Several new phenomena in the formation of ring-banded spherulites of poly(epsilon-caprolactone) and poly(styrene-random-acrylonitrile) blends

Ring-banded spherulites in crystallization of poly(epsilon-caprolactone) and poly (styrene-random-acrylonitrile) blends were observed with polarizing optical microscopy and digital image analysis technique was applied directly to the image obtained by polarizing microscope, Several new interesting phenomena were found. One is that the ring-banded structure is still clearly seen after the analyzer was removed and this astonished phenomenon couldn't result from the general concept about formation mechanism of ring-banded spherulite - lamellae twisting, Another one is that there is a slight, dark line in the bright band when cross polars were added, which may be related to the formation process and mechanism of ring-banded spherulites in the blends of poly (epsilon-caprolactone) and poly (styrene-random-acrylonitrile).

The formation of ring-banded spherulites of poly(epsilon-caprolactone) in its miscible mixtures with poly(styrene-co-acrylonitrile)

Ring-banded spherulites in polymer blends of poly(epsilon-caprolactone) (PCL) and poly(styrene-co-acrylonitrile) (SAN) were investigated by optical microscopy equipped with a digital image analysis system. PCL/SAN blends exhibit not only spherulites with a Maltese cross, but also distinct extinction rings. The periodic distance of rings changes with blend ratio and crystallization temperature and was plotted as a function of the undercooling and overall mobility of the mixtures, respectively. It was found that the overall mobility of chain segments in the mixtures could be mainly attributed to the origin of the formation of ring-banded spherulites. It was believed that for the first time a quantitative experimental result was obtained about the relationship of periodic distance of rings and the overall mobility of the mixtures. This relationship may be useful to explain the formation mechanism of ring-banded spherulites in polymer blends or even in homopolymers in the future. (C) 1977 Elsevier Science Ltd.

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.

A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration

We formulate and interpret several multi-modal registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the "auto-information function", as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the "auto-information" as well as verify them empirically on multi-modal imagery. Among the useful aspects of the "auto-information function" is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

Three-Dimensional Recognition of Solid Objects from a Two-Dimensional Image

This thesis addresses the problem of recognizing solid objects in the three-dimensional world, using two-dimensional shape information extracted from a single image. Objects can be partly occluded and can occur in cluttered scenes. A model based approach is taken, where stored models are matched to an image. The matching problem is separated into two stages, which employ different representations of objects. The first stage uses the smallest possible number of local features to find transformations from a model to an image. This minimizes the amount of search required in recognition. The second stage uses the entire edge contour of an object to verify each transformation. This reduces the chance of finding false matches.

Hypothesis Formation and Evaluation in Medical Diagnosis

This thesis describes some aspects of a computer system for doing medical diagnosis in the specialized field of kidney disease. Because such a system faces the spectre of combinatorial explosion, this discussion concentrates on heuristics which control the number of concurrent hypotheses and efficient "compiled" representations of medical knowledge. In particular, the differential diagnosis of hematuria (blood in the urine) is discussed in detail. A protocol of a simulated doctor/patient interaction is presented and analyzed to determine the crucial structures and processes involved in the diagnosis procedure. The data structure proposed for representing medical information revolves around elementary hypotheses which are activated when certain disposing of findings, activating hypotheses, evaluating hypotheses locally and combining hypotheses globally is examined for its heuristic implications. The thesis attempts to fit the problem of medical diagnosis into the framework of other Artifcial Intelligence problems and paradigms and in particular explores the notions of pure search vs. heuristic methods, linearity and interaction, local vs. global knowledge and the structure of hypotheses within the world of kidney disease.

Search by Shape Examples: Modeling Nonrigid Deformation

We describe our work on shape-based image database search using the technique of modal matching. Modal matching employs a deformable shape decomposition that allows users to select example objects and have the computer efficiently sort the set of objects based on the similarity of their shape. Shapes are compared in terms of the types of nonrigid deformations (differences) that relate them. The modal decomposition provides deformation "control knobs" for flexible matching and thus allows for selecting weighted subsets of shape parameters that are deemed significant for a particular category or context. We demonstrate the utility of this approach for shape comparison in 2-D image databases; however, the general formulation is applicable to signals of any dimensionality.

Deformable Prototypes for Encoding Shape Categories in Image Databases

We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.

ImageRover: A Content-Based Image Browser for the World Wide Web

ImageRover is a search by image content navigation tool for the world wide web. To gather images expediently, the image collection subsystem utilizes a distributed fleet of WWW robots running on different computers. The image robots gather information about the images they find, computing the appropriate image decompositions and indices, and store this extracted information in vector form for searches based on image content. At search time, users can iteratively guide the search through the selection of relevant examples. Search performance is made efficient through the use of an approximate, optimized k-d tree algorithm. The system employs a novel relevance feedback algorithm that selects the distance metrics appropriate for a particular query.

Combining Textual and Visual Cues for Content-based Image Retrieval on the World Wide Web

Some WWW image engines allow the user to form a query in terms of text keywords. To build the image index, keywords are extracted heuristically from HTML documents containing each image, and/or from the image URL and file headers. Unfortunately, text-based image engines have merely retro-fitted standard SQL database query methods, and it is difficult to include images cues within such a framework. On the other hand, visual statistics (e.g., color histograms) are often insufficient for helping users find desired images in a vast WWW index. By truly unifying textual and visual statistics, one would expect to get better results than either used separately. In this paper, we propose an approach that allows the combination of visual statistics with textual statistics in the vector space representation commonly used in query by image content systems. Text statistics are captured in vector form using latent semantic indexing (LSI). The LSI index for an HTML document is then associated with each of the images contained therein. Visual statistics (e.g., color, orientedness) are also computed for each image. The LSI and visual statistic vectors are then combined into a single index vector that can be used for content-based search of the resulting image database. By using an integrated approach, we are able to take advantage of possible statistical couplings between the topic of the document (latent semantic content) and the contents of images (visual statistics). This allows improved performance in conducting content-based search. This approach has been implemented in a WWW image search engine prototype.

The What-And-Where Filter: A Spatial Mapping Neural Network for Object Recognition and Image Understanding

The What-and-Where filter forms part of a neural network architecture for spatial mapping, object recognition, and image understanding. The Where fllter responds to an image figure that has been separated from its background. It generates a spatial map whose cell activations simultaneously represent the position, orientation, ancl size of all tbe figures in a scene (where they are). This spatial map may he used to direct spatially localized attention to these image features. A multiscale array of oriented detectors, followed by competitve and interpolative interactions between position, orientation, and size scales, is used to define the Where filter. This analysis discloses several issues that need to be dealt with by a spatial mapping system that is based upon oriented filters, such as the role of cliff filters with and without normalization, the double peak problem of maximum orientation across size scale, and the different self-similar interpolation properties across orientation than across size scale. Several computationally efficient Where filters are proposed. The Where filter rnay be used for parallel transformation of multiple image figures into invariant representations that are insensitive to the figures' original position, orientation, and size. These invariant figural representations form part of a system devoted to attentive object learning and recognition (what it is). Unlike some alternative models where serial search for a target occurs, a What and Where representation can he used to rapidly search in parallel for a desired target in a scene. Such a representation can also be used to learn multidimensional representations of objects and their spatial relationships for purposes of image understanding. The What-and-Where filter is inspired by neurobiological data showing that a Where processing stream in the cerebral cortex is used for attentive spatial localization and orientation, whereas a What processing stream is used for attentive object learning and recognition.

A mechanistic study of anodic formation of porous InP

When porous InP is anodically formed in KOH electrolytes, a thin layer ~40 nm in thickness, close to the surface, appears to be unmodified. We have investigated the earlier stages of the anodic formation of porous InP in 5 mol dm-3 KOH. TEM clearly shows individual porous domains which appear triangular in cross-section and square in plan view. The crosssections also show that the domains are separated from the surface by a ~40 nm thick, dense InP layer. It is concluded that the porous domains have a square-based pyramidal shape and that each one develops from an individual surface pit which forms a channel through this near-surface layer. We suggest that the pyramidal structure arises as a result of preferential pore propagation along the <100> directions. AFM measurements show that the density of surface pits increases with time. Each of these pits acts as a source for a pyramidal porous domain, and these domains eventually form a continuous porous layer. This implies that the development of porous domains beneath the surface is also progressive in nature. Evidence for this was seen in plan view TEM images. Merging of domains continues to occur at potentials more anodic than the peak potential, where the current is observed to decrease. When the domains grow, the current density increases correspondingly. Eventually, domains meet, the interface between the porous and bulk InP becomes relatively flat and its total effective surface area decreases resulting in a decrease in the current density. Quantitative models of this process are being developed.

Effect of reflow profile and thermal cycle ageing on the intermetallic formation and growth in lead-free soldering

The formation and growth of intermetallic compound layer thickness is one of the important issues in search for reliable electronic and electrical connections. Intermetallic compounds (IMCs) are an essential part of solder joints. At low levels, they have a strengthening effect on the joint; but at higher levels, they tend to make solder joints more brittle. If the solder joint is subjected to long-standing exposure of high temperature, this could result in continuous growth of intermetallic compound layer. The brittle intermetallic compound layer formed in this way is very much prone to fracture and cold therefore lead to mechanical and electrical failure of the joint. Therefore, the primary aim of this study is to investigate the growth of intermetallic compound layer thickness subjected to five different reflow profiles. The study also looks at the effect of three different temperature cycles (with maximum cycle temperature of 25 0C, 40 0C and 60 0C) on intermetallic compound formation and their growth behaviour.. Two different Sn-Ag-Cu solder pastes (namely paste P1 and paste P2) which were different in flux medium, were used for the study. The result showed that the growth of intermetallic compound layer thickness was a function of ageing temperature. It was found that the rate of growth of intermetallic compound layer thickness of paste P1 was higher than paste P2 at the same temperature condition. This behaviour could be related to the differences in flux mediums of solder paste samples used.

Under-water scene investigation using ground penetrating radar (GPR) in the search for a sunken jet ski, Northern Ireland

A search for a submerged jet ski and the lost limb of its driver involved in a collision with a speedboat was made in a shallow lake in Northern Ireland. The location of both was crucial to establishing events at the time of the accident. Local intelligence suggested both objects were likely to be partially-buried by lacustrine silt. To avoid sediment churning, this required non-invasive, completely non-destructive assessment and mapping of the scene. A MALA RAMAC ground-penetrating radar system (GPR) mounted on floats for surveying from walkways and jetties or placed in a small rubber dinghy for offshore profiling was used. A grid was established and each line surveyed with 100, 200 and 400MHz antennae. In waters over 6m deep GPR data showed the form of the lake floor but excessive ringing occurred in the data. In waters less than 6m deep ringing diminished on both 100 and 200MHz data, the latter displaying the best trade-off between depth penetration and horizontal object resolution. 400MHz data failed to be of use in waters over 2m deep and at these depths showed only limited improvement of image quality compared to 200MHz data. Surface objects such as a wooden walkway caused interference on 200 and 400MHz data when antennae were oriented both normal and parallel to survey direction; this may be a function of the low attenuation of radar waves in freshwater, allowing excellent lateral and vertical radar wave penetration. On 200MHz data the damaged jet-ski was clearly imaged in a location that contradicted the speedboat driver's account of the accident.

Image segmentation based on multi-scan constraint satisfaction neural network

A novel image segmentation method based on a constraint satisfaction neural network (CSNN) is presented. The new method uses CSNN-based relaxation but with a modified scanning scheme of the image. The pixels are visited with more distant intervals and wider neighborhoods in the first level of the algorithm. The intervals between pixels and their neighborhoods are reduced in the following stages of the algorithm. This method contributes to the formation of more regular segments rapidly and consistently. A cluster validity index to determine the number of segments is also added to complete the proposed method into a fully automatic unsupervised segmentation scheme. The results are compared quantitatively by means of a novel segmentation evaluation criterion. The results are promising.