Targetless image-based method for measuring displacements and strains on concrete surfaces with a consumer camera

Measurement of concrete strain through non-invasive methods is of great importance in civil engineering and structural analysis. Traditional methods use laser speckle and high quality cameras that may result too expensive for many applications. Here we present a method for measuring concrete deformations with a standard reflex camera and image processing for tracking objects in the concretes surface. Two different approaches are presented here. In the first one, on-purpose objects are drawn on the surface, while on the second one we track small defects on the surface due to air bubbles in the hardening process. The method has been tested on a concrete sample under several loading/unloading cycles. A stop-motion sequence of the process has been captured and analyzed. Results have been successfully compared with the values given by a strain gauge. Accuracy of our methods in tracking objects is below 8 μm, in the order of more expensive commercial devices.

(Table 1) Ice floe size and other characteristics during aerial observations in Prydz Bay, Antarctica

On the basis of aerial photographs of sea ice floes in the marginal ice zone (MIZ) of Prydz Bay acquired from December 2004 to February 2005 during the 21st Chinese National Antarctic Research Expedition, image processing techniques are employed to extract some geometric parameters of floes from two merged transects covering the whole MIZ. Variations of these parameters with the distance into the MIZ are then obtained. Different parameters of floe size, namely area, perimeter, and mean caliper diameter (MCD), follow three similar stages of increasing, flat and increasing again, with distance from the open ocean. Floe shape parameters (roundness and the ratio of perimeter to MCD), however, have less significant variations than that of floe size. Then, to modify the deviation of the cumulative floe size distribution from the ideal power law, an upper truncated power-law function and a Weibull function are used, and four calculated parameters of the above functions are found to be important descriptors of the evolution of floe size distribution in the MIZ. Among them, Lr of the upper truncated power-law function indicates the upper limit of floe size and roughly equals the maximum floe size in each square sample area. L0 in the Weibull distribution shows an increasing proportion of larger floes in squares farther from the open ocean and roughly equals the mean floe size. D in the upper truncated power-law function is closely associated with the degree of confinement during ice breakup. Its decrease with the distance into MIZ indicates the weakening of confinement conditions on floes owing to wave attenuation. The gamma of the Weibull distribution characterizes the degree of homogeneity in a data set. It also decreases with distance into MIZ, implying that floe size distributes increase in range. Finally, a statistical test on floe size is performed to divide the whole MIZ into three distinct zones made up of floes of quite different characteristics. This zonal structure of floe size also agrees well with the trends of floe shape and floe size distribution, and is believed to be a straightforward result of wave-ice interaction in the MIZ.

Shell-based chronologies for the Irish Sea

We demonstrate here that the growth increment variability in the shell of the long-lived bivalve mollusc Arctica islandica can be interpreted as an indicator of marine environmental change in the climatically important North Atlantic shelf seas. Multi-centennial (up to 489-year) chronologies were constructed using five detrending techniques and their characteristics compared. The strength of the common environmental signal expressed in the chronologies was found to be fully comparable with equivalent statistics for tree-ring chronologies. The negative exponential function using truncated increment-width series from which the first thirty years have been removed was chosen as the optimal detrending technique. Chronology indices were compared with the Central England Temperature record and with seawater temperature records from stations close to the study site in the Irish Sea. Statistically significant correlations were found between the chronology indices and (a) mean air temperature for the 14-month period beginning in the January preceding the year of growth, (b) mean seawater temperatures for February-October in the year preceding the year of growth (c) late summer and autumn air temperatures and sea surface temperatures for the year of growth and (d) the timing of the autumn decline in SST. Changes through time in the correlations with air and seawater temperatures and changes towards a deeper water origin for the shells in the chronology were interpreted as an indication that shell growth may respond to stratification dynamics.

WALSHSTORE : the application of burst processing to fail-soft storage systems using Walsh transforms /

"UILU-ENG 77 1753."

A prototype binary image scaling facility /

"UILU-ENG 84 1703"--Cover.

Multispectral image maps from Landsat thematic mapper data /

"September 1991."

Multigradient field-active contour model for multilayer boundary detection of ultrasound rectal wall image

Extraction and reconstruction of rectal wall structures from an ultrasound image is helpful for surgeons in rectal clinical diagnosis and 3-D reconstruction of rectal structures from ultrasound images. The primary task is to extract the boundary of the muscular layers on the rectal wall. However, due to the low SNR from ultrasound imaging and the thin muscular layer structure of the rectum, this boundary detection task remains a challenge. An active contour model is an effective high-level model, which has been used successfully to aid the tasks of object representation and recognition in many image-processing applications. We present a novel multigradient field active contour algorithm with an extended ability for multiple-object detection, which overcomes some limitations of ordinary active contour models—"snakes." The core part in the algorithm is the proposal of multigradient vector fields, which are used to replace image forces in kinetic function for alternative constraints on the deformation of active contour, thereby partially solving the initialization limitation of active contour for rectal wall boundary detection. An adaptive expanding force is also added to the model to help the active contour go through the homogenous region in the image. The efficacy of the model is explained and tested on the boundary detection of a ring-shaped image, a synthetic image, and an ultrasound image. The experimental results show that the proposed multigradient field-active contour is feasible for multilayer boundary detection of rectal wall

A fuzzy approach to texture segmentation

The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.

Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.

Dynamic breast MRI: Image registration and its impact on enhancement curve estimation

A novel algorithm for performing registration of dynamic contrast-enhanced (DCE) MRI data of the breast is presented. It is based on an algorithm known as iterated dynamic programming originally devised to solve the stereo matching problem. Using artificially distorted DCE-MRI breast images it is shown that the proposed algorithm is able to correct for movement and distortions over a larger range than is likely to occur during routine clinical examination. In addition, using a clinical DCE-MRI data set with an expertly labeled suspicious region, it is shown that the proposed algorithm significantly reduces the variability of the enhancement curves at the pixel level yielding more pronounced uptake and washout phases.

Mixtures of probabilistic principal component analysers

Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Previous attempts to formulate mixture models for PCA have therefore to some extent been ad hoc. In this paper, PCA is formulated within a maximum-likelihood framework, based on a specific form of Gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context of clustering, density modelling and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition.

Parallel implementation of image segmentation algorithms on a network of transputers

Image segmentation is one of the most computationally intensive operations in image processing and computer vision. This is because a large volume of data is involved and many different features have to be extracted from the image data. This thesis is concerned with the investigation of practical issues related to the implementation of several classes of image segmentation algorithms on parallel architectures. The Transputer is used as the basic building block of hardware architectures and Occam is used as the programming language. The segmentation methods chosen for implementation are convolution, for edge-based segmentation; the Split and Merge algorithm for segmenting non-textured regions; and the Granlund method for segmentation of textured images. Three different convolution methods have been implemented. The direct method of convolution, carried out in the spatial domain, uses the array architecture. The other two methods, based on convolution in the frequency domain, require the use of the two-dimensional Fourier transform. Parallel implementations of two different Fast Fourier Transform algorithms have been developed, incorporating original solutions. For the Row-Column method the array architecture has been adopted, and for the Vector-Radix method, the pyramid architecture. The texture segmentation algorithm, for which a system-level design is given, demonstrates a further application of the Vector-Radix Fourier transform. A novel concurrent version of the quad-tree based Split and Merge algorithm has been implemented on the pyramid architecture. The performance of the developed parallel implementations is analysed. Many of the obtained speed-up and efficiency measures show values close to their respective theoretical maxima. Where appropriate comparisons are drawn between different implementations. The thesis concludes with comments on general issues related to the use of the Transputer system as a development tool for image processing applications; and on the issues related to the engineering of concurrent image processing applications.