An efficient detection of vanishing points using inverted coordinates image space

Vanishing Point Detection with an Intersection Point Neighborhood

“…We follow the ideas in [2], [3] and [4]. A first consideration is that the size of the bins for some accumulator for straight lines or intersection points should depend on the number of measurable straight lines that intersect an image I.…”

“…Those neighborhoods replace the reciprocal transformation in [2] and the bins in [3]. Although they form no topological space they define a concept of proximity and connectedness: two positions p, p ∈ Z 2 are close if p ∈ N (p ) and p ∈ N (p) and a set S ⊆ Z 2 is connected if for any two points p, p ∈ S there exist points…”

“…We expect to find two or three vanishing points with certain geometrical restrictions. Thus, we first follow the idea of Seo [2] and group all measured lines according to their angle α. For this we use two independent k-harmonic-means clustering [7] runs, one time with two clusters and the other time with three clusters.…”

Single view based pose estimation from circle or parallel lines

“…detect the edge points in the image by Canny edge detector; 2. extract and fit the line segments in the image by orthogonal regression (Schmid and Zisserman, 1997); 3. compute the vanishing points from parallel lines via one of the methods in (Almansa et al, 2003;Liebowitz et al, 1999;Seo et al, 2006); 4. compute the vanishing line and the ICPs; 5. compute the vanishing point v ? 1 of the orthogonal direction and recover the pose of the camera according to Proposition 1.…”

What can we learn about the scene structure from three orthogonal vanishing points in images

“…There are many techniques for accurately and automatically detecting the vanishing points from images (Almansa et al, 2003;McLean and Kotturi, 1995;Seo et al, 2006). The vanishing points play an important role in the Euclidean reconstruction and is widely studied in literature.…”