Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes

Models that captures the common structure of an object class have appeared few years ago in the literature (Jojic and Caspi in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 212---219, 2004; Winn and Jojic in Proceedings of International Conference on Computer Vision (ICCV), pp. 756---763, 2005); they are often referred as "stel models." Their main characteristic is to segment objects in clear, often semantic, parts as a consequence of the modeling constraint which forces the regions belonging to a single segment to have a tight distribution over local measurements, such as color or texture. This self-similarity within a region in a single image is typical of many meaningful image parts, even when across different images of similar objects, the corresponding parts may not have similar local measurements. Moreover, the segmentation itself is expected to be consistent within a class, although still flexible. These models have been applied mostly to segmentation scenarios. In this paper, we extent those ideas (1) proposing to capture correlations that exist in structural elements of an image class due to global effects, (2) exploiting the segmentations to capture feature co-occurrences and (3) allowing the use of multiple, eventually sparse, observation of different nature. In this way we obtain richer models more suitable to recognition tasks. We accomplish these requirements using a novel approach we dubbed stel component analysis. Experimental results show the flexibility of the model as it can deal successfully with image/video segmentation and object recognition where, in particular, it can be used as an alternative of, or in conjunction with, bag-of-features and related classifiers, where stel inference provides a meaningful spatial partition of features

Statistical part-based models for object detection in large 3D scans

3D scanning technology has matured to a point where very large scale acquisition of high resolution geometry has become feasible. However, having large quantities of 3D data poses new technical challenges. Many applications of practical use require an understanding of semantics of the acquired geometry. Consequently scene understanding plays a key role for many applications. This thesis is concerned with two core topics: 3D object detection and semantic alignment. We address the problem of efficiently detecting large quantities of objects in 3D scans according to object categories learned from sparse user annotation. Objects are modeled by a collection of smaller sub-parts and a graph structure representing part dependencies. The thesis introduces two novel approaches: A part-based chain structured Markov model and a general part-based full correlation model. Both models come with efficient detection schemes which allow for interactive run-times.Die Technologie für 3-dimensionale bildgebende Verfahren (3D Scans) ist mittlerweile an einem Punkt angelangt, an dem hochaufglöste Geometrie-Modelle für sehr große Szenen erstellbar sind. Große Mengen dreidimensionaler Daten stellen allerdings neue technische Herausforderungen. Viele Anwendungen von praktischem Nutzen erfordern ein semantisches Verständnis der akquirierten Geometrie. Dementsprechend spielt das sogenannte “Szenenverstehen” eine Schlüsselrolle bei vielen Anwendungen. Diese Dissertation beschäftigt sich mit 2 Kernthemen: 3D Objekt-Detektion und semantische (Objekt-) Anordnung. Das Problem hierbei ist, große Mengen von Objekten effizient in 3D Scans zu detektieren, wobei die Objekte aus bestimmten Objektkategorien entstammen, welche mittels gerinfügiger Annotationen durch den Benutzer gelernt werden. Dabei werden Objekte modelliert durch eine Ansammlung kleinerer Teilstücke und einer Graph-Struktur, welche die Abhängigkeiten der Einzelteile repäsentiert. Diese Arbeit stellt zwei neuartige Ansätze vor: Ein Markov-Modell, das aus einer teilebasierten Kettenstruktur besteht und einen generellen Ansatz, der auf einem Modell mit voll korrelierten Einzelteilen beruht. Zu beiden Modellen werden effiziente Detektionsschemata aufgezeigt, die interaktive Laufzeiten ermöglichen

Object Recognition with Hierarchical Stel Models

We propose a new generative model, and a new image similarity kernel based on a linked hierarchy of probabilistic segmentations. The model is used to efficiently segment multiple images into a consistent set of image regions. The segmentations are provided at several levels of granularity and links among them are automatically provided. Model training and inference in it is faster than most local feature extraction algorithms, and yet the provided image segmentation, and the segment matching among images provide a rich backdrop for image recognition, segmentation and registration tasks