Region growing

Tablo 2.1 : Elektromanyetik yayılımın doku ile etkileşimleri .……………… Tablo 4.1 : Gerçek ve bulunan model parametreleri (konum değerleri benek cinsindendir)…………………...………………………………….. Tablo 4.2 : İşlenmemiş/işlenmiş görüntüler için bölütleme başarımı ………. Tablo 5.1 : Çalışmada kullanılan görüntüler ………………………………... Tablo 5.2 : Bölütleme başarımları …………………………………………... vii ŞEKİL LİSTESİ Sayfa No Şekil 2.1 Şekil 2.2 Şekil 2.3 Şekil 2.4 Şekil 2.5 Şekil 2.6 Şekil 2.7 Şekil 2.8 Şekil 2.9 Şekil 2.10 Şekil 2.11 Şekil 3.1

Tagged magnetic resonance (MR) imaging makes it possible to image the motion of tissues such as the muscles found in the heart and tongue. The harmonic phase (HARP) method largely automates the process of tracking points within tagged MR images. It works by finding spatial points in successive images that retain the same two harmonic phase values throughout the entire image sequence. Given a set of tracked points, many interesting and useful motion properties such as regional displacement or rotation, elongation, strain, and twist, can be computed. When there is a large motion between successive image frames, HARP tracking can fail, and this results in mistracked points and erroneous motion estimates. In this paper, we present a novel HARP refinement method based on seeded region growing that addresses this problem. Starting from a given seed point which is determined by the user to be correctly tracked throughout the entire sequence, this method can reliably track the motion of the whole tissue. A novel cost function is used in the region growing to assure that points that can be most reliably tracked are tracked first. Experimental results on tagged MR images of the tongue demonstrate very reliable tracking.

In evaluating pathological changes in drug efficacy and toxicity studies, morphometric analysis can be quite robust. In this experiment, we examined whether morphometric changes of major pathological findings in various tissue specimens stained with hematoxylin and eosin could be recognized and quantified using image processing software. Using Tissue Studio, hypertrophy of hepatocytes and adrenocortical cells could be quantified based on the method of a previous report, but the regions of red pulp, white pulp, and marginal zones in the spleen could not be recognized when using one setting condition. Using Image-Pro Plus, lipid-derived vacuoles in the liver and mucin-derived vacuoles in the intestinal mucosa could be quantified using two criteria (area and/or roundness). Vacuoles derived from phospholipid could not be quantified when small lipid deposition coexisted in the liver and adrenal cortex. Mononuclear inflammatory cell infiltration in the liver could be quantified to some extent, except for specimens with many clustered infiltrating cells. Adipocyte size and the mean linear intercept could be quantified easily and efficiently using morphological processing and the macro tool equipped in Image-Pro Plus. These methodologies are expected to form a base system that can recognize morphometric features and analyze quantitatively pathological findings through the use of information technology.

We examine the changeover in the particle configurations and the dynamics in dense Lennard-Jones binary mixtures composed of small and large particles. By varying the composition at a low temperature, we realize crystal with defects, polycrystal with small grains, and glass with various degrees of disorder. In particular, we show configurations where small crystalline regions composed of the majority species are enclosed by percolated amorphous layers composed of the two species. We visualize the dynamics of configuration changes using the method of bond breakage and following the particle displacements. In quiescent jammed states, the dynamics is severely slowed down and is highly heterogeneous at any compositions. In shear, plastic deformations multiply occur in relatively fragile regions, growing into large-scale shear bands where the strain is highly localized. Such bands appear on short time scales and change on long time scales with finite life times.

The radiotherapy treatment planning requires the delineation of the therapy structures that will be submitted to the radiation beams. When executed manually, this delineation is a slow process and can result in human errors due to the amount of X-ray Computed Tomography (CT) images that are analyzed in each radiotherapy planning. This process needs precision, minimizing the radiation on healthy areas, close to the target tissues. A new system for automatic segmentation of images of clinical structures is proposed in this work. The algorithm is based on multi-region growing followed by watershed transform. The main contributions are the method of seed pixels selection and predicate of the multi-region growing algorithm and the segmentation results achieved. The system was tested in 400 images and its efficiency was measured by two different statistical methods, correlation and the t-test. The clinical structures of interest are the rectum, bladder and seminal vesicles.

Image segmentation is an important process for most medical image analysis tasks. One of the familiar segmentation technique is using clustering algorithm. At present, clustering algorithm has been used in many fields including machine learning, data mining, pattern recognition, image processing, and bioinformatics. These methods partition or group the objects based on similarities and differences. This study aims to assist hematologist or technologist by automating the visual assessment to identify blasts in acute leukemia blood samples. In this study, an automated image segmentation technique using a combination between saturation component based on HSI color space and clustering algorithm which are Moving k-Means and Fuzzy c-means are proposed. Then, 7x7 pixels median filter and region growing algorithms was applied to remove the unwanted noises. The comparison performance of the proposed technique was investigated. The experimental results yield a promising result for the combination between saturation component with Moving k-means clustering algorithm, 7x7 pixels median filter and region growing algorithms.

Image segmentation is the process of partitioning a digital image into multiple segments or set of pixels. The objective of image segmentation is to group pixels into a prominent image region. In this paper, segmentation of Gray level images is used to provide information such as anatomical structure and identifying the Region of Interest i.e. locate tumor, lesion and other abnormalities. The image segmentation methods may be classified into several types: image-based, model-based and hybrid methods. Purely image-based methods perform segmentation based only on information available within the image. These include thresholding, region growing, morphological operations, active contours level sets, watershed, fuzzy connectedness, and graph cuts (Gcs).Image based methods perform well on high-quality images. However, the results are not as good when the image quality is inferior or boundary information is missing .One advantage of model based methods is that, even when some object infor...

Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared wit...

This paper reports on an automatic method for ventricular cavity segmentation in angiographic images. The first step of the method consists in applying a linear regression model that exploits the functional relationship between the original input image and a smoothed version. This intermediate result is used as input to a clustering algorithm, which is based on a region growing technique. The clustering algorithm is a two stage process. In the first stage an initial segmentation is achieved using as input the result of the linear regression and the smoothed version of the input image. The second stage is intended for refining the initial segmentation based on feature vectors including the area, the gray-level average and the centroid of each candidate region. The segmentation method is conceptually simple and provides an accurate contour detection for the left ventricle cavity.

A methodology is described for mapping the Amazon Basin Wetlands using region growing segmentation and region classification of multi-date JERS-1 data. The proposed methodology includes the following steps: imagery segmentation, feature extraction, unsupervised region classification, merging and mapping of unsupervised classes to three basic ones, editing, quality control through digital and visual revision, and, finally, addition to the basin mosaic.

The primary aim of this work is to propose and investigate the effectiveness of a novel unsupervised tissue clustering and classification algorithm for diffusion tensor MRI (DTI) data. The proposed algorithm utilizes information about the degree of homogeneity of the distribution of diffusion tensors within voxels. We adapt frameworks proposed by Hext and Snedecor, where the null hypothesis of diffusion tensors belonging to the same distribution is assessed by an F-test. Tissue type is classified according to one of the four possible diffusion models, the assignment of which is determined by a parsimonious model selection framework based on Schwarz Criterion. Both numerical phantoms and diffusion-weighted imaging (DWI) data obtained from excised rat and pig spinal cords are used to test and validate these tissue clustering and classification approaches. The unsupervised clustering method effectively identifies distinct regions of interest (ROIs) in phantoms and real experimental DTI data.

Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method...

We present techniques for creating an approximate implicit representation of space curves and of surfaces of revolution. In both cases, the proposed techniques reduce the problem to that of implicitization of planar curves. For space curves, which are described as the intersection of two implicitly defined surfaces, we show how to generate an approximately orthogonalized implicit representation. In the case of surfaces of revolution, we address the problem of avoiding unwanted branches and singular points in the region of interest.

A simple extension method of the ICP algorithm using point quality based on the distance and incident angle is presented for improving the registration accuracy of TLS point clouds. First, using a structured point cloud representation, the distance and incident angle of each point are calculated. Then, quality measures for the distance and incident angle are defined, considering the relationship between them and the scanning errors. Finally, an extension method of the ICP algorithm by the weighting using the estimated point qualities is described. A method for efficient matching using point properties is also introduced. The proposed method is applied to two datasets of TLS point clouds of a small plant and an outdoor environment, and the effectiveness is evaluated by comparing registration errors with the ground truths obtained using the scan target and a total station.

The technique presented in this paper is based on fuzzy clustering in order to achieve robust automatic detection of active regions in solar images. The first part of the detection process is based on seed selection and region growing. After that, the regions obtained are grouped into real active regions using a fuzzy clustering algorithm. The procedure developed has been tested on 400 full-disk solar images (corresponding to 4 days) taken from the satellite SOHO. The results are compared with those manually generated for the same days and a very good correspondence is found, showing the robustness of the method described.

Seeded segmentation with minimum spanning forests, also known as segmentation by watershed cuts, is a powerful method for supervised image segmentation. Given that correct segmentation labels are provided for a small set of image elements, called seeds, the watershed cut method completes the labeling for all image elements so that the boundaries between different labels are optimally aligned with salient edges in the image. Here, a randomized version of watershed segmentation, the targeted stochastic watershed, is proposed for performing multi-label targeted image segmentation with stochastic seed input. The input to the algorithm is a set of probability density functions (PDFs), one for each segmentation label, defined over the pixels of the image. For each pixel, we calculate the probability that the pixel is assigned a given segmentation label in seeded watershed segmentation with seeds drawn from the input PDFs. We propose an efficient algorithm (quasi-linear with respect to the number of image elements) for calculating the desired probabilities exactly.

Segmentation plays a vital role in medical imaging. Segmentation of an image is the partition or separation of the image into disjoint regions of similar feature. We propose a method that integrates Fuzzy C-Means (FCM) clustering and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the usual watershed algorithm for medical image analysis is common because of its advantages, such as always being able to construct a complete division of the image. However, its downsides include over-segmentation and sensitivity to false edges. We concentrate on the downsides of the usual watershed algorithm when it is applied to medical images by using Fuzzy C-Means clustering to produce a primary segmentation of the image before we apply marker controlled watershed segmentation algorithm to it. The Fuzzy C-Means clustering is an unsupervised learning algorithm, while the marker controlled watershed segmentation algorithm makes use of automated thresholding...

In chemoembolization, chemotherapy drugs and thrombotic agents are directly injected into the liver tumor through a catheter navigated to the artery that supplies the tumor. In order to help surgeons to train their hand-eye coordination skills to reduce the risk of injecting the thrombotic agents incorrectly and deprive normal tissue of its blood supply, this paper proposes a method for rendering the flow particles in simulation of chemotherapy drugs injection. Firstly, a three dimensional (3-D) region growing technique is used to extract vessels from clinical CT images. These vessels are skeletonized using a 3-D thinning algorithm, and their geometries are reconstructed using cubic b-splines. Quadrilaterals which are aligned along the viewing direction are rendered to visualize the movement of particles through the flow modeled using Hagen-Poiseuille Flow. Our visualization method achieves a computational efficient and good visual approximation of the flow of particles inside the vessels under fluoroscopic imaging.

Reverse engineering (RE) is a process to cerate computer-aided design (CAD) models from the scanned data of an existing part acquired using a CMM machine. This paper presents a common problem in the segmentation and measuring of physical object. We create a new feature-based model of reverse engineering, where we can predefine the different simple parts of an object based on a feature library. With the help of our feature library we can reconstruct not only simple element of geometry, but also generated surfaces of object. The feature library contains the generic definition of geometric features required for performing the necessary task in our application.

In chemoembolization, chemotherapy drugs and thrombotic agents are directly injected into the liver tumor through a catheter navigated to the artery that supplies the tumor. In order to help surgeons to train their hand-eye coordination skills to reduce the risk of injecting the thrombotic agents incorrectly and deprive normal tissue of its blood supply, this paper proposes a method for rendering the flow particles in simulation of chemotherapy drugs injection. Firstly, a three dimensional (3-D) region growing technique is used to extract vessels from clinical CT images. These vessels are skeletonized using a 3-D thinning algorithm, and their geometries are reconstructed using cubic b-splines. Quadrilaterals which are aligned along the viewing direction are rendered to visualize the movement of particles through the flow modeled using Hagen-Poiseuille Flow. Our visualization method achieves a computational efficient and good visual approximation of the flow of particles inside the vessels under fluoroscopic imaging.

Background and purpose: Magnetic resonance imaging (MRI) is increasingly used in radiation therapy planning of prostate cancer (PC) to reduce target volume delineation uncertainty. This study aimed to assess and validate the performance of a fully automated segmentation tool (AST) in MRI based radiation therapy planning of PC. Material and methods: Pelvic structures of 65 PC patients delineated in an MRI-only workflow according to established guidelines were included in the analysis. Automatic vs manual segmentation by an experienced oncologist was compared with geometrical parameters, such as the dice similarity coefficient (DSC). Fifteen patients had a second MRI within 15 days to assess repeatability of the AST for prostate and seminal vesicles. Furthermore, we investigated whether hormonal therapy or body mass index (BMI) affected the AST results. Results: The AST showed high agreement with manual segmentation expressed as DSC (mean, SD) for delineating prostate (0.84, 0.04), bladder (0.92, 0.04) and rectum (0.86, 0.04). For seminal vesicles (0.56, 0.17) and penile bulb (0.69, 0.12) the respective agreement was moderate. Performance of AST was not influenced by neoadjuvant hormonal therapy, although those on treatment had significantly smaller prostates than the hormone-naïve patients (p < 0.0001). In repeat assessment, consistency of prostate delineation resulted in mean DSC of 0.89, (SD 0.03) between the paired MRI scans for AST, while mean DSC of manual delineation was 0.82, (SD 0.05). Conclusion: Fully automated MRI segmentation tool showed good agreement and repeatability compared with manual segmentation and was found clinically robust in patients with PC. However, manual review and adjustment of some structures in individual cases remain important in clinical use.

Dead wood is an important habitat characteristic in forests. However, dead wood lying on the ground below a canopy is difficult to detect from remotely sensed data. Data from airborne laser scanning include measurement of surfaces below the canopy, thus offering the potential to model objects on the ground. This paper describes a new line template matching algorithm for detecting lines along the ground. The line template matching is done directly to the laser point cloud and results in a raster showing the support of the line in each raster cell. Line elements are vectorized based on the raster to represent lying tree stems. The results have been validated versus field-measured lying tree stems. The number of detected lines was 845, of which 268 could be automatically linked to the 651 field-measured stems. The line template matching produced a raster which visually showed linear elements in areas where lying tree stems where present, but the result is difficult to compare with the field measurements due to positioning errors. The study area contained big piles of storm-felled trees in some places, which made it an unusually complex test site. Longer line structures such as ditches and roads also resulted in detected lines and further analysis is needed to avoid this, for example by specifically detecting longer lines and removing them.

It always takes a skilled neurologist to detect a tumor in the MRI scans, which the numerologist does with the naked eye. Doctors have had only 2D cross sectional images for viewing the tumor in the MRI scans. This research presents a method for automatic tumor detection with an added feature of reconstructing its 3D image. The research involves implementation of various steps of detecting and extracting the tumor from the 2D slices of MRI brain images by Seeded region growing technique along with automatic seed selection and designing software for reconstructing 3D image from a set of 2D tumor images. The seeded region growing method is very attractive method for semantic image segmentation which involves high level knowledge of image components during the seed selection procedure. The volume of the tumor is also estimated based on the computation of these images to assist the radiologist.

The positron emission tomography (PET) is a tool reference in routine clinical oncology. His applications has concerned the management and therapeutic monitoring, identification and definition of targets for radiotherapy, and quantification. The most frequently radiotracer&#39;s used are fluo-deoxyglucose(FDG) and Fluo-choline (18F-choline). In addition to the functional information, PET with algorithms of post treatment could give information on the stage of cell differentiation in HCC. The objective is to determine the stage of cell differentiation in HCC on imaging PET. We have apply the process of imaging post treatment on the image of PET Choline and PET FDG of patient whose stage of cell differentiation is known at histology. The same result as histology were found. The volume of HCC lesions detected using 18F-FDG was slightly better than that using 18F-choline. The capacity of these two radiotracers to detect rate of fixation of HCC depend of the type of lesion. This combinat...

Laser range-scanners are used in fields as diverse as product design, reverse engineering, and rapid prototyping to quickly acquire geometric surface data of parts and models. This data is often in the form of a dense, noisy surface mesh that must be simplified into piecewise-smooth surfaces. The method presented here facilitates this time-consuming task by automatically segmenting a dense mesh into regions closely approximated by single surfaces. The algorithm first estimates the noise and curvature of each vertex. Then it filters the curvatures and partitions the mesh into regions with fundamentally different shape characteristics. These regions are then contracted to create seed regions for region growing. For each seed region, the algorithm iterates between region growing and surface fitting to maximize the number of connected vertices approximated by a single underlying surface. The algorithm finishes by filling segment holes caused by outlier noise. We demonstrate the algorithm effectiveness on real data sets.

Text segmentation, whether printed, handwritten or cursive, is one of the most complicated phases in any OCR. The accuracy of recognition will be heavily reliant on good segmentation. Image segmentation is a crucial component of image analysis and the field of computer vision. Researchers have developed several techniques for segmentation, each of which is used for different types of segmented objects. At present no any universal method is available for image segmentation. Existing image segmentation techniques are not capable to deal with images of any types. This survey looked at a variety of image segmentation techniques, evaluated them, and discussed the issues that came up as a result of using them.

With the recent advancements in deep neural computation, we devise a method to recover superquadric parameters from range images using a convolutional neural network. By training our simple, fullyconvolutional architecture on synthetic data images, containing a single superquadric, we achieve encouraging results. In a fixed rotation scenario, the model could already be used in practice, but we still need to improve on prediction of arbitrary rotational parameters in the future

In this paper we examine the influence of periodic islands within a time periodic chaotic flow on the evolution of a scalar tracer. The passive scalar tracer is injected into the flow field by means of a steady source term. We examine the distribution of the tracer once a periodic state is reached, in which the rate of injected scalar balances advection and the molecular diffusion κ. We study the two-dimensional velocity field u(x, y, t) = 2 cos 2 (ωt)(0, sin x) + 2 sin 2 (ωt)(sin y, 0). As ω is reduced from an O(1) value the flow alternates through a sequence of states which are either globally chaotic, or contain islands embedded in a chaotic sea. The evolution of the scalar is examined numerically using a semi-Lagrangian advection scheme.

Image segmentation to be basic for image analysis and recognition process. Segmentation divides the image into several regions based on the unique homogeneous image pixel. Image segmentation classify homogeneous pixels basedon several features such as color, texture and others. Color contains a lot of information and human vision can see thousands of color combinations and intensity compared with grayscale or with black and white (binary). The method is easy to implement to segementation is clustering method such as the Fuzzy C-Means (FCM) algorithm. Features to beextracted image is color and texture, to use the color vector L* a* b* color space and to texture using Gabor filters. However, Gabor filters have poor performance when the image is segmented many micro texture, thus affecting the accuracy of image segmentation. As support in improving the accuracy of the extracted micro texture used method of Local Binary Patterns (LBP). Experimental use of color features compared with grayscales increased 16.54% accuracy rate for texture Gabor filters and 14.57% for filter LBP. While the LBP texture features can help improve the accuracy of image segmentation, although small at 2% on a grayscales and 0.05% on the color space L* a* b*.

Airborne laser scanning systems provide high quality 3D point clouds from the earth's surface. A Digital Surface Model (DSM) is provided from the LIDAR data after removing the outliers from the point clouds. Generating Digital Terrain Models (DTM) is one of the most important applications of the LIDAR data. During the past few years many methods have been proposed for DTM generation from LIDAR DSM data. Almost all of them work properly in smooth and non-undulated urban areas. Problems appear especially in hilly urban areas where the 3D objects (e.g. buildings) are situated on and around hills. In such areas it happens frequently that the height of the ground at one side of a building is much different from the height at the other side. Another problem is the discrimination of tops of steep hills from buildings situated on these hilltops. In this paper an approach based on geodesic morphological reconstruction (Arefi and Hahn, 2005) is proposed. The experimental investigation shows the potential and reliability of this algorithm.

In this paper we describe the construction of hierarchical feature clustering and show how to overcome general problems of region growing algorithms such as seed point selection and processing order. Access to medical knowledge inherent in medical image databases requires content-based descriptions to allow non-textual retrieval, e.g., for comparison, statistical inquiries, or education. Due to varying medical context and questions, data structures for image description must provide all visually perceivable regions and their topological relationships, which poses one of the major problems for content extraction. In medical applications main criteria for segmenting images are local features such as texture, shape, intensity extrema, or gray values. For this new approach, these features are computed pixel-based and neighboring pixels are merged if the Euclidean distance of corresponding feature vectors is below a threshold. Thus, the planar adjacency of clusters representing connected image partitions is preserved. A cluster hierarchy is obtained by iterating and recording the adjacency merging. The resulting inclusion and neighborhood relations of the regions form a hierarchical region adjacency graph. This graph represents a multiscale image decomposition and therefore an extensive content description. It is examined with respect to application in daily routine by testing invariance against transformation, run time behavior, and visual quality For retrieval purposes, a graph can be matched with graphs of other images, where the quality of the matching describes the similarity of the images.

Segmentation of tumor from magnetic resonance image (MRI) brain images is an emergent research area in the field of medical image segmentation. As segmentation of brain tumor plays an important role for necessary treatment and planning of tumor surgery. However, segmentation of the brain tumor is still a great challenge in clinics, specially automatic segmentation. In this paper we proposed hybridized multilevel thresholding and level set method for automatic segmentation of brain tumor. The innovation for this paper is to interface the initial segmentation from multilevel thresholding and extract a fine portrait using level set method with morphological operations. The results are compared with the existing method and also with radiologist manual segmentation which confirm the effectiveness of this hybridized paradigm for brain tumor segmentation.

We show that the entanglement entropy associated to a region grows faster than the area of its boundary surface. This is done by proving a special case of a conjecture due to Widom that yields a surprisingly simple expression for the leading behaviour of the entanglement entropy. Copyright line will be provided by the publisher An interesting model for the area law of Bekenstein's black-hole entropy is a local quantum field theory for which one restricts all observations to the complement of a compact spatial region Ω. One should think of Ω as the interior of the horizon although one does not require Ω to have any gravitational relevance. In fact, we will restrict our attention to a region in the n-dimensional flat Euclidean space R n . Even if the quantum field is globally in a pure state (e.g., the vacuum), the state restricted to the complement of Ω will in general be mixed with finite von Neumann entropy, called the entanglement entropy, S(Ω) . For many types of quantum field theories, it was observed that in the semi-classical limit of large Ω, the entanglement entropy S(Ω) scales like the area of ∂Ω. In other words, if we rescale Ω ⊂ R n by a factor R, the entanglement entropy, S(RΩ), should asymptotically scale like R n-1 for large R. Recently, it was observed by Gioev and Klich , that for free fermions at zero temperature, however, the entanglement entropy of the ground state scales like R n-1 log(R) for R → ∞ based on a conjecture by Widom [6,. They provide a lower bound on the entanglement entropy in terms of the trace of a quadratic function of the restricted state. For the latter, we have proved in the leading asymptotic behaviour. This establishes indeed that the entanglement entropy scales at least like R n-1 log(R), which violates of the beforementioned area law scaling. For notational clarity we will often write equalities that only hold asymptotically for large R, that is, we will drop subleading terms that are not central to our argument and only mention it in the text. General arguments imply that for a pure state, the entanglement entropy of this state with respect to Ω is the same as for the complement R n \ Ω. For simplicity, we will restrict the state to a compact region Ω. The ground state, ρ Γ , of a system of non-interacting fermions in R n is given in terms of the Fermi surface at Fermi energy ǫ F : All one-particle states with momentum p ∈ Γ = {p ∈ R n |E(p) ≤ ǫ F } and energy 1 E(p) are occupied. The ground state ρ Γ is then characterised by the one-particle Fermi projector, P Γ , defined in momentum space by the kernel P Γ (p, p ′ ) = χ Γ (p)δ(pp ′ ). Here, and in the following, χ A denotes the indicator function of a 1 We will not discuss here assumptions on the dispersion relation E(p) but one may, of course, think of the example E(p) = p 2 . We require that Γ and Ω are compact sets in R n with sufficiently smooth boundaries.

In order to detect a fruit, a well-defined segmentation algorithm is required. The segmentation process requires finding the adequate thresholds in order to extract the wanted features. In this project 15 methods were applied to determine the thresholds of thirty images in order to detect the tart-cherry fruits. Then, the resulted binary images were assessed. The images were acquired under uncontrolled daylight conditions and the main idea was to find at least one fruit feature in each image. All of the algorithms were based ...

Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c-means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

SAR Interferometry allows the obtention of digital elevation models (DEM) by combination of two synthetic aperture radar images acquired from slightly different viewpoints. ERS-1 SAR interferometry has already been validated over small areas. When processing wider areas, new adaptative filtering algorithms such as the short time Fourier transform are required. However, phase unwrapping is still the critical step. LMS an weighted LMS are two phase unwrapping algorithms which overcome some disadvantages of traditional residue based methods. Some results are shown obtained from ERS-1 images from Tarragona.

This paper presents a complete image analysis system which, from high-resolution color infrared (CIR) digital images, and a Digital Surface Model (DSM), extracts, segments and classifies vegetation in high density urban areas, with very high reliability. The process starts with the extraction of all vegetation areas using a supervised classification system based on a Support Vector Machines (SVM) classifier. The result of this first step is further on used to separate trees from lawns using texture criteria computed on the DSM. Tree crown borders are identified through a robust region growing algorithm based on tree -shape criteria. A SVM classifier gives the species class for each tree-region previously identified. This classification is used to enhance the appearance of 3D city models by a realistic representation of vegetation according to the vegetation land use, shape and tree species.

This paper presents a complete high resolution aerial-images processing workflow to detect and characterize vegetation structures in high density urban areas. We present a hierarchical strategy to extract, analyze and delineate vegetation areas according to their height. To detect urban vegetation areas, we develop two methods, one using spectral indices and the second one based on a Support Vector Machines (SVM) classifier. Once vegetation areas detected, we differentiate lawns from treed areas by computing a texture operator on the Digital Surface Model (DSM). A robust region growing method based on the DSM is proposed for an accurate delineation of tree crowns. Delineation results are compared to results obtained by a Random Walk region growing technique for tree crown delineation. We evaluate the accuracy of the tree crown delineation results to a reference manual delineation. Results obtained are discussed and the influential factors are put forward.

In this paper, we develop a new methodology to estimate past changes of growing season temperature at Fontainebleau (northern France). Northern France temperature fluctuations have been documented by homogenised instrumental temperature records (at most 140 year long) and by grape harvest dates (GHD) series, incorporated in some of the European-scale temperature reconstructions. We have produced here three new proxy records: δ 18 O and δ 13 C of latewood cellulose of living trees and timbers from Fontainebleau Forest and Castle, together with ring widths of the same samples. δ 13 C data appear to be influenced by tree and age effects; ring widths are not controlled by a single climate parameter. By contrast, δ 18 O and Burgundy GHD series exhibit strong links with Fontainebleau growing season maximum temperature. Each of these records can also be influenced by other factors such as vine growing practices, local insolation, or moisture availability. In order to reduce the influence of these potential biases, we have used a linear combination of the two records to reconstruct interannual fluctuations of Fontainebleau growing season temperature from 1596 to 2000. Over the instrumental period, the reconstruction is well correlated with the temperature data (R 2 =0.60). This reconstruction is associated with an uncertainty of ∼1.1 • C (1.5 standard deviation), and is expected to provide a reference series for the variability of growing season maxi-

In this paper different classifier are used to identifying different type of cracks on road surface. As our experience shows Region Growing Classifier (RGC) method can be used to divide all surface road images in two main groups. First group covers alligator and ...

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Early-stage fruit disease detection will ensure the natural product quality for the organic agriculture business. The potential of using K-Means segmentation for diagnosing tomatoes fruit disease was intended to be explored by this proposed method. The main goal of paper is to increase classification accuracy by locating tomatoes with Ground Bud Necrosis Virus in Tomatoes disease using an image segmentation approach. The K-means clustering algorithm is intended to boost segmentation effectiveness. In the end product, the images are divided into three classes: Grade 0—00-15%; Grade 1—16-35%; Grade 2—36-65%; Grade 3—66-85%; and Class 4—86-100%. Moreover, the tested results of the proposed approach explore a variety of unhealthy images and disease Tomatoes and demonstrate that, when compared to existing methods, the proposed method has the highest accuracy.

Este artigo descreve a aplicação de um classificador por máquina de vetores suporte, ou SVM (Support Vector Machine), na seleção de atributos através de busca exaustiva. O módulo, implementado em IDL, foi incorporado a um sistema de processamento de imagens já existente, desenvolvido por alunos do INPE.

Baryon number inhomogeneities may be generated during the epoch when the baryon asymmetry of the universe is produced, e.g. at the electroweak phase transition. The regions with excess baryon number will have a lower temperature than the background temperature of the universe. Also the value of the quark hadron transition temperature T c will be different in these regions as compared to the background region. Since a first-order quark hadron transition is very susceptible to small changes in temperature, we investigate the effect of the presence of such baryonic lumps on the dynamics of quarkhadron transition. We find that the phase transition is delayed in these lumps for significant overdensities. Consequently, we argue that baryon concentration in these regions grows by the end of the transition. We briefly discuss some models which may give rise to such high overdensities at the onset of the quark-hadron transition.

Baryon number inhomogeneities may be generated during the epoch when the baryon asymmetry of the universe is produced, e.g. at the electroweak phase transition. The regions with excess baryon number will have a lower temperature than the background temperature of the universe. Also the value of the quark hadron transition temperature T c will be different in these regions as compared to the background region. Since a first-order quark hadron transition is very susceptible to small changes in temperature, we investigate the effect of the presence of such baryonic lumps on the dynamics of quarkhadron transition. We find that the phase transition is delayed in these lumps for significant overdensities. Consequently, we argue that baryon concentration in these regions grows by the end of the transition. We briefly discuss some models which may give rise to such high overdensities at the onset of the quark-hadron transition.