Research project grouping and ranking by using adaptive Mahalanobis clustering

The paper discusses the problem of grouping and ranking of research projects submitted for a call. The projects are grouped into clusters based on the assessment obtained in the review procedure and by using the adaptive Mahalanobis clustering method as a special case of the Expectation Maximization algorithm. The cluster of projects assessed as best is specially analyzed and ranked. The paper outlines several possibilities for the use of data obtained in the review procedure, and the proposed method is illustrated with the example of internal research projects at the University of Osijek

Image based crop row detection using global optimization methods

U ovoj doktorskoj disertaciji naglasak je stavljen na razvoj metoda za prepoznavanje redova usjeva na slici. Cilj je razviti metodu koja posjeduje sljedeca svojstva: robusnost s obzirom na prisutnost korova i sjene, mogucnost prepoznavanja redova usjeva za razlicite kulture u razlicitim stadijima rasta, mogucnost prepoznavanja razlicitog broja proizvoljno razmaknutih redova usjeva te mogucnost prepoznavanja ravnih i zakrivljenih redova usjeva. Razvijene su dvije metode koje su nazvane CRDI metoda i TMGEM metoda. CRDI metoda zasnovana je na inkrementalnoj metodi za trazenje priblizno optimalne particije skupa podataka te primjeni DIRECT algoritma za globalnu optimizaciju. Metoda je pogodna za prepoznavanje ravnih redova usjeva, pri cemu broj redova usjeva mora biti unaprijed poznat. TMGEM metoda zasnovana je racunanju podudaranja s predloskom i minimizaciji funkcije globalne energije primjenom dinamickog programiranja. Funkcija globalne energije, uz podatke sa slike, koristi i prethodno znanje o geometrijskoj strukturi redova usjeva, cime se postize veca tocnost prepoznavanja redova usjeva. Predlozena metoda ima mogucnost prepoznavanja proizvoljnog broja redova usjeva za razlicite kulture u razlicitim stadijima rasta te pri razlicitim geometrijama polja. Nadalje, predlozena TMGEM metoda robusna je na zakrivljenost redova usjeva. Osim navedenih metoda, u radu je predlozen novi evaluacijski okvir za usporedbu metoda prepoznavanja redova usjeva koji ukljucuje bazu slika redova usjeva, postupak rucnog generiranja referentnih vrijednosti redova usjeva te kriterije za odredivanje tocnosti prepoznavanja redova usjeva. Na slikama u bazi snimljeni su redovi usjeva kukuruza, celera, krumpira, luka, suncokreta i soje. Koristenjem predlozenog evaluacijskog okvira moguce je ekasnije i objektivnije usporediti novo razvijene metode za prepoznavanje redova usjeva s postojecim metodama. U eksperimentalnoj evaluaciji CRDI metoda je usporedena s IMLD i HT metodom na umjetno generiranim skupovima podataka. Tocnost prepoznavanja odredena je primjenom CRHID kriterija, a rezultati pokazuju da CRDI metoda prepoznaje redove usjeva sa znatno vecom tocnoscu u odnosu na ostale razmatrane metode. TMGEM metoda testirana je na 225 stvarnih slika redova usjeva iz baze slika te je usporedena s HT, HTT i LR metodom. Tocnost prepoznavanja redova usjeva odredena je koristenjem CRDA kriterija, a rezultati pokazuju da predlozena metoda znacajno nadmasuje ostale tri razmatrane metode pri prepoznavanju ravnih redova usjeva te je robusna na zakrivljenost redova usjeva.The topic of this doctoral thesis is the development of image based crop row detection methods. The main goal of the research is to develop a method which is: highly insensitive to the presence of weeds and shadows, capable of detecting crop rows of dierent crop types at dierent stages of growth, capable of detecting straight and curved crop rows and insensitive to the number and spacing of crop rows. Two methods are proposed entitled CRDI and TMGEM. CRDI method is based on incremental method of searching for an approximate globally optimal partition of a set of data points and on the DIRECT algorithm for global optimization. The method is capable of detecting straight crop rows, wherein the number of crop rows must be known in advance. TMGEM method is based on template matching followed by global energy minimization with dynamic programming technique. For accurate crop row detection, the global energy function combines image evidence and prior knowledge about the geometric structure of crop rows. The proposed method is insensitive to the number and spacing of crop rows and is capable of detecting crop rows of dierent crop types at dierent stages of growth. Furthermore, the proposed TMGEM method is capable of detecting curved crop rows. A new evaluation framework is proposed that consists of a crop row image database, manual ground truth image creation approach and two crop row detection performance measures. The image database includes images of dierent crop types including maize, celery, potato, onion, sun ower and soybean. The proposed evaluation framework enables ecient and objective comparison of new crop row detection methods with existing ones. The experimental evaluation of CRDI method includes comparison with IMLD and HT method on synthetic datasets, based on the proposed CRHID performance measure. The results show that CRDI method outperforms other considered methods. TMGEM method is evaluated on a set of 225 real-world crop row images from the image database and it is compared with HT, HTT and LR method. The proposed CRDA measure is used as a performance measure in the comparison. The results show that TMGEM signicantly outperforms the other considered methods in straight crop row detection and is capable of detecting curved crop rows