基于方向能量的植物叶脉提取方法

植物叶片的脉络与形态是研究植物特征的重要出发点,其包含了植物的内在属性及重要遗传信息,对植物种类的识别起着关键作用.由于植物叶片边缘及叶脉的复杂多变性,已有的叶脉提取方法难以有效地提取叶脉细节.针对以往方法的不足,利用图像方向能量方法进行植物叶脉提取,实验结果表明该方法能够提出较丰富的叶脉细节和边缘信息,提高了提取的精确性.     

低分辨率医学图像的多图谱分割方法

基于多图谱的图像分割方法因其分割精度高和鲁棒性强,在医学图像分割领域被广泛研究,主要包含图像配准和标签融合两个步骤.目前对多图谱分割方法的研究通常都是在图谱图像和待分割目标图像具有相同分辨率的情况下展开的.然而,由于受图像采集时间,采集设备等影响,临床实践中采集的影像大多是低分辨率数据,使得目前在影像研究中广泛使用的方法无法有效应用于临床实践.因此,针对这一问题,我们结合图像超分辨率恢复方法,提出了精确鲁棒的低分辨率医学图像的多图谱分割方法,实验结果显示提出的方法显著地提高了多图谱分割方法的分割精度.     

用于JPEG2000图像认证的鲁棒性水印算法

针对JPEG2000图像压缩标准所具有的渐进传输、一次编码多次解码等特性,提出了一种基于图像特征的鲁棒性图像认证算法.该算法在JPEG2000编码过程中,先根据图像不变特征,生成认证水印,再根据实际的鲁棒性认证需求,在量化后的小波系数中确定每个子带的认证水印嵌入位平面,最后基于小波系数位平面的特征嵌入认证水印.算法不仅能适应JPEG2000各种灵活的编码方式,还能定位图像篡改的位置.实验结果验证了图像认证算法对可允许图像操作的鲁棒性以及对图像篡改的敏感性.     

Error estimation in measuring strain fields with DIC on planar sheet metal specimens with a non-perpendicular camera alignment

The determination of strain fields based on displacement components obtained via 2D-DIC is subject to several errors that originate from various sources. In this contribution, we study the impact of a non-perpendicular camera alignment to a planar sheet metal specimen's surface. The errors are estimated in a numerical experiment. To this purpose, deformed images - that were obtained by imposing finite element (FE) displacement fields on an undeformed image - are numerically rotated for various Euler angles. It is shown that a 3D-DIC stereo configuration induces a substantial compensation for the introduced image-plane displacement gradients. However, higher strain accuracy and precision are obtained - up to the level of a perfect perpendicular alignment - in a proposed “rectified” 2D-DIC setup. This compensating technique gains benefit from both 2D-DIC (single camera view, basic amount of correlation runs, no cross-camera matching nor triangulation) and 3D-DIC (oblique angle compensation). Our conclusions are validated in a real experiment on SS304.     

Segmentation of Pulmonary Nodule Images Using 1-Norm Minimization

Total variation minimization (in the 1-norm) has edge preserving and enhancing properties which make it suitable for image segmentation. We present Image Simplification, a new formulation and algorithm for image segmentation. We illustrate the edge enhancing properties of 1-norm total variation minimization in a discrete setting by giving exact solutions to the problem for piecewise constant functions in the presence of noise. In this case, edges can be exactly recovered if the noise is sufficiently small. After optimization, segmentation is completed using edge detection. We find that our image segmentation approach yields good results when applied to the segmentation of pulmonary nodules.     

On the Image Regularity Condition

In this paper, we continue the analysis of the image regularity condition (IRC) as introduced in a previous paper where we have proved that IRC implies the existence of generalized Lagrange-John multipliers with first component equal to 1. The term generalized is connected with the fact that the separation (in the image space) is not necessarily linear (when we have classic Lagrange-John multipliers), but it can be also nonlinear. Here, we prove that the IRC guarantees, also in the nondifferentiable case, the fact that 0 is a solution of the first-order homogeneized (linearized) problem obtained by means of the Dini-Hadamard derivatives.     

Experimental Determination of the Longitudinal Dispersivity During the Injection of a Micro-Cement Grout in a One-Dimensional Soil Column

In order to describe the basic phenomenon of miscible grout propagation during the injection in a saturated and deformable porous medium, a general mathematical model was proposed, which accounts for the existing coupling between the fluid flow analysis, the miscible grout transport, and the solid skeleton deformation, leading to a realistic prediction of the injected zone. The development of the model was made concurrently with laboratory experiments to determine the model parameters. This paper describes the determination of the longitudinal dispersivity during the transport of a micro-cement grout injected in a one-dimensional soil column, by relying on image processing analysis. The application concerned here is the injection of soils for improving their properties, that is, to reduce permeability, increase mechanical strength or reduce the deformability. The longitudinal dispersion coefficient is an essential parameter governing the mixing process and the evolution of the transition zone, and thus the injection efficiency. Finally, the comparison between simulated results from the developed model and experimental data, essential to validate the model from a phenomenological standpoint, is made.