High-temperature thermal stability of C/C−ZrC−SiC composites via region labeling method

To investigate the thermal stability of ceramic-matrix composites,three kinds of C/C−ZrC−SiC composites with different Zr/Si molar ratios were synthesized by reactive melt infiltration.Employing region labeling method,the high-temperature thermal stability of the composites was systematically studied by changing the temperature and holding time of thermal treatment.Results show that the mass loss rate of low Si composites has a growth trend with increasing temperature,and a crystal transformation from β-SiC toα-SiC occurs in the composites.In the calibrated area,SiC phase experiences Ostwald ripening and volume change with location migration,while ZrC phase experiences a re-sintering process with diffusion.Moreover,it is found that increasing temperature has a more obvious effect on the thermal stability than extending holding time,which is mainly attributed to the faster diffusion rate of atoms.

3D face registration based on principal axis analysis and labeled regions orientation

A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed.The pre-registration is achieved by transforming the multi-pose models to the standard frontal model＇s reference frame using the principal axis analysis algorithm.Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics.These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration.Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.

Circular object recognition based on shape parameters

To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.

EFFICIENT IMAGE SEGMENTATION FOR SEMANTIC OBJECT GENERATION

This letter presents an efficient and simple image segmentation method for semantic object spatial segmentation. First, the image is filtered using contour-preserving filters. Then it is quasi-flat labeled. The small regions near the contour are classified as uncertain regions and are eliminated by region growing and merging. Further region merging is used to reduce the region number. The simulation results show its efficiency and simplicity. It can preserve the semantic object shape while emphasize on the perceptual complex part of the object. So it conforms to the human visual perception very well.