Results 1  10
of
9,856
Computing and verifying depth orders
 SIAM J. Comput
, 1994
"... A depth order on a set of objects is an order such that object a comes before object a ' in the order when a lies behind a', or, in other words, when a is (partially) hidden by a'. We present efficient algorithms for the computation and verification of depth orders of sets of n rods ..."
Abstract

Cited by 36 (6 self)
 Add to MetaCart
A depth order on a set of objects is an order such that object a comes before object a ' in the order when a lies behind a', or, in other words, when a is (partially) hidden by a'. We present efficient algorithms for the computation and verification of depth orders of sets of n rods
Computing and Verifying Depth Orders*
"... A depth order on a set of objects is an order such that objeet a comes before object a ’ in the order when a lies behind a’, or. in other words, when a is (partially) hidden by a’. We present efficient algorithms for the computation and verification of depth orders of sets of n rods in 3–space. Our ..."
Abstract
 Add to MetaCart
A depth order on a set of objects is an order such that objeet a comes before object a ’ in the order when a lies behind a’, or. in other words, when a is (partially) hidden by a’. We present efficient algorithms for the computation and verification of depth orders of sets of n rods in 3–space
Computing Depth Orders and Related Problems
 IN PROC. 4TH SCAND. WORKSHOP ALGORITHM THEORY
, 1994
"... Let K be a set of n nonintersecting objects in 3space. A depth order of K, if exists, is a linear order ! of the objects in K such that if K;L 2 K and K lies vertically below L then K ! L. We present a new technique for computing depth orders, and apply it to several special classes of objects. ..."
Abstract

Cited by 22 (9 self)
 Add to MetaCart
Let K be a set of n nonintersecting objects in 3space. A depth order of K, if exists, is a linear order ! of the objects in K such that if K;L 2 K and K lies vertically below L then K ! L. We present a new technique for computing depth orders, and apply it to several special classes of objects
Detection of the depth order of defocused images
 VISION RESEARCH
, 2005
"... The sign of an accommodative response is provided by differences in chromatic aberration between under and overaccommodated images. We asked whether these differences enable people to judge the depth order of two stimuli in the absence of other depth cues. Two vertical edges separated by an illumi ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
The sign of an accommodative response is provided by differences in chromatic aberration between under and overaccommodated images. We asked whether these differences enable people to judge the depth order of two stimuli in the absence of other depth cues. Two vertical edges separated
Layered depth images
, 1997
"... In this paper we present an efficient image based rendering system that renders multiple frames per second on a PC. Our method performs warping from an intermediate representation called a layered depth image (LDI). An LDI is a view of the scene from a single input camera view, but with multiple pix ..."
Abstract

Cited by 456 (29 self)
 Add to MetaCart
system, McMillan's warp ordering algorithm can be successfully adapted. As a result, pixels are drawn in the output image in back to front order. No zbuffer is required, so alphacompositing can be done efficiently without depth sorting. This makes splatting an efficient solution to the resampling
A learning based framework for depth ordering
 In CVPR
, 2012
"... Depth ordering is instrumental for understanding the 3D geometry of an image. We as humans are surprisingly good at depth ordering even with abstract 2D line drawings. In this paper we propose a learning based framework for discrete depth ordering inference. Boundary and junction characteristics ar ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Depth ordering is instrumental for understanding the 3D geometry of an image. We as humans are surprisingly good at depth ordering even with abstract 2D line drawings. In this paper we propose a learning based framework for discrete depth ordering inference. Boundary and junction characteristics
Secondorder motion conveys depthorder information
"... Psychophysical and neurophysiological studies have revealed that the visual system is sensitive to both “firstorder” motion, in which moving features are defined by luminance cues, and “secondorder ” motion, in which motion is defined by nonluminance cues, such as contrast or flicker. Here we show ..."
Abstract
 Add to MetaCart
show psychophysically that common types of secondorder stimuli provide potent cues to depth order. Although motion defined exclusively by nonluminance cues may be relatively rare in natural scenes, the depthorder cues offered by secondorder stimuli arise ubiquitously as a result of occlusion of one
Secondorder motion conveys depthorder information
"... Psychophysical and neurophysiological studies have revealed that the visual system is sensitive to both “firstorder” motion, in which moving features are defined by luminance cues, and “secondorder ” motion, in which motion is defined by nonluminance cues, such as contrast or flicker. Here we show ..."
Abstract
 Add to MetaCart
show psychophysically that common types of secondorder stimuli provide potent cues to depth order. Although motion defined exclusively by nonluminance cues may be relatively rare in natural scenes, the depthorder cues offered by secondorder stimuli arise ubiquitously as a result of occlusion of one
Motion Segmentation and Depth Ordering Using an Occlusion Detector
"... Abstract — We present a novel method for motion segmentation and depth ordering from a video sequence in general motion. We first compute motion segmentation based on differential properties of the spatiotemporal domain, and scalespace integration. Given a motion boundary, we describe two algorith ..."
Abstract

Cited by 12 (1 self)
 Add to MetaCart
Abstract — We present a novel method for motion segmentation and depth ordering from a video sequence in general motion. We first compute motion segmentation based on differential properties of the spatiotemporal domain, and scalespace integration. Given a motion boundary, we describe two
Layered Motion Segmentation and Depth Ordering by Tracking Edges
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2004
"... Abstract—This paper presents a new Bayesian framework for motion segmentation—dividing a frame from an image sequence into layers representing different moving objects—by tracking edges between frames. Edges are found using the Canny edge detector, and the ExpectationMaximization algorithm is then ..."
Abstract

Cited by 44 (0 self)
 Add to MetaCart
, in association with a Markov Random Fieldstyle prior. The identification of the relative depth ordering of the different motion layers is also determined, as an integral part of the process. An efficient implementation of this framework is presented for segmenting two motions (foreground and background) using
Results 1  10
of
9,856