Generating Dynamic Projection Images for Scene Representation and Understanding

This paper explores an interesting image projection produced by scanning dynamic scenes with a slit camera. Based on the concept of Anorthoscopic Perception, we investigate how a two-dimensionalDynamic Projection Imageof three-dimensional scenes is generated from consecutive 1-D snapshots taken through a slit, when the relative motion is homogeneous between the viewer and scenes. By moving the camera in the 3-D environment or rotating an object, we can obtain various dynamic projection images. These dynamic projection images contain major spatial and temporal information about 3-D scenes in a small amount of data. Consequently, the projection is suited for the memorization, registration, and indexing of image sequences. The generated images also directly show some of the motion properties in dynamic scenes. If a relative motion between the camera and a subject is planned properly, the dynamic projection image can even provide a texture image of the subject along with some expected photometry characteristics. Therefore, the dynamic projection can facilitate dynamic object recognition, 3-D structure acquisition, and image compression, all for a stable motion between the objects and camera. We outline various applications in vision, robotics, and multimedia and summarize the motion types and the camera setting for generating such dynamic projection images.     

图像信息对句子语义理解与表示的有效性验证与分析

近年来,图像文本建模研究已经成为自然语言处理领域一个重要的研究方向.图像常被用于增强句子的语义理解与表示.然而也有研究人员对图像信息用于句子语义理解的必要性提出质疑,原因是文本本身就能够提供强有力的先验知识,帮助模型取得非常好的效果;甚至在不使用图像的条件下就能得出正确的答案.因此研究图像文本建模需要首先回答一个问题:图像是否有助于句子语义的理解与表示?为此,本文选择一个典型的不包含图像的自然语言语义理解任务:自然语言推理,并将图像信息引入到该任务中用于验证图像信息的有效性.由于自然语言推理任务是一个单一的自然语言任务,在数据标注过程中没有考虑图像信息,因此选择该任务能够更客观地分析出图像信息对句子语义理解与表示的影响.具体而言,本文提出一种通用的即插即用框架(general plug and play framework)用于图像信息的整合.基于该框架,本文选择目前最先进的五个自然语言推理模型,对比分析这些模型在使用图像信息前后的表现,以及使用不同图像处理模型与不同图像设置时的表现.最后,本文在一个大规模公开数据集上进行了大量实验,实验结果证实图像作为额外知识,确实有助于句子语义的理解与表示.此外,还证实了不同的图像处理模型和使用方法对整个模型的表现也会造成不同的影响.     

应用神经网络方法解决小麦高产群体图像识别

应用图像分割、图像增强技术提取小麦群体图像特征，采用人工神经网络方法建立小麦群体图像识别自学习系统，并将其应用于小麦高产群体图像识别。     

Images of the rose

The surprisingly simple rose equation can provide a fertile basis for generating many visually intriguing 3D forms. This equation was originally investigated in 2D in the 18th century. However, today, using modern computer graphics techniques, full 3D versions can be visualized. The geometrical surfaces of the rose functions correspond to the zeros of the equations and are visualized as the isosurfaces of 3D fields. Despite their apparent complexity, the surfaces are not generated by iteration nor by the intersection of primitives, and it is encouraging that such simple functions can produce a wealth of interesting geometrical shapes. Solid textures and colour maps are designed using equations in a simular way. These can be used to further enhance the aesthetic sense and visual appearance of the geometrical forms.     

利用HJ星遥感进行水稻抽穗期长势分级监测研究

对水稻长势进行遥感分级监测,制作能够直观反映水稻长势等级的遥感专题图,便于农业技术人员及时制定有效的田间管理措施,达到增产的目的。以江苏省泰兴市为例,利用HJ-A/B卫星遥感影像,提取水稻的种植面积并分析抽穗期水稻的长势情况。在利用GPS实地取样调查和建立解译标志的基础上,进行HJ-A/B卫星影像校正,人机交互式判读解译等操作,并将GPS样点数据校验贯穿到整个分类过程中,面积信息解译精度在90%以上。最后,利用归一化植被指数(NDVI)反演叶面积指数(LAI)数据信息,依据LAI数据进行水稻长势分级,制作了泰兴市水稻抽穗期长势分级遥感监测专题图。     

面向照片-图形图像的二分类方法

随着科技的发展,图像应用技术日趋重要,许多图像应用技术对照片图像和图形图像的要求和效果是不同的.针对照片图像和图形图像分类的问题,本文提出了一种新的基于二分法的图像分类方法,这种方法混合了颜色、边缘和纹理三种图像特征,通过对图像特征值的K-means聚类分析,实现了照片图像和图形图像的分类.经实验验证,该方法在照片图像和图形图像的分类上取得较好的结果.     

关于反馈的作用及能力的认识

首先简述对反馈作用的定性认识，接着介绍几种典型的反馈设计方法，最后介绍近几年在探索反馈机制的能力与局限方面所得到的某些定量认识。     

用细胞神经网络提取二值与灰度图象边缘

边缘是图象的重要特征,采用细胞神经网络提取图象边缘时,网络参数的选择是一个重要问题。为了能够有效地提取图象边缘,基于高通滤波模板,选择了细胞神经网络的一组简单易行的参数,首先将其用于检测二值图象边缘,再在此基础上,通过综合灰度值各位面边缘检测的结果提取出灰度图象的边缘。与传统边缘提取方法Sobel和Log方法的比较可见,该方法是有效的,并且由于细胞神经网络具有高速并行运算、便于硬件实现等特点,因此使其在图象实时处理中具有更大的潜力。     

意象素描中的个性表现

意象素描是作者个性化的表现,是个体对客观物象的内心体验和艺术思维加工的结果,注重个体的本质认识与个性表达。在素描教学中应倡导学生通过各种素描表现形式的展现客观事物的内在,把培养创造力和艺术思维能力作为手段,把注重个体情感体验和表达作为真正的目的。意象素描是培养艺术审美素养和形成艺术个性化的学习平台。     

Images and Benford's Law

Benford's law had been proposed in the past as a way to modelize the probability distribution of the first digit in a set of natural numbers. We show in this paper that the magnitude of the gradient of an image obeys this law. We show, experimentally, that this also applies for the laplacian pyramid code. This yields to the field of entropy based coding which takes advantage of the a priori information about the probability of any symbol in the signal.     

Examination and Processing of the Images of Biomedical Microobjects

Methods for segmentation of multi-type color images of microobjects and discrimination of their external contours and those of the internal components of their elements were proposed and analyzed. For the Morfolog system intended for microscopic diagnosis, the experimental results were presented.     

Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation

Journal of Computer Science and Technology - With the recent tremendous advances of computer graphics rendering and image editing technologies, computergenerated fake images, which in general do...     

节约型设计之辨

节约型设计是基于构建节约型社会的环境而提出的,但在实践中容易产生种种不同的认识,有些认识存在着误区,与真正的节约型设计可能存在很大差距甚至相左的观念,比如认为节约型设计就是单一产品的原材料减量,节约型设计应该尽量使用低价材料,或者以降低成本为节约型设计的单一目标等等。实际上,节约型设计所提倡的,更应该是从循环经济模式的角度实现资源、能源最优化的设计;对企业、消费者、社会和环境都具有责任感的设计;对消费者的实际需求、使用环境有着周密考虑的设计;是真正能为实现节约型社会发挥各方面作用的设计。     

Understanding the curve-polygon object

In this paper the new method of understanding of the curve-polygon object is presented. The method of understanding of the curve-polygon object is part of the research aimed at developing a shape understanding method able to perform complex visual tasks connected with visual thinking. The shape understanding method is implemented as the shape understanding system (SUS). Understanding includes, among others, obtaining the visual concept in process of the visual reasoning, naming and visual explanation by generation an object from a required class. In this paper generation of the object from the selected well defined class, the curve-polygon class, as well as assigning the visual object into one of the shape classes is presented. The generation of the visual objects is used in SUS during learning of the visual concept, explanatory process and self-correcting process. The visual object is assigned into one of the shape classes during the visual reasoning process. The visual reasoning, presented in this paper, in contrast to other forms of reasoning depends on the type of objects which are analysed. In this paper visual reasoning that assigns an object to the curve-polygon class is presented. The shape understanding system consists of different types of experts that perform different processing and reasoning tasks. The self-correcting expert, that implements the new method of testing and reasoning, is invoked to test ability of the system to understand the concept of the curve-polygon shape.     

Learning the Statistics of People in Images and Video

This paper address the problems of modeling the appearance of humans and distinguishing human appearance from the appearance of general scenes. We seek a model of appearance and motion that is generic in that it accounts for the ways in which people's appearance varies and, at the same time, is specific enough to be useful for tracking people in natural scenes. Given a 3D model of the person projected into an image we model the likelihood of observing various image cues conditioned on the predicted locations and orientations of the limbs. These cues are taken to be steered filter responses corresponding to edges, ridges, and motion-compensated temporal differences. Motivated by work on the statistics of natural scenes, the statistics of these filter responses for human limbs are learned from training images containing hand-labeled limb regions. Similarly, the statistics of the filter responses in general scenes are learned to define a background distribution. The likelihood of observing a scene given a predicted pose of a person is computed, for each limb, using the likelihood ratio between the learned foreground (person) and background distributions. Adopting a Bayesian formulation allows cues to be combined in a principled way. Furthermore, the use of learned distributions obviates the need for hand-tuned image noise models and thresholds. The paper provides a detailed analysis of the statistics of how people appear in scenes and provides a connection between work on natural image statistics and the Bayesian tracking of people.     

地下管道图象的自动处理和识别

对地下管道探地雷达图象给出了自动处理和识别方法,管道的位置和深度直观地显示在计算机屏幕上,此方法已应用于一种实际探地雷达,对地下管道目标具有良好的识别能力.