@Article{ PRITCHETT/ZISSERMAN:1998, author = {Philip Pritchett and Andrew Zisserman}, title = {Matching And Reconstruction From Widely Separated Views}, year = {1998}, institution = {Department of Engineering Science University of Oxford UK}, e-mail = {pcp@robots.ox.ac.uk and az@robots.ox.ac.uk}, web = {http://www.robots.ox.ac.uk/~vgg}, keywords = {MATCHING RECONSTRUCTION HOGRAPHY WIDE-BASELINE}, annote = { M The objective of this work is to automatically estimate the trifocal tensor and feature correspondences over image triplets,under unrestricted camera motions and changes in internal parameters between views.It extends 2-view algorithm which is based on establishing feature correspondences between views together with homgraphy which enables a viewpoint inavariant affinity score.It enables feature matching and geometry estimation between two quite disparate views. } } @Article{ FITZGIBBON/CROSS/ZISSERMAN:1998; author = {Andrew W fittzgibbon and Geoff Cross and Andrew Zisserman}, title = {Automatic 3D Model Construction For Turn-Table Sequences}, year = {1998}, institution = {Robotics Research Department of Engineering Science University of Oxford UK}, e-amil = {awf@robots.ox.ac.uk and geoff@robots.ox.ac.uk and az@robots.ox.ac.uk}, web = {http://www.robots.ox.ac.uk/~vgg}, annote = { This paper descibes a system which,given a sequence of images of an object rotating about a single axis ,generates a textured 3D model automatically.The approach requires no prior information about the cameras or scene or turntable angles. It is shown that 3D structure and cameras can be estimated (including auto-caliberation) upto an overall two-parameter ambiguity.The output models are quite close to those of fully caliberated sequences.} } @InProceedings{HARTLEY:1992, author = {Richard I Hartley}, title = {Estimation Of Relative Camera Positions For Uncaliberated Cameras}, year = {1992}, month = {May}, conference = {Second European Conference on Computer Vision}, city = {Santa Margherita Ligure}, country = {Italy}, book = {Lecture Notes On Computer Science}, editor = {G Sandini}, publisher = {Springer Verlag}, pages = {579-587}, annote = { This paper considers ,the determination of internal camera parameters from two views of a point set in three dimensions. A non-iterative algorithm is given for determining the focal lengths of the two camerras,as well as their relative placement ,assuming all other internal camera parameters to be known.All this information can be obtained from a set of point correspondences.} } @Article{Schmid/Andrew, author = { Andrew Zisserman and Cordelia Schmid }, title = { Automatic line matching across Views }, institution = { Department of Engineering Science , University Of Oxford,UK OX1 3PJ }, e-mail = { az@robots.ox.ac.uk }, annote = { The papers talks of automatic matching of line segments between images of scenes mainly containing planar surfaces.Problems in this area is of reliable recovery of end points and no geometric constraint(like epipolar constraint was there for point matchings) is there for infinite lines. Matching of individual as well as group matching of line segments are existing approaches. This paper works on the intensity neighbourhood of the line. Epipolar geometry is used for point to point correspondences along the line segments.Following two algorithm are presented: 1) SHORT RANGE MOTION: This is the image motion that arises in image sequences where simple nearest neighbour tracking would almost work.(Here each segment is treated as a list of points and neighbourhood correlation is applied.) 2) LONG RANGE MOTION: that arises between views from a stereo rigs,where the baseline is significant.(Homography computed from the fundamental matrix is used for correction for large deformation between images.) The second algorithm is expensive but both work well. } } @proceeedings{GARCIA/BRUNET:1998, conference={6th International Conference On Computer Vision}, location={Mumbai,India}, publisher={Narosa}, editor={Sarat Chandran / Udai Deasi}, date={4-7}, month={January}, year={1998}, pages={1067-1072}, author={Blanca Garcia / Pere Brunet}, e-mail={brunet@lsi.upc.es} annote={ This paper is about generation of 3D image of an object given three of its images, about ninety degrees apartusing projective OCTREE and epipolar geometry.Here the camera used to take images is uncalliberated. In projective octree approach the object is considered to be completely inside a cube, whish is then divided into 8 sub-cubes by cutting along median planes.Each of the sub-cubes is then individually analyzed and said to be 'black' if it lies completely inside the object,'white' if completely outside and 'grey' if partially inside.Funadamental matrix is obtained by using known correspondences, and it is then used to establish relation between 2 or more images. Reconstruction of the image in 3D is done with projective octree approach. The algorithm to reconstrct has three parts:- 1) definition of the octree 2) estimation of the projective octree universe 3) model is improved further by considering the given correspondeces among the images taken. Advantage of this approach is that no camera calliberation is required, but the disadvantage is that the final images will have some stepped surfaces due to break-up into cubes. } } @article{WU/WANG/BAJCSY, author={C.U. Wu / D.Q. WUNG / R.U. Bajcsy}, title={Acquiring 3-D Spatial Data Of Real Object}, date={27}, month={October}, year={1983}, journal={Computer Vision Journal}, volume={28}, number={1}, pages={126-133}, institution={Computer Science & Information Science Department -University of Pennysylvania}, annote={ This method uses camera caliberation. An object is kept on a turn table and its images are taken by rotating the turntable four times.100*100 pixel resolution ccd camera was used. Steps to acquire 3-D co-ordinates of a point are :- 1) calliberation--- 6 points are located near the object for camera calliberation 2) stereo matching--- extraction upon which matching is to be performed and to solve for correspondence problem. Edges in the images are features for matching purposes. 3) computation of 3D coordinates of the object(For this we need to find matrix[H] such that [H][V3d]=[V2d], so that given x' & z' coordinates of a point in image plane we can find x,y,z of the point the 3D. To find [H] we need 6 non coplaner point. Now the first image is chosen where 3D co-ordinates same as actual; in other three views of the object we use [H] matrix to find its 3D correspondence. Error analysis : Error in length & height is <.5% depth measurement error is approx. =.5% Therefore, satisfactory accuracy in the procedure. }, }