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.     

Does decision quality (always) increase with the size of information samples? Some vicissitudes in applying the law of large numbers.

Adaptive decision making requires that contingencies between decision options and their relative assets be assessed accurately and quickly. The present research addresses the challenging notion that contingencies may be more visible from small than from large samples of observations. An algorithmic account for such a seemingly paradoxical effect is offered within a satisficing-choice framework. Accordingly, a choice is only made when the sample contingency describing the relative evaluation of the 2 options exceeds a critical threshold. Small samples, because of the high dispersion of their sampling distribution, facilitate above-threshold contingencies. Across a broad range of parameters, the resulting small-sample advantage in terms of hits is stronger than their disadvantage in false alarms. Computer simulations and experiments support the model predictions. The relative advantage of small samples is most apparent when information loss is low, when the threshold is high relative to the ecological contingency, and when the sampling process is self-truncated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

一种大尺度二维直线度精密测量的新方法

本文提出了一种远距离、大范围二维直线度测量的新方法．该方法采用特殊设计的十字线准直激光器及线阵CCD，通过特殊的光学系统结构设计，能在0-20m距离内连续测量，直线度范围可达70mm，精度优于0．1mm，实际测量结果表明，该方法可适应野外作业环境。     

Adsp-21060的主机接口在实时图像处理中的应用

简要介绍了数字信号处理器Adsp 2 10 6 0和大规模可编程逻辑器件EP1K5 0。详细讨论了Adsp 2 10 6 0的主机接口工作模式以及一个实时图像跟踪处理器的硬件组成原理 ,在这个系统中EP1K5 0充当了主机。调试结果表明 ,所提出的硬件结构设计思想工作效率高 ,完全能够胜任实时图像处理的实际需要。     

江阴长江公路大桥钢箱梁体受力分析

介绍了对大桥的风向、风力和交通流量的测试依据和内容，分析了风向、风力的测试结果以及梁体在风力及交通流量等作用下的受力情况，指出钢箱梁体主要受到的力是风力。     

A multigrid accelerated hybrid unstructured mesh method for 3D compressible turbulent flow

 A cell vertex finite volume method for the solution of steady compressible turbulent flow problems on unstructured hybrid meshes of tetrahedra, prisms, pyramids and hexahedra is described. These hybrid meshes are constructed by firstly discretising the computational domain using tetrahedral elements and then by merging certain tetrahedra. A one equation turbulence model is employed and the solution of the steady flow equations is obtained by explicit relaxation. The solution process is accelerated by the addition of a multigrid method, in which the coarse meshes are generated by agglomeration, and by parallelisation. The approach is shown to be effective for the simulation of a number of 3D flows of current practical interest. Sponsored by The Research Council of Norway, project number 125676/410 Dedicated to the memory of Prof. Mike Crisfield, a respected colleague     

频谱分解技术在塔里木盆地北部TH地区碳酸盐岩缝洞型储层预测中的应用

本文从频谱分解技术的基本原理及计算方法入手,根据正演模型,通过对比缝洞型储层与地震反射特征的关系,总结出储层发育的反射特征,相应地精细调整频谱分解处理参数,进而进行预测。通过与钻井资料对比可知,频谱分解技术可以刻画不同频率强反射、弱反射的边界。     

海拉尔盆地的古河道砂体识别方法

本文基于海拉尔盆地贝尔凹陷西北部高精度三维地震资料,通过对时间域、频率域地震属性的分析,研究了河道砂体的变化及分布规律,总结出了一套利用时间域、频率域地震属性识别古河道的方法。其中对河道砂体较为敏感的沿层属性主要有均方根振幅、反射强度梯度、有效带宽及瞬时频度等。利用这种识别方法在该区找到水下分流河道砂体和河口坝砂体,为该区的岩性勘探部署提供了具体目标。     

基于MATLAB的风能自校正自调整PID模糊控制器仿真研究

为了提高风力发电系统综合系统性能,在传统风力发电技术的基础上,对变速风力发电机组的自校正自调整PID模糊控制进行了详细的分析和研究,从而利用模糊控制和PID控制相结合的技术,取得了很好的效果。从MATLAB仿真结果看出系统精度及效率很高而稳态误差较小,鲁棒性较好。     

用增量的累积量方法测量大气风场

为了在非平稳、非线性条件下能得到真实风场的良好估计,提出了一种用空间分布天线（SA）雷达信号的增量累积量测量大气风场参数的方法。该方法基于结构函数方法,利用高阶累计量在信息处理中的特性,用零延迟的增量累计量求解平均水平风速等具体参量，并在大气散射模型基础上对增量累积量方法进行了数值模拟,得出了同理论分析一致的结果.分析和计算表明,该方法完全适用于局地平稳条件，可以减小风场参量估计对天线间距的敏感度；阶数k≥3时,对高斯噪声有很强抑制;同时可以得到风场参量的高阶估计，这是普通的二阶方法（如全相关分析、全谱分析）不具备的。