303 research outputs found
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Salient object detection increasingly receives attention as an important
component or step in several pattern recognition and image processing tasks.
Although a variety of powerful saliency models have been intensively proposed,
they usually involve heavy feature (or model) engineering based on priors (or
assumptions) about the properties of objects and backgrounds. Inspired by the
effectiveness of recently developed feature learning, we provide a novel Deep
Image Saliency Computing (DISC) framework for fine-grained image saliency
computing. In particular, we model the image saliency from both the coarse- and
fine-level observations, and utilize the deep convolutional neural network
(CNN) to learn the saliency representation in a progressive manner.
Specifically, our saliency model is built upon two stacked CNNs. The first CNN
generates a coarse-level saliency map by taking the overall image as the input,
roughly identifying saliency regions in the global context. Furthermore, we
integrate superpixel-based local context information in the first CNN to refine
the coarse-level saliency map. Guided by the coarse saliency map, the second
CNN focuses on the local context to produce fine-grained and accurate saliency
map while preserving object details. For a testing image, the two CNNs
collaboratively conduct the saliency computing in one shot. Our DISC framework
is capable of uniformly highlighting the objects-of-interest from complex
background while preserving well object details. Extensive experiments on
several standard benchmarks suggest that DISC outperforms other
state-of-the-art methods and it also generalizes well across datasets without
additional training. The executable version of DISC is available online:
http://vision.sysu.edu.cn/projects/DISC.Comment: This manuscript is the accepted version for IEEE Transactions on
Neural Networks and Learning Systems (T-NNLS), 201
Development of an 11-year (2000-2010) land surface energy balance dataset for mainland China
Abstract HKT-ISTP 2013
B
Comparison of satellite-based evapotranspiration estimates over the Tibetan Plateau
The Tibetan Plateau (TP) plays a major role in regional and global climate. The understanding of latent heat (LE) flux can help to better describe the complex mechanisms and interactions between land and atmosphere. Despite its importance, accurate estimation of evapotranspiration (ET) over the TP remains challenging. Satellite observations allow for ET estimation at high temporal and spatial scales. The purpose of this paper is to provide a detailed cross-comparison of existing ET products over the TP. Six available ET products based on different approaches are included for comparison. Results show that all products capture the seasonal variability well with minimum ET in the winter and maximum ET in the summer. Regarding the spatial pattern, the High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) ET demonstrator dataset is very similar to the LandFlux-EVAL dataset (a benchmark ET product from the Global Energy and Water Cycle Experiment), with decreasing ET from the south-east to northwest over the TP. Further comparison against the LandFlux-EVAL over different sub-regions that are decided by different intervals of normalised difference vegetation index (NDVI), precipitation, and elevation reveals that HOLAPS agrees best with LandFlux-EVAL having the highest correlation coefficient (R) and the lowest root mean square difference (RMSD). These results indicate the potential for the application of the HOLAPS demonstrator dataset in understanding the land-atmosphere-biosphere interactions over the TP. In order to provide more accurate ET over the TP, model calibration, high accuracy forcing dataset, appropriate in situ measurements as well as other hydrological data such as runoff measurements are still needed
Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking
Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent
years, due to its ability to detect object motion out of sight. Most previous
works on NLOS tracking rely on active illumination, e.g., laser, and suffer
from high cost and elaborate experimental conditions. Besides, these techniques
are still far from practical application due to oversimplified settings. In
contrast, we propose a purely passive method to track a person walking in an
invisible room by only observing a relay wall, which is more in line with real
application scenarios, e.g., security. To excavate imperceptible changes in
videos of the relay wall, we introduce difference frames as an essential
carrier of temporal-local motion messages. In addition, we propose PAC-Net,
which consists of alternating propagation and calibration, making it capable of
leveraging both dynamic and static messages on a frame-level granularity. To
evaluate the proposed method, we build and publish the first dynamic passive
NLOS tracking dataset, NLOS-Track, which fills the vacuum of realistic NLOS
datasets. NLOS-Track contains thousands of NLOS video clips and corresponding
trajectories. Both real-shot and synthetic data are included. Our codes and
dataset are available at https://againstentropy.github.io/NLOS-Track/.Comment: CVPR 2023 camera-ready version. Codes and dataset are available at
https://againstentropy.github.io/NLOS-Track
Growing season net ecosystem CO2 exchange of two desert ecosystems with alkaline soils in Kazakhstan
Central Asia is covered by vast desert ecosystems, and the majority of these ecosystems have alkaline soils. Their contribution to global net ecosystem CO(2) exchange (NEE) is of significance simply because of their immense spatial extent. Some of the latest research reported considerable abiotic CO(2) absorption by alkaline soil, but the rate of CO(2) absorption has been questioned by peer communities. To investigate the issue of carbon cycle in Central Asian desert ecosystems with alkaline soils, we have measured the NEE using eddy covariance (EC) method at two alkaline sites during growing season in Kazakhstan. The diurnal course of mean monthly NEE followed a clear sinusoidal pattern during growing season at both sites. Both sites showed significant net carbon uptake during daytime on sunny days with high photosynthetically active radiation (PAR) but net carbon loss at nighttime and on cloudy and rainy days. NEE has strong dependency on PAR and the response of NEE to precipitation resulted in an initial and significant carbon release to the atmosphere, similar to other ecosystems. These findings indicate that biotic processes dominated the carbon processes, and the contribution of abiotic carbon process to net ecosystem CO(2) exchange may be trivial in alkaline soil desert ecosystems over Central Asia
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