Image Matching for Dynamic Scenes

Hledání korespondencí mezi obrázky je důležitý krok v zaměřeních počítačového vidění jako jsou 3D rekonstrukce, hledání podobných obrázků a slepování obrázků. Vyzdvihujeme, že je důležité brát v potaz dynamické scény s různými pohyby, protože skutečný svět je dynamický. Navrhujeme rychlou hladovou metodu pro detekování několika homografií a jejich následné slučování do skupin podle pohybů. K tomuto účelu využíváme dvě vlastnosti dvou spolu se pohybujících homografií: jejich složení je planární homologie a z jejich korespondencí lze dobře vypočítat epipolární geometrii. Ukazujeme, že naše metoda funguje stejně dobře nebo lépe než sekvenční aplikování metody RANSAC. Dále navrhujeme sloučit znalost o skupinách korespondencí, které máme pro páry obrázků, do globálních skupin korespondencí, pokud máme k dispozici více než dva snímky. Tyto globání skupiny dále vložíme do klasického systému na rekonstrukci poloh kamer a bodů, abychom získali řídké rekonstrukce. Rekonstrukční systém je implementován pomocí knihovny, kterou jsme navrhli, aby byla spolehlivá, snadno rozšiřitelná a rychlá. Metodu na rekonstrukci ověřujeme na snímcích zachycující tři pohybující se objekty.Image matching is an important intermediate step for computer vision tasks such as 3D reconstruction, image retrieval, and image stitching. We argue that it is important to consider dynamic scenes with different motions because the real world is dynamic. We propose a fast greedy approach for detection of multiple homographies and their subsequent grouping into motion groups. For this purpose, we utilize two properties valid for two homographies that move together; their composition is a planar homology and epipolar geometry can be fitted well to their inliers. We show that our approach performs just as well or better than sequential application of RANSAC. Furthermore, we propose to fuse the groupings of matches available for every image pair into global grouping of n-view matches when more than two views are available. Next, we supply the groups of n-view matches to an incremental structure-from-motion pipeline to compute sparse 3D reconstructions independently. The pipeline is implemented using our library which we have designed to be reliable, easy to extend, and efficient. The approach for reconstruction of dynamic scenes is evaluated on a dataset with three moving objects

Model za inventarizaciju, monitoring i evaluaciju suhozidnih gradnji u Hrvatskoj na primjeru Starogradskoga polja na otoku Hvaru; Doktorska disertacija [sažetak]

Suhozidne gradnje karakterističan su element kulturnog krajolika jadranske Hrvatske i jedan od njenih općeprihvaćenih kulturnih simbola. Na temelju sinteze podataka iz strane i domaće literature (uključujući i uspostavljenu javnu internetsku web kartu) te vlastitih terenskih istraživanja formiran je model prostorne baze podataka za njihovu inventarizaciju, monitoring i evaluaciju. Model je upotrijebljen za izradu kvantitativnih i kvalitativnih analiza njihove distribucije na nekoliko prostornih razina: od razine države do istaknutog hrvatskog kulturnog krajolika, Starogradskog polja na otoku Hvaru

Classifying the Mediterranean terraced landscape: the case of Adriatic Croatia

This article presents a Croatian Adriatic terraced landscape classification, highlighting its natural and cultural background and proposing a classification framework for further research. The classification is based on the landscape level (i.e., the “landscape pattern level”), synthesizing its structural, biophysical, and cultural-historical dimensions. The interpretation of classes involves a combination of general land use, structure, geomorphology, local land use, crops, soil type, and historical aspects. Nine classes of terraced landscapes are identified and described, example locations are given, and they are substantiated with illustrations and photos

A Modular and Adaptive System for Business Email Compromise Detection

The growing sophistication of Business Email Compromise (BEC) and spear phishing attacks poses significant challenges to organizations worldwide. The techniques featured in traditional spam and phishing detection are insufficient due to the tailored nature of modern BEC attacks as they often blend in with the regular benign traffic. Recent advances in machine learning, particularly in Natural Language Understanding (NLU), offer a promising avenue for combating such attacks but in a practical system, due to limitations such as data availability, operational costs, verdict explainability requirements or a need to robustly evolve the system, it is essential to combine multiple approaches together. We present CAPE, a comprehensive and efficient system for BEC detection that has been proven in a production environment for a period of over two years. Rather than being a single model, CAPE is a system that combines independent ML models and algorithms detecting BEC-related behaviors across various email modalities such as text, images, metadata and the email's communication context. This decomposition makes CAPE's verdicts naturally explainable. In the paper, we describe the design principles and constraints behind its architecture, as well as the challenges of model design, evaluation and adapting the system continuously through a Bayesian approach that combines limited data with domain knowledge. Furthermore, we elaborate on several specific behavioral detectors, such as those based on Transformer neural architectures

Classifying the Mediterranean terraced landscape: the case of Adriatic Croatia

This article presents a Croatian Adriatic terraced landscape classification, highlighting its natural and cultural background and proposing a classification framework for further research. The classification is based on the landscape level (i.e., the “landscape pattern level”), synthesizing its structural, biophysical, and cultural-historical dimensions. The interpretation of classes involves a combination of general land use, structure, geomorphology, local land use, crops, soil type, and historical aspects. Nine classes of terraced landscapes are identified and described, example locations are given, and they are substantiated with illustrations and photos