Applied Mechanics and Materials Vols. 568-570

Abstract: Watermarking appraisement is the key technology for validity checking of the digital watermark algorithm. There is any comprehensive evaluation method proposed for watermarking technology oriented electronic chart. Sometimes, traditional software and testing theory can only give some appraisement indications, and those lacking of credibility. In this paper, a novel watermarking evaluation method was proposed based on certainty factor, with some factors of the proposed model, we can analyze the performance of the specified watermarking algorithm, can which can be quantified, it is also easy to compare with the optimal algorithm.

Abstract: Mesh simplification plays an important role in the process of 3D models, such as storage, transmission and real-time rendering. By analyzing the basic techniques and algorithms of mesh simplification, the paper describes the the typical methods in detail and analyzes the major characters of these methods. The future work of mesh simplification is discussed in the end.

Abstract: This paper presents an automatic approach for facial expression deformation. In order to guarantee the expression deformation is dealt with high quality and conveniently, the approach uses two key technologies: the PSO detection and the affine transformation. The simulation results show that this method can produce natural smile expression, without marking the spots manually, especially works well for some shy smile expressions.

Abstract: To detect the edge of object, an efficient scheme based on the Multi-Oriented Local Energy (MOLE) is presented. The MOLE is constructed by Multi-oriented Gaussian second differential and its Hilbert transformation. The Multi-oriented Gaussian second differential is obtained by using a linear combination of basis filters with arbitrary orientations. To overcome the affection of illumination, Phase Congruency is employed by normalizing the directional energies. At last, our method is compared with other common methods. The experimental results reveal that this method can extract more continuous edge and obtain more details.

Abstract: Non-subsampled contourlet transform (NSCT) is the combination of the multi-scale analysis and multi-directional analysis in processing high-dimensional signals and has better approximation precision and better sparse description. A novel and efficient fusion method of infrared image and visible image based on NSCT is proposed. Firstly, source fusion images can be decomposed into low-frequency coefficients and high-frequency coefficients using the NSCT. For the low-frequency coefficients, the average fusion algorithm is used. For the each directional high frequency sub-band coefficients, the larger value and region average gradient maximum criterion is used to select the better coefficients for fusion. Experimental results show that the proposed algorithm can achieve better result compared with the traditional image fusion algorithms.

Abstract: The paper proposes a improved Camshift algorithm which solve the problem of the original Camshift that have limitations when the tracking target have similar color with the background and is obstructed. The paper combines codebook model with the Camshift. The YUV space is used in foreground detection rather than the RGB. The results of experiments show that the algorithm works well in complex background, occlusion and the same color interference. At last we achieve a warning system.

Abstract: This paper presents a novel method for solving single-image super-resolution problems, based upon low-rank representation (LRR). Given a set of a low-resolution image patches, LRR seeks the lowest-rank representation among all the candidates that represent all patches as the linear combination of the patches in a low-resolution dictionary. By jointly training two dictionaries for the low-resolution and high-resolution images, we can enforce the similarity of LLRs between the low-resolution and high-resolution image pair with respect to their own dictionaries. Therefore, the LRR of a low-resolution image can be applied with the high-resolution dictionary to generate a high-resolution image. Unlike the well-known sparse representation, which computes the sparsest representation of each image patch individually, LRR aims at finding the lowest-rank representation of a collection of patches jointly. LRR better captures the global structure of image. Experiments show that our method gives good results both visually and quantitatively.

Abstract: The paper proposes a new approach to single-image super resolution (SR), which is based on sparse representation. Previous researchers just focus on the global intensive patch, without local intensive patch. The performance of dictionary trained by the local saliency intensive patch is more significant. Motivated by this, we joined the saliency detection to detect marked area in the image. We proposed a sparse representation for saliency patch of the low-resolution input, and used the coefficients of this representation to generate the high-resolution output. Compared to precious approaches which simply sample a large amount of image patch pairs, the saliency dictionary pair is a more compact representation of the patch pairs, reducing the computational cost substantially. Through the experiment, we demonstrate that our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods.

Abstract: In order to observe night vision image easily, a new image fusion method is designed to improve the detail information of night vision images in a simple and efficient way. Instead of the traditional Multi-resolution analysis and spatial transform approach, the designed method highlights the detail information of night vision images by phase modulation and image enhancement technique. In the designed approach, the phase spectrum and amplitude spectrum of the visible and infrared images are extracted using FFT firstly, and then the phase spectra of two images are exchanged and the IFFT is applied to the processed images to produce phase information images. To compensate for the blurring caused by phase modulation, the high-frequency information of the processed infrared image is segmented and applied to the reconstruction of the color night vision image. Finally, color night vision image is fused by assigning the two-modulated images to red and green channels respectively, and the segmented image to blue channel. The experimental results show that the details of the fused image by the new method are richer than those of the images fused by the traditional methods, and the designed algorithm with a little amount of calculation can be easily realized in real-time processing systems.

Abstract: To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.