Toward automatic extraction of expressive elements from motion pictures : tempo

This paper addresses the challenge of bridging the semantic gap that exists between the simplicity of features that can be currently computed in automated content indexing systems and the richness of semantics in user queries posed for media search and retrieval. It proposes a unique computational approach to extraction of expressive elements of motion pictures for deriving high-level semantics of stories portrayed, thus enabling rich video annotation and interpretation. This approach, motivated and directed by the existing cinematic conventions known as film grammar, as a first step toward demonstrating its effectiveness, uses the attributes of motion and shot length to define and compute a novel measure of tempo of a movie. Tempo flow plots are defined and derived for a number of full-length movies and edge analysis is performed leading to the extraction of dramatic story sections and events signaled by their unique tempo. The results confirm tempo as a useful high-level semantic construct in its own right and a promising component of others such as rhythm, tone or mood of a film. In addition to the development of this computable tempo measure, a study is conducted as to the usefulness of biasing it toward either of its constituents, namely, motion or shot length. Finally, a refinement is made to the shot length normalizing mechanism, driven by the peculiar characteristics of shot length distribution exhibited by movies. Results of these additional studies, and possible applications and limitations are discussed.

Towards automatic extraction of expressive elements from motion pictures : tempo

This paper proposes a unique computational approach to extraction of expressive elements of motion pictures for deriving high level semantics of stories portrayed, thus enabling better video annotation and interpretation systems. This approach, motivated and directed by the existing cinematic conventions known as film grammar, as a first step towards demonstrating its effectiveness, uses the attributes of motion and shot length to define and compute a novel measure of tempo of a movie. Tempo flow plots are defined and derived for four full-length movies and edge analysis is performed leading to the extraction of dramatic story sections and events signaled by their unique tempo. The results confirm tempo as a useful attribute in its own right and a promising component of semantic constructs such as tone or mood of a film.

Detection of dynamic background due to swaying movements from motion features

Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: 1) that of swaying tree branches and 2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

Seasonal variation in the copepod community structure from a tropical Amazon estuary, Northern Brazil

O presente estudo teve como objetivo principal avaliar a variação sazonal na estrutura da comunidade dos copépodos durante os meses de julho, setembro e novembro de 2003 (período seco) e janeiro, março e maio de 2004 (período chuvoso) no estuário do Curuçá, Norte do Brasil. As amostras foram coletadas nas marés de quadratura com auxílio de uma rede de plâncton com 200µm de abertura de malha, rebocada por meio de uma pequena embarcação a motor. As medidas de condutividade da água foram realizadas in situ utilizando-se um condutivímetro eletrônico e a salinidade foi posteriormente obtida através da transformação dos valores de condutividade. Os valores de salinidade variaram sazonalmente de 7, 2 ± 0, 1a 39, 2 ± 1, 8 (média ± desvio padrão), tendo sido principalmente influenciados pelas diferenças nas taxas de precipitação entre os períodos de amostragem estudados. Foram identificados no total 30 táxons, com Acartia tonsa constituindo a espécie mais representativa durante todo o período de estudo,seguida por Acartia lilljeborgii, Subeucalanus pileatus e Paracalanus quasimodo. Durante este trabalho, os valores de densidade, índices ecológicos e dominância das espécies de copépodos apresentaram um padrão sazonal claro, mostrando que a área estudada pode ser considerada sazonalmente heterogênea em relação a estes parâmetros investigados.

Insights into the morphology of the Serra da Cangalha impact structure from geophysical modeling

Forward modeling is commonly applied to gravity field data of impact structures to determine the main gravity anomaly sources. In this context, we have developed 2.5-D gravity models of the Serra da Cangalha impact structure for the purpose of investigating geological bodies/structures underneath the crater. Interpretation of the models was supported by ground magnetic data acquired along profiles, as well as by high resolution aeromagnetic data. Ground magnetic data reveal the presence of short-wavelength anomalies probably related to shallow magnetic sources that could have been emplaced during the cratering process. Aeromagnetic data show that the basement underneath the crater occurs at an average depth of about 1.9 km, whereas in the region beneath the central uplift it is raised to 0.51 km below the current surface. These depths are also supported by 2.5-D gravity models showing a gentle relief for the basement beneath the central uplift area. Geophysical data were used to provide further constraints for numeral modeling of crater formation that provided important information on the structural modification that affected the rocks underneath the crater, as well as on shock-induced modifications of target rocks. The results showed that the morphology is consistent with the current observations of the crater and that Serra da Cangalha was formed by a meteorite of approximately 1.4 km diameter striking at 12 km s-1.

Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation

This paper presents different application scenarios for which the registration of sub-sequence reconstructions or multi-camera reconstructions is essential for successful camera motion estimation and 3D reconstruction from video. The registration is achieved by merging unconnected feature point tracks between the reconstructions. One application is drift removal for sequential camera motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent camera motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential camera motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. In the third scenario image sequences of the same scene, which are captured under different illuminations, are registered. Several experiments with challenging real video sequences demonstrate that the presented techniques work in practice.

Constraining planet structure from stellar chemistry: the cases of CoRoT-7, Kepler-10, and Kepler-93

Aims. We explore the possibility that the stellar relative abundances of different species can be used to constrain the bulk abundances of known transiting rocky planets. Methods. We use high resolution spectra to derive stellar parameters and chemical abundances for Fe, Si, Mg, O, and C in three stars hosting low mass, rocky planets: CoRoT-7, Kepler-10, and Kepler-93. These planets follow the same line along the mass-radius diagram, pointing toward a similar composition. The derived abundance ratios are compared with the solar values. With a simple stoichiometric model, we estimate the iron mass fraction in each planet, assuming stellar composition. Results. We show that in all cases, the iron mass fraction inferred from the mass-radius relationship seems to be in good agreement with the iron abundance derived from the host star's photospheric composition. Conclusions. The results suggest that stellar abundances can be used to add constraints on the composition of orbiting rocky planets.

Learning 3D structure from 2D images using LBP features

An automatic machine learning strategy for computing the 3D structure of monocular images from a single image query using Local Binary Patterns is presented. The 3D structure is inferred through a training set composed by a repository of color and depth images, assuming that images with similar structure present similar depth maps. Local Binary Patterns are used to characterize the structure of the color images. The depth maps of those color images with a similar structure to the query image are adaptively combined and filtered to estimate the final depth map. Using public databases, promising results have been obtained outperforming other state-of-the-art algorithms and with a computational cost similar to the most efficient 2D-to-3D algorithms.