谈维米尔作品的图像特质

17世纪以来,绘画的风格丰富多彩,约翰内斯·维米尔作为17世纪典型的荷兰风俗画家之一,其绘画作品不同于同时期其他绘画的风格,具有图像和绘画语言风格的典型性和特殊性,对其绘画艺术的解读与研究也数不胜数,形成了一种别具一格的研究特性。本文重点讨论维米尔作品中的图像特质,旨在通过对维米尔创作背景和艺术形式的研究与探索,来探讨其创作过程中独特的个人技法以及这样一种绘画形式对后世的影响。

心智图像:让学生与数学深度遇见

"心智图像"是学生在数学学习中形成的一种知识"对应物"。在小学生的数学学习中,心智图像通常是从直观动作向具体形象转变,从单子分割向整体统合转变,从内在主观向外在客观转变。学生的心智图像主要有基于原型的心智图像、基于范例的心智图像、基于表征的心智图像和基于结构的心智图像。心智图像,让学生与数学深度遇见。

EVALUATION OF SMALL ANIMAL IMAGING UNDER DIFFERENT SCAN CONDITIONS

To evaluae small animal imaging with individual different high voltage, filter thickness and tube current, an animal X-ray micro-computed tomography （micro-CT） system based on panel detector is developed and a rat is scanned by using the system with individual high voltage, tube current, filter thickness, and exposure time. A model is presented based on the Monte Carlo code PENELOPE for generating the X-ray spectra of X-ray tube used in the micro-CT system. A platform developed based on Matlab allows for calculating beam quality parameters, including the average energy of X-ray beam, the change of transmition rate and the input X-ray fluence. The factors affecting the signal difference to noise ratio （SDNR） of micro-CT are investigated and the relationship between SDNR and scan combinations is analyzed. A series of tools and methods are developed for small animal imaging and imaging performance evaluation in the field of small animal imaging.

Semi-reference image quality metric for virtual view images in free-viewpoint television

A semi-reference image quality assessment metric based on similarity measurement for synthesized virtual viewpoint image （VVI） in free-viewpoint television system （FFV） is proposed in this paper. The key point of the proposed metric is taking resemblant information between VVI and its neighbor view images for quality assessment to make our metric to be extended to multi-semi-reference image quality assessment easily. The proposed metric first extracts impact factors from image features, then combines an image synthesis technique and similarity functions, in which, disparity information are taken into account for registering the resemblant regions. Experiments are divided into three phases. Phase I is to verify the validation of the proposed metric by taking impaired images and original reference into account. The experimental results show the agreement between evaluation scores and bio-characteristic of human visual system. Phase II shows the accordance with Phase I by taking neighbor view as reference. The proposed metric can be taken as a full reference one to evaluate the image quality even though the original reference is absent. Phase III is then performed to evaluate the quality of WI. Evaluation scores in the experimental results are able to evaluate the quality of VVI.

No-Reference Quality Assessment of Enhanced Images

Image enhancement is a popular technique,which is widely used to improve the visual quality of images.While image enhancement has been extensively investigated,the relevant quality assessment of enhanced images remains an open problem,which may hinder further development of enhancement techniques.In this paper,a no-reference quality metric for digitally enhanced images is proposed.Three kinds of features are extracted for characterizing the quality of enhanced images,including non-structural information,sharpness and naturalness.Specifically,a total of 42 perceptual features are extracted and used to train a support vector regression(SVR) model.Finally,the trained SVR model is used for predicting the quality of enhanced images.The performance of the proposed method is evaluated on several enhancement-related databases,including a new enhanced image database built by the authors.The experimental results demonstrate the efficiency and advantage of the proposed metric.

Image quality assessment method based on nonlinear feature extraction in kernel space

To match human perception, extracting perceptual features effectively plays an important role in image quality assessment. In contrast to most existing methods that use linear transformations or models to represent images, we employ a complex mathematical expression of high dimensionality to reveal the statistical characteristics of the images. Furthermore, by introducing kernel methods to transform the linear problem into a nonlinear one, a full-reference image quality assessment method is proposed based on high-dimensional nonlinear feature extraction. Experiments on the LIVE, TID2008, and CSIQ databases demonstrate that nonlinear features offer competitive performance for image inherent quality representation and the proposed method achieves a promising performance that is consistent with human subjective evaluation.

No-reference image quality assessment based on nonsubsample shearlet transform and natural scene statistics

A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform(NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation(SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine(SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error(RMSE) with human perception than other high performance NR IQA methods.