Propagated image Segmentation Using Edge-Weighted Centroidal Voronoi Tessellation based Methods

Propagated image segmentation is the problem of utilizing the existing segmentation of an image for obtaining a new segmentation of, either a neighboring image in a sequence, or the same image but in different scales. We name these two cases as the inter-image propagation and the intra-image propagation respectively. The inter-image propagation is particularly important to material science, where efficient and accurate segmentation of a sequence of 2D serial-sectioned images of 3D material samples is an essential step to understand the underlying micro-structure and related physical properties. For natural images with objects in different scales, the intra-image propagation, where segmentations are propagated from the finest scale to coarser scales, is able to better capture object boundaries than single-shot segmentations on a fixed image scale. In this work, we first propose an inter-image propagation method named Edge- Weighted Centroid Voronoi Tessellation with Propagation of Consistency Constraint (CCEWCVT) to effectively segment material images. CCEWCVT segments an image sequence by repeatedly propagating a 2D segmentation from one slice to another, and in each step of this propagation, we apply the proposed consistency constraint in the pixel clustering process such that stable structures identified from the previous slice can be well-preserved. We further propose a non-rigid transformation based association method to find the correspondence of propagated stable structures in the next slice when the inter-image distance becomes large. We justify the effectiveness of the proposed CCEWCVT method on 3D material image sequences, and we compare its performance against several state-of-the-art 2D, 3D, propagated segmentation methods. Then for the intra-image propagation, we propose a superpixel construction method named Hierarchical Edge-Weighted Centroidal Voronoi Tessellation (HEWCVT) to accurately capture object boundaries in natural images. We model the problem as a multilevel clustering process: superpixels in one level are clustered to obtain larger size superpixels in the next level. The clustering energy involves both color similarities and the proposed boundary smoothness of superpixels. We further extend HEWCVT to obtain supervoxels on 3D images or videos. Both quantitative and qualitative evaluation results on several standard datasets show that the proposed HEWCVT method achieves superior or comparable performances to other state-of-the-art methods. v

Accurate and discernible photocollages

There currently exist several techniques for selecting and combining images from a digital image library into a single image so that the result meets certain prespecified visual criteria. Image mosaic methods, first explored by Connors and Trivedi[18], arrange library images according to some tiling arrangement, often a regular grid, so that the combination of images, when viewed as a whole, resembles some input target image. Other techniques, such as Autocollage of Rother et al.[78], seek only to combine images in an interesting and visually pleasing manner, according to certain composition principles, without attempting to approximate any target image. Each of these techniques provide a myriad of creative options for artists who wish to combine several levels of meaning into a single image or who wish to exploit the meaning and symbolism contained in each of a large set of images through an efficient and easy process. We first examine the most notable and successful of these methods, and summarize the advantages and limitations of each. We then formulate a set of goals for an image collage system that combines the advantages of these methods while addressing and mitigating the drawbacks. Particularly, we propose a system for creating photocollages that approximate a target image as an aggregation of smaller images, chosen from a large library, so that interesting visual correspondences between images are exploited. In this way, we allow users to create collages in which multiple layers of meaning are encoded, with meaningful visual links between each layer. In service of this goal, we ensure that the images used are as large as possible and are combined in such a way that boundaries between images are not immediately apparent, as in Autocollage. This has required us to apply a multiscale approach to searching and comparing images from a large database, which achieves both speed and accuracy. We also propose a new framework for color post-processing, and propose novel techniques for decomposing images according to object and texture information

Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation

Mesh generation is a fundamental and critical problem in geometric data modeling and processing. In most scientific and engineering tasks that involve numerical computations and simulations on 2D/3D regions or on curved geometric objects, discretizing or approximating the geometric data using a polygonal or polyhedral meshes is always the first step of the procedure. The quality of this tessellation often dictates the subsequent computation accuracy, efficiency, and numerical stability. When compared with unstructured meshes, the structured meshes are favored in many scientific/engineering tasks due to their good properties. However, generating high-quality structured mesh remains challenging, especially for complex or large-scale geometric data. In industrial Computer-aided Design/Engineering (CAD/CAE) pipelines, the geometry processing to create a desirable structural mesh of the complex model is the most costly step. This step is semi-manual, and often takes up to several weeks to finish. Several technical challenges remains unsolved in existing structured mesh generation techniques. This dissertation studies the effective generation of structural mesh on large and complex geometric data. We study a general geometric computation paradigm to solve this problem via model partitioning and divide-and-conquer. To apply effective divide-and-conquer, we study two key technical components: the shape decomposition in the divide stage, and the structured meshing in the conquer stage. We test our algorithm on vairous data set, the results demonstrate the efficiency and effectiveness of our framework. The comparisons also show our algorithm outperforms existing partitioning methods in final meshing quality. We also show our pipeline scales up efficiently on HPC environment

Reconstruction of plant microstructure using distance weighted tessellation algorithm optimized by virtual segmentation

Abstract(#br)The accurate reconstruction model of plant microstructure is important for obtaining the mechanical properties of plant tissues. In this paper, a virtual segmentation technique is proposed to optimize Delaunay triangulation. Based on the optimized Delaunay triangulation, an Optimized Distance Weighted Tessellation (ODWT) algorithm is developed. Two different structures, namely carrot and retting maize vascular bundles, were reconstructed via the ODWT algorithm. The accuracy of ODWT is evaluated statistically by comparing with Centroid-based Voronoi Tessellation (CVT) and Area Weighted Tessellation (AWT). The results show that ODWT has distinct advantages over CVT and AWT. It is worth mentioning that ODWT has better performance than CVT when there exists large diversity in adjacent cell area. It is found that CVT and AWT fail to reconstruct cells with elongated and concave shapes, while ODWT shows excellent feasibility and reliability. Furthermore, ODWT is capable of establishing finite tissue boundary, which CVT and AWT have failed to realize. The purpose of this work is to develop an algorithm with higher accuracy to implement the preprocessing for further numerical study of plants properties. The comparison results of the simulated values of the longitudinal tensile modulus with the experimental value show that ODWT algorithm can improve the prediction accuracy of multi-scale models on mechanical properties