Alin Hou

In order to improve the practical application property of the two-dimensional barcode Quick Response (QR) code, we investigate the coding and decoding process of the QR code image. Run-length coding is applied to binary QR code image so as to accelerate the identification of QR code image. The QR code is transformed into many runs of data in alternate pixels of black and white. The related runs of data among adjacent rows are formed a unit module. After the whole image has been scanned, all of such modules in binary QR code image can be generated accordingly. With a noisy QR image captured by an industrial camera as an example, the experiments of image binarization, image seeking and localization adjustment are accomplished in sequence. Also the error correction algorithm is discussed in detail. A decoding system of QR code is designed and the online detection experiments are carried out. The satisfied results are achieved.

The measurement of safe driving distance based on stereo vision is proposed. The model of camera imaging is established using traditional camera calibration method firstly. Secondly, the projection matrix is deduced according to camera's internal and external parameter and used to calibrate the camera. The method of camera calibration based on two-dimensional target plane is adopted. Then the distortion parameters are calculated when the nonlinear geometric model of camera imaging is built. Moreover, the camera's internal and external parameters are optimized on the basis of the projection error' least squares criterion so that the un-distortion image can be obtained. The matching is done between the left image and the right image corresponding to angular point. The parallax error and the distance between the target vehicle and the camera can be calculated. The experimental results show that the measurement scheme is an effective one in a security vehicles spacing survey. The proposed system is convenient for driver to control in time and precisely. It is able to increase the security in intelligent transportation vehicles.

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

With the development of the micro-processing, silicon material technique, the silicon structural design, mass production, the scientific researches on integrated circuit have been developed rapidly. Defect detection and fault diagnosis as critical design requirements is necessary to achieve high-quality, cost-effective multichip systems. An increasingly difficult task, however, is the inherent need to accurately locate, indentify the defects within the microchip. A method of defect detection of IC wafer have been investigated using mathematical morphology. Firstly, the differential charts of the pending images and the intact image of IC wafer are computed and digitized to two gray levels, i.e. black and white. Secondly, the brightness of the pending images have been transformed to the same brightness as that of intact image in order to make the arithmetics is robust for various illumination conditions. Next, the defects on the binary image of differential chart can be found and dealt with mathematical morphology. Finally, several representative characteristics are proposed to extract and describe the defects of IC wafer image, for example perimeter, area, macro axis, minor axis, eccentricity ratio, centroid, circularity and rectangular degree, etc.

In order to meet the requirements of users, a content-based image retrieval algorithm for electronic commerce has been investigated combining color features with shape features. The algorithm of image retrieval using both the invariant moment and the Fourier descriptor is proposed to retrieve the shape features of image. The secondary retrieval has been done in succession by use of modified color histogram. To avoid the influence of background, a method of image background elimination is proposed prior to the image retrieval using multi-features. Experimental results show that the algorithm is accuracy. The methods of garment image retrieval based on multi-features are promising to be useful for applications of online shopping.