Hongjun Zhou

Segmenting, removing and ranking partial blur

Image blur is a common phenomenon in daily life. Due to the great challenge, image restoration fascinates researchers to find out the solutions. Considering different types of blur, we propose a framework to segment the partial blur from a single image and then restore the latent information. In general, some morphological technologies are applied to separate the blur area. Traditionally, blind deconvolution method is applied in underdetermined conditions. In this research, we marginalize the kernel estimation by separating the problem into two stages, both of which are combined with different useful priors. A criterion of ranking the blur degree of a partial blur image is also proposed at the end of this paper. Experimental results demonstrate the accuracy and superiority of our approach.

Adaptive detection method of degraded parameters in motion blur image

Image restoration technique is an important method to improve the quality of image in flights photograph and machine vision. On account of blur emerge in relative motion and other condition, the paper analyse its mathematic principle, then for each motion pattern we adopt different deblurring method. Be concrete, we first judge the type of motion, for linear motion blur we take both hough transform and error-parameter to estimate the blur parameters, yet for rotate motion blur we take curve-fitting and polar transformation to estimate parameters. After that we apply Wiener filtering to recover image and improve image quality. We perform experiments with linear motion blur parameter (30,70) and rotate motion blur parameter ((128,128), 20°). The contrast results of the experiments indicate that the algorithm can enhance image quality effectively. Compared with traditional handling, ours have the advantage of being high precision, robustness and speediness‥

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.

Subspace Culling for Continuous Collision Detection

This paper presents a novel efficient culling method for continuous collision detection (CCD) problem performed by dimension reduction in subspace. The basic idea is to use a fast one-dimension (1D) reduced filter and a fast two-dimension (2D) reduced filter that remove large amount of false positives and elementary tests between the primitives. The culling method could be combined with other techniques. The algorithm has been implemented and tested on two benchmarks, including cloth-ball simulations and N-body simulations. The results demonstrate that the algorithm can efficiently reduce the number of elementary tests one order of magnitude, and improve the overall performance of collision query about one half.

A Novel Method of Mesh Simplification Using Hausdoff Distance

This paper describes a novel method for surface mesh simplification. Given an initial surface mesh, the goal is to reduce the number of mesh elements and preserve the geometric approximation as well as the shape quality of the resulting mesh. We present a novel method - triangle contraction to simplify the mesh, and two tolerance areas with respect to the reference mesh have been introduced to preserve the geometry of the surface. The reference mesh is then simplified and optimized in order that the resulting mesh belongs to these tolerance areas.

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.

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.