Applied Mechanics and Materials Vols. 373-375

Abstract: Intra4×4 mode decision has correlation between enhancement layer and base layer in scalable video coding (SVC). In this paper, a new intra mode decision algorithm is proposed based on this correlation and the proximity of prediction directions of nine Intra4×4 prediction modes. This method is used in enhancement layer to decrease the mode numbers in enhancement layer. Experiments show that the proposed algorithm can save 27.52% encoding time on average with negligible PSNR change and small bit-rate loss.

Abstract: In order to get an image with every object in focus, an image fusion process is required to fuse the images under different focal settings. In this paper, a new multifocus image fusion algorithm is proposed. The algorithm is based on Laplacian pyramid and Gabor filters. The source images are decomposed by Laplacian pyramid, then the directional edges feature and detail information can be obtained by Gabor filters. Different fusion rules are applied to the low frequency and high frequency coefficients. The experimental results show that the algorithm is simple and effective.

Abstract: Robust and efficient indistinctive feature matching and outliers removal is an essential problem in many computer vision applications. In this paper we present a simple and fast algorithm named as LDGTH (Local Descriptor Generalized Hough Transform) to handle this problem. The main characteristics of the proposed method include: (1) A novel local descriptor generalized hough transform framework is presented in which the local geometric characteristics of invariant feature descriptors are fused together as a global constraint for feature correspondence verification. (2) Different from standard generalized hough transform, our approach greatly reduces the computational and storage requirements of parameter space through taking advantage of the invariant feature correspondences. (3) The proposed algorithm can be seamlessly embedded into the existing image matching framework, and significantly improve the image matching performance both in speed and robustness in challenge conditions. In the experiment we use both synthetic image data and real world data with high outliers ratio and severe changes in view point, scale, illumination, image blur, compression and noises to evaluate the proposed method, and the results demonstrate that our approach achieves achieves faster and better matching performance compared to the traditional algorithms.

Abstract: A method that the adaptive super-resolution reconstruction for Terahertz (THz) image based on the Markov random field (MRF) is proposed. The adaptive Gaussian weighting factor based on the Markov prior distribution is applied to the smoothness of the image edge. The gradient-based optimization converges to the optimal solution fast. It simulates the fact Terahertz image to verify the feasibility of the method comparing with the traditional maximum a posteriori (MAP) super-resolution algorithm. The experimental results show that the adaptive Gaussian weighting super-resolution algorithm not only has high super-resolution performance, but also can better maintain the image edge information and reduce the noise of restored images, and get an ideal THz image. An adaptive super-resolution reconstruction method can be used for Terahertz image reconstruction.

Abstract: The Surendra background update and novel fast model matching were mixed which can reduce the matching region. A Yuntai tracking system was present for people tracking, the fuzzy control tracking based on polar coordinates was also present, which makes the tracking of people object always in the video range and the Yuntai neednt move frequency. Results indicate that the algorithm is superior to the previously published variants of the model matching and the Yuntai system track people in real time.

Abstract: Medical ultrasonic B-scans often suffer from intensity inhomogeneities that originates from the nonuniform attenuation properties of the sonic beam within the body. In order to correct signal attenuation in the tissue, time gain compensation (TGC) is routinely used in medical ultrasound scanners. However, TGC assumes a uniform attenuation coefficient for all body tissues. Since this assumption is evidently inaccurate, over-amplification or under-amplification sometimes appear. This is a major problem for intensity-based, automatic segmentation of video-intensity images because conventional threshold-based or intensity-statistic-based approaches do not work well in the presence of such image distortions. The main contribution of this paper is that additional spatial image features are incorporated to improve inhomogeneity correction and to make it more dynamic besides most commonly used intensity features, so that local intensity variations can be corrected more efficiently. The degraded image is corrected by the inverse of the image degradation model. The image degradation process is described by a linear model, consisting of a multiplicative and an additive component which are modeled by a combination of smoothly varying basis functions. Spatial information about intensity nonuniformity is obtained using cubic spline smoothing and entropy minimizing. Gray-level histogram information of the image corrupted by intensity inhomogeneity is exploited from a signal processing perspective. We explain how this model can be related to the ultrasonic physics of image formation to justify our approach. Experiments are presented on synthetic images and real US data to evaluate quantitatively the accuracy of the method.

Abstract: In order to deal with the influence of adverse weather (such as dust fog) over vision system of the outdoor machine, this paper proposes a real-time image defogging method basing on sparsity. This method estimates the radiation intensity of airlight based on the dark channel prior statistical law and adopts sparse decomposition and reconstruction to figure out the atmospheric veil, taking advantage of the sparsity of this issue. By solving the equation for imaging based on the atmospheric scattering model, we can obtain the atmospheric radiation intensity under the ideal condition and succeed in image defogging. Experimental results show that this method can effectively improve the degradation of image in adverse weather (foggy) and raise its sharpness.

Abstract: SPOT-5 image is widely used in every trade by its special advantage of high spatial resolution and stereo mapping. SPOT-5 image is difficult to composite true color without blue wavelength channel. This article aims to achieve approximation from RS images colors to natural colors through true-color processing. The experiential arithmetic module of true-color composite is established by Using Erdas Imagine, which is the software for processing RS image, and ERDAS Macro Language (EML), which is modeling language. Loading module and some simulations get more effective images of approximating natural colors by all kinds of surface features of SPOT-5 image.

Abstract: In order to improve the resolution and accuracy of the inversion, this paper proposed a new inversion method. By introducing constraint sparse spike inversion, the new method can fully take the advantages of high vertical resolution of logging data and the preferable transverse continuity of the seismic data to improve the resolution of the profiles and the quality of imaging and inversion in specific areas. Experimental results showed that this solution can deduce more precise and reasonable inversion result than other inversion solution. Constraint sparse spike inversion can generate reflection coefficients with broad frequency band and solve the marking problems preferably, thereby makes the impedance model obtained from the inversion even close to the actual situation underground.

Abstract: In this paper, we present a multi-hypothesis scheme of Temporal Error Concealment (TEC). The lost block is concealed by several hypothesis blocks in the previous frame, and the corresponding Motion Vectors (MVs) of hypothesis blocks are derived using a kalman filter based on the similarity of local MVs. The processing is at the level of 8x8 block and the tendency in horizontal and vertical orientations is considered to predict the candidate MVs. Experimental results demonstrate that the proposed method can provide better performance as compared with the conventional approaches.