The mobile phone as a platform for assisting the independent living ...

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The process of tagging images with location could be partially automated, by using aspeech recognition interface on the phone, so that the database creator can tag images byannouncing her location while pictures are taken.6.4.2 Matching stageAfter the creation of the database, we can proceed to the matching stage of the sceneanalysis technique, where we can test our localization system.For the image comparison we use three off-the-shelf, open source algorithms for imageretrieval developed in C#. The first algorithm is part of the AForge.NET framework [17]which provides an exhaustive template matching algorithm. The algorithm performscomplete scan of source image, comparing each pixel with corresponding pixel oftemplate. The second algorithm is part of the Eye.Open image processing library [18]and finds similar images using RBG horizontal and vertical projections. The thirdalgorithm is found in the Image Recognition project [19] at CodePlex Open SourceCommunity and performs image retrieval using edge detection and colour recognition.When the web server receives a query image, it compares it with the images stored in thedatabase. Every image included in the database is assigned a weight by each algorithmwhich reflects the degree of similarity between the two images. The similarity level is afloat number in the interval [0,1], where 1 is the maximum degree of similarity betweentwo images and thus, exact match. The total weight of an image is calculated as a linearcombination of the weights assigned by each algorithm. If the weight of the best match isless than a certain threshold value, the query image is discarded. This is necessary toprevent wrong location updates from being sent to the client. Once the weights of theimages in the database with respect to the query image are known, we can return thelocation of the image that matches the query image with maximum weight. A screenshotof the application that performs the matching stage is shown in Figure 12.37
Fig.12: The matching stageThe query image is shown at the up-left corner of the application. We use a combo-boxto return the top n matches from the image database, where. We draw thelocation returned on the house map as an ellipse, using different colours for eachalgorithm (AForge.NET, Eye.Open image processing, Image Recognition and linearcombination of all algorithms).At the right side of the application we print in a textbox the results of the matching stage.For this reason we defined the 1meter accuracy as success in the task of localization. Todescribe a matching result with the term “Success” or “Fail” programmatically, we testedour application with some images that we used in our training set. The database containsthe exact location for these images in the form of Cartesian coordinates. In the returningmatches we discarded the top match with similarity level equal to 1, which of course isthe same as the query image and kept the second best match. The Euclidean distancebetween the query image and the second best match gives the accuracy of the locationcalculation, as the application behaves as the query image was never in the training set.6.5 EvaluationIn order to evaluate the proposed indoor localization system, we created a database of200 photographs of the apartment, following the method described in the matchingphase of the fingerprinting technique. In the photographs of the training set we tried tocapture every place on the house and especially the positions that match with daily life’s38
Page 1: ATHENS UNIVERSITY OF ECONOMICS AND
Page 5 and 6: AbstractMobile phones or Personal D
Page 7 and 8: Chapter 7: Implementation of an app
Page 9 and 10: List of TablesTable 1: Advantages a
Page 11 and 12: suffering from memory and orientati
Page 13 and 14: Chapter 2Smart Homes2.1 Smart Homes
Page 15 and 16: • Automate specific tasks that a
Page 17 and 18: Fig. 1: A smart home solution 1It i
Page 19 and 20: lighting strips and passive infra-r
Page 21 and 22: The Amigo project [10] is a joint e
Page 23 and 24: However, inside a building, that un
Page 25 and 26: with co-ordinates in the form of la
Page 27 and 28: At last, the utilized technology mu
Page 29 and 30: In this work we concentrate on achi
Page 31 and 32: In order to estimate the user locat
Page 33 and 34: 4.2.1 Cell-based methodsCell-based
Page 35 and 36: correlation between distance and si
Page 37 and 38: thus increasing the storage and mem
Page 39 and 40: egions. Examining images based on t
Page 41 and 42: Chapter 6Implementation of an indoo
Page 43 and 44: 6.3 Experimental environment, hardw
Page 45: Fig. 10: The database table that st
Page 49 and 50: 6.6 Future workIn this section we p
Page 51 and 52: According to the Eye Diseases Preva
Page 53 and 54: Fig. 14: Use of image processing te
Page 55 and 56: list, printing the best match as th
Page 57 and 58: Chapter 8Conclusion8.1 ConclusionMo
Page 59 and 60: [13] Gilles Virone, Norbert Noury,
Page 61 and 62: [40] Andrew M. Ladd, Kostas E. Bekr
Page 63: [65] X. Chen and A.L. Yuille, “A

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