Shaoping Xu

A Nonlinear Viscoelastic Tensor-Mass Visual Model for Surgery Simulation

The linear elastic models of soft tissue are widely used in virtual-reality-based surgery simulation due to their computational efficiency; however, it is well known that these models are only a coarse approximation of the real biological soft tissue. To achieve realistic simulation, the deformable model should incorporate many other tissue properties such as nonlinearity, anisotropy, and viscoelasticity. Among these properties, viscoelasticity is a very important one, and it directly determines the behaviors of the tissue when it is cut, deformed, or torn. In this paper, we proposed to incorporate the property of viscoelasticity into the visual model of soft tissue. One significant advantage of the developed model is its fast computation. Experiments show that the incorporation of nonlinear viscoelasticity makes the simulated tissue look much more realistic than other models, whereas the computation time is increased by only approximately 5% compared with models without the consideration of viscoelasticity.

A New Hybrid Soft Tissue Model for Visio-Haptic Simulation

A new hybrid soft tissue model, which is mainly based on the mass-spring model (MSM) and the 3-D finite strain nonlinear anisotropic elasticity theory, is presented for visio-haptic simulations, such as surgery simulators. One significant difference from conventional MSMs is that the internal forces among mass nodes are derived within the framework of nonlinear continuum mechanics. As a result, the new hybrid model is much more realistic in the sense that it incorporates the typical biological properties and behaviors of living tissue such as nonlinearity, anisotropy, viscoelasticity, and incompressibility. From the implementation point of view, the proposed model can be regarded as a hybrid of finite-element and MSMs, which enables it to maintain largely the advantage of the MSM, such as a simple architecture, low memory usage, and fast computation. The new model is validated in several benchmark problems, and the results show very good agreement with real experimental data reported in the literature. An example simulating a human kidney is given to demonstrate the capabilities of the proposed model in describing the nonlinearity, anisotropy, viscoelasticity, and incompressibility of typical soft tissue.

A fast nonlocally centralized sparse representation algorithm for image denoising

The sparsity from self-similarity properties of natural images, which has received significant attention in the image processing community of researchers, is widely applied for image denoising. The recently proposed nonlocally centralized sparse representation (NCSR) algorithm that takes advantage of the sparse representations (SRs) and the nonlocal estimate of sparse coefficients (NESCs) has shown promising results with respect to noise reduction. Despite successful combination of the above two techniques, the iterative dictionary learning and the nonlocal estimate of unknown sparse coefficients make this algorithm computationally demanding, which largely limits its applicability in many applications. To address this problem, a fast version of the NCSR algorithm called FNCSR algorithm, which is based on pre-learned dictionary and adaptive parameter setting approaches, was proposed in this paper. Specifically, we adopted the same dictionary learning approach, i.e, the K-means and principal component analysis (PCA), with the NCSR algorithm to obtain a dictionary for each image in a selected image dataset including high-quality natural and texture images. Then we applied PNSR index to objectively assess the image quality of the reconstructed images using these dictionaries throughout the image dataset. The dictionary providing the best average reconstructed quality was selected as fixed dictionary, i.e., the pre-learned dictionary, for sparse coding throughout the iterative denoising process, which implies that it no longer requires dictionary learning procedure within the framework of the proposed FNCSR algorithm, resulting in greatly decreased execution time. In order to further improve computational efficiency, we employed quality-aware features and support vector regression (SVR) technique to build a fast noise level estimator (NLE) to estimate the noise level from a single noisy image. The parameters related to the NESC, i.e., the search window and the search step, which influences the computational performance of the NCSR algorithm strongly, were chosen automatically according to the estimated noise level. Compared to the original NCSR algorithm, these modifications lead to substantial benefits in computational efficiency (a performance gain of about 90% can be achieved) without sacrificing image quality too much (the largest decline is less than 0.55dB and 0.014 in terms of PSNR and SSIM indices). Compared with other state-of-the-art denoising algorithms, experimental results show that the proposed FNCNR algorithm also achieves comparable performance in terms of both quantitative measures and visual quality. HighlightsA fast version of the nonlocally centralized sparse representation (FNCSR) algorithm is proposed.We utilize the pre-learned dictionary instead of the adaptive dictionary constructed runtime.We utilize the training-based NLE for automatic and optimal parameter setting.The thorough quantitative and qualitative results demonstrate that FNCSR achieves better results than state-of-the-art algorithms.

Similarity measures for content-based image retrieval based on intuitionistic fuzzy set theory

In this paper, a new intuitionistic fuzzy model for images based on the HSV color histogram is proposed. The image can be treated as an Attanassov’s intuitionistic fuzzy set (IFS) with this new model. A new and simple calculation of similarity measurement called IFSL1 based on similarity measurement of intuitionistic fuzzy set L1 is presented. Unlike general fuzzy similarity measure that consider only the membership degree, the new intuitionistic similarity measure takes into account the membership degree, the nonmembership degree and the hesitation degree, these have been found to be highly useful in dealing with vagueness. The similarity measure IFSL1 is used for content-based image retrieval (CBIR).With the similarity measure IFSL1, image retrieval can be carried out more rapidly than with many other existing similarity measurements and the results better coincide with human perception.

An improved realistic mass-spring model for surgery simulation

An improved realistic mass-spring model, which is mainly based on the 3D finite strain nonlinear anisotropic elasticity theory, is presented for virtual reality based surgery simulation. Compared with the conversional mass-spring model, the proposed model is able to describe typical behaviors of living tissues such as incompressibility, nonlinearity and anisotropy. The nonlinear viscoelasticity is also incorporated into the soft tissue model by employing a numerical scheme. In terms of implementation, the model proposed can be seen as a mixture of finite-element and mass-spring models, which enables it to still maintain the advantage of mass-spring model, such as simple architectures, low memory usage and fast computation. An example to use this model to simulate human kidney is given to demonstrate its capability of describing the typical behaviors of soft tissue.

Simulation of soft tissue using mass-spring model with simulated annealing optimization

A key challenge of the simulation of deformable soft tissue is to satisfy the conflicting requirements of real-time interactivity and physical realism. The behavior of soft tissue can be described by a mass-spring model provided that correct parameters, such as spring stiffness and viscosity, are used. In practice, such parameters are often determined by trial-and-error based on the visual effects of simulation. Therefore, it is very difficult to obtain accurate values, and the process is tedious and time consuming. In this paper, a heuristic optimization technique is proposed to identify these parameters of the mass-spring model for soft tissue simulation. We employ the simulated annealing algorithm to tune the parameters automatically until the deformation of simulated soft tissue approximates the reference one as defined by a new modified tensor-mass model in which we have taken into account the viscoelasticity of soft tissue. The proposed technique provides an automatic method to determine the parameters in mass-spring-based models.

An improved switching vector median filter for image-based haptic texture generation

In this paper, we present a novel approach to generation of haptic texture from visual image for modelling the constraint forces in tangent direction of a surface, based on which the texture force (friction) can be calculated. One significant difference from conventional image-based haptic texture is that an improved switching vector median filter (ISVMF) was employed to replace the gray-scale Gaussian filter for preserving fine detail information of the image. As a result, the new haptic texture rendering algorithm can convey tactile patterns based on fine features of the image and is much more realistic than those published in the literature.

Recognition of pedestrian activity based on dropped-object detection

Abstract Aiming at recognizing dropped objects and matching their owners, this paper presents a method for analyzing pedestrian activity based on dropped-object detection in video surveillance. The recognition results may be applied to further analyzing human activity and intentions such as determining whether the dropped-objects are intentional hazardous or unconsciously lost articles according to the appearance of dropped-objects. The method consists of dropped-object detection and recognition. The dropped-object detection algorithm uses foreground detection based on bi-directional background modeling, MeanShift tracking, and pixel-based regional information at the drop-off point. It analyzes the relationship between the dropped objects and pedestrians at the pixel level in complex environments with noises and occlusions. Afterwards, an algorithm based on moment invariant and Principal Component Analysis (PCA) is proposed to further recognize the dropped-objects viewed from different directions and locations from video cameras. In addition, in order to solve the limitation of the centralized video processing model for large-scale video streams in real time, the proposed method is designed and accomplished in a distributed model. The experimental results showed that the proposed method can effectively and efficiently recognize the pedestrian activity through the dropped objects in real-time video data.

A Controller Combining Positive Velocity Feedback with Negative Angle Feedback for a Two-Wheeled Robot

Abstract The two-wheeled robot is a nonlinear system of multi-variables, higherorder and strong coupling. This paper presented a PID Controller with Double Loops (PCDL) to control the tilt angle and velocity of a two-wheeled robot. The angle controller is the regular negative feedback, while the velocity control is the positive feedback. The Double Loops work cooperatively to endow the system with strong anti-interference ability. The stability of the whole system is analyzed and the criterion of the system stability is developed. The simulation and experiments showed that the two-wheeled robot can self-balance and move at an expected velocity and the system has strong anti-interference ability.

AN UNSUPERVISED COLOR-TEXTURE SEGMENTATION USING TWO-STAGE FUZZY c-MEANS ALGORITHM

Unsupervised image segmentation is a fundamental but challenging problem in computer vision. In this paper, we propose a novel unsupervised segmentation algorithm, which could find diverse applications in pattern recognition, particularly in computer vision. The algorithm, named Two-stage Fuzzy c-means Hybrid Approach (TFHA), adaptively clusters image pixels according to their multichannel Gabor responses taken at multiple scales and orientations. In the first stage, the fuzzy c-means (FCM) algorithm is applied for intelligent estimation of centroid number and initialization of cluster centroids, which endows the novel segmentation algorithm with adaptivity. To improve the efficiency of the algorithm, we utilize the Gray Level Co-occurrence Matrix (GLCM) feature extracted at the hyperpixel level instead of the pixel level to estimate centroid number and hyperpixel-cluster memberships, which are used as initialization parameters of the following main clustering stage to reduce the computational cost while keeping the segmentation performance in terms of accuracy close to original one. Then, in the second stage, the FCM algorithm is utilized again at the pixel level to improve the compactness of the clusters forming final homogeneous regions. To examine the performance of the proposed algorithm, extensive experiments were conducted and experimental results show that the proposed algorithm has a very effective segmentation results and computational behavior, decreases the execution time and increases the quality of segmentation results, compared with the state-of-the-art segmentation methods recently proposed in the literature.