Contour-based Model Research Articles

Train rolling stock video segmentation and classification for bogie part inspection automation: a deep learning approach

Train rolling stock examination (TRSE) is a physical procedure for inspecting the bogie parts during transit at a little over 30 kmph. Currently, this process is manually performed across many railway networks across the world. This work proposes to automate the process of TRSE using artificial intelligence techniques. The previous works have proposed active contour-based models for the segmentation of bogie parts. Though accurate, the models require manual intervention and are found to be iterative making them unsuitable for real-time operations. In this work, we propose a segmentation model followed by a deep learning classifier that can accurately increase the deployability of such systems in real time. We apply the UNet model for the segmentation of bogie parts which are further classified using an attention-based convolutional neural network (CNN) classifier. In this work, we propose a shape deformable attention model to identify shape variations occurring in the video sequence due to viewpoint changes during the train movement. The TRSNet is trained and tested on the high-speed train bogie videos captured across four different trains. The results of the experimentation have been shown to improve the recognition accuracy of the proposed system by 6% over the state-of-the-art classifiers previously developed for TRSE.

Design load estimation with IFORM-based models considering long-term extreme response for mooring systems

ABSTRACT In this paper, we present an extension of the alternative environmental contour approach based on inverse first-order reliability theory in a three-dimensional model that accounts for short-term extreme response uncertainties. Subsequently, the long-term extreme tension under wave excitation loads is investigated to evaluate return levels for the design of mooring systems. Tension extreme data are derived from time-domain simulations of a floating coupled system using the peak-over-threshold method to determine short-term load distribution. A linear interpolation scheme is utilised to establish the parametric model using distribution parameters and wave data. Long-term extreme loads are estimated using a simplified discrete approach combined with Monte Carlo simulations, which helps avoid the tedious task of direct integration. The applicability of these load assessment models is demonstrated and discussed using an example of a semi-submersible platform situated at a 500-m water depth, and the results are compared with one- and two-dimensional environmental contour-based models.

Automated Individualization of Size-Varying and Touching Neurons in Macaque Cerebral Microscopic Images.

In biomedical research, cell analysis is important to assess physiological and pathophysiological information. Virtual microscopy offers the unique possibility to study the compositions of tissues at a cellular scale. However, images acquired at such high spatial resolution are massive, contain complex information, and are therefore difficult to analyze automatically. In this article, we address the problem of individualization of size-varying and touching neurons in optical microscopy two-dimensional (2-D) images. Our approach is based on a series of processing steps that incorporate increasingly more information. (1) After a step of segmentation of neuron class using a Random Forest classifier, a novel min-max filter is used to enhance neurons’ centroids and boundaries, enabling the use of region growing process based on a contour-based model to drive it to neuron boundary and achieve individualization of touching neurons. (2) Taking into account size-varying neurons, an adaptive multiscale procedure aiming at individualizing touching neurons is proposed. This protocol was evaluated in 17 major anatomical regions from three NeuN-stained macaque brain sections presenting diverse and comprehensive neuron densities. Qualitative and quantitative analyses demonstrate that the proposed method provides satisfactory results in most regions (e.g., caudate, cortex, subiculum, and putamen) and outperforms a baseline Watershed algorithm. Neuron counts obtained with our method show high correlation with an adapted stereology technique performed by two experts (respectively, 0.983 and 0.975 for the two experts). Neuron diameters obtained with our method ranged between 2 and 28.6 μm, matching values reported in the literature. Further works will aim to evaluate the impact of staining and interindividual variability on our protocol.

Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model

Optical Coherence Tomography (OCT) constitutes an imaging technique that is increasing its popularity in the ophthalmology field, since it offers a more complete set of information about the main retinal structures. Hence, it offers detailed information about the eye fundus morphology, allowing the identification of many intraretinal pathological signs. For that reason, over the recent years, Computer-Aided Diagnosis (CAD) systems have spread to work with this image modality and analyze its information. A crucial step for the analysis of the retinal tissues implies the identification and delimitation of the different retinal layers. In this context, we present in this work a fully automatic method for the identification of the main retinal layers that delimits the retinal region. Thus, an active contour-based model was completely adapted and optimized to segment these main retinal boundaries. This fully automatic method uses the information of the horizontal placement of these retinal layers and their relative location over the analyzed images to restrict the search space, considering the presence of shadows that are normally generated by pathological or non-pathological artifacts. The validation process was done using the groundtruth of an expert ophthalmologist analyzing healthy as well as unhealthy patients with different degrees of diabetic retinopathy (without macular edema, with macular edema and with lesions in the photoreceptor layers). Quantitative results are in line with the state of the art of this domain, providing accurate segmentations of the retinal layers even when significative pathological alterations are present in the eye fundus. Therefore, the proposed method is robust enough to be used in complex environments, making it feasible for the ophthalmologists in their routine clinical practice.

Human Body Segmentation Using Level Set-Based Active Contours With Application on Activity Recognition

Activity recognition (AR) methods require the ability to identify activities of crucial parts in a human body. Body segmentation techniques employed in video-based AR methods outlines significance of areas (parts) in a human body image that are essential for the follow-up methods, such as feature extraction. Existing body segmentation techniques generally make use of body modeling with or without its background and essentially entail large training datasets. These techniques cannot cope with the changes that may occur in images over time. For example, illumination and clothing changes are some of the key concerns in practical AR systems. This paper presents a new approach to employ active contour-based model that exhibits robustness to illumination and clothing changes. Existing techniques based on active contours effectively address this problem in still images and hence lack the ability to cope with illumination and changes. We present an optical flow that 1) ensures the smooth working of the model over the data that is used for estimation into contiguous frames, and 2) ensures to place the initial contour within the current frame. Our proposed approach does not depend on prior knowledge about the data and and does not require the training data, hence it is an unsupervised approach. We use the existing effective method curvelet transform to extract prominent features from activity frames. Curvelet transform keeps the line and edge information in an image intact. We employ the linear discriminant analysis (LDA) for dimension space reduction and the hidden Markov model (HMM) for activity recognition. We show that AR using our proposed method for segmentation has the accuracy comparable to those that make use of manual segmentation methods and prove its feasibility in practical AR systems.

Virtual downsizing for decision support in mitral valve repair.

Various options are available for the treatment of mitral valve insufficiency, including reconstructive approaches such as annulus correction through ring implants. The correct choice of general therapy and implant is relevant for an optimal outcome. Additional to guidelines, decision support systems (DSS) can provide decision aid by means of virtual intervention planning and predictive simulations. Our approach on virtual downsizing is one of the virtual intervention tools that are part of the DSS workflow. It allows for emulating a ring implantation based on patient-specific lumen geometry and vendor-specific implants. Our approach is fully automatic and relies on a lumen mask and an annulus contour as inputs. Both are acquired from previous DSS workflow steps. A virtual surface- and contour-based model of a vendor-specific ring design (26-40mm) is generated. For each case, the ring geometry is positioned with respect to the original, patient-specific annulus and additional anatomical landmarks. The lumen mesh is parameterized to allow for a vertex-based deformation with respect to the user-defined annulus. Derived from post-interventional observations, specific deformation schemes are applied to atrium and ventricle and the lumen mesh is altered with respect to the ring location. For quantitative evaluation, the surface distance between the deformed lumen mesh and segmented post-operative echo lumen close to the annulus was computed for 11 datasets. The results indicate a good agreement. An arbitrary subset of six datasets was used for a qualitative evaluation of the complete lumen. Two domain experts compared the deformed lumen mesh with post-interventional echo images. All deformations were deemed plausible. Our approach on virtual downsizing allows for an automatic creation of plausible lumen deformations. As it takes only a few seconds to generate results, it can be added to a virtual intervention toolset without unnecessarily increasing the pipeline complexity.

Enabling scanning electron microscope contour-based optical proximity correction models

A scanning electron microscope (SEM) is the metrology tool used to accurately characterize very fine structures on wafers, usually by extracting one critical dimension (CD) per SEM image. This approach for optical proximity correction (OPC) modeling requires many measurements resulting in a lengthy cycle time for data collection, review, and cleaning, and faces reliability issues when dealing with critical two-dimensional (2-D) structures. An alternative to CD-based metrology is to use SEM image contours for OPC modeling. To calibrate OPC models with contours, reliable contours matched to traditional CD-SEM measurements are required along with a method to choose structure and site selections (number, type, and image space coverage) specific to a contour-based OPC model calibration. The potential of SEM contour model-based calibration is illustrated by comparing two contour-based models to reference models, one empirical model and a second rigorous simulation-based model. The contour-based models are as good as or better than a CD-based model with a significant advantage in the prediction of complex 2-D configurations with a reduced metrology work load.

Automatic Contour-Based Road Network Design for Optimized Wind Farm Micrositing

Constructing the access roads between wind turbines requires a significant cost when a wind farm is built in hills or mountains. An optimized design of road network can substantially reduce construction costs and increase investment returns. In this paper, we consider a challenging problem of the road network design for a wind farm with complex topography. An automatic contour-based model is developed for road network design, and is incorporated into the optimization of wind farm micrositing to maximize investment returns of the wind farm. The directions of access roads are first deduced from a contour tree, the route projections are then designed by a gradient-bounded algorithm, and the most cost-effective road network is finally obtained by the minimum spanning tree algorithm. The topographic speedup, wind resource grid and Park wake model are incorporated to accurately evaluate the power production of the wind farm. Both the profit of wind energy sales and the cost of access roads are combined into a cost function called net present value, which is optimized using genetic algorithms. Simulation results illustrate that the length of access roads is decreased significantly while the locations of wind turbines are optimized. The proposed method simultaneously optimizes the turbine layout and road network of the wind farm, therefore achieving a more practical and profitable wind farm micrositing.

Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual information

We present a novel solution to automatic semantic modeling of indoor scenes from a sparse set of low-quality RGB-D images. Such data presents challenges due to noise, low resolution, occlusion and missing depth information. We exploit the knowledge in a scene database containing 100s of indoor scenes with over 10,000 manually segmented and labeled mesh models of objects. In seconds, we output a visually plausible 3D scene, adapting these models and their parts to fit the input scans. Contextual relationships learned from the database are used to constrain reconstruction, ensuring semantic compatibility between both object models and parts. Small objects and objects with incomplete depth information which are difficult to recover reliably are processed with a two-stage approach. Major objects are recognized first, providing a known scene structure. 2D contour-based model retrieval is then used to recover smaller objects. Evaluations using our own data and two public datasets show that our approach can model typical real-world indoor scenes efficiently and robustly.

컨투어기반 잎맥 패턴의 절차적 모델링

컨투어기반 잎맥 패턴의 절차적 모델링

Detecting shapes in noise: the role of contour-based and region-based representations

We investigated the detection of shapes (closed contours) in noise as a function of their complexity. As in previous work (VSS2012), we quantified complexity in several ways reflecting alternate shape-generating models. In a contour-based model, complexity is defined as the integrated surprisal along the contour, that is, the summed negative log probability of the sequence of turning angles defining it. Alternatively, in a skeleton-based model of shape, complexity is defined as the surprisal of the shape given its generating skeleton, that is, the negative log probability of the shape's boundary conditioned on an estimated skeleton. The two models have the same basic probabilistic conception but differ in assumed generating model. In VSS2012 we applied these measures to natural shapes (animals and leaves), but these shapes necessarily result in an irregular and sparse sampling of the shape space. Here we present a series of more controlled experiments in which we manipulated the structure of the target shape in order to distinguish the potentially separate effects of contour complexity and different components of skeleton-based shape complexity, such as the log prior and log likelihood. Subjects detected closed contours embedded in background noise (2IFC task). We parametrically varied several shape factors, including the number of parts in the shape, and the variability of the contour around its internal skeleton (hence the amplitude of contour noise). The number of parts influences the prior probability of the skeleton, while the variability influences the complexity under the contour model as well as the likelihood under the skeletal model. Both factors show significant influences on shape detectability, providing evidence for both contour-based and region-based representational formats. The results shed light on basic processes of perceptual organization, including object segmentation (extracting whole shapes from cluttered scenes) and shape representation. Meeting abstract presented at VSS 2013

A Brief Review on Models of Animal Tracking in Video

The observation on motion and behaviors of the animal is still important in scientific research fields, and various behaviors of animals are tracked and analyzed for many different purposes. The paper concentrates on the technologies employed for animal tracking in video, and reviews the tracking models related to animal motions and behaviors including shape-based model, contour-based model, articulated model, Bag-Of-Feature based model, Markov Model, etc. and gives a brief summary of these models respectively.

Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection

This paper presents automated segmentation of structures in the Head and Neck (H&N) region, using an active contour-based joint registration and segmentation model. A new atlas selection strategy is also used. Segmentation is performed based on the dense deformation field computed from the registration of selected structures in the atlas image that have distinct boundaries, onto the patient’s image. This approach results in robust segmentation of the structures of interest, even in the presence of tumors, or anatomical differences between the atlas and the patient image. For each patient, an atlas image is selected from the available atlas-database, based on the similarity metric value, computed after performing an affine registration between each image in the atlas-database and the patient’s image. Unlike many of the previous approaches in the literature, the similarity metric is not computed over the entire image region; rather, it is computed only in the regions of soft tissue structures to be segmented. Qualitative and quantitative evaluation of the results is presented.

Viscosity solutions for geodesic active contour under geometrical conditions

This paper addresses the issue of introducing geometrical constraints into segmentation processes in image analysis. This question has emerged from the analysis of classical tools: indeed, usual methods such as deformable models, fast marching prove to be fruitless when image data are missing or of poor quality or when the image owns homogeneous-textured regions. To cope with these hindrances, the idea developed hereafter consists of integrating geometrical constraints in the modelling to make the segmentation process easier to handle. We have devised a geodesic active contour-based model, in which we are trying to determine a curve that best approaches the given points (geometrical data) while detecting the object under consideration (see also Apparato et al., Segmentation of medical image sequence under constraints: application to non-invasive assessment of pulmonary arterial hypertension, Int. J. Comput. Math. 5(2004), pp. 527–536 and Gout and Vieira-Testé C. Gout and S. Vieira-Testé, An algorithm for segmentation under interpolation conditions using deformable models, Int. J. Comput. Math. 80(1) (2003), pp. 47–54 where other approaches are given for this kind of problems). In this paper the main results concern the study of the existence and uniqueness of the viscosity solution of this problem (following three different approaches). We also give numerical examples on real data sets.

Reconstruction of deforming aortas in two-photon autofluorescence image sequences.

Information loss may occur frequently in the imaging of living tissues by using two-photon fluorescence microscopy due to the intensive deformation of the tissue. A landmark-based optical flow interpolation scheme is proposed for image reconstruction of living aorta walls in two-photon autofluorescence image sequences. Landmarks are extracted and evaluated by an active contour-based aorta model, and are aligned and reconstructed by use of a hierarchical algorithm. The accuracy of the calculation of optical flow is improved by applying landmark-based image warping. Experimental results show that the proposed scheme outperforms commonly used optical flow interpolation techniques for the reconstruction of intensively deforming tissues.

Triangulated irregular network optimization from contour data using bridge and tunnel edge removal

This paper proposes an optimization methodology associated with the conversion process from a contour-based elevation model to a TIN model and two algorithms that are combined to reach an optimized result. The first algorithm uses a constrained Delaunay triangulation, in which each contour is used as a non-crossing break line. The second algorithm is designed to eliminate edges known as bridge and tunnel edges by inserting a point using parabolic interpolation at the middle of such features.