validating the systematic position of {\i}t Plationus Segers, Murugan \& Dumont, 1993 (Rotifera: Brachionidae) using sequences of the large subunit of the nuclear ribosomal DNA and of cytochrome C oxidase},
	journal = {Hydrobiologia},
	volume = {644},
	year = {2010},
	month = {2010/05//},
	pages = {361 - 370},
	abstract = {Members of the family Brachionidae are free-living organisms that range in size from 170 to 250 microns. They comprise part of the zooplankton in freshwater and marine systems worldwide. Morphologically, members of the family are characterized by a single piece loricated body without furrows, grooves, sulci or dorsal head shields, and a malleate trophi. Differences in these structures have been traditionally used to recognize 217 species that are classified into seven genera. However, the validity of the species, Plationus patulus, P. patulus macracanthus P. polyacanthus, and P. felicitas have been confused because they were alternatively assigned in Brachionus or Platyias, when considering only morphological and ecological characters. Based on scanning electron microscope (SEM) images of the trophi, these taxa were assigned in a new genus, Plationus. In this study, we examined the systematic position of P. patulus and P. patulus macracanthus using DNA sequences of two genes: the cytochrome oxidase subunit 1 (cox1) and domains D2 and D3 of the large subunit of the nuclear ribosomal RNA (LSU). In addition, the cox1 and LSU sequences representing five genera of Brachionidae (Anuraeopsis, Brachionus, Keratella, Plationus, and Platyias) plus four species of three families from the order Ploima were used as the outgroup. The maximum likelihood (ML) analyses were conducted for each individual gene as well as for the combined (cox1 + LSU) data set. The ML tree from the combined data set yielded the family Brachionidae as a monophyletic group with weak bootstrap support (< 50\%). Five main clades in this tree had high (> 85\%) bootstrap support. The first clade was composed of three populations of P. patulus + P. patulus macracanthus. The second clade was composed of a single species of Platyias. The third clade was composed of six species of Brachionus. The fourth clade included a single species of the genus Anuraeopsis, and the fifth clade was composed of three species of the genus Keratella. The genetic divergence between Plationus and Platyias ranged from 18.4 to 19.2\% for cox1, and from 4.5 to 4.9\% for LSU, and between Brachionus and Plationus, it ranged from 16.9 to 23.1\% (cox1), and from 7.3 to 9.1\% (LSU). Morphological evidence, the amount of genetic divergence, the systematic position of Plationus within the family Brachionidae, and the position of Plationus as a sister group of Brachionus and Platyias support the validity of Plationus patulus and P. patulus macracanthus into the genus Plationus.},
	keywords = {Cox1;, likelihood;, LSU;, maximum, Phylogeny, Plationus;},
	doi = {DOI 10.1007/s10750-010-0203-1},
	author = {Reyna-Fabian,ME and Laclette,J. P and Cummings, Michael P. and Garc{\'\i}a-Varela,M}
}
@conference {13186,
	title = {Fast multiple object tracking via a hierarchical particle filter},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/10//},
	pages = {212 - 219 Vol. 1 - 212 - 219 Vol. 1},
	abstract = {A very efficient and robust visual object tracking algorithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge orientation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To accelerate the algorithm while retaining robustness we adopt several enhancements in the algorithm. The first is the use of integral images for efficiently computing the color features and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robustness against clutter or short period occlusions. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.},
	keywords = {(numerical, algorithm;, analysis;, Color, colour, Computer, Convergence, detection;, edge, fast, filter;, Filtering, hierarchical, histogram;, image, images;, integral, likelihood;, methods);, methods;, multiple, numerical, object, observation, of, orientation, particle, processes;, quasirandom, random, sampling;, tracking, tracking;, vision;, visual},
	doi = {10.1109/ICCV.2005.95},
	author = {Yang,Changjiang and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {12766,
	title = {Probabilistic recognition of human faces from video},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {I-41 - I-44 vol.1 - I-41 - I-44 vol.1},
	abstract = {Most present face recognition approaches recognize faces based on still images. We present a novel approach to recognize faces in video. In that scenario, the face gallery may consist of still images or may be derived from a videos. For evidence integration we use classical Bayesian propagation over time and compute the posterior distribution using sequential importance sampling. The probabilistic approach allows us to handle uncertainties in a systematic manner. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach in both still-to-video and video-to-video scenarios with appropriate model choices.},
	keywords = {Bayes, Bayesian, CMU;, distribution;, Face, faces;, gallery;, handling;, human, image, images;, importance, likelihood;, methods;, NIST/USF;, observation, posterior, probabilistic, probability;, processing;, propagation;, recognition;, sampling;, sequential, signal, still, Still-to-video, Uncertainty, video, Video-to-video},
	doi = {10.1109/ICIP.2002.1037954},
	author = {Chellapa, Rama and Kruger, V. and Zhou,Shaohua}
}
