Unsupervised segmentation of road images. A multicriteria approach

This paper presents a region-based segmentation algorithm which can be applied to various problems since it does not requir e a priori knowledge concerning the kind of processed images . This algorithm, based on a split and merge method, gives reliable results both on homogeneous grey level images and on textured images . First, images are divided into rectangular sectors . The splitting algorithm works independently on each sector, and uses a homogeneity criterion based only on grey levels . The mergin g is then achieved through assigning labels to each region obtained by the splitting step, using extracted feature measurements . We modeled exploited fields (data field and label field) by Markov Random Fields (MRF), the segmentation is then optimall y determined using the Iterated Conditional Modes (ICM) . Input data of the merging step are regions obtained by the splitting step and their corresponding features vector. The originality of this algorithm is that texture coefficients are directly computed from these regions . These regions will be elementary sites for the Markov relaxation process . Thus, a region- based segmentation algorith m using texture and grey level is obtained . Results from various images types are presented .Nous présentons ici un algorithme de segmentation en régions pouvant s'appliquer à des problèmes très variés car il ne tient compte d'aucune information a priori sur le type d'images traitées. Il donne de bons résultats aussi bien sur des images possédant des objets homogènes au sens des niveaux de gris que sur des images possédant des régions texturées. C'est un algorithme de type division-fusion. Lors d'une première étape, l'image est découpée en fenêtres, selon une grille. L'algorithme de division travaille alors indépendamment sur chaque fenêtre, et utilise un critère d'homogénéité basé uniquement sur les niveaux de gris. La texture de chacune des régions ainsi obtenues est alors calculée. A chaque région sera associé un vecteur de caractéristiques comprenant des paramètres de luminance, et des paramètres de texture. Les régions ainsi définies jouent alors le rôle de sites élémentaires pour le processus de fusion. Celui-ci est fondé sur la modélisation des champs exploités (champ d'observations et champ d'étiquettes) par des champs de Markov. Nous montrerons les résultats de segmentation obtenus sur divers types d'images

Mind the Gap: Developments in Autonomous Driving Research and the Sustainability Challenge

Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modeling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions

THREE-DIMENSIONAL VISION FOR STRUCTURE AND MOTION ESTIMATION

1997/1998Questa tesi, intitolata Visione Tridimensionale per la stima di Struttura e Moto, tratta di tecniche di Visione Artificiale per la stima delle proprietà geometriche del mondo tridimensionale a partire da immagini numeriche. Queste proprietà sono essenziali per il riconoscimento e la classificazione di oggetti, la navigazione di veicoli mobili autonomi, il reverse engineering e la sintesi di ambienti virtuali. In particolare, saranno descritti i moduli coinvolti nel calcolo della struttura della scena a partire dalle immagini, e verranno presentati contributi originali nei seguenti campi. Rettificazione di immagini steroscopiche. Viene presentato un nuovo algoritmo per la rettificazione, il quale trasforma una coppia di immagini stereoscopiche in maniera che punti corrispondenti giacciano su linee orizzontali con lo stesso indice. Prove sperimentali dimostrano il corretto comportamento del metodo, come pure la trascurabile perdita di accuratezza nella ricostruzione tridimensionale quando questa sia ottenuta direttamente dalle immagini rettificate. Calcolo delle corrispondenze in immagini stereoscopiche. Viene analizzato il problema della stereovisione e viene presentato un un nuovo ed efficiente algoritmo per l'identificazione di coppie di punti corrispondenti, capace di calcolare in modo robusto la disparità stereoscopica anche in presenza di occlusioni. L'algoritmo, chiamato SMW, usa uno schema multi-finestra adattativo assieme al controllo di coerenza destra-sinistra per calcolare la disparità e l'incertezza associata. Gli esperimenti condotti con immagini sintetiche e reali mostrano che SMW sortisce un miglioramento in accuratezza ed efficienza rispetto a metodi simili Inseguimento di punti salienti. L'inseguitore di punti salienti di Shi-Tomasi- Kanade viene migliorato introducendo uno schema automatico per lo scarto di punti spuri basato sulla diagnostica robusta dei campioni periferici ( outliers ). Gli esperimenti con immagini sintetiche e reali confermano il miglioramento rispetto al metodo originale, sia qualitativamente che quantitativamente. Ricostruzione non calibrata. Viene presentata una rassegna ragionata dei metodi per la ricostruzione di un modello tridimensionale della scena, a partire da una telecamera che si muove liberamente e di cui non sono noti i parametri interni. Il contributo consiste nel fornire una visione critica e unificata delle più recenti tecniche. Una tale rassegna non esiste ancora in letterarura. Moto tridimensionale. Viene proposto un algoritmo robusto per registrate e calcolare le corrispondenze in due insiemi di punti tridimensionali nei quali vi sia un numero significativo di elementi mancanti. Il metodo, chiamato RICP, sfrutta la stima robusta con la Minima Mediana dei Quadrati per eliminare l'effetto dei campioni periferici. Il confronto sperimentale con una tecnica simile, ICP, mostra la superiore robustezza e affidabilità di RICP.This thesis addresses computer vision techniques estimating geometrie properties of the 3-D world /rom digital images. Such properties are essential for object recognition and classification, mobile robots navigation, reverse engineering and synthesis of virtual environments. In particular, this thesis describes the modules involved in the computation of the structure of a scene given some images, and offers original contributions in the following fields. Stereo pairs rectification. A novel rectification algorithm is presented, which transform a stereo pair in such a way that corresponding points in the two images lie on horizontal lines with the same index. Experimental tests prove the correct behavior of the method, as well as the negligible decrease oLthe accuracy of 3-D reconstruction if performed from the rectified images directly. Stereo matching. The problem of computational stereopsis is analyzed, and a new, efficient stereo matching algorithm addressing robust disparity estimation in the presence of occlusions is presented. The algorithm, called SMW, is an adaptive, multi-window scheme using left-right consistency to compute disparity and its associated uncertainty. Experiments with both synthetic and real stereo pairs show how SMW improves on closely related techniques for both accuracy and efficiency. Features tracking. The Shi-Tomasi-Kanade feature tracker is improved by introducing an automatic scheme for rejecting spurious features, based on robust outlier diagnostics. Experiments with real and synthetic images confirm the improvement over the original tracker, both qualitatively and quantitatively. 111 Uncalibrated vision. A review on techniques for computing a three-dimensional model of a scene from a single moving camera, with unconstrained motion and unknown parameters is presented. The contribution is to give a critical, unified view of some of the most promising techniques. Such review does not yet exist in the literature. 3-D motion. A robust algorithm for registering and finding correspondences in two sets of 3-D points with significant percentages of missing data is proposed. The method, called RICP, exploits LMedS robust estimation to withstand the effect of outliers. Experimental comparison with a closely related technique, ICP, shows RICP's superior robustness and reliability.XI Ciclo1968Versione digitalizzata della tesi di dottorato cartacea

Three-dimensional scene recovery for measuring sighting distances of rail track assets from monocular forward facing videos

Rail track asset sighting distance must be checked regularly to ensure the continued and safe operation of rolling stock. Methods currently used to check asset line-of-sight involve manual labour or laser systems. Video cameras and computer vision techniques provide one possible route for cheaper, automated systems. Three categories of computer vision method are identified for possible application: two-dimensional object recognition, two-dimensional object tracking and three-dimensional scene recovery. However, presented experimentation shows recognition and tracking methods produce less accurate asset line-of-sight results for increasing asset-camera distance. Regarding three-dimensional scene recovery, evidence is presented suggesting a relationship between image feature and recovered scene information. A novel framework which learns these relationships is proposed. Learnt relationships from recovered image features probabilistically limit the search space of future features, improving efficiency. This framework is applied to several scene recovery methods and is shown (on average) to decrease computation by two-thirds for a possible, small decrease in accuracy of recovered scenes. Asset line-of-sight results computed from recovered three-dimensional terrain data are shown to be more accurate than two-dimensional methods, not effected by increasing asset-camera distance. Finally, the analysis of terrain in terms of effect on asset line-of-sight is considered. Terrain elements, segmented using semantic information, are ranked with a metric combining a minimum line-of-sight blocking distance and the growth required to achieve this minimum distance. Since this ranking measure is relative, it is shown how an approximation of the terrain data can be applied, decreasing computation time. Further efficiency increases are found by decomposing the problem into a set of two-dimensional problems and applying binary search techniques. The combination of the research elements presented in this thesis provide efficient methods for automatically analysing asset line-of-sight and the impact of the surrounding terrain, from captured monocular video.EThOS - Electronic Theses Online ServiceGBUnited Kingdo