We present a scheme for recovering the orientation of a planar scene from a single translation ally-motion blurred image. By leveraging the homography relationship among image coordinates of 3D points lying on a plane, and by exploiting natural correspondences among the extremities of the blur kernels derived from the motion blurred observation, the proposed method can accurately infer the normal of the planar surface. We validate our approach on synthetic as well as real planar scenes. © 2014 IEEE.

A. N. Rajagopalan

Department of Electrical Engineering

Makkena Purnachandra Rao

Software engineering

A-plane

HOMOGRAPHIES

IMAGE COORDINATES

MOTION BLURRED IMAGE

MOTION-BLURRED

Planar surface

PLANE ORIENTATION

Pattern Recognition

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Inferring Plane Orientation from a Single Motion Blurred Image

Proceedings - International Conference on Pattern Recognition

In this work, we investigate the relation between the edge profiles present in a motion blurred image and the underlying camera motion responsible for causing the motion blur. While related works on camera motion estimation (CME) rely on the strong assumption of space-invariant blur, we handle the challenging case of general camera motion. We first show how edge profiles 'alone' can be harnessed to perform direct CME from a single observation. While it is routine for conventional methods to jointly estimate the latent image too through alternating minimization, our above scheme is best-suited when such a pursuit is either impractical or inefficacious. For applications that actually favor an alternating minimization strategy, the edge profiles can serve as a valuable cue. We incorporate a suitably derived constraint from edge profiles into an existing blind deblurring framework and demonstrate improved restoration performance. Experiments reveal that this approach yields state-of-the-art results for the blind deblurring problem. © 2017 IEEE.

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

From local to global: Edge profiles to camera motion in blurred images

Existing image registration methods that work in the presence of motion blur assume the scene to be fronto-parallel. In this work, we extend the state-of-the-art by cueing motion blur itself to infer plane inclination. This is achieved by matching extremities of blur kernels computed at different locations in the image. Because these extremities correspond to the same homography, we show that it is possible to find the orientation. Following this, we propose a registration method that reorients the source image to the target plane within a reblur-difference framework to detect the actual changes. © 2015 SPIE.

Proceedings of SPIE - The International Society for Optical Engineering

Cueing motion blur for registration of inclined planar scenes

We propose a framework for robust estimation of normal of a planar scene from a single motion blurred observation. We first reveal how feature points can be extracted from blur kernels and matched to generate several point correspondences. Although these points correspond to different homographies, the fact that they conform to the same normal yields a rank-3 constraint which we harness within a hierarchical clustering framework to estimate the normal accurately. © 2015 IEEE.

Proceedings - International Conference on Image Processing, ICIP

Tapping motion blur for robust normal estimation of planar scenes

Estimation of the orientation of a textured planar surface is one of the basic tasks in the area of "shape from texture". For the solution of this task, many successful approaches were proposed. In this paper, we have examined a few unaddressed questions: First, is there a mathematical formulation that relates the spectral characteristics of the texture pattern and the orientation of an inclined planar surface in a polar-coordinate system? Second, is there a good wavelet-based approach that produces an accurate estimate of the orientation angle of the textured planar surface by analyzing the spectral behavior of one single uncalibrated image? To answer these questions at first we present the formulation of a "texture projective equation", which relates the depth and orientation of an inclined planar surface in a polar coordinate system with the spectral properties of its image texture. A suitable imaging geometry has been considered to enable separable analysis of the effect of inclination of the texture surface. Next, a method for shape from texture is presented based on discrete wavelet analysis to estimate the orientation of the planar surface. This approach although designed mainly for M-channel wavelets, is also applicable for dyadic wavelet analysis. Texture characteristics in the subbands of wavelet decomposition are analyzed using scalograms, and quantitatively evaluated based on texture projective equations. The proposed method of estimation of the orientation of a planar texture surface is evaluated using a set of simulated and real world textured images. © 2009 Elsevier Ltd. All rights reserved.

Estimation of orientation of a textured planar surface using projective equations and separable analysis with M-channel wavelet decomposition

Procedings of the British Machine Vision Conference 2012

Detecting planes and estimating their orientation from a single image

CVPR 2011

Structure from motion blur in low light

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Non-uniform deblurring for shaken images

Computer Vision – ECCV 2010 Lecture Notes in Computer Science

Single Image Deblurring Using Motion Density Functions

Inferring plane orientation from a single motion blurred image

Journal	Data powered by TypesetProceedings - International Conference on Pattern Recognition
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
ISSN	10514651
Open Access	No