Jianming Wang

Circle Marker Based Distance Measurement Using a Single Camera

A new distance measurement method with the use of a single camera and a circle marker is presented. The distance measurement is based on the idea that a circle marker at a longer distance forms a smaller image when the parameters of the imaging system remain unchanged. Firstly, the image region of the circle marker is segmented and rotation correction algorithm is designed and applied to recover the supposed image region when the circle marker is perpendicular to the camera optical axis. The distance value can be calculated by the corrected image region area and pinhole camera model. Finally, experimental results show that distance information can be retrieved by the proposed method.

Online GRF: Semi-Automatically Labeling Objects in Video

In this paper, semi-automatic methods based on Gaussian random field (GRF) for online object labeling in video were presented. With a user specified region of interest (ROI), the interested object in all of the frames can be labeled. Two methods, i.e. Updated GRF with fixed SmartLabel (UGFS) method and fixed GRF with fixed SmartLabel (FGFS) method were proposed and compared. Evaluations on object categories have indicated that the UGFS method not only improves the real time performance of object labeling in video, but also has relatively high labeling accuracy.

Research on path planning algorithm of indoor mobile robot

In recent years, indoor mobile robot navigation problems have become the key issues research of the scholars. In order to meet the requirements of a simple robot control, path planning algorithm should have two characteristic at least: the least inflection point and the shortest path, but the elements are often contradictory. The paper has established a rectangular obstacle model according to the features of indoor obstacle. First, it is to determine whether there are obstructions on the pro-domain path between the start point and end point. Secondly, the path is selected according to the number of nodes and overall distance. Finally, the paper has made the simulation of different situations.

Defects detection based on sparse regularization method for electromagnetic tomography (EMT)

In this paper, we propose a new metal defect detection method using electromagnetic tomography (EMT) technique, which is used to measure the alternating magnetic signal modulated by defects in the metal, and then the distribution of defects is reconstructed. Due to the sparsity of the defect distribution, the l1 regularization method for EMT reconstruction is presented to solve the sparse problem. As a result, the l2 regularization can be over-smoothing effect of traditional avoided effectively. A simulation model is designed and the forward problem of the model is calculated using electromagnetic finite-element method. Furthermore, the laboratory experiment and simulation results indicate that the sizes and positions of defects can be effectively distinguished by the new method.

Dynamic imaging based on spatio-temporal information for electrical impedance tomography

Imaging objects in electrical impedance tomography (EIT) measurement are often in a dynamic evolution process, and exploiting the spatial-temporal properties for the dynamic reconstruction objects is crucial for the improvement of the reconstruction quality. In this paper, the linearized equation system for dynamic EIT is established based on spatio-temporal representation. Due to the spatial redundancy and the temporal correlations, the low-rank property of a two-dimensional difference image at a specific time window is introduced to objective functional. The Augmented Lagrange Multiplier (ALM) method is applied to solve the new objective functional. The effectiveness of the proposed scheme is illustrated on both simulation and experiment data sets.

Patch-based sparse reconstruction for electrical impedance tomography

Purpose n n n n nElectrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise. n n n n nDesign/methodology/approach n n n n nThis paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary optimization and image reconstruction are performed alternately. Two patch-based sparsity, namely, square-patch sparsity and column-patch sparsity, are discussed and compared with the global sparsity. n n n n nFindings n n n n nBoth simulation and experimental results indicate that the patch based sparsity method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages. n n n n nOriginality/value n n n n nEIT image is reconstructed based on patch-based sparse representation. Square-patch sparsity and column-patch sparsity are proposed and compared. Sparse dictionary optimization and image reconstruction are performed alternately. The new method tolerates a relatively high level of noise in measured voltages.

Visual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images

With the rapid development of CT technology, especially the higher resolution of CT machine and a sharp increase in the amount of slices, to extract and three-dimensionally display aortic dissection from the huge medical image data became a challenging task. In this paper, active shape model combined with spatial continuity was adopted to realize automatic reconstruction of aortic dissection. First, we marked aortic feature points from big data sample library and registered training samples to build a statistical model. Meanwhile, gray vectors were sampled by utilizing square matrix, which set the landmarks as the center. Posture parameters of the initial shape were automatically adjusted by the method of spatial continuity between CT sequences. The contrast experiment proved that the proposed algorithm could realize accurate aorta segmentation without selecting the interested region, and it had higher accuracy than GVF snake algorithm (93.29% versus 87.54% on aortic arch, 94.30% versus 89.25% on descending aorta). Aortic dissection membrane was extracted via Hessian matrix and Bayesian theory. Finally, the three-dimensional visualization of the aortic dissection was completed by volume rendering based on the ray casting method to assist the doctors in clinical diagnosis, which contributed to improving the success rate of the operations.

Accelerated reconstruction of electrical impedance tomography images via patch based sparse representation

Electrical impedance tomography (EIT) reconstruction is a nonlinear and ill-posed problem. Exact reconstruction of an EIT image inverts a high dimensional mathematical model to calculate the conductivity field, which causes significant problems regarding that the computational complexity will reduce the achievable frame rate, which is considered as a major advantage of EIT imaging. The single-step method, state estimation method, and projection method were always used to accelerate reconstruction process. The basic principle of these methods is to reduce computational complexity. However, maintaining high resolution in space together with not much cost is still challenging, especially for complex conductivity distribution. This study proposes an idea to accelerate image reconstruction of EIT based on compressive sensing (CS) theory, namely, CSEIT method. The novel CSEIT method reduces the sampling rate through minimizing redundancy in measurements, so that detailed information of reconstruction is not lost. In order to obtain sparse solution, which is the prior condition of signal recovery required by CS theory, a novel image reconstruction algorithm based on patch-based sparse representation is proposed. By applying the new framework of CSEIT, the data acquisition time, or the sampling rate, is reduced by more than two times, while the accuracy of reconstruction is significantly improved.

An Initial Positioning Method on Magnetic Rotary Encoder Applying to PMSM

Magnetic rotary encoder can be used to detect the rotor position of PMSM, in order to realize vector control of PMSM. An initial positioning method on magnetic rotary encoder based on open loop control system was presented to calculate absolute difference of average through setting Rotor position angle on multi point.Experimental results show that the method was simple and suitable to measure rotor angle exactly and easily.

An Efficient Method of Image-Sound Conversion Based on IFFT for Vision Aid for the Blind

The vOICe technology is a kind of vision aid technology for the blind, which offers the experience of live camera views through image-sound conversion. To reduce the computational complexity of image-sound conversion, a novel image-sound conversion method is proposed in the paper. The new method takes each column of an image as the Discrete Fourier Transform (DFT) of an audio signal, and inverse Fast Fourier Transform (IFFT) is utilized to implement the inverse DFT and calculate the converted audio signal. The final result represent that this method is more effectively and has a better real time performance. Vision is the most important way for human to perceive the outside world, more than 80% of the information we obtain is derived from vision. The visually impaired has difficulty to gain information from eyes because of the impairment, meanwhile it also gives living or economic