STM image of tungstosilicic acid molecular layer on highly oriented pyrolytic graphite

Molecular layer of tungstosilicic acid (H4SiW12O40) deposited on freshly-cleaved highly oriented pyrolytic graphite (HOPG) was observed by scanning tunneling microscopy (STM) in air at room temperature. The molecular dimension (11.5 Angstrom) of H4SiW12O40 measured by STM is consistent with known crystallographic parameter. We also imaged the boundary of H4SiW12O40 molecular layer on HOPG showing that molecular layer of H4SiW12O40 was formed. It has been proved that individual tungstosilicic acid species is imaged. The probable reason for the formation of the molecular layer is also discussed.

IN-SITU SCANNING-TUNNELING-MICROSCOPY IMAGE OF A HIGHLY ORIENTED PYROLYTIC-GRAPHITE ELECTRODE MODIFIED WITH POTASSIUM BIS(11-MOLYBDOSILICATO) DYSPROSATE(III) IN SODIUM-SULFATE SOLUTION

The variation in molecule adsorption mode on pretreated highly oriented pyrolytic graphite electrodes, modified with the title complex K10H3[Dy(SiMo11O39)(2)] by cyclic voltammetry in the title complex solution, was observed in situ by electrochemical scanning tunnelling microscopy (ECSTM) with molecular resolution in sodium sulphate solution. According to the ECSTM images and the known molecular structure we conclude that the adsorption mode of the title complex modified electrode changed during potential cycling from ''vertical'' to ''inclined'' and then ''horizontal'' or ''flat'' mode, i.e. the title complex adsorbed on the surface of electrode by one ligand of the complex at first, then began to incline and was finally adsorbed by two ligands of the complex. This result indicates that the adsorption mode on the modified electrode surface changed during potential cycling in the sulphate solution and a much more stable molecular layer was formed. The change in adlattice of adsorbates on the modified electrode surface from hexagonal to rectangular was also observed by ECSTM. A plausible model was given to explain this process.

On the nonlinear integral transform of an ocean wave spectrum into an along-track interferometric synthetic aperture radar image spectrum

We present a new nonlinear integral transform relating the ocean wave spectrum to the along-track interferometric synthetic aperture radar (AT-INSAR) image spectrum. The AT-INSAR, which is a synthetic aperture radar (SAR) employing two antennas displaced along the platform's flight direction, is considered to be a better instrument for imaging ocean waves than the SAR. This is because the AT-INSAR yields the phase spectrum and not only the amplitude spectrum as with the conventional SAR. While the SAR and AT-INSAR amplitude spectra depend strongly on the modulation of the normalized radar cross section (NRCS) by the long ocean waves, which is poorly known, the phase spectrum depends only weakly on this modulation. By measuring the phase difference between the signals received by both antennas, AT-INSAR measures the radial component of the orbital velocity associated with the ocean waves, which is related to the ocean wave height field by a well-known transfer function. The nonlinear integral transform derived in this paper differs from the one previously derived by Bao et al. [1999] by an additional term containing the derivative of the radial component of the orbital velocity associated with the long ocean waves. By carrying out numerical simulations, we show that, in general, this additional term cannot be neglected. Furthermore, we present two new quasi-linear approximations to the nonlinear integral transform relating the ocean wave spectrum to the AT-INSAR phase spectrum.

Depth inversion in coastal water based on SAR image of waves

Wave-number spectrum technique is proposed to retrieve coastal water depths by means of Synthetic Aperture Radar (SAR) image of waves. Based on the general dispersion relation of ocean waves, the wavelength changes of a surface wave over varying water depths can be derived from SAR. Approaching the analysis of SAR images of waves and using the general dispersion relation of ocean waves, this indirect technique of remote sensing bathymetry has been applied to a coastal region of Xiapu in Fujian Province, China. Results show that this technique is suitable for the coastal waters especially for the near-shore regions with variable water depths.

长时记忆中图形不同特征的加工与提取

Information can be represented both conceptually and imaginarily in long-term memory. However, it seems that only conceptual representation appears, neglecting imaginary information, in most of the long-term memory (LTM) models. In the matter of fact, picture can be stored in LTM directly and conceptually. There is no evidence for what specific type of information, conceptual or imaginary, for the color, shape, or texture to be represented. However, it is evident that the shape and color can be represented separately in LMT. Further research is needed on whether features are represented separately or not, such as color and texture, texture and shape etc. Rehearsal plays important role in picture memory besides the types of storage and representation. Memory of picture is indeed enhanced by rehearsal. There are two types of rehearsal. One is for creating image, another is articulatory loop. Which one will be taken during picture memory process depends on the characteristics of stimuli, subjects' encoding preferences and/or task requirements. Nevertheless, the relation between two types of rehearsal is not very clear yet up to now. Different features could be activated at different time course or possibilities since they can be represented separately. Six experiments were conducted dealing with the characteristics of representation, rehearsal and retrieval of picture in LTM. From these experiments, further understanding of picture information processing was expected. It would add more evidence to the LTM models, and make practical sense to the computer visual identification. The first two experiments were based on the paradigm from Hanna et al.(1996) to investigate separable representation of texture and shape, texture and color. The results indicated that texture could be represented separately with color and shape respectively. It suggested that different features might be processed in different way during remembering. Another interest finding is that recognition performance for shape, color and texture are quite different. What for shape is highest, for color is lowest, and for texture is between of them. Three features of picture can be represented separately. How about the roles of rehearsal when they enter the LTM from short-term memory(STM)? The second three experiments assigned three different types of rehearsal, i. e. visual, verbal, and subject-run(might be both of visual and verbal). The findings are that performances of picture memory were affected significantly by different types of rehearsal. Both visual and verbal rehearsal played important role during remembering process. It seems that verbal rehearsal, which might enhance the relative strength of memory trace, was much more effective than visual one. In addition, subjects tended to choose those difficult-to-name, features to rehearse, to improve the memory performance. Only two features were changed in each of the first two experiments. They might interact (facilitate or disturb) each other when they were retrieved. So it was difficult to identify the retrieval difference between them. In the last experiment, easy-to-name pictures were studied, and only one feature could be recognized. The results indicated that the retrieval performances of three features(shape, color, and texture) were quite different. They were different on the relative strength of memory trace, with the shape was strongest, color was lightest, and texture was in between. No difference was found on the absolute strength of them.

Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers fo the lower dimensional maniforlds that define the boundaries between classes, for clouds of multi-dimensional, mult-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the applicatin of these extensions are also given.

Towards an Example-Based Image Compression Architecture for Video-Conferencing

This paper consists of two major parts. First, we present the outline of a simple approach to very-low bandwidth video-conferencing system relying on an example-based hierarchical image compression scheme. In particular, we discuss the use of example images as a model, the number of required examples, faces as a class of semi-rigid objects, a hierarchical model based on decomposition into different time-scales, and the decomposition of face images into patches of interest. In the second part, we present several algorithms for image processing and animation as well as experimental evaluations. Among the original contributions of this paper is an automatic algorithm for pose estimation and normalization. We also review and compare different algorithms for finding the nearest neighbors in a database for a new input as well as a generalized algorithm for blending patches of interest in order to synthesize new images. Finally, we outline the possible integration of several algorithms to illustrate a simple model-based video-conference system.

Linear Object Classes and Image Synthesis from a Single Example Image

The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced techniques that are applicable under restricted conditions but simpler. The approach exploits image transformations that are specific to the relevant object class and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a new technique by extending the notion of linear class first proposed by Poggio and Vetter. For linear object classes it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high- resolution face images from a single 2D view.

Recognition of Surface Reflectance Properties from a Single Image under Unknown Real-World Illumination

This paper describes a machine vision system that classifies reflectance properties of surfaces such as metal, plastic, or paper, under unknown real-world illumination. We demonstrate performance of our algorithm for surfaces of arbitrary geometry. Reflectance estimation under arbitrary omnidirectional illumination proves highly underconstrained. Our reflectance estimation algorithm succeeds by learning relationships between surface reflectance and certain statistics computed from an observed image, which depend on statistical regularities in the spatial structure of real-world illumination. Although the algorithm assumes known geometry, its statistical nature makes it robust to inaccurate geometry estimates.

The Image Dissector "Eyes"

This is a collection of data on the construction operation and performance of the two image dissector cameras. Some of this data is useful in deciding whether certain shortcomings are significant for a given application and if so how to compensate for them.

Recovering Intrinsic Images from a Single Image

We present an algorithm that uses multiple cues to recover shading and reflectance intrinsic images from a single image. Using both color information and a classifier trained to recognize gray-scale patterns, each image derivative is classified as being caused by shading or a change in the surface's reflectance. Generalized Belief Propagation is then used to propagate information from areas where the correct classification is clear to areas where it is ambiguous. We also show results on real images.

Shape Recipes: Scene Representations that Refer to the Image

The goal of low-level vision is to estimate an underlying scene, given an observed image. Real-world scenes (e.g., albedos or shapes) can be very complex, conventionally requiring high dimensional representations which are hard to estimate and store. We propose a low-dimensional representation, called a scene recipe, that relies on the image itself to describe the complex scene configurations. Shape recipes are an example: these are the regression coefficients that predict the bandpassed shape from bandpassed image data. We describe the benefits of this representation, and show two uses illustrating their properties: (1) we improve stereo shape estimates by learning shape recipes at low resolution and applying them at full resolution; (2) Shape recipes implicitly contain information about lighting and materials and we use them for material segmentation.

Multiple Resolution Image Classification

Binary image classifiction is a problem that has received much attention in recent years. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. We then apply that system to images at one-half through one-sixteenth resolution, and consider the corresponding error rates. In addition, we further observe generalization performance as it depends on the number of training images, and lastly, compare the system's best error rates to that of a human performing an identical classification task given teh same set of test images.

Inferring 3D Structure with a Statistical Image-Based Shape Model

We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure'' model. The 3D shape of a class of objects may be represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes can then be estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We augment the shape model to incorporate structural features of interest; novel examples with missing structure parameters may then be reconstructed to obtain estimates of these parameters. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a dataset of thousands of pedestrian images generated from a synthetic model, we can perform accurate inference of the 3D locations of 19 joints on the body based on observed silhouette contours from real images.