181,101 research outputs found
Context-Specific Preference Learning of One Dimensional Quantitative Geospatial Attributes Using a Neuro-Fuzzy Approach
Change detection is a topic of great importance for modern geospatial information systems. Digital aerial imagery provides an excellent medium to capture geospatial information. Rapidly evolving environments, and the availability of increasing amounts of diverse, multiresolutional imagery bring forward the need for frequent updates of these datasets. Analysis and query of spatial data using potentially outdated data may yield results that are sometimes invalid. Due to measurement errors (systematic, random) and incomplete knowledge of information (uncertainty) it is ambiguous if a change in a spatial dataset has really occurred. Therefore we need to develop reliable, fast, and automated procedures that will effectively report, based on information from a new image, if a change has actually occurred or this change is simply the result of uncertainty. This thesis introduces a novel methodology for change detection in spatial objects using aerial digital imagery. The uncertainty of the extraction is used as a quality estimate in order to determine whether change has occurred. For this goal, we develop a fuzzy-logic system to estimate uncertainty values fiom the results of automated object extraction using active contour models (a.k.a. snakes). The differential snakes change detection algorithm is an extension of traditional snakes that incorporates previous information (i.e., shape of object and uncertainty of extraction) as energy functionals. This process is followed by a procedure in which we examine the improvement of the uncertainty at the absence of change (versioning). Also, we introduce a post-extraction method for improving the object extraction accuracy. In addition to linear objects, in this thesis we extend differential snakes to track deformations of areal objects (e.g., lake flooding, oil spills). From the polygonal description of a spatial object we can track its trajectory and areal changes. Differential snakes can also be used as the basis for similarity indices for areal objects. These indices are based on areal moments that are invariant under general affine transformation. Experimental results of the differential snakes change detection algorithm demonstrate their performance. More specifically, we show that the differential snakes minimize the false positives in change detection and track reliably object deformations
Fuzzy geometry, entropy, and image information
Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described
Les manifestations violentes
Abstract. An automatic human shape-motion analysis method based on a fusion architecture is proposed for human action recognition in videos. Robust shape-motion features are extracted from human points detection and tracking. The features are combined within the Transferable Belief Model (TBM) framework for action recognition. The TBMbased modelling and fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. Action recognition is performed by a multilevel analysis. The sequencing is exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of jumps: high jump, pole vault, triple jump and long jump.
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
High-resolution optical and SAR image fusion for building database updating
This paper addresses the issue of cartographic database (DB) creation or updating using high-resolution synthetic aperture radar and optical images. In cartographic applications, objects of interest are mainly buildings and roads. This paper proposes a processing chain to create or update building DBs. The approach is composed of two steps. First, if a DB is available, the presence of each DB object is checked in the images. Then, we verify if objects coming from an image segmentation should be included in the DB. To do those two steps, relevant features are extracted from images in the neighborhood of the considered object. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of DempsterâShafer evidence theory
Deep Learning-aided Brain Tumor Detection: An Initial âExperience based Cloud Framework â
Lately, the uncertainty of diagnosing diseases increased and spread due to the huge intertwined and ambiguity of symptoms, that leads to overwhelming and hindering the reliability of the diagnosis âprocess. Since tumor detection from âMRI scans depends mainly on the specialist experience, âmisdetection will result an inaccurate curing that might cause âcritical harm consequent results. In this paper, detection service for brain tumors is introduced as âan aiding function for both patients and specialist. The âpaper focuses on automatic MRI brain tumor detection under a cloud based framework for multi-medical diagnosed services. The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase. The contour âdetection and binary segmentation were applied to extract the region âof interest and reduce the unnecessary information before injecting the data into the model for training. The brain tumor âdata was obtained from Kaggle datasets, it contains 2062 cases, ââ1083 tumorous and 979 non-tumorous after preprocessing and âaugmentation phases. The training and validation phases have been âdone using different imagesâ sizes varied between (16, 16) to ââ (128,128). The experimental results show 97.3% for detection âaccuracy, 96.9% for Sensitivity, and 96.1% specificity. Moreover, âusing small filters with such type of images ensures better and faster âperformance with more deep learning.
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
The analysis of glandular morphology within colon histopathology images is an
important step in determining the grade of colon cancer. Despite the importance
of this task, manual segmentation is laborious, time-consuming and can suffer
from subjectivity among pathologists. The rise of computational pathology has
led to the development of automated methods for gland segmentation that aim to
overcome the challenges of manual segmentation. However, this task is
non-trivial due to the large variability in glandular appearance and the
difficulty in differentiating between certain glandular and non-glandular
histological structures. Furthermore, a measure of uncertainty is essential for
diagnostic decision making. To address these challenges, we propose a fully
convolutional neural network that counters the loss of information caused by
max-pooling by re-introducing the original image at multiple points within the
network. We also use atrous spatial pyramid pooling with varying dilation rates
for preserving the resolution and multi-level aggregation. To incorporate
uncertainty, we introduce random transformations during test time for an
enhanced segmentation result that simultaneously generates an uncertainty map,
highlighting areas of ambiguity. We show that this map can be used to define a
metric for disregarding predictions with high uncertainty. The proposed network
achieves state-of-the-art performance on the GlaS challenge dataset and on a
second independent colorectal adenocarcinoma dataset. In addition, we perform
gland instance segmentation on whole-slide images from two further datasets to
highlight the generalisability of our method. As an extension, we introduce
MILD-Net+ for simultaneous gland and lumen segmentation, to increase the
diagnostic power of the network.Comment: Initial version published at Medical Imaging with Deep Learning
(MIDL) 201
Catalog Extraction in SZ Cluster Surveys: a matched filter approach
We present a method based on matched multifrequency filters for extracting
cluster catalogs from Sunyaev-Zel'dovich (SZ) surveys. We evaluate its
performance in terms of completeness, contamination rate and photometric
recovery for three representative types of SZ survey: a high resolution single
frequency radio survey (AMI), a high resolution ground-based multiband survey
(SPT), and the Planck all-sky survey. These surveys are not purely flux
limited, and they loose completeness significantly before their point-source
detection thresholds. Contamination remains relatively low at <5% (less than
30%) for a detection threshold set at S/N=5 (S/N=3). We identify photometric
recovery as an important source of catalog uncertainty: dispersion in recovered
flux from multiband surveys is larger than the intrinsic scatter in the Y-M
relation predicted from hydrodynamical simulations, while photometry in the
single frequency survey is seriously compromised by confusion with primary
cosmic microwave background anisotropy. The latter effect implies that
follow-up observations in other wavebands (e.g., 90 GHz, X-ray) of single
frequency surveys will be required. Cluster morphology can cause a bias in the
recovered Y-M relation, but has little effect on the scatter; the bias would be
removed during calibration of the relation. Point source confusion only
slightly decreases multiband survey completeness; single frequency survey
completeness could be significantly reduced by radio point source confusion,
but this remains highly uncertain because we do not know the radio counts at
the relevant flux levels.Comment: 14 pages, 13 figures, replaced to match version accepted for
publication in A&
- âŠ