In this article, the authors present an orientation-preserving spectral correspondence for three-dimensional (3D) shape analysis, which is robust and efficient for topological and deformable changes, even for non-isometric shapes. Our technique introduces an optimal spectral representation by combining the eigendecomposition with principal components analysis (PCA) to the heat kernel Laplacian matrix, and we further propose an efficient symmetry detection method based on so-called dominant eigenfunctions. Finally, a 3D descriptor encoding intrinsic symmetry structure and local geometric feature is constructed which effectively reveals the consistent structure between the deformable shapes. Consequently, sufficient orientation-preserving correspondence can be established in our embedding space. Experimental results showed that our method produces stable matching results in comparison with state-of-the-art methods.

Mean squared error (MSE) has long been the most useful objective image quality assessment (IQA) metric due to its mathematical tractability and computational simplicity, although it has shown poor correlations with the perceived visual quality for distorted images. Contrary to the MSE, recent IQA methods are more closely related with measured visual quality. However, their applications are somewhat limited due to their heavy computational costs and inapplicability in optimization process. In order to develop a better IQA method that will be closer to the perceived visual quality, the authors aimed to incorporate simple yet powerful linear features into the form of MSE while retaining the advantages of computational simplicity and desirable mathematical properties of MSE. Through comprehensive experiments, the authors found that Difference of Gaussians (DoG) kernel significantly improves the prediction performance while keeping the aforementioned advantages in the form of MSE. The proposed method performs better as the DoG filtering well approximates the behaviors of neural response functions in the visual cortex of the human visual system, thus extracting perceptually important features. At the same time, it holds the computational simplicity and mathematical properties of MSE since DoG is a very simple linear kernel. Their extensive experiments showed that the proposed method provides competitive prediction performance to the recent IQA methods with a significantly lower computational complexity.

A classification problem involving multi-class samples is typically divided into a set of two-class sub-problems. The pairwise probabilities produced by the binary classifiers are subsequently combined to generate a final result. However, only the binary classifiers that have been trained with the unknown real class of an unlabeled sample are relevant to the multi-class problem. A distance-based relative competence weighting (DRCW) combination mechanism can estimate the competence of the binary classifiers. In this work, we adapt the DRCW mechanism to the support vector machine (SVM) approach for the classification of remote sensing images. The application of DRCW can allow the competence of a binary classifier to be estimated from the spectral information. It is therefore possible to distinguish the relevant and irrelevant binary classifiers. The SVM+DRCW classification approach is applied to analyzing the land-use/land-cover patterns in Guangzhou, China from the remotely sensed images from Landsat-5 TM and SPOT-5. The results show that the SVM+DRVW approach can achieve higher classification accuracies compared to the conventional SVM and SVMs combined with other combination mechanisms such as weighted voting (WV) and probability estimates by pairwise coupling (PE).

A novel hardware-oriented image contrast enhancement algorithm is proposed in this study for intelligent autonomous vehicles. It utilizes a weighted filter and calculates the brightness values of an image based on the adjusted image. The brightness values are processed to either reduce or increase the brightness values of the points. To further improve the quality of an image, the algorithm implements a block-based pixel processing as opposed to a per image frame processing. The brightness values for each block or area in the image are used to improve the contrast of the image. This is accomplished by reducing or increasing the different brightness values of the pixel or lifting point in each block. Simulation results showed that compared with previously proposed algorithms, this work improved on the average discrete entropy by 1% and increased the average color enhancement factor by 8.5%. The proposed novel algorithm was realized using TSMC 0.18 μm CMOS cell process. The VLSI design has a total gate count of 6028 and operates with a frequency of 201 MHz and a power rating of 17.47 mW.

A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

In this paper, we propose a method of image conversion that changes unglossy images into glossy ones using deep photo style transfer. This style of transfer is expected to reproduce the desired image with a metallic appearance based on the technique used for texture transfer. A practical challenge was undertaken to create a gold, metallic image by transferring the style images of a gold ingot. To avoid excessive loss of color balance, we applied the YC_rC_b separation technique and used only the Y component to reproduce a glossy appearance. The luminance and saturation of the style image were altered to investigate the influence on the convergence of appearance because the converted images should be dependent on the contents of the images. The results of transfer were assessed by subjective evaluation using the semantic differential method. A style image with an appropriate amount of change in contrast was found suitable for an appropriate glossy appearance. Moreover, contrast in the style image can be appropriately chosen depending on the contents of the original images.

Hyperspectral images (HSIs) acquired actually often contain various types of noise, such as Gaussian noise, impulse noise, and dead lines. On the basis of land covers, the spectral vectors in HSI can be separated into different classifications, which means the spectral space can be regarded as a union of several low-rank (LR) subspaces rather than a single LR subspace. Recently, LR constraint has been widely applied for denoising HSI. However, those LR-based methods do not constrain the intrinsic structure of spectral space. And these methods cannot make better use of the spatial or spectral features in an HSI cube. In this article, a framework named subspace low-rank representation combined with spatial–spectral total variation regularization (SLRR-SSTV) is proposed for HSI denoising, where the SLRR is introduced to more precisely satisfy the low-rank property of spectral space, and the SSTV regularization is involved for the spatial and spectral smoothness enhancement. An inexact augmented Lagrange multiplier method by alternative iteration is employed for the SLRR-SSTV model solution. Both simulated and real HSI experiment results demonstrate that the proposed method can achieve a state-of-the-art performance in HSI denoising.