Edge enhancement of noisy ultrasound images is important for medical diagnosis. Conventional edge enhancement methods are mainly directed to emphasizing the high-frequency components of the image. Because these methods emphasize also the noise of image, they are not suitable for noisy ultrasound images with speckle noise. In this paper, we propose an edge enhancement method using mathematical morphology based on a geometrical characteristics of the image, using locally variable structuring elements. We show that the proposed method enhances the edge of ultrasound images without noise emphasis.

Bidirectional reflection distribution functions (BRDFs) of commercially pure titanium sheets with three different kinds of surface morphology were measured. Those experimental BRDFs were analyzed by using Phong's reflection model. Topographic measurements of the specimens' surfaces were performed with using a stylus-method. An explicit microfacet model based on topographic data was proposed. With using the explicit microfacet model and geometrical optics the calculated BRDFs were obtained and then compared with the experimental BRDFs. Both of them were in a good agreement. Through this comparison physical meanings of Phong's reflection model were discussed. We concluded that with using the explicit microfacet model it will be possible to calculate the BRDF of the materials' surface in arbitrary illumination conditions and that this modeling will be useful to develop new aesthetic surface appearance in material industries, computer graphics, architectural design and surface science.

In this paper, a novel approach is proposed to identify the detailed typeface of Gothic characters in document images. The identification is performed by evaluating two types of typeface models, named the Gs-pattern and the Gd-pattern according to the principle of MDL. The typeface models are generated from the observed character image by using morphology and are viewed as approximating expressions of the observed character. Consequently, this method is unique in that it is free from both character recognition and dictionary lookup.

It is proposed a novel method that optimizes nonlinear filters by unsupervised learning using a novel definition of morphological pattern spectrum, called "morphological opening/closing spectrum (MOCS)." The MOCS can separate smaller portions of image objects from approximate shapes even if the shapes are degraded by noisy pixels. Our optimization method analogizes the linear low-pass filtering and Fourier spectrum: filter parameters are adjusted to reduce the portions of smaller sizes in MOCS, since they are regarded as the contributions of noises like high-frequency components. This method has an advantage that it uses only target noisy images and requires no example of ideal outputs. Experimental results of applications of this method to optimization of morphological open-closing filter for binary images are presented.

Homogeneous but distinct visual objects having low-contrast boundaries are usually merged in most of the segmentation algorithms. To alleviate this problem, an efficient image segmentation algorithm based on a bottom-up approach is proposed by using spatial domain information only. For initial image segmentation, we adopt a new marker extraction algorithm conforming to the human visual system. It generates dense markers in visually complex areas and sparse markers in visually homogeneous areas. Then, two region-merging algorithms are successively applied so that homogeneous visual objects can be represented as simple as possible without destroying low-contrast real boundaries among them. The first one is to remove insignificant regions in a proper merging order. And the second one merges only homogeneous regions, based on ternary region classification. The resultant segmentation describes homogeneous visual objects with few regions while preserving semantic object shapes well. Finally, a size-based region decision procedure may be applied to represent complex visual objects simpler, if their precise semantic contents are not necessary. Experimental results show that the proposed image segmentation algorithm represents homogeneous visual objects with a few regions and describes complex visual objects with a marginal number of regions with well-preserved semantic object shapes.

This paper describes a theoretical foundation of fuzzy morphological operations and architectural extension of the shared-weight neural network (SWNN). The network performs shift-invariant filtering using fuzzy-morphological operations for feature extraction. The nodes in the feature extraction stage employ the generalized-mean operator to implement fuzzy-morphological operations. The parameters of the SWNN, weights, morphological structuring element and fuzziness, are optimized by the error back-propagation (EBP) training method. The parameter values of the trained SWNN are then implanted into the extended SWNN (ESWNN) which is a simple convolution neural network. The ESWNN architecture dramatically reduces the amount of computation by avoiding segmentation process. The neural network is applied to automatic recognition of a vehicle in visible images. The network is tested with several sequences of images that include targets ranging from no occlusion to almost full occlusion. The results demonstrate an ability to detect occluded targets, while trained with non-occluded ones. In comparison, the proposed network was superior to the Minimum-Average Correlation filter systems and produced better results than the ordinary SWNN.

The pattern spectrum has been proposed to represent morphological size distribution of an image. However, the conventional pattern spectrum cannot extract approximate shape information from image objects spotted by noisy pixels since this is based only on opening. In this paper, a novel definition of the pattern spectrum, morphological multiresolution pattern spectrum (MPS), involving both opening and closing is proposed. MPS is capable of distinguishing details from approximate information of the image.

In this paper, we discuss the principle of the clumsy painter method proposed for extracting interested regions from image signals automatically. We theoretically clarify the reason why the clumsy painter method is effective so well. We compare its algorithm with the opening operation in mathematical morphology, and prove that the clumsy painter method has the advantage over the opening operation in mathematical morphology on removing uninterested regions from image signals. Simulating these two methods on two simple geometrical models, we show that the extracted redults by the opening operation are included in those by the clumsy painter method.

We thought that multiple skeletons were inherent in an ordinary three-dimensional object. A thinning method is developed to extract multiple skeletons using 333 templates for boundary deletion based on the hit or miss transformation and 222 templates for checking one voxel thickness. We prepared twelve sets of deleting templates consisting of total 194 templates and 72 one voxel checking templates. One repetitive iteration using one sequential use of the template sets extracts one skeleton. Some of the skeletons thus obtained are identical; however, multiple independent skeletons are extracted by this method. These skeletons fulfill the well-recognized three conditions for a skeleton. We extracted three skeletons from the cube, two from the space shuttle model and four from the L-shaped figure by Tsao and Fu. The digital medial skeleton, which is not otherwise extracted, is extracted by comparing the multiple skeletons with the digital medial-axis-like-figure. One of our skeletons for the cude agreed with the ideal medial axis. The locations of the gravity center of the multiple skeletons are compared with that of the original shape to evaluate how uniform or non-biased skeletons are extracted. For the L-shaped figure, one of our skeletons is found to be most desirable from the medial and uniform points of view.

A self-induced-transparent (SIT) system that takes advantage of morphology dependent resonances (MDR's) in a Mie-sized microsphere doped with a resonant material is proposed. The present system is doubly resonant: one has microscopic origin (the two-level system), while the other has macroscopic origin (the MDR). In this geometry, owing to the feedback action of MDR's, the pulse area can be much expanded, and thus the electric-field amplitude of the incident pulse can be reduced substantially compared with the conventional one-way SIT propagation. Theoretical results that incorporate dephasing due to structural imperfections are shown.

Almost stoichiometric YBa2Cu3O7-δ(110) or (103) and SrTiOx(110) films, and multilayer films consisting of them have successfully been grown epitaxially on hot SrTiO3 substrates by 90off-axis rf magnetron sputtering with facing targets. Their whole composition, compositional distribution in depth, crystallinity and surface morphology were examined by inductively coupled plasma spectroscopy, Auger electron spectroscopy, reflection high-energy electron diffraction, and scanning tunneling microscopy or atomic force microscope, respectively. When any YBa2Cu3O7-δ film was exposed to air after deposition, a Ba-rich layer was formed in a near surface region of the film. However, such a compositional distribution in depth of the film was improved by in situ deposition of a SrTiOx film on it. Moreover, the surface roughness of the YBa2Cu3O7-δ film was improved by predeposition of a SrTiOx film under it. On the basis of these results, both YBa2Cu3O7-δ/SrTiOx/YBa2Cu3O7-δ/SrTiO3(sub.) and YBa2Cu3O7-δ/SrTiOx/YBa2Cu3O7-δ/SrTiOx/SrTiO3(sub.) multilayer films with average surface roughness of 3 nm were grown reproducibly, which had uniform compositional distribution throughout the depth of the film except a near surface region of the top YBa2Cu3O7-δ layer. A new 222 structure described by Sr8Ti8O20 (Sr2Ti2O5) with a long range ordered arrangement of oxygen vacancies was formed in the SrTiOx films deposited epitaxially on YBa2Cu3O7-δ films.

The character binarization method MTC is developed for enhancing the recognition of characters in general outdoor images. Such recognition is traditionally difficult because of the influence of illumination changes, especially strong shadow, and also changes in character, such as apparent character sizes. One way to overcome such difficulties is to restrict objects to be processed by using strong hypotheses, such as type of object, object orientation and distance. Several systems for automatic license plate reading are being developed using such strong hypotheses. However. their strong assumptions limit their applications and complicate the extension of the systems. The MTC method assumes the most reasonable hypotheses possible for characters: they occupy plane areas, consist of narrow lines, and external shadow is considerably larger than character lines. The first step is to eliminate the effect of local brightness changes by enhancing feature including characters. This is achieved by applying mathematical morphology by using a logarithmic function. The enhanced gray-scale image is then binarized. Accurate binarization is achieved because local thresholds are determined from the edges detected in the image. The MTC method yields stable binary results under illumination changes, and, consequently, ensures high character reading rates. This is confirmed with a large number of images collected under a wide variety of weather conditions. It is also shown experimentally that MTC permits stable recognition rate even if the characters vary in size.

A YBCO/CeO2/Au MIS structure (YBCO:YBa2Cu3O7y) is fabricated on a MgO(100) substrate with the help of the all-in-situ electron-beam and heater coevaperation system. The current-voltage (I-V) characteristics of the deposited YBCO film under various gate voltages are examined. Small modulation of the I-V characteristics by gate voltages can be observed. Meanwhile, the surface morphology is also studied by means of an atomic force microscope (AFM). The relation between the field effect and the surface morphology of a thin YBCO film is discussed.

Mathematical morphology clarified geometrical properties of shape analysis algorithms for binary pictures. Results of labelling, distance transform, and adjacent numbering are, however, coded pictures. For full descriptions of shape analysis algorithms in the framework of mathematical morphology, it is necessary to extend morphological operations to code-labelled pictorial data. Nevertheless, extensions of morphology to code-labelled pictures have never discussed though the theory of gray morphology is well studied by several authors. Hence, this paper proposes a theory of the coded morphology which is based on the binary scaling of labels of pixels. The method uses n-layered binary sub-pictures for the processing of a picture with 2n labels. By introducing morphological operations for the coded point sets, we express some coding functions in the manner of the mathematical morphology. We also derive multidimensional array registers and gates which store and process coded pictures and morphological operations to them by proposing basic gates which compute parallelly logical operations for elements of Boolean layered arrays. These gates and registers are suitable for the implementation of the shape analysis processors on the three-dimensional VLSI and ULSI.