Search results for " Segmentation"
showing 10 items of 462 documents
Segmentation and differential post-rift uplift at the Angola margin as recorded by the transform-rifted Benguela and oblique-to-orthogonal-rifted Kwa…
2010
29 pages; International audience; We analyse tectonic and sedimentary field and subsurface data for the Angola onshore margin together with free-air gravity anomaly data for the offshore margin. This enables us to characterize the mode of synrift tectonism inherited from the Precambrian and its impact on the segmentation of the Angola margin. We illustrate that segmentation by the progressive transition from the Benguela transform-rifted margin segment to the oblique-rifted South Kwanza and orthogonal-rifted North Kwanza margin segments. The spatial variation in the intensity of post-rift uplift is demonstrated by the study of a set of geomorphic markers detected in the post-rift succession…
Artificial intelligence for image-guided prostate brachytherapy procedures
2020
Radiotherapy procedures aim at exposing cancer cells to ionizing radiation. Permanently implanting radioactive sources near to the cancer cells is a typical technique to cure early-stage prostate cancer. It involves image acquisition of the patient, delineating the target volumes and organs at risk on different medical images, treatment planning, image-guided radioactive seed delivery, and post-implant evaluation. Artificial intelligence-based medical image analysis can benefit radiotherapy procedures. It can help to facilitate and improve the efficiency of the procedures by automatically segmenting target organs and extrapolating clinically relevant information. However, manual delineation…
Comparison of Micro X-ray Computer Tomography Image Segmentation Methods: Artificial Neural Networks Versus Least Square Support Vector Machine
2013
Micro X-ray computer tomography (XCT) is a powerful non-destructive method for obtaining information about rock structures and mineralogy. A new methodology to obtain porosity from 2D XCT digital images using artificial neural network and least square support vector machine is demonstrated following these steps: the XCT image was first preprocessed, thereafter clustering algorithms such as K-means, Fuzzy c-means and self-organized maps was used for image segmentation. Then artificial neural network was applied for image classification. For comparison, least square support vector machine approach was used for classification labeling of the scan images. The methodology shows how artificial ne…
Fuzzy C-Means Segmentation on Brain MR Slices Corrupted by RF-Inhomogeneity
2007
Brain MR Images corrupted by RF-Inhomogeneity exhibit brightness variations in such a way that a standard Fuzzy C-Means (fcm) segmentation algorithm fails. As a consequence, modified versions of the algorithm can be found in literature, which take into account the artifact. In this work we show that the application of a suitable pre-processing algorithm, already presented by the authors, followed by a standard fcm segmentation achieves good results also. The experimental results ones are compared with those obtained using SPM5, which can be considered the state of the art algorithm oriented to brain segmentation and bias removal.
Simultaneous segmentation and beam-hardening correction in computed microtomography of rock cores
2013
We propose a post-reconstruction correction procedure for the beam-hardening artifact that neither requires knowledge of the X-ray spectrum nor of the attenuation coefficients in multi-mineral geologic samples. The beam-hardening artifact in polychromatic X-ray computer tomography (CT) hampers segmentation of the phase assemblage in geologic samples. We show that in cylindrically shaped samples like rock cores, the X-ray attenuation value for a single phase depends mainly on the distance from the center of the cylinder. This relationship could be easily extracted from the CT data for every phase and used to infer the presence of these phases at all positions in the sample. Our new approach …
A Study of Perceptron Mapping Capability to Design Speech Event Detectors
2006
Event detection is a fundamental yet critical component in automatic speech recognition (ASR) systems that attempt to extract knowledge-based features at the front-end level. In this context, it is common practice to design the detectors inside well-known frameworks based on artificial neural network (ANN) or support vector machine (SVM). In the case of ANN, speech scientists often design their detector architecture relying on conventional feed-forward multi-layer perceptron (MLP) with sigmoidal activation function. The aim of this paper is to introduce other ANN architectures inside the context of detection-based ASR. In particular, a bank of feed-forward MLPs using sinusoidal activation f…
Distinguishing Onion Leaves from Weed Leaves Based on Segmentation of Color Images and a BP Neural Network
2006
A new algorithm to distinguish onion leaves from weed leaves in images is suggested. This algorithm is based on segmentation of color images and on BP neural network. It includes: discarding soil for conserving only plants in the image, color image segmentation, merging small regions by analyzing the frontier rates and the averages of color indices of the regions, at last a BP neural network is used to determine if the small regions belongs to onion leaf or not. The algorithm has been applied to many images and the correct identifiable percents for onion leaves are between 80%~ 90%.
Emotions and visitors’ satisfaction at a museum
2014
Purpose – This research aims to investigate whether emotions can be considered as a suitable variable to segment visitors at a museum. Furthermore, it seeks to analyse whether emotions influence visitor satisfaction and whether this depends on objective variables (such as age, gender and level of education) or not. Design/methodology/approach – A structured questionnaire was developed and data were collected at the National Museum of Archaeology “G.A. Sanna” in Sardinia (Italy) via 410 face-to-face interviews. Hierarchical and non-hierarchical cluster analyses and a series of chi-squared tests were run for the purpose of the study. Findings – Two segments were identified. The cluster with …
Destination Attractiveness Drivers among Urban Hostel Tourists: An Analysis of Frustrators and Delighters
2015
This study is based on data from 679 tourists staying at hostel accommodation facilities in Zagreb, a propulsive city-break destination in Central Europe. Besides providing insight into the socio-demographic and behavioural characteristics of hostel tourists, this study uncovers determinant destination attributes influencing their perceptions of destination attractiveness. In particular, impact asymmetry analysis identified potential sources of delight and frustration among hostel tourists. Since the hostel tourist segment, as such, is almost completely unconsidered in contemporary tourism research, partly covered only by a few studies on youth travellers and backpackers, the results of thi…
Sequential Lip Region Segmentation Using Fuzzy Clustering with Spatial and Temporal Information
2012
For many visual speech recognition and visual speaker authentication systems, lip region extraction is of vital important. In order to segment the lip region accurately and robustly from a lip sequence, a new fuzzy-clustering based algorithm is proposed. In the proposed method, a new dissimilarity measure is introduced to take all the color, spatial and temporal information into consideration. An iterative optimization method is employed to derive the optimal lip region membership map and the final segmentation result. From the experimental results, it is observed that the proposed algorithm can provide superior results compared with other traditional methods.