论中国古代哲学中的缘象比附──读列维·布留尔《原始思维》

本文将中国古代构造天人合一的思维方式称为缘象比附，并将缘象比附的种类分 理为数字比附、秩序比附、时空比附、属性比附、符号比附和异象比附。作者认为，由于工商业的不 够发达，列维·布留尔所说的原始思维的“互渗律”在古代中国没有完全消失，而是通过缘象比附的 思维方式继续延续。作者也指出了缘象比附对原始思维的超越。 缘象比附是中国科学精神形成的巨大障碍，在今天仍有残存，必须予以清除。

缘象悟道 托象喻道——老子悟道、言道方式解析

老子因水、陀螺、草木等"象"而悟道的同时,巧妙地用寓象于言的方式而喻道。"象"既是老子悟道的途径,也是老子喻道的载体。其思想之所以如此深刻而又独具特色,很大程度上便在于其对"象"的深刻洞察和独到见解。老子借此而通往智慧之境,构筑起他匠心独运的意义世界。

辨体·缘象·遵路·明旨——摭谈文学类文本阅读的宏观把握

对文学类文本阅读考查越来越受到命题者的青睐,面对考生此类试题普遍得分不高的现状,笔者拟从辨体、缘象、遵路、明旨等宏观层面来谈谈阅读文学类文本的一些基本方法。

Edge Enhancement for Remote Sensing Image Using Norm Algorithm in L_2 Vector Space

The Householder transformation-norm structure function in L2 vector space of linear algebra is introduced, and the edge enhancement for remote sensing images is realized. The experiment result is compared with traditional Laplacian and Sobel edge enhancements and it shows that the effect of the new method is better than that of the traditional algorithms.

Adaptive edge image enhancement based on maximum fuzzy entropy

Based on the maximum fuzzy entropy principle,the edge image with low contrast is optimally classified into two classes adaptively,under the condition of probability partition and fuzzy partition.The optimal threshold is used as the classified threshold value,and a local parametric gray-level transformation is applied to the obtained classes.By means of two parameters representing,the homogeneity of the regions in edge image is improved.The excellent performance of the proposed technique is exercisable through simulation results on a set of test images.It is shown how the extracted and enhanced edges provide an efficient edge-representation of images.It is shown that the proposed technique possesses excellent performance in homogeneity through simulations on a set of test images,and the extracted and enhanced edges provide an efficient edge-representation of images.

Watershed-based Image Segmentation with Region Merging and Edge Detection

The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A watershed transformation technique is then employes. This includes: gradient of the classified image, dividing the image into markers, checking the Marker Image to see if it has zero points (watershed lines). The watershed lines are then deleted in the Marker Image created by watershed algorithm. A Region Adjacency Graph (RAG) and Region Adjacency Boundary (RAB) are created between two regions from Marker Image. Finally region merging is done according to region average intensity and two edge strengths (T1, T2). The approach of the authors is tested on remote sensing and brain MR medical images. The final segmentation result is one closed boundary per actual region in the image.

A NEW IMAGE REGISTRATION METHOD FOR GREY IMAGES

The proposed algorithm relies on a group of new formulas for calculating tangent slope so as to address angle feature of edge curves of image. It can utilize tangent angle features to estimate automatically and fully the rotation parameters of geometric transform and enable rough matching of images with huge rotation difference. After angle compensation, it can search for matching point sets by correlation criterion, then calculate parameters of affine transform, enable higher-precision emendation of rotation and transferring. Finally, it fulfills precise matching for images with relax-tense iteration method. Compared with the registration approach based on wavelet direction-angle features, the matching algorithm with tangent feature of image edge is more robust and realizes precise registration of various images. Furthermore, it is also helpful in graphics matching.

AN APPLIED RESEARCH ON APPROACH OF DYADIC WAVELET TRANSFORM FOR REMOTE SENSING IMAGE EDGE DETECTION

In the edge detection of Remote Sensing (RS) image, the useful detail losing and the spurious edge often appear. To solve the problem, the authors uses the dyadic wavelet to detect the edge of surface features by combining the edge detecting with the multi-resolution analyzing of the wavelet transform. Via the dyadic wavelet decomposing, the RS image of a certain appropriate scale is obtained, and the edge data of the plane and the upright directions are respectively figured out, then the gradient vector module of the surface features is worked out. By tracing them, the authors get the edge data of the object, therefore build the RS image which obtains the checked edge. This method can depress the effect of noise and examine exactly the edge data of the object by rule and line. With an experiment of an RS image which obtains an airport, the authors certificate the feasibility of the application of dyadic wavelet in the object edge detection.

Image Edge Detection Based on Oscillation

A new method for image edge detection based on a pulse neural network is proposed in this paper. The network is locally connected. The external input of each neuron of the network is gray value of the corresponding pixel. The synchrony of the neuron and its neighbors is detected by detection neurons. The edge of the image can be read off at minima of the total activity of the detection neurons.

Analysis of principle deviation of subpixel Zernike moments operator

In original Zernike moments subpixel edge operator, ideal step edge model is chosen for calculating four parameters of a pixel point, and therefore principle deviation is caused by edge model. In order to discuss the effect of principle deviation on edge location, sampled edge model is chosen for calculating edge parameters, principle deviation of edge translation l is derived based on Zernike moments edge detection theory, and modified formula of l is given for Zernike moments operator with masks of 5×5 size. Both theory analysis and testing result demonstrate that principle deviation is zero when edge translation l is limited in central pixel of a sampled window. In another case, the modification of l should be considered, which results in edge location accuracy of non-subpixel level for this subpixel edge operator.

Pyramidal Edge Detection Method Based on AWFM Filtering and Fuzzy Linking Model

A novel multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean(AWFM)filtering and fuzzy linking model, is presented in this paper. The algorithm first constructs a pyramidal structure by repetitive AWFM filtering and subsampling of original image. Then it utilizes multiple heuristic linking criteria between the edge nodes of two adjacent levels and considers the linkage as a fuzzy model, which is trained offline. Through this fuzzy linking model, the boundaries detected at coarse resolution are propagated and refined to the bottom level from the coarse-to fine edge detection. The validation experiment results demonstrate that the proposed approach has superior performance compared with standard fixed resolution detector andprevious multiresolution approach, especially in impulse noise environment.

Objects Description and Extraction by the Use of Straight Line Segments in Digital Images

An advanced edge-based method of feature detection and extraction is developed for object description in digital images. It is useful for the comparison of different images of the same scene in aerial imagery, for describing and recognizing categories, for automatic building extraction and for finding the mutual regions in image matching. The method includes directional filtering and searching for straight edge segments in every direction and scale, taking into account edge gradient signs. Line segments are ordered with respect to their orientation and average gradients in the region in question. These segments are used for the construction of an object descriptor. A hierarchical set of feature descriptors is developed, taking into consideration the proposed straight line segment detector. Comparative performance is evaluated on the noisy model and in real aerial and satellite imagery.

AN ADAPTIVE COLOR PLANE INTERPOLATION METHOD BASED ON EDGE DETECTION

In this letter, an adaptive interpolation algorithm based on edge detection is proposed. With this algorithm, all the missing green values can be reconstructed in Bayer pattern image by using edge detection interpolation method. Reconstructed images composed of green pixels are classified according to the high frequency components in image, and the threshold T needed for all kinds of green images in the edge detection is determined through experiments. The edge detection is carried out based on the one Dimensional (1D) gradient operator. If the gradient value is greater than T, this pixel is located on the edge; otherwise the pixel is in the smooth area of the image. Finally, the simple bilinear interpolation is used for the smooth area while the Laplacian interpolation with the second-order correction term is adopted to reconstruct the other red/blue values on the edge. This algorithm resolves effectively the conflicts between reconstructing high quality color image and reducing computational complexity, and thus largely enhances the processing speed for the reconstructed color image.

Automatic extraction of floating ice at Antarctic continental margin from remotely sensed imagery using object-based segmentation

Information of Antarctic iceberg and sea ice are valuable to Antarctic ice melting patterns studies which are helpful to understand climate conditions and general trends of our planet.This paper presents an automatic floating ice extraction method based on image segmentation technology using region growing.It effectively solves the over-segmentation and under-segmentation problems by merging the gray,contour,location and other information of each ice-object.A pixel-based extraction method is proposed to extract the small ices within 5 pixels.LANDSAT TM data,Chinese environment disaster satellite HJ1B data,and MODIS 1B data used to detect Floating ice at Antarctic continental margin respectively.The results showed that the extraction accuracies of the three kinds of data were all higher than 81 percent,while the accuracies of both TM data and HJ1B data were higher than 90%.Object-based information extraction methods can not only obtain the total area and number of floating ice objects in the whole region,but also provide precise details of single objects,including area,perimeter,contour,average brightness.

The relationship between extension of lower crust and displacement of the shelf break

With deep sea petroleum explorations become more and more popular,some geological phenomena have emerged:extension of lower crust and upper crust is inhomogeneous;shelf break has been moved rapidly after crustal stretching.These geological phenomena are important to the continental margin evolution.To investigate the thinning of the whole crust and the contribution of the upper crust versus the lower crust to the crustal stretching since the Cenozoic,we calculated the stretching factors of the upper and the lower crust based on the 13 seismic lines in the Baiyun Sag from CNOOC.The results indicated that the whole crustal thickness decreases seaward while the whole crustal stretching factor increases from shelf to slope.Our calculations showed that the lower crustal stretching factor is higher than that of the upper crust in the Baiyun Sag.In the Cenozoic,deformation of the Baiyun Sag is controlled mainly by ductile shearing rather than brittle shearing.Based on the numerical modeling,we can conclude the initial crust in the Baiyun Sag is thermally attenuated.The stretching factor(β)of the lower crust increases from the north to the south of the continental margin,indicating two stretching centers:the Baiyun Sag and the Liwan Sag.The geometry of the shelf break and theβisoline trap have the similar trend in 23.8 and 13.8 Ma,both located in the intense deforming zone of the lower crust,and therefore we conclude the stretching and flowing of the lower crust cause the displacement of the shelf break before and after 23.8 Ma.