直方图累积器——一个图象处理硬件

本文介绍了一个直方图累积器的工作原理,设计方案,实验结果以及某些应用。     

TES过程概述

TES(Transform-Expend—Sample)过程是一种自相关的随机变量序列。它具有任意的边缘分布和很宽范围的自相关函数。这里主要对TES过程作了描述。     

基于子块直方图的图像检索算法研究

基于全图直方图图像检索算法过于简单及查全率、查准率过低的情况,本文提出把图像分割成子块的算法,寻找空间分布差异,间接捕捉到色彩的空间分布特征,从而能够区别色彩分布相似但空间分布不同的图像,尤其适用于纹理丰富的图像的检索,获得满意的结果。     

Optimal data-based binning for histograms and histogram-based probability density models

Histograms are convenient non-parametric density estimators, which continue to be used ubiquitously. Summary quantities estimated from histogram-based probability density models depend on the choice of the number of bins. We introduce a straightforward data-based method of determining the optimal number of bins in a uniform bin-width histogram. By assigning a multinomial likelihood and a non-informative prior, we derive the posterior probability for the number of bins in a piecewise-constant density model given the data. In addition, we estimate the mean and standard deviations of the resulting bin heights, examine the effects of small sample sizes and digitized data, and demonstrate the application to multi-dimensional histograms.     

基于自适应融合的手掌静脉增强方法

针对掌脉轮廓不清晰，图像对比度低、亮度低，进而导致识别性能降低的现象，提出一种自适应融合的手掌静脉增强方法。首先，基于暗原色先验（DCP）去雾算法，根据掌脉图像变异系数自适应选择去雾系数，得到DCP增强图像，并且基于部分子块重叠直方图均衡（POSHE）算法得到POSHE增强图像；然后，将图像分为16个子块，依据图像灰度均值与标准差确定各子块权重；最后，根据各子块权重对DCP和POSHE增强图像进行自适应融合，得到最终增强图像。该方法既保留了DCP算法在增强图像对比度和亮度的同时不引入明显噪声的优点，又保留了POSHE算法在增强图像对比度和亮度的同时不损失局部细节的特点；同时，两者的自适应融合既解决了DCP图像阴影部分掌脉缺失现象，又削弱了POSHE产生的块效应。在对两个公开库和自建库分别进行的实验中，三个数据库的等错误率分别为0.0004、0.0472、0.0579，识别率分别为99.98%、94.27%、92.05%。实验结果表明，与现有的图像增强方法相比，该方法降低了等错误率，提高了识别精度。     

Underwater image quality enhancement through integrated color model with Rayleigh distribution

The physical properties of water cause light-induced degradation of underwater images. Light rapidly loses intensity as it travels in water, depending on the color spectrum wavelength. Visible light is absorbed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Underwater images with low contrast are captured due to the degradation effects of light spectrum. Therefore, the valuable information from these images cannot be fully extracted for further processing. The current study proposes a new method to improve the contrast and reduce the noise of underwater images. The proposed method integrates the modification of image histogram into two main color models, Red–Green–Blue (RGB) and Hue-Saturation-Value (HSV). In the RGB color model, the histogram of the dominant color channel (i.e., blue channel) is stretched toward the lower level, with a maximum limit of 95%, whereas the inferior color channel (i.e., red channel) is stretched toward the upper level, with a minimum limit of 5%. The color channel between the dominant and inferior color channels (i.e., green channel) is stretched to both directions within the whole dynamic range. All stretching processes in the RGB color model are shaped to follow the Rayleigh distribution. The image is converted into the HSV color model, wherein the S and V components are modified within the limit of 1% from the minimum and maximum values. Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue-green effect, and minimizes under- and over-enhanced areas in the output image. For quantitative analysis, the test with 300 underwater images shows that the proposed method produces average mean square error (MSE) and peak signal to noise ratio (PSNR) of 76.76 and 31.13, respectively, which outperform six state-of-the-art methods.     

Multimodal hand gesture recognition combining temporal and pose information based on CNN descriptors and histogram of cumulative magnitudes

In this paper, we present a new approach for dynamic hand gesture recognition. Our goal is to integrate spatiotemporal features extracted from multimodal data captured by the Kinect sensor. In case the skeleton data is not provided, we apply a novel skeleton estimation method to compute temporal features. Furthermore, we introduce an effective method to extract a fixed number of keyframes to reduce the processing time. To extract pose features from RGB-D data, we take advantage of two different approaches: (1) Convolutional Neural Networks and (2) Histogram of Cumulative Magnitudes. We test different integration methods to fuse the extracted spatiotemporal features to boost recognition performance in a linear SVM classifier. Extensive experiments prove the effectiveness and feasibility of the proposed framework for hand gesture recognition.     

基于C3D-GRNN模型的人群异常行为识别算法

针对传统人群行为识别算法受人工主观因素影响较大等问题,综合三维卷积神经网络（C3D）与广义回归神经网络（GRNN）的优势和特点,提出并实现了基于C3D-GRNN模型的人群异常行为识别算法。该算法首先采用ViBe算法确定运动目标区域,然后通过改进C3D网络提取目标的HOG时空特征,再加入GRNN层进行分类训练,最后根据训练好的C3D-GRNN模型完成人群异常行为的识别与分类。实验结果表明：所提算法的HOG时空特征能够明显提升对人群行为的表达能力,减少了特征提取工作量,并且该方法的准确度和鲁棒性均高于支持向量机等其他同类方法,为小样本数据集的分类问题提供解决新思路,具有较高的应用价值。     

梯度直方图卷积特征的胶囊网络在交通监控下的车型分类

为了充分利用图像信息以提高现有交通监控下车型分类的效果,在胶囊网络的基础上增加梯度直方图卷积（HOG-C）特征提取方法,提出HOG-C特征的胶囊网络模型——HOG-C CapsNet。首先,使用梯度统计特征提取层对图像中的梯度信息进行统计,构建方向梯度直方图（HOG）特征图;其次,使用卷积层提取出图像的颜色信息,把提取出的颜色信息与HOG特征图融合构成HOG-C特征图;最后,输入卷积层提取HOG-C特征图的抽象特征,并通过胶囊网络对提取的抽象特征进行具有三维空间特征表达的胶囊封装,使用动态路由算法实现车型分类。在BIT-Vehicle数据集上对该模型和其他相关模型进行的对比实验中,该模型得到98.17%的准确率、97.98%的平均精确率均值（MAP）、98.42%的平均召回率均值（MAR）和98.20%的综合评价指标。实验结果表明,该模型在交通监控下的车型分类上具有更好的效果。     

SEM IPS10型图像分析仪测量晶体粒径

ＳＥＭＩＰＳ１０ 型图像分析仪系统可以自动、准确地测量晶体等目标的尺寸,对大量测量数据进行统计计算,求出平均直径、标准偏差等,并绘制晶体粒径分布直方图。