Automatic coregistration of three-dimensional building models with image features

Namen naše raziskave je samodejna določitev tekstur streh in fasad stavb z infrardečih (IR) posnetkov za teksturiranje obstoječega trirazsežnega (3D) modela stavb. Za to je treba izboljšati natančnosti neposredno izmerjenih parametrov zunanje orientacije IR-kamere, pritrjene na mobilno platformo. Ta prispevek opisuje metodo, razvito za izboljšanje parametrov zunanje orientacije, ki temelji na ujemanju točk samodejno zaznanih grafičnih gradnikov z IR-videoposnetka in žičnega modela stavb. Najprej proučimo zaznavo različnih tipov grafičnih gradnikov na testnem IR-posnetku. Förstnerjeve in presečiščne točke izberemo kot primerne grafične gradnike z a predstavitev obravnavanih značilnosti stavb na IR-posnetku. 3D-model stavb projiciramo na vsak posamezen posnetek videosekvence ob upoštevanju orientacijskih parametrov, od katerih so parametri zunanje orientacije podani s približnimi vrednostmi. Nato izvedemo samodejno koregistracijo 3D-modela stavb, projiciranega na videoposnetek, in grafičnih gradnikov, zaznanih z istega IR-videoposnetka. Samodejno ujemanje 3D-modela stavb in zaznanih grafičnih gradnikov poteka iterativno in skupaj z izravnavo parametrov zunanje orientacije z metodo najmanjših kvadratov. Razvito metodologijo za koregistracijo in izravnavo zunanjih orientacijskih parametrov smo preizkusili na strnjenem poseljenem območju. Kakovost metodologije ocenimo s petimi parametri: učinkovitostjo metodologije, popolnostjo in pravilnostjo algoritmov za ujemanje in zaznavo grafičnih gradnikov.The aim of this article is to investigate methods for the automatic extraction of the infrared (IR) textures for the roofs and facades of existing building models. We focus on the correction of the measured exterior orientation parameters of the IR camera mounted on a mobile platform. The developed method is based on point-to-point matching of the features extracted from IR images with a wire-frame building model. Firstly, the extraction of different feature types is studied on a sample IR imageFörstner and intersection points are chosen for a representation of the image features. Secondly, the three-dimensional (3D) building model is projected into each frame of the IR video sequence using orientation parametersonly coarse exterior orientation parameters are known. Then the automatic co-registration of a 3D building model projection into the image sequence with image features is carried out. The matching of a model and extracted features is applied iteratively, and exterior orientation parameters are adjusted with least square adjustment. The method is tested on a dataset of a dense urban area. Finally, an evaluation of the developed method is presented with five quality parameters, i.e. efficiency of the method, completeness and correctness of matching and extraction

Searching for unwanted drug interactions

Drug interactions are interweaving effects between two or more drugs that can have desirable or harmful effects on patients health. In this thesis we for searched harmful drug interactions. Our approach is based on two machine learning algorithms for association rule mining. We use two given hierarchies, one for drugs (ATC), the other for diseases (ICD), and one proprietary interaction database LexiComp. A generalized association rule algorithm tries to find rules that contain basic elements as well as elements from given hierarchies. The second algorithm uses high-utility pattern mining. The utility function was designed to use statistical information from both the data and the hierarchies. Algorithms were tested on artificial data and on a dataset from University Medical Centre Ljubljana. Detected rules were reviewed, analyzed, commented and evaluated by pharmacists. The results are promising as several interesting new rules and patterns are detected

Analysis of survey consistency with machine learning

We researched the quality of survey responses. We don't know if answers really reect the opinion of interviewees. We believe that inconsistent, respondents can be detected with the use of machine learning techniques. Our idea is to build a prediction model for every question of a survey. With the models, we get a probability distribution for every answer in the survey. We use cross-validation to get distributions for all instances. We evaluate them with Brier score, information score, probabilities, classification accuracy, Birer ranking, information score ranking, probability ranking and classification accuracy ranking. We merge these scores, and get an inconsistency score for every instance (interviewee) of the survey. We visualize these inconsistent cases for a better comprehension. We developed the method with the statistical system R and packages CORElearn [14], MASS [20] and rpart [16]. For the visualization we used package CORElearn and data mining software Orange [5]. For testing purposes we used data sets Monk, B2B, B2C, DPS and hearnig aid. As prediction models we mostly used random forests, because of their superb accuraccy. Missing values were imputed with the use of k-nearest neighbor (kNN), modus, mean, or the instance was simply removed from the data. We generated inconsistent data and tried to identify these cases. There were some variance in our incosistency scores, so we reduced it by averaging the scores. For a better comprehension and indetification, we have plotted the cases that were identi_ed as inconsistent. The results depend on the data and evaluation method. Brier score, probabilities, Brier ranking and prabability ranking in most cases identified all inconsistent instances (interviewees). Other methods sometimes failed to identify inconsistent cases. The approach is computationaly demanding for larger datasets

Searching for unwanted drug interactions

Interakcije zdravil so prepletanja učinkov zdravil, ki lahko povzročijo želene ali škodljive učinke na pacientu. V nalogi smo z orodjem strojnega učenja iskali interakcije zdravil, ki lahko na zdravje bolnikov vplivajo negativno. Naš pristop k reševanju problema temelji na dveh algoritmih strojnega učenja. Pri tem smo upoštevali hierarhiji zdravil in bolezni ter bazo LexiComp. Algoritem posplošenih povezovalnih pravil poskuša s pomočjo hierarhij poiskati pravila, ki poleg osnovnih elementov upoštevajo tudi njihove posplošitve v hierarhiji. Drugi uporabljeni algoritem je iskanje pravil s koristnostno funkcijo, ki uporablja statistične informacije podatkov. Algoritme smo testirali na umetno generiranih podatkih in na realnih podatkih pacientov iz Univerzitetnega kliničnega centra v Ljubljani. Najdena pravila so pregledali farmacevti, ki so jih podrobno analizirali in komentirali. Rezultati algoritmov so obetavni, saj smo odkrili nekaj zanimivih novih pravil in vzorcev.Drug interactions are interweaving effects between two or more drugs that can have desirable or harmful effects on patients health. In this thesis we for searched harmful drug interactions. Our approach is based on two machine learning algorithms for association rule mining. We use two given hierarchies, one for drugs (ATC), the other for diseases (ICD), and one proprietary interaction database LexiComp. A generalized association rule algorithm tries to find rules that contain basic elements as well as elements from given hierarchies. The second algorithm uses high-utility pattern mining. The utility function was designed to use statistical information from both the data and the hierarchies. Algorithms were tested on artificial data and on a dataset from University Medical Centre Ljubljana. Detected rules were reviewed, analyzed, commented and evaluated by pharmacists. The results are promising as several interesting new rules and patterns are detected

Searching for unwanted drug interactions

Interakcije zdravil so prepletanja učinkov zdravil, ki lahko povzročijo želene ali škodljive učinke na pacientu. V nalogi smo z orodjem strojnega učenja iskali interakcije zdravil, ki lahko na zdravje bolnikov vplivajo negativno. Naš pristop k reševanju problema temelji na dveh algoritmih strojnega učenja. Pri tem smo upoštevali hierarhiji zdravil in bolezni ter bazo LexiComp. Algoritem posplošenih povezovalnih pravil poskuša s pomočjo hierarhij poiskati pravila, ki poleg osnovnih elementov upoštevajo tudi njihove posplošitve v hierarhiji. Drugi uporabljeni algoritem je iskanje pravil s koristnostno funkcijo, ki uporablja statistične informacije podatkov. Algoritme smo testirali na umetno generiranih podatkih in na realnih podatkih pacientov iz Univerzitetnega kliničnega centra v Ljubljani. Najdena pravila so pregledali farmacevti, ki so jih podrobno analizirali in komentirali. Rezultati algoritmov so obetavni, saj smo odkrili nekaj zanimivih novih pravil in vzorcev.Drug interactions are interweaving effects between two or more drugs that can have desirable or harmful effects on patients health. In this thesis we for searched harmful drug interactions. Our approach is based on two machine learning algorithms for association rule mining. We use two given hierarchies, one for drugs (ATC), the other for diseases (ICD), and one proprietary interaction database LexiComp. A generalized association rule algorithm tries to find rules that contain basic elements as well as elements from given hierarchies. The second algorithm uses high-utility pattern mining. The utility function was designed to use statistical information from both the data and the hierarchies. Algorithms were tested on artificial data and on a dataset from University Medical Centre Ljubljana. Detected rules were reviewed, analyzed, commented and evaluated by pharmacists. The results are promising as several interesting new rules and patterns are detected

Searching for unwanted drug interactions

Drug interactions are interweaving effects between two or more drugs that can have desirable or harmful effects on patients health. In this thesis we for searched harmful drug interactions. Our approach is based on two machine learning algorithms for association rule mining. We use two given hierarchies, one for drugs (ATC), the other for diseases (ICD), and one proprietary interaction database LexiComp. A generalized association rule algorithm tries to find rules that contain basic elements as well as elements from given hierarchies. The second algorithm uses high-utility pattern mining. The utility function was designed to use statistical information from both the data and the hierarchies. Algorithms were tested on artificial data and on a dataset from University Medical Centre Ljubljana. Detected rules were reviewed, analyzed, commented and evaluated by pharmacists. The results are promising as several interesting new rules and patterns are detected