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Embedded steel billet character is low contrast. The uneven illumination distribution and oxidation would affect embedded character detection in images correctly. A novel method based on structured light for embedded character acquisition and extraction is proposed. First the embedded character is irradiated by structured light, embedded character would bend the structured light. The processing algorithm based on Fourier transform, picks up the carrier wave from the reflected image. The reflected image is demodulated and filtered to extract the embedded character. Experimental results show that the algorithm is conciseness and effective. The algorithm has the integrity, is not easy to be interfered by noise. The proposed method has reliability and application.

Steel code location is the key point to realize billet detection and recognition in production line scene with complex illumination. However, due to high temperature and complex scene in the rolling line, the steel code location at the end of billet is quite different from optical character location with simple background and vehicle license plate location. In the process of billet detection and recognition, how to determine steel code target location at the end of billet from the complex illumination scene is first necessary in steel intelligent recognition system. In order to solve this problem, a novel method for steel code location is proposed in this paper. First of all, production line scene image is restrained by Mean Shift filtering and iterative segmentation filter, and then candidate character region can be found by clustering character connected domain with same features. At last, the quantitative model is established for candidate region and the statistical decision algorithm can be used to complete screening object region. The experimental results show that the proposed location method is very precise in most different scenes.

The characters at the billet ends are connected and broken due to the complex scene. Good segmentation of these connected and broken characters is very important in the characters recognition. However, it's difficult for the existing segmentation algorithm to deal with these connected and broken characters. A segmentation algorithm based on multiple feature decision is proposed to divide the connected and broken billet characters. It operates by projection to divide the characters roughly and then multiple feature decision to judge and divide the connected and broken characters. A series of segmentation experimental results show that the algorithm proposed is more effective than traditional segmentation algorithm in deal with the connected and broken characters.

A novel automatic pavement crack detection approach based on texture feature is proposed. The bidirectional multi-level median filter is applied in pretreatment process to eliminate noise while maintain the details of crack edge. Improved center-symmetric local binary pattern (ICS-LBP) texture feature, local correlation texture feature and relative standard deviation texture feature are combined to detect the pavement cracks. Trained-decision strategy is applied to allocate each weight of features and texture features are extracted to train the weights. Experimental results show that the proposed algorithm provides better detection result in comparison with various crack extraction algorithms, and can detect the pavement crack quickly and effectively.

In this paper, a method of automatic pavement breakage extraction based on patching feature is presented for the requirement of pavement patching extraction with complex background on road surface. The median filter is adopted in the process of preprocessing, with improved noise attenuation and excellent edge response. After median filter, an algorithm based on patching feature space is incorporated into the process of segmentation. The comparison of various segmentation algorithms indicate that our approach can provide a better segmentation. A series of experiments have been performed to test the proposed algorithm. It is proven that the proposed automatic extraction method is advanced and effective.

A method for real-time fast detection and symptom analysis of cracks in highway asphalt pavement is proposed. At the first step, the fissure characteristic of the acquired and de-noised pavement images is analyzed and then extracted by the method based on gray value comparison between the current pixel and its neighboring pixels, the false cracks are deleted by using the computed measurement of crack features, thus the true fissures are detected. The most important step is the symptom analysis of the cracks in the pavement image, all the data could be analyzed and be the basis for the agencies to remedy and manage the pavement. Quantities of images are processed and the results show that the proposed method can detect the pavement distress information actually, and has robustness and availability.

There are some disadvantages existing in traditional artificial method of detecting road rut feature, such as consuming time, consuming energy, low accuracy and danger. In order to solve those problems listed above, a new method of detecting features automatically based on the laser image processing technology is proposed. The first step is to start pretreatments such as fuzzy compensating and enhancing the original images. Then adopting an improved watershed serial segmentation method, we can get binary segmentation images. Used multi-level filtering technology combined with statistical methods, the accurate road rut feature can be got. It has been proved by experiments that the detection method has high accuracy and stability.

A double recursive algorithm based on fuzzy entropy for image thresholding is proposed. The inner recursive step is to calculate the threshold between the given gray level interval, and the outer recursive step is to calculate the optimal for the image with small objects. Experiments on steel billet image show that the proposed algorithm can threshold image fast and exactly, which has effectiveness and application for real time image segmentation.

The restoration of rotational motion blurred image involves a lot of interpolations operators in rectangular-to-polar transformation and its inversion of polar-to-rectangular. The technique of interpolation determines the quality of restoration and computational complexity. In this paper, we incorporate orthogonal chebyshev polynomials interpolations into the processing of restoration of rotational motion blurred image, in which the space-variant blurs are decomposed into a series of space-invariant blurs along the blurring paths, and the blurred gray-values of the discrete pixels of the blurring paths are calculated by using of orthogonal chebyshev polynomials' interpolations and the space-variant blurs can be removed along the blurring paths in the polar system. At same way, we use orthogonal chebyshev polynomials' interpolations to perform polar-to-rectangular transformation to put the restored image back to its original rectangular format. In order to overcome the interference of noise, an optimization restoration algorithm based on regularizations is presented, in which non-negative and edge-preserving smoothing are incorporated into the process of restoration. A series of experiments have been performed to test the proposed interpolation method, which show that the proposed interpolations are effective to preserve edges.

Owing to the effect of the high temperature, complex illumination and bad-imaging environment, there are a lot of difficulties in the target of character detection algorithm in complex background. For the requirement of identification and trace of steel billet in the procedure of manufacture and management, the ends of steel billets are all printed a series of digital characters before they are transported to furnace. Owing to the high temperature and the complicated-imaging background, the character detection of digital characters at ends of steel billets is more difficult than general character detection. For the former methods dissatisfied with practical demands, a method using an improved OTSU algorithm and extracted the target character by projection in this paper.

A new method to detect laser parameter is proposed in this paper. The improved Michelson Interferometer is used as experiment system to detect parameter. In the proposed detection method, the interference images in variant atmosphere visibility by regulating the polarizer in the system simulate the transmissibility of laser. Experimental results show that the proposed scheme can simulate laser parameter in true atmosphere environment and has good application value.

Ferroelectric Pb(Zr 0.53 Ti 0.47 )O 3 (PZT) thin films were grown on silicon substrates by modified So1-Gel method using C 4 H 6 O 4 Pb•3H 2 O, ZrO(NO 3 ) 2 •2H 2 O and Ti(OC 4 H 9 ) as raw materials. PbTiO 3 (PT) thin film was introduced as buffer layer. The microstructures of PZT thin films were characterized by XRD and SEM. Electrical properties such as C-V and leakage current characteristics of the films was investigated. The results show that PT buffer layer was helpful to improve the dielectric and ferroelectric properties of PZT thin films. PT buffer layer prevented the interface reaction and reduced the leakage current density of PZT/PT/Si structure about 10 -2 compared with that of PZT/Si structure.

"Ox-eye" interference image analysis is an important means to acquire related optical information such as wavelength and the incidence angle of laser from far away. For the unavoidable noise, it is very difficult to extract object pattern from the noise background, so the contrast enhancement and thresholding is a crucial step for such image processing. In this paper, a novel method is proposed for "ox-eye" interference image processing based on maximum fuzzy entropy. In the new method, the maximum fuzzy entropy is used to conduct contrast enhancement and optimum threshold selecting. By comparing with the traditional methods, the presented method can process images more quickly and can extract the parameters more accurately.