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Fringe projection profilometry technique has attracted more and more attention in the computer vision and found wide applications in the areas of high-precision measurement. This paper presents an accurate and robust system calibration method to formulate the phase-to-height conversion. This method uses the Levenberg-Marquardt nonlinear optimization algorithm to fit the phase value of a virtual reference plane, which is parallel to the line between the optical centers of the camera and the projector. The final expression just has three coefficients. With no need to manually adjust the position of components, several experiments were conducted to evaluate the performance of proposed method and demonstrated that the novel system calibration outperforms state-of-the-art methods in terms of stability and accuracy.

The accuracy of camera calibration depends significantly on the control point detection which customarily subjects to image noise, distortion and resolution. This paper uses concentric circle grids as the calibration pattern and accordingly proposes a robust and accurate control point detection method for camera calibration. Each set of concentric circles on the grids pattern consists of multiple circles whose radii decrease progressively. The geometric structure of multi-hierarchies can not only robustly extract control points from complicated scenarios but also build a voting scheme to avoid numerical instability resulting from the estimation of projected centers. According to the common self-polar triangle of concentric circles and the local sub-pixel edge detection algorithm, we efficiently estimated sub-pixel coordinates of control points under projective transformation. Simulations and real experiments were conducted to demonstrate that the performance of calibration using concentric circle grids is superior to the popular grid patterns with respect to the image noise, distortion and resolution.

This paper mainly proposed a kind of algorithm based on moving object detection and direction recognition, with a variety of data in video surveillance system, constructing a moving objects trajectories system with a combination of artificial and video surveillance system, to find escape routes of suspects accurately and make work easy for investigators. This system was created by Python language, MySQL database language and Open CV direction recognition technology, completing usage of map, creating database structure and recognizing the direction of a moving object, to predict object moving direction.

At present, a person-specific 3D model generation technology has been an active research topic for scientists in the field of computer vision and computer graphics. The paper proposes a 3D modeling method based on single photograph, by analysis of individual difference like geometric features which present the shape and locations of facial components. An extension of the basic Active Appearance Model is proposed to localize some facial feature points in the frontal image and all these feature points are aligned to fit the generic 3D face model to a specialized one to reflect the given person's face, being both more robust and faster. Then an optimized texture mapping of cylindrical projection and model adjustment technologies are presented, to synthesize personal virtual face realistically. This method rectifies these previous defects, such as the difficulty in gaining access to acquiring the accurate data of a 3D face model, slowly manual location methods, incomplete facial information mining. The approach gives a significant improvement in both the reliability and the overall accuracy of 3D modeling.

On the basis of the characters of footprint shape, a toe shape description method based on geometric characteristic values of toe image is proposed. Corner detection is carried out on toe region, and the characteristic points which can describe the toe shape are confirmed by the edge of toe image. Through finding characteristic points whose distances to the center are stable and which can distinguish different toe shapes and the correlation among them, the toe shape characteristic vector is built. On the basis of toe shape characteristic vector, a toe shape recognition method based on fuzzy neural networks is proposed. Firstly, according to the statistical distribution disciplinarians of different shape toe images, membership functions are constructed respectively from the angle, length and region parameters, and these values are used as single judgment factors. The comprehensive judgment vector can be obtained through the operation among these single judgment factors. At last, the distance vector between comprehensive judgment vector and four model vectors are computed to feed into neural networks in order to judge. Because the method based on fuzzy neural network can reflect subjectively and correctly the different shapes of toe image, so the total automatic recognition rate amounts to 92.80%.

A feature-level image fusion method based on segmentation region and neural networks is proposed in this paper. Firstly, the source images are segmented and merged into a set of common regions which are used for guiding the whole fusion process; then selecting the corresponding segmentation regions from the source images respectively and extracting features representing clarity in the two regions; at last the features are fed into a neural networks to judge clear region to reconstruct the final fusion image. The experimental results show that the fusion effect is better.