Artwork classification based on image features

In this thesis we are trying to discover a method that allows us to attribute a painting to a particular artist with the help of image analysis. We are testing two methods. In the first one, we are trying to identify the style of a painter by analysing the way in which he translates a human face from a photograph into a painting. We are testing whether the differences on facial proportions in photographs and paintings are statistically significant. With the other method, we describe every painting with a set of features. The features look at the image color, texture and dimensions to form a feature vector. We test this on 10 pictures for each of the 3 painters with different styles. We are trying to test, whether we can correctly attribute these paintings to a painter just with these feature vectors

Utilisation of sludge from municipal wastewater treatment plant

In this diploma I will present the possibilities of using sewage sludge from municipal wastewater treatmentplant.\ud \ud The initial part of the diploma are the official documents and various literature to describe what should be the future application of this type of waste and uses. The following thesis has described some common options of sewage sludge, and products that can be extracted from the sewage sludge. In addition I described restrictions on the parameters of sludge from wastewater treatment plant that shall not exceed for a particular use. The final part of the diploma presents an analysis of sludge from wastewater treatment plant Ivančna Gorica.\ud \ud Based on a sample of sludge was used to determine suitability for continued use of sludge and its potential use. These results will serve us, so we can remove mud in legal acceptable way.\ud \ud It is necessary to emphasize that in the past for this purpose, cost was not necessary large, because the mad was cart away on agricultural land. Now, legislation is so strict that the possibility of using sewage sludge from municipal wastewater treatment plant is very limited and associated with a high cost of municipal wastewater treatment plant operator itself.\ud \u

Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities

Artwork classification based on image features

V tej diplomski nalogi želimo preizkusiti metodo, ki nam omogoči, da s pomočjo računalniške analize sliko pripišemo določenemu slikarju. Testiramo dva načina. Pri prvem pristopu želimo identificirati slikarja glede na način, s katerim preslika človeške obrazne poteze iz fotografije na naslikan portret. Zanima nas, ali so razlike v obraznih razmerjih na fotografiji in sliki statistično pomembne. Pri drugi metodi vsako sliko opišemo z vektorjem značilnic. Značilnice obsegajo barvo, teksturo in dimenzije slike, katerih kombinacija tvori vektor značilnic. Princip testiramo na 3 slikarjih z različnimi stili. Za vsakega od njih imamo množico desetih testnih slik. Zanima nas, ali lahko z gručenjem sliko pravilno pripišemo slikarju samo na podlagi teh vektorjev značilnic.In this thesis we are trying to discover a method that allows us to attribute a painting to a particular artist with the help of image analysis. We are testing two methods. In the first one, we are trying to identify the style of a painter by analysing the way in which he translates a human face from a photograph into a painting. We are testing whether the differences on facial proportions in photographs and paintings are statistically significant. With the other method, we describe every painting with a set of features. The features look at the image color, texture and dimensions to form a feature vector. We test this on 10 pictures for each of the 3 painters with different styles. We are trying to test, whether we can correctly attribute these paintings to a painter just with these feature vectors

Face aging using deep generative neural networks

Staranje obrazov je področje, ki se ukvarja z modeliranjem staranja osebe iz ene same referenčne slike. Želimo ustvariti generativni model, ki nam s pomočjo nevronskih mrež ustvari slike referenčne osebe pri različnih starostnih skupinah. Pri našem pristopu smo želeli cilj doseči z uporabo različnih generativnih arhitektur. Preizkusili smo nekaj uveljavljenih pristopov ter implementirali nekaj lastnih idej, ki se niso izkazale za najuspešnejše. Dobljeni končni rezultati so bili pod pričakovanji, vendar naloga naredi pregled nad preizkušenimi pristopi in njihovo implementacijo. Naloga predstavlja dobro podlago za nadaljnje raziskovanje na tem področju, saj naredi pregled nad uspešnimi in neuspešnimi pristopi ter težavami, ki se pojavljajo pri raziskovanju tega področja.Face aging as a research topic is dealing with modelling human aging from a reference photo. We want a generative model that, using generative neural networks, generates images of a reference person at a different age. We implemented some existing approaches and developed some of our own, however, they didn\u27t return results that we wished for. The final results were below expectations, however, the thesis makes a good overview over the implemented approaches and their implementation. The thesis creates a good foundation for further research. It gives a good overview over successful and non successful approaches and the difficulties that arise when doing research on this topic

Artwork classification based on image features

V tej diplomski nalogi želimo preizkusiti metodo, ki nam omogoči, da s pomočjo računalniške analize sliko pripišemo določenemu slikarju. Testiramo dva načina. Pri prvem pristopu želimo identificirati slikarja glede na način, s katerim preslika človeške obrazne poteze iz fotografije na naslikan portret. Zanima nas, ali so razlike v obraznih razmerjih na fotografiji in sliki statistično pomembne. Pri drugi metodi vsako sliko opišemo z vektorjem značilnic. Značilnice obsegajo barvo, teksturo in dimenzije slike, katerih kombinacija tvori vektor značilnic. Princip testiramo na 3 slikarjih z različnimi stili. Za vsakega od njih imamo množico desetih testnih slik. Zanima nas, ali lahko z gručenjem sliko pravilno pripišemo slikarju samo na podlagi teh vektorjev značilnic.In this thesis we are trying to discover a method that allows us to attribute a painting to a particular artist with the help of image analysis. We are testing two methods. In the first one, we are trying to identify the style of a painter by analysing the way in which he translates a human face from a photograph into a painting. We are testing whether the differences on facial proportions in photographs and paintings are statistically significant. With the other method, we describe every painting with a set of features. The features look at the image color, texture and dimensions to form a feature vector. We test this on 10 pictures for each of the 3 painters with different styles. We are trying to test, whether we can correctly attribute these paintings to a painter just with these feature vectors

CMIX: Cloud Mask Intercomparison eXercise

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masks have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. Here, we summarize results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10-30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), were evaluated within the CMIX. Those algorithms varied in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs were evaluated against existing reference cloud mask datasets. Those datasets varied in sampling methods, geographical distribution, sample unit (points, polygons, or full image labels), and generation approach (experts annotations, machine learning, or sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in cloud definitions used when producing the reference datasets. Average overall accuracy (across algorithms) varied 80.0±5.3% to 89.4±2.4% for Sentinel-2, and 79.8±7.1% to 97.6±0.8% for Landsat 8, depending on the reference dataset. An overall accuracy of 90% yields half the errors than an overall accuracy of 80%. The study identified algorithms that provided a balance between commission and omission errors, as well as algorithms, which are cloud conservative (high user’s accuracy) and non-cloud (clear) conservative (high producer’s accuracy). With repetitive observations like those of Sentinel-2, it seems reasonable to favor non-cloud conservative approaches, with maybe the exception of very cloudy regions where every cloud free observation is critical. When thin/semi-transparent clouds were not considered in the reference datasets algorithms’ performance generally improved: overall accuracy values increased by +1.5% to 7.4%. It should be noted though that these clouds are commonly occurring and are often present in optical imagery. Within CMIX, we also developed recommendations for further activities, which include provision of a quantitative definition for clouds (targeting moderate spatial resolution imagery by Landsat 8 and Sentinel-2), generation of new reference datasets, and expansion of the analysis framework (for example, multi-temporal analysis and application-driven validation). Such intercomparison studies will hopefully help the community to improve the algorithms and move towards standardization of cloud masking. Given the importance of cloud masking in optical satellite imagery we encourage CEOS to continue the CMIX activities