ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization

Clip-art image segmentation is widely used as an essential step to solve many vision problems such as colorization and vectorization. Many of these applications not only demand accurate segmentation results, but also have little tolerance for time cost, which leads to the main challenge of this kind of segmentation. However, most existing segmentation techniques are found not sufficient for this purpose due to either their high computation cost or low accuracy. To address such issues, we propose a novel segmentation approach, ECISER, which is well-suited in this context. The basic idea of ECISER is to take advantage of the particular nature of cartoon images and connect image segmentation with aliased rasterization. Based on such relationship, a clip-art image can be quickly segmented into regions by re-rasterization of the original image and several other computationally efficient techniques developed in this paper. Experimental results show that our method achieves dramatic computational speedups over the current state-of-the-art approaches, while preserving almost the same quality of results.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

多输入单输出逆高斯分布流域汇流模型

本文将逆高斯分布应用于由水库和区间组成的复杂的流域汇流系统，并建立了多输入单输出逆高斯单位线模型。讨论了模型参数的估计方法，并对沾益流域的雨洪资料进行了实例分析。     

Cluster analysis in industrial market segmentation through artificial neural network

Market segmentation has commonly applied cluster analysis. This study intends to make the comparison of conventional two-stage method with proposed two-stage method through the simulated data. The proposed two-stage method is the combination of self-organizing feature maps and K-means method. The simulation results show that the proposed scheme is better than the conventional two-stage method based on the rate of misclassification.     

川中丘陵区一种新型小流域径流输沙模型的建立与应用

径流输沙模型对定量评价水沙的输出响应具有重要的意义。本研究在已有研究成果的基础上，通过对四川盐亭县林山西沟小流域的径流、泥沙进行现场观测与分析，提出含沙量单位线的定义及其确定方法，建立了适应该小流域的径流输沙模型。并应用实测资料进行比较，表明模型的模拟结果可靠，模拟过程确定性系数达0．8以上，为川中丘陵区小流域径流输沙过程的预测预报提供了一个新方法。     

A Novel Segmentation and Feedback Model for Resolving Contention in Optical Burst Switching

A novel segmentation and feedback model (SFM) applied to resolve contention is proposed. Simulation and performance analyses show that the SFM effectively avoids contentions in optical burst switching (OBS). The long delay time of deflection routing and the immature technology of wavelength converters and optical buffers are not deployed in the SFM. The SFM does not only realize fast switching but also allows preemption by higher priority bursts.     

An Interval Fuzzy Multiobjective Watershed Management Model for the Lake Qionghai Watershed, China

Integrated watershed management is required to ensure the reasonable use of resources and reconcile interactions among natural and human systems. In the present study, an interval fuzzy multiobjective programming (IFMOP) method was used to solve an integrated watershed management problem. Based on system analysis, an IFMOP model suitable for a lake watershed system {IFMOPLWS} was developed and applied to the Lake Qionghai watershed in China. Scenario analysis and an interactive approach were used in the solution process. In this manner, various system components were incorporated into one framework for holistic consideration and optimization. Integrality and uncertainty, as well as the multiobjective and dynamic characteristics of the watershed system, were well addressed. Using two scenarios, two planning schemes were generated. Agriculture, tourism, macroeconomics, cropland use, water supply, forest coverage, soil erosion, and water pollution were fully interpreted and compared to identify a preferable planning alternative for local agencies. This study showed that the IFMOPLWS is a powerful tool for integrated watershed management planning and can provide a solid base for sustainable watershed management.     

Learning-based dynamic coupling of discrete and continuous trackers

We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.     

Recognizing the importance of scale in the ecology and management of riverine fish

Processes affecting fish populations range in scale from local to global. Fish response is also scale‐dependent, with some activities varying locally while others depend on large‐scale connectivity within or between watersheds. These issues are still only partly recognized, with large‐scale research often affected by non‐independent sampling, weak inference, poor model testing or model over‐extrapolation. Available multi‐scale studies can reach different conclusions about factors affecting fishes from local studies, but results vary between examples. Potential explanations are (i) effects on fishes are context‐dependent; (ii) different species or life‐stages are limited in different ways; (iii) multi‐scale studies are too few for generalization. We advocate improved use of geostatistical tools to guide sampling or interpret the spatial extent of management problems, and we illustrate this using brown trout in Welsh streams. Our strongest recommendation is that fish ecologists recognize the importance of interactions across scales in quantifying effects on fishes so that management decisions can be better based on evidence rather than judgement or extrapolation. Copyright © 2006 John Wiley & Sons, Ltd.     

An improved watershed algorithm based on efficient computation of shortest paths

The present paper describes a new algorithm to calculate the watershed transform through rain simulation of greyscale digital images by means of pixel arrowing. The efficiency of this method is based on limiting the necessary neighbouring operations to compute the transform to the outmost, and in the total number of scannings performed over the whole image. The experiments demonstrate that the proposed algorithm is able to significantly reduce the running time of the fastest known algorithm without involving any loss of efficiency.