Two-phase synthesis of shape-controlled colloidal zirconia nanocrystals and their characterization

We have developed a two-phase approach for the synthesis of shape-controlled colloidal zirconia nanocrystals, including spherical-, teardrop-, rod-, and rice grain-shaped particles. We found that the key factors for controlling the shape were the reaction time, the nature of the capping agent, and the monomer concentration. We have analyzed the morphologies, crystallinity, optical properties, and structural features of the as-prepared ZrO2 nanoparticles by using transmission electron microscopy (TEM), high-resolution TEM, X-ray powder diffraction, and UV-vis absorption and fluorescence spectroscopy. The possible nucleation and growth process is also discussed.

Surfactantless synthesis of multiple shapes of gold nanostructures and their shape-dependent SERS spectroscopy

A useful method for the synthesis of various gold nanostructures is presented. The results demonstrated that flowerlike nanoparticle arrays, nanowire networks, nanosheets, and nanoflowers were obtained on the solid substrate under different experimental conditions. In addition, surface-enhanced Raman scattering (SERS) spectra of 4-aminothiophenol (4-ATP) on the as-prepared gold nanostructures of various shapes were measured, and their shape-dependent properties were evaluated. The intensity of the SERS signal was the smallest for the gold nanosheets, and the flowerlike nanoparticle arrays gave the strongest SERS signals.

A solvothermal route to size- and shape-controlled CdSe and CdTe nanocrystals

The size- and shape-controlled CdSe and CdTe nanocrystals, which exhibit obvious quantum confinement effect, have been synthesized by a solvothermal route. It is found that initial precursor concentrations are key factors in controlling the shape of the resulting nanocrystals. Moreover, the obtained nanocrystals are all of zinc blende structure, regardless of their sizes and shapes. A possible mechanism for the formation and growth of the nanocrystals is put forward. It is inferred that the adhesion and subsequent recrystallization of nanocrystals with an assistance of remaining monomers should be a major reason for formation and growth of the elongated nanocrystals.

Polylactide-based polyurethane and its shape-memory behavior

A series of polylactide polyurethanes (PLAUs) were synthesized from poly(L-lactide) diols, hexamethylene diisocyanate (HDI), and 1,4-butanediol (BDO). Their thermal and mechanical properties and shape-memory behavior were studied by infrared spectroscopy (IR), differential scanning calorimetry (DSC), wide angle X-ray diffraction (WAXID), tensile testing, and thermal mechanical analysis (TMA). The T(g)s of these polymers were in the range of 33-53 degrees C, and influenced by the Mn of the PLA diol and the ratio of the soft-segment to the hard-segment. These materials can restore their shapes almost completely after 150% elongation or twofold compression. By changing the M-n of the PLA diol and the ratio of the hard-to-soft-segment, their Ts and shape-recovery temperatures can be adjusted to the neighborhood of the body temperature. Therefore, these PLAUs are expected to find practical medical applications.

An N-shape thermal front in the western South Yellow Sea in winter

An N-shape thermal front in the western South Yellow Sea (YS) in winter was detected using Advanced Very High Resolution Radiation (AVHRR) Sea Surface Temperature data and in-situ observations with a merged front-detecting method. The front, which exists from late October through early March, consists of western and eastern wings extending roughly along the northeast-southwest isobaths with a southeastward middle segment across the 20-50 m isobaths. There are north and south inflexions connecting the middle segment with the western and eastern wings, respectively. The middle segment gradually moves southwestward from November through February with its length increasing from 62 km to 107 km and the southern inflexion moving from 36.2A degrees N to 35.3A degrees N. A cold tongue is found to coexist with the N-shape front, and is carried by the coastal jet penetrating southward from the tip of the Shandong Peninsula into the western South YS as revealed by a numerical simulation. After departing from the coast, the jet flows as an anti-cyclonic recirculation below 10 m depth, trapping warmer water originally carried by the compensating Yellow Sea Warm Current (YSWC). A northwestward flowing branch of the YSWC is also found on the lowest level south of the front. The N-shape front initially forms between the cold tongue and warm water involved in the subsurface anti-cyclonical recirculation and extends upwards to the surface through vertical advection and mixing. Correlation analyses reveal that northerly and easterly winds tend to be favorable to the formation and extension of the N-shape front probably through strengthening of the coastal jet and shifting the YSWC pathway eastward, respectively.

Elliptic Object Detection Based on Shape Preserving and Active Contour

本文通过形状约束方程(组)与一般主动轮廓模型结合,将目标形状与主动轮廓模型融合到统一能量泛函模型中,提出了一种形状保持主动轮廓模型即曲线在演化过程中保持为某一类特定形状。模型通过参数化水平集函数的零水平集控制演化曲线形状,不仅达到了分割即目标的目的,而且能够给出特定目标的定量描述。根据形状保持主动轮廓模型,建立了一个用于椭圆状目标检测的统一能量泛函模型,导出了相应的Euler-Lagrange常微分方程并用水平集方法实现了椭圆状目标检测。此模型可以应用于眼底乳头分割,虹膜检测及相机标定。实验结果表明,此模型不仅能够准确的检测出给定图像中的椭圆状目标,而且有很强的抗噪、抗变形及遮挡性能。

Direct Computation of 3D Shape Invariants and the Focus of Expansion

Structure from motion often refers to the computation of 3D structure from a matched sequence of images. However, a depth map of a surface is difficult to compute and may not be a good representation for storage and recognition. Given matched images, I will first show that the sign of the normal curvature in a given direction at a given point in the image can be computed from a simple difference of slopes of line-segments in one image. Using this result, local surface patches can be classified as convex, concave, parabolic (cylindrical), hyperbolic (saddle point) or planar. At the same time the translational component of the optical flow is obtained, from which the focus of expansion can be computed.

Vectorizing Face Images by Interpreting Shape and Texture Computations

The correspondence problem in computer vision is basically a matching task between two or more sets of features. In this paper, we introduce a vectorized image representation, which is a feature-based representation where correspondence has been established with respect to a reference image. This representation has two components: (1) shape, or (x, y) feature locations, and (2) texture, defined as the image grey levels mapped onto the standard reference image. This paper explores an automatic technique for "vectorizing" face images. Our face vectorizer alternates back and forth between computation steps for shape and texture, and a key idea is to structure the two computations so that each one uses the output of the other. A hierarchical coarse-to-fine implementation is discussed, and applications are presented to the problems of facial feature detection and registration of two arbitrary faces.

Shape-Time Photography

We introduce a new method to describe, in a single image, changes in shape over time. We acquire both range and image information with a stationary stereo camera. From the pictures taken, we display a composite image consisting of the image data from the surface closest to the camera at every pixel. This reveals the 3-d relationships over time by easy-to-interpret occlusion relationships in the composite image. We call the composite a shape-time photograph. Small errors in depth measurements cause artifacts in the shape-time images. We correct most of these using a Markov network to estimate the most probable front surface, taking into account the depth measurements, their uncertainties, and layer continuity assumptions.

Properties and Applications of Shape Recipes

In low-level vision, the representation of scene properties such as shape, albedo, etc., are very high dimensional as they have to describe complicated structures. The approach proposed here is to let the image itself bear as much of the representational burden as possible. In many situations, scene and image are closely related and it is possible to find a functional relationship between them. The scene information can be represented in reference to the image where the functional specifies how to translate the image into the associated scene. We illustrate the use of this representation for encoding shape information. We show how this representation has appealing properties such as locality and slow variation across space and scale. These properties provide a way of improving shape estimates coming from other sources of information like stereo.

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.

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.

Virtual Visual Hulls: Example-Based 3D Shape Estimation from a Single Silhouette

Recovering a volumetric model of a person, car, or other object of interest from a single snapshot would be useful for many computer graphics applications. 3D model estimation in general is hard, and currently requires active sensors, multiple views, or integration over time. For a known object class, however, 3D shape can be successfully inferred from a single snapshot. We present a method for generating a ``virtual visual hull''-- an estimate of the 3D shape of an object from a known class, given a single silhouette observed from an unknown viewpoint. For a given class, a large database of multi-view silhouette examples from calibrated, though possibly varied, camera rigs are collected. To infer a novel single view input silhouette's virtual visual hull, we search for 3D shapes in the database which are most consistent with the observed contour. The input is matched to component single views of the multi-view training examples. A set of viewpoint-aligned virtual views are generated from the visual hulls corresponding to these examples. The 3D shape estimate for the input is then found by interpolating between the contours of these aligned views. When the underlying shape is ambiguous given a single view silhouette, we produce multiple visual hull hypotheses; if a sequence of input images is available, a dynamic programming approach is applied to find the maximum likelihood path through the feasible hypotheses over time. We show results of our algorithm on real and synthetic images of people.