Early developmental stages of two Secutor species (Family: Leiognathidae) collected from the Bak-khali river estuary of the Bay of Bengal, Bangladesh

Early developmental stages of two Secutor species, Secutor insidiator (Bloch) (11.9-36.0 mm standard length, SL) and Secutor ruconius (Hamilton-Buchanan) (14.0-33.0 mm SL) collected by ichthyoplankton net from the Bak-khali river estuary of the Bay of Bengal, Cox's Bazar, Bangladesh are described and illustrated. All of the fins with supporting spines and rays were present in the smallest collected sizes of both species. With growth of the specimens, significant changes in melanophore patterns were found. S. insidiator is similar to S. ruconius in having upward protracting mouth parts and body colouration, but can be distinguished easily by its more elongate body shape (body depth 38-47% of SL compared with 46-52% of SL in Secutor ruconius). Both the species occurred round the year from August 1998 to July 1999. The surface water temperature and salinity during the study period varied from 22.0-32) C and 10-37 ppt respectively.

Learning shape priors for single view reconstruction

In this paper, we aim to reconstruct free-from 3D models from a single view by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: 1) a probabilistic framework for prior-based reconstruction we propose, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects, and 2) an attempt at automatic reconstruction of more complex 3D shapes, like human bodies, from 2D silhouettes only. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach. ©2009 IEEE.

Species identification of family Lutjanidae and separation of populations of John’s snapper (Lutjanus johnii) in Persian Gulf and Oman Sea based on otolith shape analysis

The anatomical and morphometric (shape indices, contour descriptors and otolith weight) characterizations of sagittal otoliths were investigated in 13 species of Lutjanus spp. inhabiting the Persian Gulf. This is the first study that compares the efficiency of three different image analysis techniques for discriminating species based on the shape of the outer otolith contour, including elliptical Fourier descriptors (EFD), fast Fourier transform (FFT) and wavelet transform (WT). Sagittal otoliths of snappers are morphologically similar with some small specific variations. The use of otolith contour based on wavelets (WT) provided the best results in comparison with the two other methods based on Fourier descriptors, but only the combination of the all three methods (EFD, FFT and WT) was useful to obtain a robust classification of species. The species prediction improved when otolith weight was included. In relation to the shape indices, only the aspect ratio provided a clear grouping of species. Also, another study was carried on to test the possibility of application of shape analysis and comparing otolith contour of otoliths of Lutjanus johnii from Persian Gulf and Oman Sea to identify potential stocks. The results showed the otoliths have differences in contour shape and can be contribute to two different stocks.

Automatic object segmentation from calibrated images

This paper addresses the problem of automatically obtaining the object/background segmentation of a rigid 3D object observed in a set of images that have been calibrated for camera pose and intrinsics. Such segmentations can be used to obtain a shape representation of a potentially texture-less object by computing a visual hull. We propose an automatic approach where the object to be segmented is identified by the pose of the cameras instead of user input such as 2D bounding rectangles or brush-strokes. The key behind our method is a pairwise MRF framework that combines (a) foreground/background appearance models, (b) epipolar constraints and (c) weak stereo correspondence into a single segmentation cost function that can be efficiently solved by Graph-cuts. The segmentation thus obtained is further improved using silhouette coherency and then used to update the foreground/background appearance models which are fed into the next Graph-cut computation. These two steps are iterated until segmentation convergences. Our method can automatically provide a 3D surface representation even in texture-less scenes where MVS methods might fail. Furthermore, it confers improved performance in images where the object is not readily separable from the background in colour space, an area that previous segmentation approaches have found challenging. © 2011 IEEE.

The influence of particle shape on the stress-strain and creep response of a fine silica sand

The influence of particle shape on the stress-strain response of fine silica sand is investigated experimentally. Two sands from the same source and with the same particle size distribution were examined using Fourier descriptor analysis for particle shape. Their grains were, on average, found to have similar angularity but different elongation. During triaxial stress path testing, the stress-strain behavior of the sands for both loading and creep stages were found to be influenced by particle elongation. In particular, the behavior of the sand with less elongated grains was more like that of rounded glass beads during creep. The results highlight the role of particle shape in stress transmission in granular packings and suggest that shape should be taken more rigorously into consideration in characterizing geomaterials. © 2005 Taylor & Francis Group.

Silhouette-based object phenotype recognition using 3D shape priors

This paper tackles the novel challenging problem of 3D object phenotype recognition from a single 2D silhouette. To bridge the large pose (articulation or deformation) and camera viewpoint changes between the gallery images and query image, we propose a novel probabilistic inference algorithm based on 3D shape priors. Our approach combines both generative and discriminative learning. We use latent probabilistic generative models to capture 3D shape and pose variations from a set of 3D mesh models. Based on these 3D shape priors, we generate a large number of projections for different phenotype classes, poses, and camera viewpoints, and implement Random Forests to efficiently solve the shape and pose inference problems. By model selection in terms of the silhouette coherency between the query and the projections of 3D shapes synthesized using the galleries, we achieve the phenotype recognition result as well as a fast approximate 3D reconstruction of the query. To verify the efficacy of the proposed approach, we present new datasets which contain over 500 images of various human and shark phenotypes and motions. The experimental results clearly show the benefits of using the 3D priors in the proposed method over previous 2D-based methods. © 2011 IEEE.

The potential for thermal energy recovery from wastewater treatment works in Southern England

There is potential to extract energy from wastewater in a number of ways, including: kinetic energy using micro-hydro systems, chemical energy through the incineration of sludge, biomass energy from the biogas produced after anaerobic sludge digestion, and thermal energy as heat. This paper considers the last option and asks how much heat could be recovered under UK climatic conditions and can this heat be used effectively by wastewater treatment plants to reduce their carbon footprint? Four wastewater treatment sites in southern England are investigated and the available heat that can be recovered at those sites is quantified. Issues relating to the environmental, economic and practical constraints on how energy can be realistically recovered and utilised are discussed .The results show there is a definite possibility for thermal energy recovery with potential savings at some sites of up to 35,000 tonnes of total long-cycle carbon equivalent (fossil fuel) emissions per year being achievable. The paper also shows that the financial feasibility of three options for using the heat (either for district heating, sludge drying or thermophilic heating in sludge digestion processes) is highly dependant upon the current shadow price of carbon. Without the inclusion of the cost of carbon, the financial feasibility is significantly limited. An environmental constraint for the allowable discharge temperature of effluent after heat-extraction was found to be the major limitation to the amount of energy available for recovery. The paper establishes the true potential of thermal energy recovery from wastewater in English conditions and the economic feasibility of reducing the carbon footprint of wastewater treatment operations using this approach.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Automated Detection of Concrete Columns from Visual Data

After earthquakes, licensed inspectors use the established codes to assess the impact of damage on structural elements. It always takes them days to weeks. However, emergency responders (e.g. firefighters) must act within hours of a disaster event to enter damaged structures to save lives, and therefore cannot wait till an official assessment completes. This is a risk that firefighters have to take. Although Search and Rescue Organizations offer training seminars to familiarize firefighters with structural damage assessment, its effectiveness is hard to guarantee when firefighters perform life rescue and damage assessment operations together. Also, the training is not available to every firefighter. The authors therefore proposed a novel framework that can provide firefighters with a quick but crude assessment of damaged buildings through evaluating the visible damage on their critical structural elements (i.e. concrete columns in the study). This paper presents the first step of the framework. It aims to automate the detection of concrete columns from visual data. To achieve this, the typical shape of columns (long vertical lines) is recognized using edge detection and the Hough transform. The bounding rectangle for each pair of long vertical lines is then formed. When the resulting rectangle resembles a column and the material contained in the region of two long vertical lines is recognized as concrete, the region is marked as a concrete column surface. Real video/image data are used to test the method. The preliminary results indicate that concrete columns can be detected when they are not distant and have at least one surface visible.

Scale-invariant vote-based 3D recognition and registration from point clouds

This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2013 Springer-Verlag Berlin Heidelberg.

Growth and shape control of disks by bending and extension

Differential growth of thin elastic bodies furnishes a surprisingly simple explanation of the complex and intriguing shapes of many biological systems, such as plant leaves and organs. Similarly, inelastic strains induced by thermal effects or active materials in layered plates are extensively used to control the curvature of thin engineering structures. Such behaviour inspires us to distinguish and to compare two possible modes of differential growth not normally compared to each other, in order to reveal the full range of out-of-plane shapes of an initially flat disk. The first growth mode, frequently employed by engineers, is characterised by direct bending strains through the thickness, and the second mode, mainly apparent in biological systems, is driven by extensional strains of the middle surface. When each mode is considered separately, it is shown that buckling is common to both modes, leading to bistable shapes: growth from bending strains results in a double-curvature limit at buckling, followed by almost developable deformation in which the Gaussian curvature at buckling is conserved; during extensional growth, out-of-plane distortions occur only when the buckling condition is reached, and the Gaussian curvature continues to increase. When both growth modes are present, it is shown that, generally, larger displacements are obtained under in-plane growth when the disk is relatively thick and growth strains are small, and vice versa. It is also shown that shapes can be mono-, bi-, tri- or neutrally stable, depending on the growth strain levels and the material properties: furthermore, it is shown that certain combinations of growth modes result in a free, or natural, response in which the doubly curved shape of disk exactly matches the imposed strains. Such diverse behaviour, in general, may help to realise more effective actuation schemes for engineering structures. © 2012 Elsevier Ltd. All rights reserved.

Shape vs. volume: Invariant shape descriptors for 3D region of interest characterization in MRI

We propose a new approach for quantifying regions of interest (ROIs) in medical image data. Rotationally invariant shape descriptors (ISDs) were applied to 3D brain regions extracted from MRI scans of 5 Parkinson's patients and 10 control subjects. We concentrated on the thalamus and the caudate nucleus since prior studies have suggested they are affected in Parkinson's disease (PD). In the caudate, both the ISD and volumetric analyses found significant differences between control and PD subjects. The ISD analysis however revealed additional differences between the left and right caudate nuclei in both control and PD subjects. In the thalamus, the volumetric analysis showed significant differences between PD and control subjects, while ISD analysis found significant differences between the left and right thalami in control subjects but not in PD patients, implying disease-induced shape changes. These results suggest that employing ISDs for ROI characterization both complements and extends traditional volumetric analyses. © 2006 IEEE.

A high-resolution atlas and statistical model of the human heart from multislice CT

Atlases and statistical models play important roles in the personalization and simulation of cardiac physiology. For the study of the heart, however, the construction of comprehensive atlases and spatio-temporal models is faced with a number of challenges, in particular the need to handle large and highly variable image datasets, the multi-region nature of the heart, and the presence of complex as well as small cardiovascular structures. In this paper, we present a detailed atlas and spatio-temporal statistical model of the human heart based on a large population of 3D+time multi-slice computed tomography sequences, and the framework for its construction. It uses spatial normalization based on nonrigid image registration to synthesize a population mean image and establish the spatial relationships between the mean and the subjects in the population. Temporal image registration is then applied to resolve each subject-specific cardiac motion and the resulting transformations are used to warp a surface mesh representation of the atlas to fit the images of the remaining cardiac phases in each subject. Subsequently, we demonstrate the construction of a spatio-temporal statistical model of shape such that the inter-subject and dynamic sources of variation are suitably separated. The framework is applied to a 3D+time data set of 138 subjects. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. The obtained level of detail and the extendability of the atlas present an advantage over most cardiac models published previously. © 1982-2012 IEEE.

Cortical thickness estimation of the proximal femur from multi-view Dual-Energy X-ray Absorptiometry (DXA)

Hip fracture is the leading cause of acute orthopaedic hospital admission amongst the elderly, with around a third of patients not surviving one year post-fracture. Although various preventative therapies are available, patient selection is difficult. The current state-of-the-art risk assessment tool (FRAX) ignores focal structural defects, such as cortical bone thinning, a critical component in characterizing hip fragility. Cortical thickness can be measured using CT, but this is expensive and involves a significant radiation dose. Instead, Dual-Energy X-ray Absorptiometry (DXA) is currently the preferred imaging modality for assessing hip fracture risk and is used routinely in clinical practice. Our ambition is to develop a tool to measure cortical thickness using multi-view DXA instead of CT. In this initial study, we work with digitally reconstructed radiographs (DRRs) derived from CT data as a surrogate for DXA scans: this enables us to compare directly the thickness estimates with the gold standard CT results. Our approach involves a model-based femoral shape reconstruction followed by a data-driven algorithm to extract numerous cortical thickness point estimates. In a series of experiments on the shaft and trochanteric regions of 48 proximal femurs, we validated our algorithm and established its performance limits using 20 views in the range 0°-171°: estimation errors were 0:19 ± 0:53mm (mean +/- one standard deviation). In a more clinically viable protocol using four views in the range 0°-51°, where no other bony structures obstruct the projection of the femur, measurement errors were -0:07 ± 0:79 mm. © 2013 SPIE.

Multi-objective shape optimisation for horizontal-axis wind turbine blades

We are developing a wind turbine blade optimisation package CoBOLDT (COmputa- tional Blade Optimisation and Load De ation Tool) for the optimisation of large horizontal- axis wind turbines. The core consists of the Multi-Objective Tabu Search (MOTS), which controls a spline parameterisation module, a fast geometry generation and a stationary Blade Element Momentum (BEM) code to optimise an initial wind turbine blade design. The objective functions we investigate are the Annual Energy Production (AEP) and the fl apwise blade root bending moment (MY0) for a stationary wind speed of 50 m/s. For this task we use nine parameters which define the blade chord, the blade twist (4 parameters each) and the blade radius. Throughout the optimisation a number of binary constraints are defined to limit the noise emission, to allow for transportation on land and to control the aerodynamic conditions during all phases of turbine operation. The test case shows that MOTS is capable to find enhanced designs very fast and eficiently and will provide a rich and well explored Pareto front for the designer to chose from. The optimised blade de- sign could improve the AEP of the initial blade by 5% with the same flapwise root bending moment or reduce MY0 by 7.5% with the original energy yield. Due to the fast runtime of order 10 seconds per design, a huge number of optimisation iterations is possible without the need for a large computing cluster. This also allows for increased design flexibility through the introduction of more parameters per blade function or parameterisation of the airfoils in future. © 2012 by Nordex Energy GmbH.