How to define Saliency ? • Modeling the 3D mesh surface • Adaptive local patches construction • Saliency computation • Results • Comparisons with the state-of-the-art • Robustness and stability of the approach • Perspectives 2
2 attentional mechanisms in the human vision: Bottom-up: Saliency here is involuntary and it is related to the properties of the stimuli «The 3D object jumps out ». Top-down: Saliency depends on what the observer is looking for. « The ease of finding a target object ». The aim: Determine saliency related to the Bottom-up process, called also Attentional saliency. How to define saliency ? 5
the human eyes to strong fluctuations and discontinuities. If a vertex from the mesh stands out strongly from the mesh, then, it could be considered as a salient 3D point. The basis of our approach ? Flat Surface without fluctuations Flat surface with strong fluctuations 6 How to define saliency ?
Define into every vertex of the mesh a normal vector and a 2D tangent plane. ! To do so : ! the set of the vertices the set of edges connecting a couple of vertices and the weight of the edge . (vi, vj) 2 E G = (V, E, w) V E = V ⇥ V Let M a mesh represented by a non-oriented graph where: 8 w(vi, vj) Modeling the surface of a 3D mesh
values 3 eigen vectors normal vector directional vector directional vector Computations : Center of gravity: cov ( vi) = X j2N(vi) ( vj ˆ vi)( vj ˆ vi)T 2 R3⇥3 Covariance matrix: ~ x ~ y Guide the normal vectors outward using the minimum spanning tree of the graph (MST). 9 Modeling the surface of a 3D mesh ˆ vi = 1 |N(vi)| X j2N(vi) vj with vj ⇠ vi
a spherical neighborhood of the target vertex: S"(vi) = vj | ||~ vj ~ vi ||2 2 " • Projection of the neighboring vertices on the 2D tangent plane : ~ v 0 j = [( ~ vj ~ vi) · ~ x ( vi) , ( ~ vj ~ vi) · ~ y ( vi)]T ~ P(vi) • Adaptive size of the patch offers better consideration of the irregularities of the surface: Td( vi) = max ( ~ v0 j , ~ v0 k )2 ~ P (vi) ( || ~ v0d j ~ v0d k ||2 2) d : represents the x or y coordinate. : the coordinate of the vector . : Euclidian norm. v0d j d ~ v0 j ||.||2 11 Local adaptive patch construction
of the patch into cells : l ⇥ l • Filling cells with the absolute value of the sum of the projections heights: H(~ v0 j ) = ||(~ vj ~ v0 j )||2 2 12 Local adaptive patch construction where is vector of the accumulated heights into the cells of the patch. H(~ v0 j ) index d = $ ~ v0d j Td( vi) /l % with vj ⇠ vi
computation: ( vi) = max vk ⇠vi ( ||~ vi ~ vk ||2) scale parameter: : the weight of the edge between and . w(vi, vj) vi vj • Visual saliency: : the cardinality of the neighboring. 15 Computation of saliency w( vi , vj) = exp " || ~ H ( vi) ~ H ( vj) ||2 2 ( vi) ⇤ ( vj) # with vj ⇠ vi Saliency(vi) = ✓ 1 |N(vi)| ◆ X vi ⇠vj w(vi, vj) |N(vi)|
stability Independence of our approach to any pretreatment to define saliency: ! - Simplification ! - Smoothing ! Robustness to 2 types of distortions: ! - Noise (aleatory displacement of vertices). ! - Simplification with the algorithm of Garland M. and Heckbert P.S . ! ! 24
Centaur noised 3D mesh Centaur severely noised Saliency on the noised 3D mesh Centaur with our approach Robustness to noise 26 Robustness and stability Saliency on the noised 3D mesh Centaur with our approach
Centaur simplified to 68.63% 3D mesh Centaur simplified to 79.5% Saliency on the 3D mesh Centaur with our approach Robustness to simplification 27 Robustness and stability Saliency on the 3D mesh Centaur with our approach
Improvement of the saliency map. • Add and investigate the multi-scale aspect. Implement saliency for some applications like: 3D objects Compression : Salient regions are less compressed. (details preservation). Source coding and transmission: Allocate more bits for salient regions to preserve them while the transmission process. Best viewpoints selection : Present the most attractive areas on the 3D objects. 28