由单幅图像恢复物体三维形状的应用研究

由阴影恢复物体表面形状的方法是计算机三维视觉领域中的一个重要分支。首先给出了利用多幅图像由阴影恢复物体表面三维形状算法的计算结果 ,并进行了详细分析 ,进而提出了针对单幅图像的改进算法 ,包括引入表面光滑约束、边界条件和误差的灰度加权调整等。利用合成图像验证了这种算法的准确性。最后结合实际焊接过程熔池成像特点 ,应用这种算法利用单幅焊接熔池图像成功地获取了焊接熔池表面三维形状     

一种改进型保持形状的图像对比度增强算法

提出了一种改进的保持形状的图像对比度增强算法.改进包含两个方面：1) 修改并推广了原有的基于PDE的灰度增强方法,使之可适用于任何设定的灰度拉伸函数;2）给出了一种通用的分段线性拉伸函数设计方法,它较采用累积直方图作为灰度变换函数可达到更好的直方图均衡化效果.还提出了一种松弛阈值化方法用以消除局部反差增强时可能出现的“斑点效应”.实验结果表明,本文提出的改进算法适用于多种自然图像,增强效果良好.     

改进灰度世界颜色校正算法

讨论了灰度世界算法,提出了两种改进的灰度世界算法——基于标准差加权的算法和基于图像熵约束的算法,从而避免了当给定图像仅有少量几种颜色或大块单一颜色时的冥法失效问题.通过对算法颜色校正结果进行主观(视觉上)评价,并对算法性能通过使用色差数据进行了客观评价.实验结果表明:改进算法对实际采集的图像和下载的图像都有较好的校正效果.     

基于网格点投影灰度相似性的三维重建新方法

基于双目立体视觉的三维重构是计算机视觉技术的主要内容之一,在机器人视觉导航、航空测绘、医学成像和工业检测等很多领域都有广泛的应用.提出一种基于网格点投影灰度相似性的双日立体视觉的三维重建新方法.首先将被测物体所在的世界坐标系划分成问距卡日等的矩形网格,将网格节点作为潜在的物点投影到左右图像坐标系上,然后根据不同深度的空间点在两幅图像上相应的灰度相似性来判断被测物体在三维空间中的深度信息.通过Matlab平台下的仿真实验证明了本方法的三维重建效果和计箅效率都要优于传统方法.与传统的图像匹配方法相比,具有算法简单、速度快、精度高、且不受摄像装置非线性畸变影响的优点.     

基于局部和全局信息的自适应水平集图像分割

针对仅采用局部或全局信息无法快速准确分割灰度不均匀图像的问题,提出了一种基于局部和全局信息的自适应水平集图像分割模型。首先,利用图像局部信息和全局信息建立局部能量项和全局能量项,并且利用演化曲线轮廓内外小邻域的灰度均值差作为自变量,建立了权重函数模型,实现了局部能量项和全局能量项之间权重的自适应调整,提高了模型分割灰度不均匀图像的效率和准确性。其次,提出了一种新的能量惩罚项,避免了水平集函数的重新初始化,增强了数值计算的稳定性。最后,为验证模型的优越性,将模型与CV模型、LBF模型和LGIF模型进行了对比,并通过分割时间、迭代次数以及相似度等指标对分割结果进行了客观、定量分析。最终结果表明:该模型不但对初始轮廓具有较高鲁棒性,而且对灰度不均匀图像具有较高的分割准确性与分割效率。     

标准互相关的灰度无关特性及其应用

对用于图像特征定位的标准互相关算子灰度无关特性展开了研究.由于在显微视觉中的大多数图像灰阶较低,接近于三值甚至二值图像;可籍此灰度无关特性对模板匹配的算法进行优化.实验结果表明,标准互相关的灰度无关特性可以在实际中指导设计模板图像,降低相关运算计算速度,以及指导改善照明条件.     

标准互相关的灰度无关特性及其应用

对用于图像特征定位的标准互相关算子灰度无关特性展开了研究.由于在显微视觉中的大多数图像灰阶较低,接近于三值甚至二值图像;可籍此灰度无关特性对模板匹配的算法进行优化.实验结果表明,标准互相关的灰度无关特性可以在实际中指导设计模板图像,降低相关运算计算速度,以及指导改善照明条件.     

基于改进主动轮廓模型的全景海天线检测

提出了一种基于改进主动轮廓模型的海天线检测算法,检测全景海域图像中的椭圆海天线。通过全分辨率算法获取图像的视觉显著图,提取全景设备区在全景图像中的位置,以消除全景设备区干扰对海天线检测的不良影响。根据全景海天线为椭圆形这一特征,将构造的形状能量约束项加入主动轮廓模型中作为新的能量函数,使活动轮廓线的形状在收敛过程中受到限制,从而成功收敛到全景海天线上。实验结果表明,该算法对不同拍摄条件下的全景图像均适用,检测准确率达到96%,普适性和稳健性良好。     

列图像中遮挡缺损图像补偿算法

针对动态图像的遮挡缺损问题,提出了基于图像块的帧间预测算法,可以检测出序列图像中的遮挡缺损区域;利用帧间灰度相关性修正补偿算法进行遮挡缺损区域补偿,利用修正的像元对缺损区域进行补偿能够克服传统插值补偿算法补偿痕迹明显的缺点.仿真结果表明:该算法补偿处理区域与邻域灰度过渡平滑,消除了补偿痕迹,补偿效果比较理想.     

基于FPGA的实时图像增强设计

局部直方图均衡是以全局直方图均衡化方法为基础,对图像中每个像素点所在的邻域范围求出灰度转换函数,然后仅应用在该中心点处。为了提高算法的运算速度,特别是在处理视频图像时,采取传统的DSP的设计方法在速度上很难满足需要,因此,利用FPGA实现是一个很好的选择。为使局部直方图均衡方法能够在FPGA上具体实现,从空间域的角度改进了图像灰度直方图均衡算法,并利用VHDL语言对算法进行了完全可综合的RTL级描述,最后在硬件平台上验证了结果。     

基于IR-SFS算法空间目标红外影像3D重建

在利用单幅影像的明暗恢复形状（Shape From Shading,SFS）三维重构算法的基础上,提出同时考虑外界辐射源和目标自身红外辐射的IR-SFS（infrared-SFS）算法。首先通过分析SFS算法原理和空间目标的红外成像特性,建立IR-SFS辐射方程并进行了仿真研究,然后利用温度场估计获得红外差值图,在人工合成的理想半球体、半圆柱体卫星红外影像上进行算法测试,并以美国STS107真实红外影像作为实验目标进行三维重建。实验结果表明,所提出IR-SFS算法经过参数优化后,与原SFS算法相比,重建模型的峰值信噪比更高,对STS107顶部舱门、尾翼、机舱、机舱内方形部件具有更佳显示度,整体效果得到明显改善。     

Fast algebra algorithm of shape-from-shading with specular reflectance

Shape-from-shading (SFS) is to reconstruct three-dimensional (3D) shape from a single gray image, which is an important problem in computer vision. We propose a novel SFS method based on hybrid reflection model which contains both diffuse reflectance and specular reflectance. The intensity gradient of image is in the direction that the shape of urface changes most, so we use directional derivative of the reflectance map as parts of objective function. When discrete characteristic of digital images is considered, finite difference approximates differential operator. So the reflectance map equation described by a partial differential equation (PDE) turns into an algebra equation about the nknown surface height correspondingly. Using iterative numeric computation, a new SFS method is gained. Experiments on synthesis and real images show that the proposed SFS method is accurate and fast.     

Analysis and synthesis of multicolored objects in a single image

We present a method with which to recover the intrinsic shading and reflectance characteristics of multicolored three-dimensional objects in a single image, with which realistic new scenes can be synthesized. A color watershed algorithm, which is based on a regularized dichromatic fitting error, is proposed for robust image segmentation. For shading recovery in small regions, a weighted interpolation is employed, whereas in large regions the reflectance and shading are calculated based on the assumption of gradual shape variation. It is demonstrated that the proposed method is promising and can be applied in image simulation.     

利用曲面逼近进行三维形状恢复

通过对表面形状的平滑性约束，给出了一个关于表面深度和图像亮度的能量函数。并通过对预先给曲面进行调速使得这个能量函数达到极小值，来逼近实际的表面，从而恢复表面形状。这一方法同时利用了图像的局部和全局阴影，避免了只利用局部或全局阴影所带来的误差。通过对合成图像以及实际图像进行的实验，证明该方法是行之有效的。     

Brain MRI tissue classification based on local Markov random fields

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field (MRF) priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous MRF and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume probabilistic atlases. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model. For the maximization task, a novel genetic algorithm almost free of initialization dependency is applied. The algorithm is tested on both simulated and real brain MR images. The experiments confirm that the new method offers a useful improvement of the tissue classification accuracy when the basic tissue characteristics vary across the brain and the noise level of the images is reasonable. The method also offers better protection against intensity non-uniformity artifact than the corresponding method based on a global (whole image) modeling scheme.     

基于单幅测量图像的三维缺陷检测技术

为了对缺陷进行更加全面的判断,设计了一种新型三维缺陷检测方法,只需要根据单幅实时测量的工件图像,就可获得0°~180°范围内工件形貌的三维数据,进而对缺陷的平面及深度尺寸进行全面判断。其核心技术是根据单幅测量图像中留下的三维线索—灰度信息,进行亮度分析和转换,利用倾角和偏角计算物体深度信息。在工业现场磁性材料缺陷检测中,该方法在X和Y方向的分辨力达到0.1 mm,Z方向的分辨力达到0.007 mm。实验证明所使用的三维缺陷检测方法,工作方式简单,硬件成本低,处理速度快,精度高,适宜在工业现场应用。     

Detection of meso-micro scale surface features based on microcanonical multifractal formalism

Synthetic aperture radar(SAR) is an effective tool to analyze the features of the ocean. In this paper, the microcanonical multifractal formalism is used to analyze SAR images to obtain meso-micro scale surface features. We use the Sobel operator to calculate the gradient of each point in the image, then operate continuous variable scale wavelet transform on this gradient matrix. The relationship between the wavelet coefficient and scale is built by linear regression. This relationship decides the singular exponents of every point in the image which contain local and global features. The manifolds in the ocean can be revealed by analyzing these exponents. The most singular manifold, which is related to the streamlines in the SAR images, can be obtained with a suitable threshold of the singular exponents. Experiments verify that application of the microcanonical multifractal formalism to SAR image analysis is effective for detecting the meso-micro scale surface information.     

基于球扩展光源模型的物体三维形状的恢复

计算机视觉中传统的物体三维形状恢复方法大都基于光源是无穷远点光源的假设，如由单幅图像恢复三维形状（ｓｈａｐｅｆｒｏｍｓｈａｄｉｎｇ）光度立体视觉（ｐｈｏｔｏｍｅｔｒｉｃｓｔｅｒｅｏ）方法等。实际环境中的光源往往不能满足这个假设，因此大大限制了这些方法的应用，本文提出了一种更加实用的球扩光源模型，并推导出此光源下反射物体表面的反射图，此模型在三维坐标系中描述了球光源位置，亮度，漫反射物体表面反射度与     

Part-Aware Mask-Guided Attention for Thorax Disease Classification

Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.