Jinjin Zheng

Medical image processing by denoising and contour extraction

This paper proposes a new algorithm of medical image denoising and contour extraction that can be used in image processing such as medical image reconstruction, image enhancement, image smoothing, feature extraction and image analysis. The algorithm consists of two parts. First, combining median filtering with wavelet transform to enhance and denoise image. Second, Canny edge detection and contour tracing are repeatedly applied to extract the continuous contour lines of image. The method is validated through experiments on real images. The results show that the algorithm achieves good effects, can deals with low quality or marginal vague images which have high spatial redundancy, low contrast and biggish noise, and has a potential of certain practical use.

A Method of 3D Reconstruction from Image Sequence

In this paper, a method of 3D reconstruction from an image sequence acquired by a moving camera is presented. The internal parameters and the motion of the camera are absolutely unknown. Firstly, the features of the reconstruction object are detected in each image, and are matched between image pairs. A new method of angel filtering is used to eliminate false correspondences. Fundamental matrix can be computed and projective reconstruction is set up. Secondly, camera self- calibration method is used to get the internal parameters. Epipolar geometry is used to obtain a pairwise image rectification. Dense correspondence matches are then computed. Finally, the 3D model is reconstructed based on the self- calibrated result and dense correspondences map. The feasibility of this approach is illustrated by the experiment.

Solving the large airline disruption problems using a distributed computation approach to integer programming

In this paper, a distributed implementation of Dangs iterative method [1] is proposed to solve the airline disruption problems caused by the airport closures. The problem consists of two subproblems, and the solution domain of the first subproblem is divided into several segments by the initial seeds cluster division method. Dangs method [1] is applied to solve the first subproblem in each segment simultaneously in a distributed computation network, and the obtained feasible flight routes are used to solve the second subproblem. Numerical results show that the proposed method is better than CPLEX and Liu et al. [2].

Study on the roughness evolution of optical surfaces during ion beam sputtering

Abstract. Ion beam machining technology has been extensively adopted to obtain an ultraprecision surface in ultraviolet lithography optics. However, there exist complex mechanisms leading the surface to evolve complicated topographies and increasing roughness. We build a kinetic model integrating with the typical sputter theory and a bond-counting Monte Carlo algorithm based on the compound materials to investigate the surface roughness evolution during ion beam sputtering. The influences of primary sputter, reflection, secondary sputter, geometrical shadowing, redeposition, and thermal diffusion were all taken into consideration to compose a dynamic evolution process. In calculation, using this model the surface first possesses a period of smoothing and then goes into a roughening stage, where the roughness follows the regular power law. Quantitative analyses of surface roughness derived from calculations are also examined and compared with experiments.

Fast detection and segmentation of partial image blur based on discrete Walsh–Hadamard transform

Abstract Image signals can be blurred due to defocus or motion. Blur may be undesirable for image sensing, but may also contain useful information. Therefore, detecting the blurriness of each pixel and segmenting the partial blur regions in natural images are important and yet challenging in the field of machine vision. A concise no-reference method based on discrete Walsh–Hadamard transform is proposed to detect and segment partial blur in this paper. First, a re-blurring strategy is performed over multiple overlapping image patches extracted from the test image. Then, for both test image and re-blurred image, discrete Walsh–Hadamard transforms are utilized in each image patches to obtain the blur map. This blur map can characterize the blurriness of each pixel in test image. Based on it, combined with K-Means clustering and region-growing, the test image can be segmented into blurry/non-blurry regions. The experiments, performed on a public dataset, demonstrate the capability of the proposed metric in the detection and segmentation of the blur region. Comparative results with the state-of-the-art show the superiority of the proposed approach in image segmentation for both defocus and motion blur images. The proposed approach is compendious without data training and possesses a high time efficiency because of the fast sequency transform.

3D Reconstruction of human bones based on dictionary learning

An effective method for reconstructing a 3D model of human bones from computed tomography (CT) image data based on dictionary learning is proposed. In this study, the dictionary comprises the vertices of triangular meshes, and the sparse coefficient matrix indicates the connectivity information. For better reconstruction performance, we proposed a balance coefficient between the approximation and regularisation terms and a method for optimisation. Moreover, we applied a local updating strategy and a mesh-optimisation method to update the dictionary and the sparse matrix, respectively. The two updating steps are iterated alternately until the objective function converges. Thus, a reconstructed mesh could be obtained with high accuracy and regularisation. The experimental results show that the proposed method has the potential to obtain high precision and high-quality triangular meshes for rapid prototyping, medical diagnosis, and tissue engineering.

Study on mechanism and process of ion beam machining on high- precision large optical surfaces

Ion beam machining has become an important means adopted to high-precision large optical mirror processing. This study has conducted a bitmap-style model, calculation and analysis on low-energy Ar+ ion beam sputtering optical surface, based on Sigmund Theory and the CCOS principle. We have obtained the relationship of the removal function and the removal rate with major technological parameters of ion beam machining (e.g. dwell time, work distance, ion energy, etc.) also via orthogonal experiments of single point removal. Results indicated that the removal rate of amorphous SiO2 (fused silica) by Ar+ ions with 600~1200 electron volts increases with the increase of ion energy and dwell time at different extents, decreases exponentially with the increase in work distance. On the basis of computational analysis and experimental investigations, we optimized process conditions and further figured the plane mirror with the clear aperture of 130 millimeters, utilizing technologically optimized low-energy Ar+ ion beam machining. Eventually we obtained the high-precision figure shape with the post-machined surface roughness of 0.43~0.44 nm rms and the post-machined figure error of 1 nm rms.

Magnetic Field Analysis of a New 3-Axis Optical Pickup Actuator Based on ANSYS

The magnetic field and the force distribution of a new 3-axis optical pickup actuator are simulated based on ANSYS.

Method to Interpolate Scattered Rainfall Data Using the Thin Plate Spline, B-Spline and Energy Fairing

This paper aims at constructing a smooth surface using scattered rainfall data to gives us a visual distribution of rainfall. A grid is superimposed on the map within the data boundary, and thin plate spline (TPS) method is used to interpolate rainfall data to obtain the values of the grid points. Due to the irregularity of the data boundary, the grid has to be divided into several small regular grids then B-Spline is used to interpolate these grid points to construct surfaces. Finally an energy method is used to join these surfaces together, and it gives us the smoothest joined surfaces in vision.

A Novel Adaptive Algorithm of Catmull-Clark Subdivision Surfaces

The character of the subdivision surfaces is that the number of facets grows exponentially with the level of subdivision. A novel adaptive subdivision algorithm based on Catmull-Clark scheme is presented in this paper by calculating the distance smoothness value of the vertices. The new method generates preferable rendering results than the traditional adaptive approaches such as computing a vertex’s curvature or distance between the control mesh and their limit surfaces. The new method has obvious geometric meaning, programming simplicity, numerical stability and performs efficiently when applied to the model whose mesh has more sharp features.