View subspaces for indexing and retrieval of 3D models

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithmsComment: Three-Dimensional Image Processing (3DIP) and Applications (Proceedings Volume) Proceedings of SPIE Volume: 7526 Editor(s): Atilla M. Baskurt ISBN: 9780819479198 Date: 2 February 201

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

3D Shape Correspondence by Isometry Driven Greedy Optimization

We present an automatic method that establishes 3D correspondence between isometric shapes. Our goal is to find an optimal correspondence between two given (nearly) isometric shapes, that minimizes the amount of deviation from isometry. We cast the problem as a complete surface correspondence problem. Our method first divides the given shapes to be matched into surface patches of equal area and then seeks for a mapping between the patch centers which we refer to as base vertices. Hence the correspondence is established in a fast and robust manner at a relatively coarse level as imposed by the patch radius. We optimize the isometry cost in two steps. In the first step, the base vertices are transformed into spectral domain based on geodesic affinity, where the isometry errors are minimized in polynomial time by complete bipartite graph matching. The resulting correspondence serves as a good initialization for the second step of optimization in which we explicitly minimize the isometry cost via an iterative greedy algorithm in the original 3D Euclidean space. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes for some of which the ground-truth correspondence is available

3D Isometric Shape Correspondence

3B izometrik şekiller arasındaki eşleme problemini ele alıyoruz. Önerdiğimiz yöntem, verilen iki izometrik şekil arasındaki izometrik sapmayı enküçülten optimal eşlemeyi otomatik olarak bulabilmektedir.İzometri hatasını iki adımda eniyiliyoruz. İlk adımda, şekil yüzeylerinden örneklenmiş bir örnek 3B noktalar kesel ilginlik bilgisine dayanarak spektral uzaya aktarılır.İlk eşleme spektral uzayda tam iki kısımlı bir çizge eşleştirme yöntemi kullanarak izometri hatasının polinom zamanda enküçültülmesiyle elde edilir. Elde edilen bu ilk eşleme ikinci adımda, geliştirdiğimiz döngülü fırsatçı bir algoritmayla izometri maliyetini 3B Oklit uzayda enküçülterek iyileştirilir. Yöntemimizi tam olarak ya da neredeyse izometrik şekil çiftleri üzerinde sınıyor ve başarımını gerçekeşleme bilgisine dayanarak veriyoruz.We address the problem of correspondence between 3D isometric shapes. We present an automatic method that finds the optimal correspondence between two given (nearly) isometric shapes by minimizing the amount of deviation from isometry. We optimize the isometry error in two steps. In the first step, the 3D points uniformly sampled from the shape surfaces are transformed into spectral domain based on geodesic affinity, where the isometry errors are minimized in polynomial time by complete bipartite graph matching. The second step of optimization, which is well-initialized by the resulting correspondence of the first step, explicitly minimizes the isometry cost via an iterative greedy algorithm in the original 3D Euclidean space. Our method is put to test using (nearly) isometric pairs of shapes and its performance is measured via ground-truth correspondence information when available