Biological object representation for identification

This thesis is concerned with the problem of how to represent a biological object for computerised identification. Images of biological objects have been generally characterised by shapes and colour patterns in the biology domain and the pattern recognition domain. Thus, it is necessary to represent the biological object using descriptors for the shape and the colour pattern. The basic requirements which a description method should satisfy are those such as invariance of scale, location and orientation of an object; direct involvement in the identification stage; easy assessment of results. The major task to deal with in this thesis was to develop a shape-description method and a colour-pattern description method which could accommodate all of the basic requirements and could be generally applied in both domains. In the colour-pattern description stage, an important task was to segment a colour image into meaningful segments. The most efficient method for this task is to apply Cluster Analysis. In the image analysis and pattern recognition domains, the majority of approaches to this method have been constrained by the problem of dealing with inordinate amounts of data, i.e. a large number of pixels of an image. In order to directly apply Cluster Analysis to the colour image segmentation, data structure, the Auxiliary Means is developed in this thesis

A new method for improved standardisation in three-dimensional computed tomography cephalometry

Interest for three-dimensional computed tomography cephalometry has risen over the last two decades. Current methods commonly rely on the examiner to manually point-pick the landmarks and/or orientate the skull. In this study, a new approach is presented, in which landmarks are calculated after selection of the landmark region on a triangular model and in which the skull is automatically orientated in a standardised way. Two examiners each performed five analyses on three skull models. Landmark reproducibility was tested by calculating the standard deviation for each observer and the difference between the mean values of both observers. The variation can be limited to 0.1 mm for most landmarks. However, some landmarks perform less well and require further investigation. With the proposed reference system, a symmetrical orientation of the skulls is obtained. The presented methods contribute to standardisation in cephalometry and could therefore allow improved comparison of patient data

Nanoscale cuticle density variations correlate with pigmentation and color in butterfly wing scales

How pigment distribution correlates with cuticle density within a microscopic butterfly wing scale, and how both impact final reflected color remains unknown. We used ptychographic X-ray computed tomography to quantitatively determine, at nanoscale resolutions, the three-dimensional mass density of scales with pigmentation differences. By comparing cuticle densities with pigmentation and color within a scale, we determine that the lower lamina structure in all scales has the highest density and lowest pigmentation. Low pigment levels also correlate with sheet-like chitin structures as opposed to rod-like structures, and distinct density layers within the lower lamina help explain reflected color. We propose that pigments, in addition to absorbing specific wavelengths, can affect cuticle polymerization, density, and refractive index, thereby impacting reflected wavelengths that produce structural colors

Development of deep learning neural network for ecological and medical images

Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology also shows its effective in shape representation, contour detection and image smoothing. The experimental results in the morphological neural network shows this type of deep learning model is also effective in ecology datasets and medical dataset. Compared with CNN, the MNN could achieve a similar or better result in the following datasets. The chest X-ray images are notoriously difficult to analyze for the radiologists due to their noisy nature. The existing models based on convolutional neural networks contain a giant number of parameters and thus require multi-advanced GPUs to deploy. In this research, the morphological neural networks are developed to classify chest X-ray images, including the Pneumonia Dataset and the COVID-19 Dataset. A novel structure, which can self-learn a morphological dilation or erosion, is proposed for determining the most suitable depth of the adaptive layer. Experimental results on the chest X-ray dataset and the COVID-19 dataset show that the proposed model achieves the highest classification rate as comparing against the existing models. More significant improvement is that the proposed model reduces around 97% computational parameters of the existing models. Automatic identification of pneumonia on medical images has attracted intensive studies recently. The model for detecting pneumonia requires both a precise classification model and a localization model. A joint-task joint learning model with shared parameters is proposed to combine the classification model and segmentation model. To accurately classify and localize pneumonia area. Experimental results using the massive dataset of Radiology Society of North America have confirmed the efficiency of showing a test mean interception over union (IoU) of 89.27% and a mean precision of area detection result of 58.45% in segmentation model. Then, two new models are proposed to improve the performance of the original joint-task learning model. Two new modules are developed to improve both classification and segmentation accuracies in the first model. These modules including an image preprocessing module and an attention module. In the second model, a novel design is used to combine both convolutional layers and morphological layers with an attention mechanism. Experimental results performed on the massive dataset of the Radiology Society of North America have confirmed its superiority over other existing methods. The classification test accuracy is improved from 0.89 to 0.95, and the segmentation model achieves an improved mean precision result from 0.58 to 0.78. Finally, two weakly-supervised learning methods: class-saliency map and grad-cam, are used to highlight corresponding pixels or areas which have significant influence on the classification model, such that the refined segmentation can focus on the correct areas with high confidence

Reverse Engineering of Materials Using Surfacelet-Based Methods for CAD-Material Integration

To integrate material information into CAD systems, geometric features of material microstructure must be recognized and represented, which is the focus of this paper. Linear microstructure features, such as fibers or grain boundaries, can be found computationally from microstructure images using surfacelet based methods, which include the Radon or Radon-like transform followed by a wavelet transform. By finding peaks in the transform results, linear features can be recognized and characterized by length, orientation, and position. The challenge is that often a feature will be imprecisely represented in the transformed parameter space. In this paper, we demonstrate surfacelet-based methods to recognize microstructure features in parts fabricated by additive manufacturing. We will provide an explicit mathematical method to recognize and to quantify linear geometric features from an image.Mechanical Engineerin

3D tomographic analysis of the order-disorder interplay in the Pachyrhynchus congestus mirabilis weevil

The bright colors of Pachyrhynchus weevils originate from complex dielectric nanostructures within their elytral scales. In contrast to previous work exhibiting highly ordered single-network diamond-type photonic crystals, we here show by combining optical microscopy and spectroscopy measurements with 3D FIB tomography that the blue scales of P. congestus mirabilis differ from that of an ordered diamond structure. Through the use of FIB tomography on elytral scales filled with Pt by electron beam-assisted deposition, we reveal that the red scales of this weevil possess a periodic diamond structure, while the network morphology of the blue scales exhibit diamond morphology only on the single scattering unit level with disorder on longer length scales. Full wave simulations performed on the reconstructed volumes indicate that this local order is sufficient to open a partial photonic bandgap even at low dielectric constant contrast between chitin and air in the absence of long-range or translational order. The observation of disordered and ordered photonic crystals within a single organism opens up interesting questions on the cellular origin of coloration and studies on bio-inspired replication of angle-independent colors.Comment: 13 pages, 10 figure

Automatic separation of laminar-turbulent flows on aircraft wings and stabilisers via adaptive attention butterfly network

Many of the laminar-turbulent flow localisation techniques are strongly dependent upon expert control even-though determining the flow distribution is the prerequisite for analysing the efficiency of wing & stabiliser design in aeronautics. Some recent efforts have dealt with the automatic localisation of laminar-turbulent flow but they are still in infancy and not robust enough in noisy environments. This study investigates whether it is possible to separate flow regions with current deep learning techniques. For this aim, a flow segmentation architecture composed of two consecutive encoder-decoder is proposed, which is called Adaptive Attention Butterfly Network. Contrary to the existing automatic flow localisation techniques in the literature which mostly rely on homogeneous and clean data, the competency of our proposed approach in automatic flow segmentation is examined on the mixture of diverse thermographic observation sets exposed to different levels of noise. Finally, in order to improve the robustness of the proposed architecture, a self-supervised learning strategy is adopted by exploiting 23.468 non-labelled laminar-turbulent flow observations