Artificial Neural Network Performing the Forward Operation of Color Appearance Model

A method of the forward operation of color appearance (from colorimetric attributes to color appearance attributes) using an artificial neural network (ANN) is presented The neural network model developed is a multilayer feedforward neural network model for predicting color appearance model (CAM). This method greatly decreased the mathematical computation in color appearance prediction. The error backed-propagation (BP) algorithm was applied in the training of the neural networks, and it was trained and tested by the LUTCHI color appearance datasets which are the most comprehensive one in testing color appearance model. CRT was selected as a typical example in experiment because it is usually used as self-luminous object in fact, and several ways for choosing training samples were included and compared each other. The testing results show that the color appearance prediction using artificial neural network is well consistent with visual evaluation.

Simulation of CIECAM02 color appearance model based on Chinese color system

Color appearance model （CAM） can be used to determine the required colors for repro- duction across changes in cross-media. CIECAM02 color appearance model prediction is implemen- ted by artificial neural networks in this paper, which includes forward and reversed prediction. 1333 color samples as training samples and other 1332 color samples as test samples are selected in the Chinese color system. In order to test the prediction accuracy of neural networks after simulation of CIECAM02 color appearance model, the color-difference formula can be used for the evaluation of forward and reversed models. Results have shown that BP neural-network has acceptable accuracy in simulation of CIECAM02 color appearance model for colors of Chinese color system.

Efficient model building in active appearance model for rotated face

This paper proposes the efficient model building in active appearance model(AAM) for the rotated face.Finding an exact region of the face is generally difficult due to different shapes and viewpoints.Unlike many papers about the fitting method of AAM,this paper treats how images are chosen for fitting of the rotated face in modelling process.To solve this problem,databases of facial rotation and expression are selected and models are built using Procrustes method and principal component analysis(PCA).These models are applied in fitting methods like basic AAM fitting,inverse compositional alignment(ICA),project-out ICA,normalization ICA,robust normalization inverse compositional algorithm(RNIC)and efficient robust normalization algorithm(ERN).RNIC and ERN can fit the rotated face in images efficiently.The efficiency of model building is checked using sequence images made by ourselves.

Effectively Lossless Subspace Appearance Model Compression Using Prior Information

Subspace appearance models are widely used in computer vision and image processing tasks to compactly represent the appearance variations of target objects. In order to ensure algorithm performance, they are typically stored in high-precision formats; this results in a large storage footprint, rendering redistribution costly and difficult. Since for most image and vision applications, pixel values are quantized to 8 bits by the acquisition apparatuses, we show that it is possible to construct a fixed-width, effectively Iossless representation of the bases vectors, in the sense that reconstructions from the original bases and from the quantized bases never deviate by more than half of the quantization step-size. In addition to directly applying this result to Iosslessly compress individual models, we also propose an algorithm to compress appearance models by utilizing prior information on the modeled objects in the form of prior appearance subspaces. Experiments conducted on the compression of person-specific face appearance models demonstrate the effectiveness of the proposed algorithms.

Deep appearance modeling:A survey

Appearance modeling is an essential task in computer graphics for capturing and reproducing rich appearance of real world materials under different lighting and viewing conditions.With recent advances of deep learning techniques,a set of deep learning based approaches have been proposed for improving the efficiency and result quality of appearance modeling.In this paper,we provide a survey of these deep appearance modeling techniques from both graphics and machine learning perspectives,and discuss the challenges and opportunities along this direction.

3D fluid model analysis on the generation of negative hydrogen ions for negative ion source of NBI

A radio-frequency(RF) inductively coupled negative hydrogen ion source(NHIS) has been adopted in the China Fusion Engineering Test Reactor(CFETR) to generate negative hydrogen ions.By incorporating the level-lumping method into a three-dimensional fluid model,the volume production and transportation of H^(-) in the NHIS,which consists of a cylindrical driver region and a rectangular expansion chamber,are investigated self-consistently at a large input power(40 k W) and different pressures(0.3–2.0 Pa).The results indicate that with the increase of pressure,the H^(-) density at the bottom of the expansion region first increases and then decreases.In addition,the effect of the magnetic filter is examined.It is noteworthy that a significant increase in the H^(-) density is observed when the magnetic filter is introduced.As the permanent magnets move towards the driver region,the H^(-) density decreases monotonically and the asymmetry is enhanced.This study contributes to the understanding of H-distribution under various conditions and facilitates the optimization of volume production of negative hydrogen ions in the NHIS.

Research on degradation mechanism of trichlorobenzene and Hg^(0) by nonthermal plasma catalysis

Aiming at mercury and dioxin in fire coal gas as research objects,nonthermal plasma(NTP)catalytic technology was used to investigate the degradation effect of operating condition parameters on mixed pollutants in mixed flue gas condition,and to explore the synergistic degradation of Hg0and TCB(1,2,3-trichlorobenzene,TCB)under mixed flue gas conditions.The research results showed that the conversion efficiency of mercury and TCB increased with the additional output of voltage,and decreased with the increase of the gas flow rate.Under optimal reaction conditions:voltage=17 k V,frequency=300 Hz,gas flow rate=21 min^(-1),the conversion efficiency of Hg^(0)and TCB reached the highest 91.4%and 84.98%,respectively.In the NTP catalytic system,active free radicals played an important role in the synergistic conversion of mercury and TCB,which have a competitive effect,to make the conversion efficiency of mixed pollutants lower than a single substance.In the mixed flue gas condition,the mixed gas has an inhibitory effect on the synergistic conversion of mercury and TCB.Kinetic modeling of NTP catalytic synergistic reaction was established.Under three conditions of TCB,mercury and TCB,mixed simulated flue gas,the NTP catalytic technology showed a quasi-firstorder kinetic reaction for the degradation of TCB.According to the synergistic effect of NTP and composites,the transformation and degradation of TCB mainly included two processes:TCB and ring opening,and Hg^(0)was finally oxidized to Hg^(2+).

Multiview SVBRDF capture from unified shape and illumination

This paper proposes a stable method for reconstructing spatially varying appearances (SVBRDFs) frommultiview images captured under casual lighting conditions. Unlike flat surface capture methods, ourscan be applied to surfaces with complex silhouettes. The proposed method takes multiview images asinputs and outputs a unified SVBRDF estimation. We generated a large-scale dataset containing themultiview images, SVBRDFs, and lighting appearance of vast synthetic objects to train a two-streamhierarchical U-Net for SVBRDF estimation that is integrated into a differentiable rendering networkfor surface appearance reconstruction. In comparison with state-of-the-art approaches, our methodproduces SVBRDFs with lower biases for more casually captured images.