Procrustes &amp;#x2014; based shape prior for parametric active contours

Kamandar, Mehdi; Seyedin, Seyed Alireza

doi:10.1109/icmv.2007.4469287

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…One such example, [9] makes use of "landmark-based" shape analysis [7] to impose a shape prior based on the statistics of similarityinvariant point sets. Recent advancements in the modeling of shapes as continuous curves, given by elastic shape analysis, e.g.…”

Section: Past Work On Prior-driven Active Contoursmentioning

confidence: 99%

Bayesian Active Contours with Affine-Invariant, Elastic Shape Prior

Bryner

Srivastava

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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Active contour, especially in conjunction with priorshape models, has become an important tool in image segmentation. However, most contour methods use shape priors based on similarity-shape analysis, i.e. analysis that is invariant to rotation, translation, and scale. In practice, the training shapes used for prior-shape models may be collected from viewing angles different from those for the test images and require invariance to a larger class of transformation. Using an elastic, affine-invariant shape modeling of planar curves, we propose an active contour algorithm in which the training and test shapes can be at arbitrary affine transformations, and the resulting segmentation is robust to perspective skews. We construct a shape space of affinestandardized curves and derive a statistical model for capturing class-specific shape variability. The active contour is then driven by the true gradient of a total energy composed of a data term, a smoothing term, and an affine-invariant shape-prior term. This framework is demonstrated using a number of examples involving the segmentation of occluded or noisy images of targets subject to perspective skew.

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Section: Past Work On Prior-driven Active Contoursmentioning

confidence: 99%

Bayesian Active Contours with Affine-Invariant, Elastic Shape Prior

Bryner

Srivastava

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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“…The base shape s 0 is the mean shape and the vectors s i are the m eigenvectors corresponding to the m largest eigenvalues. Before applying PCA we can normalize the meshes using a Procrustes analysis [16] to remove variation due to a chosen global shape normalising transformation. Therefore the resulting PCA is only concerned with local, non-rigid shape deformation.…”

Section: Constrained Local Modelsmentioning

confidence: 99%

Robust Facial Expression Recognition Using Near Infrared Cameras

Jeni¹,

Hashimoto²,

Kubota³

2012

J. Adv. Comput. Intell. Intell. Inform.

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In human-human communication we use verbal, vocal and non-verbal signals to communicate with others. Facial expressions are a form of non-verbal communication, recognizing them helps to improve the human-machine interaction. This paper proposes a system for pose- and illumination-invariant recognition of facial expressions using near-infrared camera images and precise 3D shape registration. Precise 3D shape information of the human face can be computed by means of Constrained Local Models (CLM), which fits a dense model to an unseen image in an iterative manner. We used a multi-class SVM to classify the acquired 3D shape into different emotion categories. Results surpassed human performance and show poseinvariant performance. Varying lighting conditions can influence the fitting process and reduce the recognition precision. We built a near-infrared and visible light camera array to test the method with different illuminations. Results shows that the near-infrared camera configuration is suitable for robust and reliable facial expression recognition with changing lighting conditions.

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“…Charmi et al [5] have proposed a shape energy term according to distance between Fourier descriptors of evolving contour and a reference template. In our method [6], shape knowledge was introduced through a similarity measurement between evolving contour and Procrustes mean shape of desired object. This similarity is measured by full Procrustes distance between these two contours that is invariant with respect to similarity transformations (translation, scaling, and rotation) and is added to traditional energy function of snakes as an extra shape energy term.…”

Section: Introductionmentioning

confidence: 99%

“…The final contour in the current frame or a prediction by Kalman filtering will be chosen as initial contour for the next frame and will be fitted the boundary of desired object by minimizing the energy function of directional Procrustes snake. The minimizing process is done by greedy algorithm [6]. Greedy is a suboptimum and fast algorithm for minimizing the energy function of snakes.…”

Section: Introductionmentioning

confidence: 99%

3D Object Tracking Using Directional Procrustes Snake

Kamandar

Seyedin

Khoshbin

2008

2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications

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A novel method of parametric active contours with geometric shape prior is presented in this paper. The main idea of the method consists in minimizing an energy function that includes additional information on a shape reference called a prototype. Shape prior introduced through a similarity measurement between evolving contour and Procrustes mean shape of desired object. This similarity measurement is full Procrustes distance between these two contours that is invariant with respect to similarity transformations (translation, scaling, and rotation). This extra shape knowledge enhances the model robustness to noise, occlusion and complex background. We also use gradient direction information in addition to gradient magnitude for more robustness to complex background. In this paper we introduce one important application of this new snake "3D object tracking". We obtain promising results for 3D object tracking which show the robustness of our method against noise, complex background, similarity transformations, occlusion, and changing viewpoint of 3D object.

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Procrustes — based shape prior for parametric active contours

Cited by 4 publications

References 13 publications

Bayesian Active Contours with Affine-Invariant, Elastic Shape Prior

Bayesian Active Contours with Affine-Invariant, Elastic Shape Prior

Robust Facial Expression Recognition Using Near Infrared Cameras

3D Object Tracking Using Directional Procrustes Snake

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