Hierarchical Representations of 2D/3D Gray-Scale Images and Their 2D/3D Two-Way Conversion

This article proposes G-octree as an extension of G-quadtree to three dimensions. A G-octree reflects in its construction a hierarchy of gray-scale level value homogeneity, as well as a hierarchy of spatial resolution. The article also develops two-way G-quadtree/Goctree conversion procedures based on the algorithms for the binary case. These procedures provide an integrated processing environment for hierarchically represented 2D/3D gray.scale images. We demonstrate our approach with an application to the color coding of macro-autoradiography images taken from rat brains.     

Sketch-based 3D model retrieval by incorporating 2D-3D alignment

Sketch-based 3D model retrieval is very important for applications such as 3D modeling and recognition. In this paper, a sketch-based retrieval algorithm is proposed based on a 3D model feature named View Context and 2D relative shape context matching. To enhance the accuracy of 2D sketch-3D model correspondence as well as the retrieval performance, we propose to align a 3D model with a query 2D sketch before measuring their distance. First, we efficiently select some candidate views from a set of densely sampled views of the 3D model to align the sketch and the model based on their View Context similarities. Then, we compute the more accurate relative shape context distance between the sketch and every candidate view, and regard the minimum one as the sketch-model distance. To speed up retrieval, we precompute the View Context and relative shape context features of the sample views of all the 3D models in the database. Comparative and evaluative experiments based on hand-drawn and standard line drawing sketches demonstrate the effectiveness and robustness of our approach and it significantly outperforms several latest sketch-based retrieval algorithms.     

3D CAD model matching from 2D local invariant features

The matching of particular types of CAD models to existing physical models can provide invaluable support to the process of CAD design and reuse. To meet the demand for fast and robust algorithms to detect predefined models in database, an local invariant model matching approach is proposed in this paper. It first maps the 3D CAD model to 2D principal image plane by its first two principal components, and then finds affine invariant key points in the 2D image. The CAD model matching problem is implemented as key points matching. Experimental results show the proposed 3D model retrieval method performs fairly well in retrieving similar models from a database of 3D CAD models.     

A theoretical 2D image model for locating 3D targets

To construct a water quality monitoring system, challenging issues need to be addressed regarding the acquisition of target information (e.g. 3D location and occlusion) as well as the behavioural analysis of aquatic organisms. This paper presents a novel 3D information acquisition and location method, by means of an information acquisition platform consisting of a monitoring terminal, frame grabbers, a single camera and a single mirror. Using this platform, we propose a theoretical 2D image model for locating 3D targets and then validate it using data obtained from both real and artificial fish. The proposed model is based on the principles of light refraction, plane mirror imaging, underwater objects and camera imaging as well as the technologies of digital to analog conversion and object segmentation. In contrast with existing methods, our method can accurately reflect 3D information of aquatic organisms, thus providing critical technical support for the development of water quality monitoring systems in the future.     

A 2D/3D model-based object tracking framework

This paper presents a robust framework for tracking complex objects in video sequences. Multiple hypothesis tracking (MHT) algorithm reported in (IEEE Trans. Pattern Anal. Mach. Intell. 18(2) (1996)) is modified to accommodate a high level representations (2D edge map, 3D models) of objects for tracking. The framework exploits the advantages of MHT algorithm which is capable of resolving data association/uncertainty and integrates it with object matching techniques to provide a robust behavior while tracking complex objects. To track objects in 2D, a 4D feature is used to represent edge/line segments and are tracked using MHT. In many practical applications 3D models provide more information about the object's pose (i.e., rotation information in the transformation space) which cannot be recovered using 2D edge information. Hence, a 3D model-based object tracking algorithm is also presented. A probabilistic Hausdorff image matching algorithm is incorporated into the framework in order to determine the geometric transformation that best maps the model features onto their corresponding ones in the image plane. 3D model of the object is used to constrain the tracker to operate in a consistent manner. Experimental results on real and synthetic image sequences are presented to demonstrate the efficacy of the proposed framework.     

2D/3D image registration using regression learning

In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object’s 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projection(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region’s motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method’s application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof.     

2D/3D image registration on the GPU

We present a method that performs a rigid 2D/3D image registration efficiently on the Graphical Processing Unit (GPU). As one main contribution of this paper, we propose an efficient method for generating realistic DRRs that are visually similar to x-ray images. Therefore, we model some of the electronic post-processes of current x-ray C-arm-systems. As another main contribution, the GPU is used to compute eight intensity-based similarity measures between the DRR and the x-ray image in parallel. A combination of these eight similarity measures is used as a new similarity measure for the optimization. We evaluated the performance and the precision of our 2D/3D image registration algorithm using two phantom models. Compared to a CPU + GPU algorithm, which calculates the similarity measures on the CPU, our GPU algorithm is between three and six times faster. In contrast to single similarity measures, our new similarity measure achieved precise and robust registration results for both phantom models.     

2D/3D Isometric Transformation Using Spring-Mass System

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Reactive 2D/3D garment pattern design modification

This paper presents a new 3D garment simulation result update algorithm for the 2D garment pattern design modification. The proposed algorithm enables the 3D garment fitting simulation result directly to react to the modification in the 2D patterns. The algorithm performs a topological invariant deformation of the 2D pattern mesh after the boundary of the 2D pattern undergoes a topological consistent modification. The length of each of the edges in the mesh defined as the equilibrium state parameter is updated and then directly used in the 3D garment fitting simulation to update the original simulation result. The advantage of the proposed algorithm is that the mesh topology of the 2D garment pattern is preserved and thus simplifies the numerical scheme by maintaining the consistency of the matrix equation. With this approach, the 3D garment fitting simulation does not need to repeat the entire simulation for every modification and can react to the 2D pattern modification efficiently and speedily.     

2D clustering based discriminant analysis for 3D head model classification

This paper introduces a novel framework for 3D head model recognition based on the recently proposed 2D subspace analysis method. Two main contributions have been made. First, a 2D version of clustering-based discriminant analysis (CDA) is proposed, which combines the capability to model the multiple cluster structure embedded within a single class with the computational advantage that is characteristic of 2D subspace analysis methods. Second, we extend the applications of 2D subspace methods to the field of 3D head model classification by characterizing these models with 2D feature sets.     

Topology based 2D engineering drawing and 3D model matching for process plant

Process plant models mainly include 3D models and 2D engineering drawings. Matching calculation between these CAD models has wide applicability in model consistency check and retrieval. In process plant, engineering design standards make 2D engineering drawing and 3D model differ in geometry, proportion and structure, leading to the inapplicability of current shape-feature based matching approaches. Since connection relationships between components are the core of a process plant, a topology based algorithm is proposed. Firstly, by exploiting components as vertices and relationships as edges, both 2D engineering drawing and 3D model are preprocessed into graph structures. Then each model’s relationship types are extracted from the graph. Finally, regarding the extracted relationship types as primary feature, feature similarity is calculated to measure the matching degree between their corresponding models. The proposed algorithm is geometric deformation invariant. Experiments with industrial applications are presented, which demonstrates the effectiveness and feasibility of the proposed algorithm.     

Preparation of 2D sequences of corneal images for 3D model building

A confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, medical practioners can extract clinical information on the state of health of the patient's cornea. In this work we are addressing problems associated with capturing and processing these images including blurring, non-uniform illumination and noise, as well as the displacement of images laterally and in the anterior–posterior direction caused by subject movement. The latter may cause some of the captured images to be out of sequence in terms of depth. In this paper we introduce automated algorithms for classification, reordering, registration and segmentation to solve these problems. The successful implementation of these algorithms could open the door for another interesting development, which is the 3D modelling of these sequences.     

Kingston KHX14400D3K2/2GN

在DDR2到DDR3的过渡阶段．各大内存厂商都在积极地推出DDR3新产品。作为老牌内存厂商,金士顿也适时地更新了他们的当家品牌——HyperX系列DDR3高端内存。来到我们面前的是型号为KHX14400D3K2／2GN的内存套装．容量为1GB×2。这款产品秉承了HyperX蓝色酷炫散热片的特点．     

Interactive 3D/2D visualization for geophysical data processing and interpretation

A new 3D/2D interactive display server was developed for the IGeoS geophysical data processing framework presented earlier. With introduction of this major component, the framework becomes conceptually complete and potentially bridges the gap between traditional processing and interpretation geophysical software.The display server utilizes Qt toolkit and OpenGL graphics libraries while taking advantage of the object-oriented design of the core data processing system. It operates by creating image object trees that are automatically propagated to the server(s) residing on the same or remote hosts and producing complex, structured, and interactive data displays. The displays support custom interactive graphical user interfaces, which are constructed entirely by the user and do not require computer coding. With over 200 specialized processing tools available, this approach allows creating 3D visualizations and building custom interactive data analysis, interpretation, and modeling tools in many areas of application. In particular, we show examples of integration of seismic ray tracing, gravity, and receiver function modeling and inversion in deep crustal studies.     

脊柱手术导航中分步式2D/3D图像配准方法

为实现微创脊柱手术导航,提出一种将脊柱术前CT与其术中X射线图像配准的方法.首先基于一种最近点迭代法进行快速粗配准;然后提取图像的梯度特征,根据投影变换原理,采用寻找具有CT最大梯度投影位置的方法进行精配准.在模拟数据及临床标本上进行实验的配准率分别为92%和78%. 实验结果表明:该方法鲁棒性强、人工干预少、适合于临床应用.     

DCT-based objective quality assessment metric of 2D/3D image

With the increasing growth of multimedia applications over the networking in recent years, users have put forward much higher requirements for multimedia quality of experience (QoE) than before. One of the representative requirements is the image quality. Therefore, the image quality assessment ranging from two-dimension (2D) image to three-dimension (3D) image has been getting much attention. In this paper, an efficient objective image quality assessment metric in block-based discrete cosine transform (DCT) coding is proposed. The metric incorporates properties of human visual system (HVS) to improve its validity and reliability in evaluating the quality of stereoscopic image. This is fulfilled by calculating the local pixel-based distortions in frequency domain, combining the simplified models of local visibility properties embodied in frequency domain, which consist of region of interest (ROI) mechanism (visual sensitivity), contrast sensitivity function (CSF) and contrast masking effect. The performance of the proposed metric is compared with other currently state-of-the-art objective image quality assessment metrics. The experimental results have demonstrated that the proposed metric is highly consistent with the subjective test scores. Moreover, the performance of the metric is also confirmed with the popular IRCCyN/IVC database. Therefore, the proposed metric is promising in term of the practical efficiency and reliability for real-life multimedia applications.     

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

     

Aesthetic 3D model evolution

A new research frontier for evolutionary 2D image generation is the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a statistically significant preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary design.     

Relative affine structure: canonical model for 3D from 2D geometryand applications

We propose an affine framework for perspective views, captured by a single extremely simple equation based on a viewer-centered invariant we call relative affine structure. Via a number of corollaries of our main results we show that our framework unifies previous work-including Euclidean, projective and affine-in a natural and simple way, and introduces new, extremely simple algorithms for the tasks of reconstruction from multiple views, recognition by alignment, and certain image coding applications