C. Jerian and R. Jain. "Polynomial methods for structure from motion". IEEE Trans. on Pattern Analysis and Machine Intelligence, 12(12):1150--1166, 1990.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....results show that accuracy can be improved by increasing the number of corresponding point pairs. Common problems with the non linear algorithms in self calibration are the multiplicity of solutions and the selection of the true solution for the camera parameters [16, 17, 12] Jerian and Jain [20] proposed using a polynomial system of equations and finding all the real roots with Sturm sequences. A hypothesis and verification scheme then follows to eliminate the incorrect ones. Tsai and Huang [36] reported that 7 point correspondences are sufficient to uniquely determine the 3D motion ....

Jerian, C., and Jain, R. Polynomial Methods for Structure from Motion. IEEE Trans. on Pattern Analysis and Machine Intelligence, 12 (1990), 1150--1165.

....combination schemes were obtained [24] For some applications, the analysis of static images may su er from inherent limitations in the amount of three dimensional information that can be extracted. Recently, dynamic scene analysis in the machine vision eld has been given increasing attention [4,37,56] (and references therein) The input to a dynamic scene analysis system is a series of images, each representing the scene at a particular time instant. The images can be produced by disparate sensors, giving information about each time step in multiple channels. One of the goals of the analysis ....

Jerian, C. and R. Jain (1990). Polynomial methods for structure from motion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 1150-1166.

....image variance. Please see [13] for more details. 2.2 Multi resolution Motion Analysis Motion perception in humans is a natural welldeveloped visual capability that is an essential survival skill. Over the last two decades there have been numerous developments in the area of motion analysis [8, 9, 1, 10, 13]. However, developing automatic computational motion analysis algorithms capable of handling various categories of motion has been a difficult task. Most of the work in motion analysis is based on the rigidity assumption where the shape of objects do not change over time. Motion of an object may ....

C. Jerain and R. Jain. Polynomial methods for structure from motion. Proc. 2nd ICCV, Tarpon Springs, Florida, 1988.

.... expressed as a high variance (mean square difference between the ground truth and the estimate) or a bias (mean difference between the ground truth and the estimate) One approach to the problem is to design algorithms to minimize the effects of noise by using a form of constrained minimization [6, 7], use statistical estimation [8, 9, 10] analyze the behavior of the error in a wide variety of configurations [11, 12, 13] analyze the Cramer Rao Lower Bound (CRLB) of the estimation [14] study the effects of bias and devising tools to reduce it [15] etc. Most of this work has been collected ....

C. Jerian and R. Jain. Polynomial methods for structure from motion. IEEE Trans. Pattern Analysis and Machine Intelligence, 12:1150--1165, 1990.

....Uniqueness of structure and motion recovery Whilst only one sensor scene motion generated the image flow, it may not always be possible to recover a unique solution. 35] proves the result that given a set of 5 image matches over 2 frames there are in general ten compatible sensor displacements. [30, 58] have also shown that a 16 th degree polynomial in one variable may be formulated whose roots encompass all solutions to the structure and motion recovery problem for 5 matched points over 2 frames, additionally [58] shows that even with a known translation (direction) all structure and motion ....

C. Jerain and R. Jain. Polynomial methods for structure from motion. IEEE Trans. Pattern Analysis and Machine Intelligence, 12(12):1150--1166, Dec 1991.

....of equations is overconstrained and the coefficients of the equations are inaccurate due to noise. As a result, the optimal solution is usually found using gradient descent methods on related least squares problems. Most of the local methods known in the literature do not work well in practice [15] and an algorithm based on global minimization is greatly desired. Sparse sensors have recently demonstrated success in recognition and localization applications [27, 20] The need for accurate localization algorithms has arisen with the emergence of these sparse probing sensor techniques. This ....

C. Jerian and R. Jain. Polynomial methods for structure from motion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(12):1150--1166, 1990.

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C. Jerian and R. Jain. "Polynomial methods for structure from motion". IEEE Trans. on Pattern Analysis and Machine Intelligence, 12(12):1150--1166, 1990.

No context found.

C. Jerian and R. Jain. Polynomial methods for structure from motion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(12):1150--1165, 1990.

No context found.

Jerian, C. and Jain, R., Polynomial Methods for Structure from Motion, PAMI, p.1150-1166, December 1990.

No context found.

C. Jerian and R. Jain, Polynomial methods for structure from motion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 1150--1166, 1990.

No context found.

C. Jerian and R. Jain, Polynomial methods for structure from motion, in Proceedings of the 2nd ICCV, Tarpon Springs, Fla., December 1988.

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