Affine invariant model-based object recognition

Lamdan, Y.; Schwartz, Jacob T.; Wolfson, Haim J.

doi:10.1109/70.62047

Cited by 276 publications

(140 citation statements)

References 20 publications

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“…When the shape is not deformable or we are not interested in recovering the deformation but only in localizing the object up to translation and scale, simple strategies can be applied, such as Geometric Hashing [23], Hough Transform [29], or exhaustive search (typically combined with Chamfer Matching [18] or classifiers [14,33]). In case of complex deformations, the parameter space becomes too large for these simple strategies.…”

Section: Related Workmentioning

confidence: 99%

From Images to Shape Models for Object Detection

2009

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We present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is made possible by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately, while needing no segmented examples for training (only bounding-boxes) Key-words: object detection, shape matching, contour features * This research was supported by the EADS foundation, INRIA and CNRS. Vittorio Ferrari was funded by a postdoctoral fellowship of the EADS foundation. D'images au modèles de forme pour la détection d'objetsRésumé : Nous proposons une nouvelle approche pour la détection de catégories d'objets qui intègre les avantages des méthodes de mise en correspondance de formes. D'une part, comme les détecteurs d'objets, notre approche est capable d'apprendre les modèles de classe directement à partir des images et de localiser de nouvelles instances malgré des variations intra-classe, des changements d'échelle ou la présence d'un fond encombré. D'autre part, tout comme les méthodes de mise en correspondance de formes, elle ne fournit pas simplement une boite englobante, mais parvient à extraire les frontières des objets. Ceci est possible grâce à une nouvelle technique d'apprentissage capable de construire le modèle de forme d'une catégorie d'objets, étant donné des images contenant des instances de cette catégorie. Afin de localiser le modèle dans des images test, nous combinons un algorithme de vote dans un espace de Hough à un algorithme de mise en correspondance de points pour les formes non rigides. Les résultats expérimentaux montrent que les frontières des objets sont localisées avec précision sans utiliser d'images d'apprentissage segmentées, les boites englobants seules suffisent.

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Section: Related Workmentioning

confidence: 99%

From Images to Shape Models for Object Detection

2009

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“…For example, [39] contains perhaps the most straightforward method that computes the correspondence between the two point sets using all possible sets of four points. This method is similar to an earlier method [32] that uses affine invariant representation of points based on a triplet of basis points. All possible triplets are considered and the affine invariants of the remaining points are computed and stored in a hash table, which is then used in the matching stage.…”

Section: Previous Workmentioning

confidence: 99%

Higher Dimensional Affine Registration and Vision Applications

Chi

Shahed

et al. 2008

Lecture Notes in Computer Science

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Abstract-Affine registration has a long and venerable history in computer vision literature, and in particular, extensive work has been done for affine registration in IR 2 and IR 3 . This paper studies affine registration in IR m with m typically ranging from 4 to 12. To justify breaking of this dimension barrier, the first part of the paper describes three novel matching problems that can be formulated and solved as affine point-set registration problems in dimensions greater than three: stereo correspondence under motion, image set matching, and covariant point-set matching, problems that are not only interesting in their own right but also have potential for important vision applications. Unfortunately, most of the existing affine registration algorithms do not generalize easily to higher dimensions due to their inefficiency. Therefore, the second part of this paper develops a novel algorithm for estimating the affine transform between two point sets in IR m . Specifically, the algorithm follows the common approach of iteratively solving the correspondences and transform. The initial correspondences are determined using the novel notion of local spectral features, features constructed from local distance matrices. Unlike many correspondence-based methods, the proposed algorithm is capable of registering point sets of different size, and the use of local features provides some degree of robustness against noise and outliers. The proposed algorithm is validated on a variety of synthetic point sets in different dimensions with varying degrees of deformation and noise, and the paper also shows experimentally that several instances of the aforementioned three matching problems can indeed be solved satisfactorily using the proposed affine registration algorithm.

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“…Our method for representing the coupler curve is similar to geometric hashing methods [Lamdan et al, 1990, Lamdan andWolfson, 1988]. Geometric hashing relies on computing invariant geometric features for the models and storing them in a hashtable.…”

Section: Related Workmentioning

confidence: 99%

A case-based approach to planar linkage design

Bose

Gini

Riley

1997

Artificial Intelligence in Engineering

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We describe a method to store and retrieve design cases of four-bar linkages. The method abstracts relevant features from the planar curve that characterizes each linkage design, and records these features at several levels of abstraction. This allows to use a fast multi-level retrieval procedure, that successively eliminates inappropriate cases at each level. The retrieval incorporates tolerance measures and allows for incomplete problem specifications. Adaptation methods are then used to transform the cases retrieved to match the new design problem. Results of experimental studies to evaluate the efficacy of our method are presented.

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Affine invariant model-based object recognition

Cited by 276 publications

References 20 publications

From Images to Shape Models for Object Detection

From Images to Shape Models for Object Detection

Higher Dimensional Affine Registration and Vision Applications

A case-based approach to planar linkage design

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