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  • 1.
    Lim, Nancy Joy
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad/GIS-Institutet.
    Åhlén, Julia
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad/GIS-Institutet.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Geovisualisation of uncertainty in simulated flood maps2014Inngår i: Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2014, Game and Entertainment Technologies 2014 and Computer Graphics, Visualization, Computer Vision and Image Processing 2014 - Part of the Multi Conference on Computer Science and Information Systems, MCCSIS 2014 / [ed] Katherine Blashki and Yingcai Xiao, IADIS Press , 2014, s. 206-214Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The paper presents a three-dimensional (3D) geovisualisation model of uncertainties in simulated flood maps that can help communicate uncertain information in the data being used. An entropy-based measure was employed for uncertainty quantification. In developing the model, Visualisation Toolkit (VTK) was utilised. Different data derived from earlier simulation study and other maps were represented in the model. Cartographic principles were considered in the map design. A Graphical User Interface (GUI), which was developed in Tkinter, was also created to further support exploratory data analysis. The resulting model allowed visual identification of uncertain areas, as well as displaying spatial relationship between the entropy and the slope values. This geovisualisation has still to be tested to assess its effectiveness as a communication tool. However, this type of uncertainty visualisation in flood mapping is an initial step that can lead to its adoption in decision-making when presented comprehensively to its users. Thus, further improvement and development is still suggested for this kind of information presentation.

  • 2.
    Ooms, Kristien
    et al.
    Department of Geography, Ghent University, Ghent, Belgium.
    Åhlén, Julia
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad, GIS.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Detecting Collapsed Buildings in Case of Disaster: Which Visualisation Works Best?2018Inngår i: Eye Tracking for Spatial Research: Proceedings of the 3rd International Workshop / [ed] Kiefer, Peter Giannopoulos, Ioannis Göbel, Fabian Raubal, Martin Duchowski, Andrew T., Zurich, 2018Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A user study is conducted to evaluate the efficiency and effectiveness of two types of visualizations to identify damage sites in case of disaster. The test consists out 36 trials (18 for each visualisation) and in each trial an area of 1x1km, located in Ghent, is displayed on a screen. This image shows the combined height information from before and after the disaster. The first visualisation, page flipping, is based on greyscale images with height information from the pre- and post-disaster situation between which users can switch manually. The second visualisation, difference image, is a result of subtracting the heights (before versus after) and assigning a blue-white-red colour ramp. In order to simulate the urgency with which the data is captured, systematic and random imperfections are introduced in the post-disaster data. All participants’ mouse and key interactions are logged, which is further complemented by the registration of their eye movements. This give insights the visualizations’ efficiency, effectiveness and the overall search strategies of the participants.

  • 3.
    Ooms, Kristien
    et al.
    Department of Geography, Ghent University, Ghent, Belgium.
    Åhlén, Julia
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad, GIS.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Efficiency and effectiveness in case of disaster: a visual damage assessment test2018Inngår i: Proceedings of the International Cartographic Association (Proceedings of the ICA), 2018, Vol. 1, artikkel-id 86Konferansepaper (Fagfellevurdert)
    Abstract [en]

     A user study is conducted to evaluate the efficiency and effectiveness of two types of visualizations to identify damages sites in case of disaster. The test consists out 36 trials (18 for each visualisation) and in each trial an area of 1×1km, located in Ghent, is displayed on a screen. This image shows the combined height information from before and after the disaster. The first visualisation, page flipping, is based on greyscale images with height information from the pre- and post-disaster situation between which users can switch manually. The second visualisation, difference image, is a result of subtracting the heights (before versus after) and assigning a blue-white-red colour ramp. In order to simulate the urgency with which the data is captured, systematic and random imperfections are introduced in the post-disaster data. All participants’ mouse and key interactions are logged, which is further complemented by the registration of their eye movements. This give insights the visualizations’ efficiency, effectiveness and the overall search strategies of the participants.

  • 4.
    Zdravkovic, Jelena
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Åhlén, Julia
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Modeling Image Processing Tasks As Flexible Workflows for Improved Quality of Service2004Inngår i: The IADIS Applied Computing / [ed] Nuno Guimarães and Pedro Isaías, 2004, s. 363-370Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Processing of nontrivial images is a difficult task performed throughout a set of ordered steps. Besides the basic functionality that a process must satisfy, quality of service goals are to be met. Images should be processed within a certain time and a certain quality should be attained. To manage image-processing tasks in the optimal way, the process goals must be defined explicitly and their fulfillment has be controlled. Modeling image-processing tasks with workflows would enable control of the fulfillment of goals. In addition, by introducing flexible semantics in the workflow, the process could be executed along optimal execution alternatives. In this paper, we propose an approach to model the class of image processing tasks with workflows that would, based on the extended semantics, allow for flexibility in the process execution toward optimal goals fulfillment.

  • 5.
    Åhlén, Julia
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Colour Correction of Underwater Images Using Spectral Data2005Doktoravhandling, monografi (Annet vitenskapelig)
  • 6.
    Åhlén, Julia
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Improvement of a Color Correction Algorithm for Underwater Images Through Compensating for Digital Camera Behaviour2004Inngår i: Proc. Swedish Symposium on Image Analysis / [ed] Bengtsson E., Eriksson M., 2004, s. 142-145Konferansepaper (Annet vitenskapelig)
  • 7.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Bengtsson, E
    On Colour Reconstruction of Underwater Images Taken in Shallow Waters2004Inngår i: Ocean Optics XVII, Fremantle, Australia, 2004, s. 25-29Konferansepaper (Annet vitenskapelig)
  • 8.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Bengtsson, E
    Sundgren, David
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för matematik och statistik.
    Evaluation of Underwater Spectral Data for Colour Correction Applications2006Inngår i: CSECS'06 Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, 2006Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    The inherent properties of water column usually affect underwater imagery by suppressing high-energy wavelengths. One of the inherent properties, diffuse attenuation, can be estimated from multi or hyper spectral data and thus give information on how fast light of different wavelengths decreases with increasing depth. Based on exact depth measurements and data from a spectrometer incoming light on an object can be calculated and diffuse attenuation coefficient can be estimated. In this work the authors introduce a mathematical model that suggests the most stable wavelengths, which corresponds to estimated coefficients, based on spectral information from each depth. These values are then used in reconstruction of colours in underwater imagery. Since there are no digital hyper spectral cameras yet we are for the time being confined to point data, but the method is general and we show how it can be applied on multi spectral images.

  • 9.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Bengtsson, Ewert
    Sundgren, David
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för matematik och statistik.
    Optimisation of Underwater Hyper Spectral Data for Colour Correction of Pseudo Hyper Spectral Images2006Inngår i: WSEAS Transactions on Signal Processing, ISSN 1790-5022, Vol. 2, nr 11, s. 1473-1479Artikkel i tidsskrift (Annet vitenskapelig)
  • 10.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad, GIS.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap. Uppsala University, Department of Information Technology, Sweden .
    Automatic water body extraction from remote sensing images using entropy2015Inngår i: Proceedings of the International Multidisciplinary Scientific GeoConference SGEM, 2015, Vol. 4, s. 517-524Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This research focuses on automatic extraction of river banks and other inland waters from remote sensing images. There are no up to date accessible databases of rivers and most of other waters objects for modelling purposes. The main reason for that is that some regions are hard to access with the traditional ground through techniques and thus the boundary of river banks are uncertain in many geographical positions. The other reason is the limitations of widely applied method for extraction of water bodies called normalized-difference water index (NDWI). There is a novel approach to extract water bodies, which is based on pixel level variability or entropy, however, the methods work somewhat satisfactory on high spatial resolution images, there is no verification of the method performance on moderate or low resolution images. Problems encounter identification of mixed water pixels and e.g. roads, which are built in attachment to river banks and thus can be classified as rivers. In this work we propose an automatic extraction of river banks using image entropy, combined with NDWI identification. In this study only moderate spatial resolution Landsat TM are tested. Areas of interest include both major river banks and inland lakes. Calculating entropy on such poor spatial resolution images will lead to misinterpretation of water bodies, which all exhibits the same small variation of pixel values as e.g. some open or urban areas. Image entropy thus is calculated with the modification that involves the incorporation of local normalization index or variability coefficient. NDWI will produce an image where clear water exhibits large difference comparing to other land features. We are presenting an algorithm that uses an NDWI prior to entropy processing, so that bands used to calculate it, are chosen in clear connection to water body features that are clearly discernible.As a result we visualize a clear segmentation of the water bodies from the remote sensing images and verify the coordinates with a given geographic reference.

  • 11.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad/GIS-Institutet.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Early Recognition of Smoke in Digital Video2010Inngår i: Advances in Communications, Computers, Systems, Circuits and Devices: European Conference of Systems, ECS'10, European Conference of Circuits Technology and Devices, ECCTD'10, European Conference of Communications, ECCOM'10, ECCS'10 / [ed] Mladenov, V; Psarris, K; Mastorakis, N; Caballero, A; Vachtsevanos, G, Athens: World Scientific and Engineering Academy and Society, 2010, s. 301-306Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a method for direct smoke detection from video without enhancement pre-processing steps. Smoke is characterized by transparency, gray color and irregularities in motion, which are hard to describe with the basic image features. A method for robust smoke description using a color balancing algorithm and turbulence calculation is presented in this work. Background extraction is used as a first step in processing. All moving objects are candidates for smoke. We make use of Gray World algorithm and compare the results with the original video sequence in order to extract image features within some particular gray scale interval. As a last step we calculate shape complexity of turbulent phenomena and apply it to the incoming video stream. As a result we extract only smoke from the video. Features such shadows, illumination changes and people will not be mistaken for smoke by the algorithm. This method gives an early indication of smoke in the observed scene.

  • 12.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad/GIS-Institutet.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Indication of Methane Gas in IR-Imagery2011Inngår i: Proceedings of IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2011, 2011, s. 187-192Konferansepaper (Fagfellevurdert)
    Abstract [en]

    There are human produced sources of methane gas, such as waste storages, that contribute to the global warmth and other negative effects. There is not much research on the correlation of such leakage and greenhouse effect. Methane gas is not visible for humans and thus impossible to detect using commercial cameras. Specially designed IR-camera can detect this gas and thus is used in this study. Using digital video taken over a waste disposal place we create a detection algorithm that is sensitive to the spectral and morphological characteristics of methane gas. Different kind of leakage can take place in waste disposal places. In case of small spot leakage there is a reason to assume failure in piping system and in case of widely spread leakage area we can state that it is caused by unsupervised storage of waste and this should be attended immediately. In digital video, background and target gas are distinguished using spectral and morphological classifiers, which are extracted from the analyzed IR-imagery. It is shown that indications of methane gas can be carried out efficiently using image processing techniques and the definition of turbulence of the image.

  • 13.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad/GIS-Institutet.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Knowledge Based Single Building Extraction and Recognition2014Inngår i: Recent Advances in Computer Engineering, Communications and Information Technology / [ed] Josip Music, WSEAS Press , 2014, s. 29-35Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Building facade extraction is the primary step in the recognition process in outdoor scenes. It is also a challenging task since each building can be viewed from different angles or under different lighting conditions. In outdoor imagery, regions, such as sky, trees, pavement cause interference for a successful building facade recognition. In this paper we propose a knowledge based approach to automatically segment out the whole facade or major parts of the facade from outdoor scene. The found building regions are then subjected to recognition process. The system is composed of two modules: segmentation of building facades region module and facade recognition module. In the facade segmentation module, color processing and objects position coordinates are used. In the facade recognition module, Chamfer metrics are applied. In real time recognition scenario, the image with a building is first analyzed in order to extract the facade region, which is then compared to a database with feature descriptors in order to find a match. The results show that the recognition rate is dependent on a precision of building extraction part, which in turn, depends on a homogeneity of colors of facades.

  • 14.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad, GIS.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap. Uppsala University, Department of Information Technology, Sweden.
    Mapping of roof types in orthophotos using feature descriptors2018Inngår i: International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM: Proceedings of the International Multidisciplinary Scientific GeoConference SGEM, 2018, Vol. 18, s. 285-291Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In the context of urban planning, it is very important to estimate the nature of the roof of every building and, in particular, to make the difference between flat roofs and gable ones. This analysis is necessary in seismically active areas. Also in the assessment of renewable energy projects such solar energy, the shape of roofs must be accurately retrieved. In order to perform this task automatically on a large scale, aerial photos provide a useful solution. The goal of this research project is the development of algorithm for accurate mapping of two different roof types in digital aerial images. The algorithm proposed in this paper includes several components: pre-processing step to reduce illumination differences of individual roof surfaces, statistical moments calculation and color indexing. Roof models are created and saved as masks with feature specific descriptors. Masks are then used to mark areas that contain elements of the different roof types (e.g. gable and hip). The final orthophoto visualize an accurate position of each of the roof types. The result is evaluated using precision recall method.

  • 15.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Seipel, Stefan
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Prediction of Ventilation Paths in Urban Environments using Digitized Maps2009Inngår i: Proceedings of the IADIS International Conference Applied Computing 2009, 2009, s. 217-221Konferansepaper (Fagfellevurdert)
  • 16.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap. Department of Information Technology, Uppsala University, Uppsala, Sweden.
    Segmentation of shadows and water bodies in high resolution images using ancillary data2016Inngår i: 16th International Multidisciplinary Scientific GeoConference SGEM 2016: SGEM2016 Conference Proceedings : Book 2, 2016, Vol. 1, s. 827-834Konferansepaper (Fagfellevurdert)
    Abstract [en]

    High spatial resolution imagery is often affected by shadows, both in urban environments with large variations in surface elevation and in vegetated areas. It is a common bias in classification when waters and shadows are registered as the same area. The radiometric response for the shadowed regions should be restored prior to classification. To enable that, separate classes of non-shadowed regions and shadowed areas should be created. Previous work on water extraction using low/medium resolution images, mainly faced two difficulties. Firstly, it is difficult to obtain accurate position of water boundary and secondly, shadows of elevated objects e.g. buildings, bridges, towers and trees are a typical source of noise when facing water extraction in urban regions. In high resolution images the problem of separation water and shadows becomes more prominent since the small local variation of intensity values gives rise to misclassification. This paper proposes a robust method for separation of shadowed areas and water bodies in high spatial resolution imagery using hierarchical method on different scales combined with classification of PCA (Principal Component Analysis) bands, which reduces the effects of radiometric and spatial differences that is commonly associated with the pixel-based methods for multisource data fusion. The method uses ancillary data to aid in classification of shadows and waters. The proposed method includes three steps: segmentation, classification and postprocessing. To achieve robust segmentation, we apply the merging region with three features (PCA bands, NSVDI (Normalized Saturation-value Difference Index) and height data). NSVDI discriminates shadows and some water. In the second step we use hierarchic region based classification to identify water regions. After that step candidates for water pixels are verified by the LiDAR DEM data. As a last step we consider shape parameters such as compactness and symmetry to completely remove shadows.

  • 17.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad/GIS-Institutet.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Time-space visualisation of Amur river channel changes due to flooding disaster2014Inngår i: Proceedings of the International Multidisciplinary Scientific GeoConference SGEM, 2014, Vol. 1:2, s. 873-882Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The analysis of flooding levels is a highly complex temporal and spatial assessment task that involves estimation of distances between references in geographical space as well as estimations of instances along the time-line that coincide with  given spatial locations. This work has an aim to interactively explore changes of Amur River boundaries caused by the severe flooding in September 2013. In our analysis of river bank changes we use satellite imagery (Landsat 7) to extract parts belonging to Amur River. We use imagery from that covers time interval July 2003 until February 2014. Image data is pre-processed using low level image processing techniques prior to visualization. Pre-processing has a purpose to extract information about the boundaries of the river, and to transform it into a vectorized format, suitable as inputs subsequent visualization. We develop visualization tools to explore the spatial and temporal relationship in the change of river banks. In particular the visualization shall allow for exploring specific geographic locations and their proximity to the river/floods at arbitrary times. We propose a time space visualization that emanates from edge detection, morphological operations and boundary statistics on Landsat 2D imagery in order to extract the borders of Amur River. For the visualization we use the time-space-cube metaphor. It is based on a 3D rectilinear context, where the 2D geographical coordinate system is extended with a time-axis pointing along the 3rd Cartesian axis. Such visualization facilitates analysis of the channel shape of Amur River and thus enabling for a conclusion regarding the defined problem. As a result we demonstrate our time-space visualization for river Amur and using some amount of geographical point data as a reference we suggest an adequate method of interpolation or imputation that can be employed to estimate value at a given location and time.   

  • 18.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap. Uppsala University, Department of Information Technology, Sweden.
    Kautz, Marie-Loise
    Technische Universität, Dresden, Germany.
    Data source evaluation for shoreline deliniation applications2017Inngår i: International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM: Conference proceedings, 2017, Vol. 17, nr 2-3, s. 849-858Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper proposes an evaluation of data acquired with various sensors and used in coastal water segmentation applications. Correct monitoring of coastal changes in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. Automatic shoreline delineation methods are preferable, especially in terms of time, cost, labor intensiveness and difficulties of in-situ measurements. Two main issues have been encountered within this application field, the quality of data and the segmentation algorithms. In this work, potential benefits of various data sources including optical and active sensors for extraction of shorelines have been investigated. The goal with shoreline detection from digital data sources is to obtain information as efficiently as possible and as reliably as necessary. Starting with that observation the paper discusses the effectiveness of coastal information extraction provided different data sources. This question is especially important to address since we observe a fast development of high spatial resolution data acquisition. There are many of segmentation algorithms described in the field of image processing and yet there is currently no single theory or method, no universal segmentation framework, that can be applied on all images to precisely and robustly extract shorelines. Nether there is a uniform standard for the assessment of segmentation results, and this process still largely relies on visual analysis and personal judgment. Out of myriads of image segmentation algorithms, we chose the most frequently and successfully applied within the application field and considering the data sources. In optical sensor data cases, the most frequently used methods are NDWI (Normalized Difference Water Index) and thresholding techniques. We do not aim to create yet another method to segment out the particular objects from remotely sensed data and then tailor it to work efficiently on that data set. Instead, we evaluate the data quality regarding the given application field. The case study is carried out on a 10 km coastal stretch facing the Baltic Sea (Sweden) and belonging to the Municipality of Gävle. In citu measurements were acquired to evaluate the extracted coastal lines and comparisons with reference were performed based on the average mean distance. A conclusion is done regarding the most reliable data source for this particular application of shoreline delineation.

  • 19.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad/GIS-Institutet.
    Seipel, Stefan
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Liu, Fei
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Datavetenskap.
    Evaluation of the Automatic methods for Building Extraction2014Inngår i: International Journal Of Computers and Communications, ISSN 2074-1294, Vol. 8, s. 171-176Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Recognition of buildings is not a trivial task, yet highly demanded in many applications including augmented reality for mobile phones. Recognition rate can be increased significantly if building façade extraction will take place prior to the recognitionprocess. It is also a challenging task since eachbuilding can be viewed from different angles or under differentlighting conditions. Natural situation outdoor is when buildings are occluded by trees, street signs and other objects. This interferes for successful building façade recognition. In this paper we evaluate the knowledge based approach  to automatically segment out the whole buildingfaçade or major parts of thefaçade. This automatic building detection algorithm is then evaluated against other segmentation methods such as SIFT and vanishing point approach. This work contains two main steps: segmentation of building façades region using two different approaches and evaluation of the methods using database of reference features. Building recognition model (BRM) includes evaluation step that uses Chamfer metrics. BMR is then compared to vanishing points segmentation. In the evaluation mode, comparison of these two different segmentation methods is done using the data from ZuBuD.Reference matching is also done using Scale Invariant Feature Transform. Theresults show that the recognition rate is satisfactory for the BMR model and there is no need to extract the whole building façade for the successful recognition.

  • 20.
    Åhlén, Julia
    et al.
    Uppsala universitet.
    Sundgren, David
    Stockholms universitet.
    Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images2003Inngår i: Proceedings of the 13th Scandinavinan Conference on Image Analysis / [ed] Bigun, J., Gustavsson, T., Berlin: Springer , 2003, s. 922-926Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Diminishing the negative effects of water column introduced on digital underwater images is the aim of a color correction algorithm presented by the authors in a previous paper. The present paper describes an experimental result and set of calculations for determining the impact of bottom reflectance on the algorithm's performance. This concept is based on the estimation of the relative reflectance of various bottom types such as sand, bleached corals and algae. We describe the adverse effects of extremely low and high bottom reflectances on the algorithm.

  • 21.
    Åhlén, Julia
    et al.
    Uppsala universitet.
    Sundgren, David
    Stockholms universitet.
    Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images2003Inngår i: SCIA, Scandinavian Conference on Image Analysis, Göteborg, 29 juni-2 juli / [ed] Bigun, J., Gustavsson, T.,, Berlin: Springer , 2003, s. 922-926Konferansepaper (Annet vitenskapelig)
  • 22.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Sundgren, David
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för matematik och statistik.
    Recognition of video sequences using low frequencies and color2008Inngår i: Advanced topics on signal processing, robotics and automation: proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation (ISPRA '08), Cambridge, UK, February 20-22 2008, WSEAS , 2008, s. 204-207Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a method for descriptive feature matching between two video streams of the same scene. The second video is a corrupted copy of the first video. Examples of reliable recognition of an original scene that is subjected for compression and noise are presented. The algorithm is invariant to image scale and rotation, providing robust matching across a substantial range of video signals. This paper shows an approach to using descriptive features for randomly chosen frames from video stream. By involving color information as a feature we increase the robustness of the identification process. The median of low frequencies is measured, followed by an average of spectral information from the same frame to identify clusters belonging to a scene in a film. Finally, a comparison analysis is done to verify the originality of the film.

  • 23.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Sundgren, David
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för matematik och statistik.
    Recognition of video signal by matching with the original video sequence2008Inngår i: Proceedings on CD-ROM, IADIS International Conference, 2008Konferansepaper (Fagfellevurdert)
    Abstract [en]

    An adequate analysis of the originality of the video is of a great importance in video processing field. This paper presents a method for feature matching between two video streams. One of the video streams is subjected for severe compression, noise and rotation. These two copies represent the same movie but only one of the versions is original. Examples of reliable recognition of an original scene that is subjected for compression and noise are presented. The algorithm is invariant to image scale and rotation, providing robust matching across a substantial range of video signals. This paper shows an approach to using descriptive features for randomly chosen unstructured frames from video stream. By involving color information as a feature together with intensity profile for frame blocks we increase the robustness of the identification process. The median of low frequencies is measured, followed by an average of spectral information from the same frame to identify clusters belonging to a scene in a film. Finally, a comparison of mean squares of the difference is done to verify the originality of the film.

  • 24.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Sundgren, David
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för matematik och statistik.
    Bengtsson, Ewert
    CBA.
    Application of underwater hyperspectral data for color correction purposes2007Inngår i: Pattern recognition and image analysis, ISSN 1555-6212, Vol. 17, nr 1, s. 170-173Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Color correction of underwater images has been considered a difficult task for a number of reasons. Those include severe absorption of the water column, the unpredictable behavior of light under the water surface, limited access to reliable data for correction purposes, and the fact that we are only able to process three spectral channels, which is insufficient for most color correction applications. Here, the authors present a method to estimate a hyperspectral image from an RGB image and pointwise hyperspectral data. This is then used to color correct the hyperspectral underwater image and transform it back into RGB color space.

  • 25.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Sundgren, David
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för matematik och statistik.
    Bengtsson, Ewert
    Pre-Processing of Underwater Images Taken in Shallow Waters for Color Reconstruction Purposes2005Inngår i: Proceedings of the 7th IASTED International Conference on Signal and Image Processing, 2005Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Coral reefs are monitored with different techniques in order to examine their health. Digital cameras, which provide an economically defendable tool for marine scientists to collect underwater data, tend to produce bluish images due to severe absorption of light at longer wavelengths. In this paper we study the possibilities of correcting for this color distortion through image processing. The decrease of red light by depth can be predicted by Beer's law. Another parameter that has to be taken into account is the image enhancement functions built into the camera. We use a spectrometer and a reflectance standard to obtain the data needed to approximate the joint effect of these functions. This model is used to pre-process the underwater images taken by digital cameras so that the red, green and blue channels show correct values before the images are subjected to correction for the effects of water column through application of Beer's law. This process is fully automatic and the amount of processed images is limited only by the speed of computer system. Experimental results show that the proposed method works well for correcting images taken at different depths with two different cameras.

  • 26.
    Åhlén, Julia
    et al.
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för datavetenskap.
    Sundgren, David
    Högskolan i Gävle, Institutionen för matematik, natur- och datavetenskap, Ämnesavdelningen för matematik och statistik.
    Lindell, Tommy
    Dissolved Organic Matters Impact on Color Reconstruction of Underwater Images2005Inngår i: Proceedings of the 14th Scandinavian Conference on Image Analysis, 2005, s. 1148-1156Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The natural properties of water column usually affect underwater imagery by supressing high-energy light. In application such as color correction of underwater images estimation of water column parameters is crucial. Diffuse attenuation coefficients are estimated and used for further processing of underwater taken data. The coeeficients will give information on how fast light of different wavelengths decreases with increasing depth. Based on the exact depth measurements and data from a spectrometrer the calculation of downwelling irradiance will be done. Chlorophyll concentration and a yellow substance factor contribute to a great variety of values of attenuation coefficients at different depth. By taking advantage of variations in depth, a method is presented to estimate the influence of dissolved organic matters and chlorophyll on color correction. Attenuation coefficients that depends on concentration of dissolved organic matters in water gives an indication on how well any spectral band is suited for color correction algorithm.

1 - 26 of 26
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