Game Vertebrate Densities in Hunted and Nonhunted Forest Sites in Manu National Park, Peru

Manu National Park of southern Peru is one of the most renowned protected areas in the world, yet large-bodied vertebrate surveys conducted to date have been restricted to Cocha Cashu Biological Station, a research station covering <0.06 percent of the 1.7Mha park. Manu Park is occupied by >460 settled Matsigenka Amerindians, 300-400 isolated Matsigenka, and several, little-known groups of isolated hunter-gatherers, yet the impact of these native Amazonians on game vertebrate populations within the park remains poorly understood. On the basis of 1495 km of standardized line-transect censuses, we present density and biomass estimates for 23 mammal, bird, and reptile species for seven lowland and upland forest sites in Manu Park, including Cocha Cashu. We compare these estimates between hunted and nonhunted sites within Manu Park, and with other Neotropical forest sites. Manu Park safeguards some of the most species-rich and highest biomass assemblages of arboreal and terrestrial mammals ever recorded in Neotropical forests, most likely because of its direct Andean influence and high levels of soil fertility. Relative to Barro Colorado Island, seed predators and arboreal folivores in Manu are rare, and generalist frugivores specializing on mature fruit pulp are abundant. The impact of such a qualitative shift in the vertebrate community on the dynamics of plant regeneration, and therefore, on our understanding of tropical plant ecology, must be profound. Despite a number of external threats, Manu Park continues to serve as a baseline against which other Neotropical forests can be gauged.

A new half-flash architecture for high speed video ADC

In this paper a new half-flash architecture for high speed video ADC is presented. Based on a high speed single-way analog switch circuit, this architecture effectively reduces the number of elements. At the same lime no sacrifice of speed is needed compared with the normal half-flash structure.

Non-goal scene analysis for soccer video

The broadcast soccer video is usually recorded by one main camera, which is constantly gazing somewhere of playfield where a highlight event is happening. So the camera parameters and their variety have close relationship with semantic information of soccer video, and much interest has been caught in camera calibration for soccer video. The previous calibration methods either deal with goal scene, or have strict calibration conditions and high complexity. So, it does not properly handle the non-goal scene such as midfield or center-forward scene. In this paper, based on a new soccer field model, a field symbol extraction algorithm is proposed to extract the calibration information. Then a two-stage calibration approach is developed which can calibrate camera not only for goal scene but also for non-goal scene. The preliminary experimental results demonstrate its robustness and accuracy. (c) 2010 Elsevier B.V. All rights reserved.

A Video Quality Assessment Metric Based on Human Visual System

It is important for practical application to design an effective and efficient metric for video quality. The most reliable way is by subjective evaluation. Thus, to design an objective metric by simulating human visual system (HVS) is quite reasonable and available. In this paper, the video quality assessment metric based on visual perception is proposed. Three-dimensional wavelet is utilized to decompose video and then extract features to mimic the multichannel structure of HVS. Spatio-temporal contrast sensitivity function (S-T CSF) is employed to weight coefficient obtained by three-dimensional wavelet to simulate nonlinearity feature of the human eyes. Perceptual threshold is exploited to obtain visual sensitive coefficients after S-T CSF filtered. Visual sensitive coefficients are normalized representation and then visual sensitive errors are calculated between reference and distorted video. Finally, temporal perceptual mechanism is applied to count values of video quality for reducing computational cost. Experimental results prove the proposed method outperforms the most existing methods and is comparable to LHS and PVQM.

Biologically Inspired Features for Scene Classification in Video Surveillance

Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.