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In the 2D–3D registration process, due to the differences in CAD model sizes, models may be too large to be displayed in full or too small to have obvious features. To address these problems, previous studies have attempted to adjust parameters manually; however, this is imprecise and frequently requires multiple adjustments. Thus, in this paper, we propose the model self-adaptive display of fixed-distance and maximization (MSDFM) algorithm. The uncertainty of the model display affects the storage costs of pose images, and pose images themselves occupy a large amount of storage space; thus, we also propose the storage optimization based on the region of interest (SOBROI) method to reduce storage costs. The proposed MSDFM algorithm retrieves the farthest point of the model and then searches for the maximum pose image of the model display through the farthest point. The algorithm then changes the projection angle until the maximum pose image is maximized within the window. The pose images are then cropped by the proposed SOBROI method to reduce storage costs. By labeling the connected domains in the binary pose image, an external rectangle of the largest connected domain is applied to crop the pose image, which is then saved in the lossless compression portable network image (PNG) format. Experimental results demonstrate that the proposed MSDFM algorithm can automatically adjust models of different sizes. In addition, the results show that the proposed SOBROI method reduces the storage space of pose libraries by at least 89.66% and at most 99.86%.

Encouraging informal firms to register with the government is a key policy issue for developing economies. However, the impact of formal registration on firm performance remains inconclusive. This paper constructs a nationally representative panel data set on registered and unregistered establishments in Cambodia by using the Economic Census in 2011 and the Inter-censal Economic Survey in 2014; the Economic Census surveyed all nonfarm establishments and enterprises without any establishment-size threshold, which served as a credible sample frame for the Inter-censal Economic Survey. To mitigate selection bias, I employ a difference-in-differences method combined with propensity-score matching and a propensity-score-weighted regression method. My results show that formalization has a significantly positive impact on sales, value added, and regularly employed workers, but yields little effect on labor productivity. While formal registration alone may not boost productivity, it can encourage the business growth of formalized firms by hiring more formal workers.

Over recent years, non-rigid registration has become a major issue in medical imaging. It consists in recovering a dense point-to-point correspondence field between two images, and usually takes a long time. This is in contrast to the needs of a clinical environment, where usability and speed are major constraints, leading to the necessity of reducing the computation time from slightly less than an hour to just a few minutes. As financial pressure makes it hard for healthcare organizations to invest in expensive high-performance computing (HPC) solutions, cluster computing proves to be a convenient solution to our computation needs, offering a large processing power at a low cost. Among the fast and efficient non-rigid registration methods, we chose the demons algorithm for its simplicity and good performances. The parallel implementation decomposes the correspondence field into spatial blocks, each block being assigned to a node of the cluster. We obtained an acceleration of 11 by using 15 2GHz PC's connected through a 1GB/s Ethernet network and reduced the computation time from 40min to 3min30. In order to further optimize the costs and the maintenance load, we investigate in the second part the transparent use of shared computing resources, either through a graphic client or a Web one.

Computer support for early detection of breast cancer requires a proper mimicking of the way radiologists compare mammographic images; by comparing bilateral (images of the left and right breasts) and temporal images. In this paper, one method for bilateral registration and intensity normalization and two methods for difference analysis are described. The bilateral registration is based on anatomical features and assumptions of how the female breast is deformed under compression. The first method for differential analysis is based on the absolute difference between the registered images while the second method is based on statistical differences between properties of corresponding neighborhoods. The methods are tested on images from the MIAS database (on 100 images with 59 abnormalities distributed over four types) and evaluated by FROC-analysis. The performances of the two methods are similar but the statistical method gives better performance at a lower false positive rate and is better in particular for detecting asymmetrical developments.

The iterative closet point (ICP) method is a dominant method for data registration that has attracted extensive attention. In this paper, a unified mathematical model of ICP based on Lie group representation is established. Under the framework, the registration problem is formulated into an optimization problem over a certain Lie group. In order to simplify the model and to reduce the dimension of parameter space, the translation part of geometric transformation is eliminated by calibrating the centers of two data sets under registration. As a result, a fast algorithm by solving an iterative linear system is designed for the optimization problem on Lie groups. Moreover, PCA and ICA methods are jointly applied to estimate the initial registration to achieve the global minimum. Finally, several illustrations and comparison experiments are presented to test the performance of the proposed algorithm.

Fiducial marker systems are composed of a number of patterns that are mounted in the environment and automatically detected by computer vision algorithms using digital techniques. Thus, these systems are valuable in augmented reality (AR), robot navigation, and other applications. This paper proposes a new AR marker called CH-marker, which uses Hamming check codes to encode multiple kinds of colors and restore binary codes in the squares occluded in the markers. The marker solves the registration failure, which occurs when the markers are partially occluded in dynamic scenes. Experiments showed that the proposed marker is effective, reliable, and can meet the application demand of AR.

Traditional vision registration technologies require the design of precise markers or rich texture information captured from the video scenes, and the vision-based methods have high computational complexity while the hardware-based registration technologies lack accuracy. Therefore, in this paper, we propose a novel registration method that takes advantages of RGB-D camera to obtain the depth information in real-time, and a binocular system using the Time of Flight (ToF) camera and a commercial color camera is constructed to realize the three-dimensional registration technique. First, we calibrate the binocular system to get their position relationships. The systematic errors are fitted and corrected by the method of B-spline curve. In order to reduce the anomaly and random noise, an elimination algorithm and an improved bilateral filtering algorithm are proposed to optimize the depth map. For the real-time requirement of the system, it is further accelerated by parallel computing with CUDA. Then, the Camshift-based tracking algorithm is applied to capture the real object registered in the video stream. In addition, the position and orientation of the object are tracked according to the correspondence between the color image and the 3D data. Finally, some experiments are implemented and compared using our binocular system. Experimental results are shown to demonstrate the feasibility and effectiveness of our method.

Many investigations of image sequences can be understood on the basis of a few concepts for which computational approaches become increasingly available. The estimation of optical flow fields is discussed, exhibiting a common foundation for feature-based and differential approaches. The interpretation of optical flow fields is mostly concerned so far with approaches which infer the 3-D structure of a rigid point configuration in 3-D space and its relative motion with respect to the image sensor from an image sequence. The combination of stereo and motion provides additional incentives to evaluate image sequences, especially for the control of robots and autonomous vehicles. Advances in all these areas lead to the desire to describe the spatio-temporal development recorded by an image sequence not only at the level of geometry, but also at higher conceptual levels, for example by natural language descriptions.

In this paper, the problem of constructing geometric models from data provided by 3-D imaging sensors is addressed. Such techniques allow for rapid modeling of sculptured free-form shapes and generation of geometric models for existing parts. In order for a complete data set to be obtained, multiple images, each from a different viewpoint, have to be merged. A technique stemming from the Iterative Closest Point (ICP) method for estimating the relative transformations among the viewpoints is developed. Computational solutions are provided for estimating shape from noisy sensory measurements using representations that conform with commonly used representations from Computer Aided Geometric Design (CAGD). In particular, NURBS and triangular surface representations are applied in shape estimation. The surface approximations are refined by the algorithms to meet a user-defined tolerance value.

The design of representative models of the human body is of great interest to medical doctors. Qualitative information about the characteristics of the brain is widely available, but due to the volume of information that needs to be analyzed and the complexity of its structure, rarely is there quantification according to a standard model. To address this problem, we propose in this paper an automatic method to retrieve corresponding structures from a database of medical images. This procedure being local and fast, will permit navigation through large databases in a practical amount of time. We present as examples of applications the building of an average volume of interest and preliminary results of classification according to morphology.

While the role and utility of Magnetic Resonance Images as a diagnostic tool are well established in current clinical practice, there are a number of emerging medical arenas in which MRI can play an equally important role. In this article, we consider the problem of image-guided surgery, and provide an overview of a series of techniques that we have recently developed in order to automatically utilize MRI-based anatomical reconstructions for surgical guidance and navigation.

The design of fiducials for precise image registration is of major practical importance in computer vision, especially in automatic inspection applications. We analyze the sub-pixel registration accuracy that can, and cannot, be achieved by some rotation-invariant fiducials, and present and analyze efficient algorithms for the registration procedure. We rely on some old and new results from lattice geometry and number theory and efficient computational-geometric methods.

To generate a 3D computer model of a free-form object, multiple range images (point clouds) covering its entire surface are acquired from different viewpoints. These views are then aligned in a common coordinate basis by minimizing the distance error between their corresponding points. Establishing correspondences automatically is an inherently challenging problem due to the lack of any type of information other than the geometrical information extracted from the point clouds. Existing "automatic" correspondence techniques achieve automatism at the expense of other important specifications namely, applicability to free-form objects, accuracy, efficiency, robustness to resolution and surface sampling, robustness to overlap, robustness to noise and finally their applicability to simultaneous multiview correspondence. There is also a lack of a review paper that describes and critically analyzes these techniques. In this paper, we present such an extensive review and carry out the analysis of each technique according to the above listed indispensable criteria. Our analysis shows that none of these techniques fully meets these criteria and that there is still a need for the development of practical automatic correspondence algorithms.

Range finder devices are quite useful to measure the geometry of real-world objects. However, the reconstruction of large and complicated objects often requires the acquisition of multiple range images showing different views of the object. With our reconstruction method, the relative orientations of all range images are determined via simultaneous registration of all range images. The registration process is based on a least-squares approach where a distance metric between the overlapping range images is minimized. Registration errors are not accumulated, and it is even possible to reconstruct large objects from an arbitrary number of small range images. A resolution hierarchy accelerates the registration substantially. In order to exploit redundancies, the overlapping surfaces are optimally adapted to each other. The processed range images are converted to a signed distance function, which is based on the idea that no part of the object can lie between the scanner and the measured surface in any view. According to the signed distance function an intermediate volumetric model is sculptured out and polygonalized with isosurface techniques. The accuracy of the generated mesh is improved by moving its vertices onto the surface implicitly defined by the registered range images.

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This paper deals with a new method of three dimensional reconstruction of coronary arteries, by using the texture-mapping technique on a myocardial nuclear image. The bi-plane CAG images are texture-mapped onto a LV surface model which is pre-determined on a nuclear 3D image. By maximizing a matching degree between two mapped CAG images, registration between CAG and nuclear image is performed automatically. By taking only true images from the mapped CAG images, we can obtain 3D reconstructed coronary image on the LV surface model. This method has the great advantage that it is not necessary to extract the feature points, nor is there a need to identify the correspondence. The obtained images give us a clear understanding of the relation between the coronary artery and the function of the myocardium.

A cost-effective method that integrates high-resolution morphology using an atomic force microscope and immunofluorescence imaging for measuring the local mechanical properties of a cell was developed. By considering the normal indentation conditions and the distribution of the underlying cytoskeleton, a criterion for selecting indentation sites was proposed. PC-12 cells cultivated under normal and high D-glucose medium are employed to demonstrate the applicability of the proposed method. The apparent Young's modulus for each indentation site was estimated by fitting the data with a pyramidal punch contact mechanics model. The results showed that the cell bodies cultivated in the high D-glucose medium were higher but their growth cones were shorter than those cultivated in a normal medium. The Young's moduli of the growth cones were positively correlated with the density of the actin filament in the cytoskeleton. The Young's moduli at the growth cone and the nucleus region of cells cultivated in the high D-glucose medium were higher and lower, respectively, than those of the control group. The results demonstrated the integrated method could correlate local mechanical properties and distribution of actin filament of the growth cone of PC-12 cell.

The fold that appeared in the micro-slice images needs to be inpainted exactly so that it can meet the requirements of the scientific experiments. The biological slice image usually consists of piecewise smooth regions with the closed loop contour which can be represented by the Bendlet function proposed in recent years. Therefore, a novel image inpainting method based on Bendlet and interval Shannon–Cosine wavelet is proposed. Since the deformation of the locust slice image is flexible, it is necessary to obtain as many feature points as possible to ensure the accuracy of the inpainting, so we introduce the curvature as a new registration element for registration. First, the homography matrix is obtained by calculating the correct feature points by our proposed registration method. Second, the fold position is located by homography matrix and inpainted by Shannon–Cosine interval wavelet interpolation. Finally, the pixel difference is eliminated through adaptive fusion. The results indicate that, in comparison to the SURF and ORB algorithms, our registration method significantly enhances the extraction of feature points, achieving a more even distribution. Furthermore, when compared to four other methods (K-SVD, BSCB, TV and Criminisi), as well as various interpolation methods such as cubic polynomial interpolation, cubic spline interpolation, and nearest neighbor interpolation, our approach consistently achieves the highest PSNR and SSIM values.

Cloud computing supports multitenancy to satisfy the users’ demands for accessing resources and simultaneously it increases revenue for cloud providers. Cloud providers adapt multitenancy by virtualizing the resources, like CPU, network interfaces, peripherals, hard drives and memory using hypervisor to fulfill the demand. In a virtualized environment, many virtual machines (VMs) can run on the same core with the help of the hypervisor by sharing the resources. The VMs running on the same core are the target for the malicious or abnormal attacks like side channel attacks. Among various side channel attacks in cloud computing, cache-based side channel attack is one that leaks private information of the users based on the shared resources. Here, as the shared resource is the cache, a process can utilize the cache usage of another by cache contention. Cache sharing provides a way for the attackers to gain considerable information so that the key used for encryption can be inferred. Discovering this side channel attack is a challenging task. This requires identification of a feature that influences the attack. Even though there are various techniques available in the literature to mitigate such attacks, an effective solution to reduce the cache-based side channel attack is still an issue. Therefore, a novel fuzzy rule-based mechanism is integrated to detect the cache side channel attackers by monitoring the cache data access (CDA). The factor that determines the attack is CDA in a log file created by the framework during authorization. The proposed framework also utilizes certain security properties including ECC and hashing for the privacy preservation and the decision is made with the aid of a fuzzy logic system.

Invasive Ductal Carcinoma (IDC) is one of the most frequently diagnosed breast cancers. IDC accounts for about 8 out of 10 of all invasive breast cancers. While early detection of breast cancer is essential for the reduction of death rate, there may be already more than 10⁷ cells in a breast cancer when it can be observed by X-ray mammogram. In contrast, the passive IR spectrogram proposed by Szu et al. was shown to be promising in detecting breast cancers several months ahead of mammogram. With energy readings from two IR cameras, middle wavelength IR (MIR, 3–5 μm) and long wavelength IR (LIR, 8–12 μm), dual-spectrum IR (DS-IR) spectrogram may be computed by using the deterministic neighborhood-based blind source separation algorithm developed by Szu et al..^4–7 To evaluate the performance of the DS-IR spectrogram on detection of IDC, a DS-IR spectrogram hardware system is built and a sub-pixel super-resolution registration is developed to implement the deterministic neighborhood-based blind source separation algorithm. Clinical tests have been carried out with the approval of Institutional Review Board of National Taiwan University Hospital. From August 2007 to June 2008, 35 patients aged between 30–66 (average age 49) with IDC breast cancers were recruited in this project. The results demonstrate that 62.86% of success rate for IDC detection may be achieved with the cross-sectional data. Longitudinal study shows that breast cancers may be detected more accurately by cross-referencing s₁ maps of multiple time-points.