From FNS to HEIV: A link between two vision parameter estimation methods

Copyright © 2004 IEEEProblems requiring accurate determination of parameters from imagebased quantities arise often in computer vision. Two recent, independently developed frameworks for estimating such parameters are the FNS and HEIV schemes. Here, it is shown that FNS and a core version of HEIV are essentially equivalent, solving a common underlying equation via different means. The analysis is driven by the search for a nondegenerate form of a certain generalized eigenvalue problem and effectively leads to a new derivation of the relevant case of the HEIV algorithm. This work may be seen as an extension of previous efforts to rationalize and interrelate a spectrum of estimators, including the renormalization method of Kanatani and the normalized eight-point method of Hartley.Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel, and Darren Gawle

FNS and HEIV: relating two vision parameter estimation frameworks

©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Problems requiring accurate determination of parameters from image-based quantities arise often in computer vision. Two recent, independently developed frameworks for estimating such parameters are the FNS and HEIV schemes. Here it is shown that FNS and a core version of HEIV are essentially equivalent, solving a common underlying equation via different means. The analysis is driven by the search for a non-degenerate form of a certain generalised eigen-value problem, and effectively leads to a new derivation of the relevant case of the HEIV algorithm. This work may be seen as an extension of previous efforts to rationalise and inter-relate a spectrum of estimators, including the renormalisation method of Kanatani and the normalised eight-point method of Hartley.Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel, Darren Gawle

A voting scheme for estimating the synchrony of moving-camera videos

Copyright © 2003 IEEERecovery of dynamic scene properties from multiple videos usually requires the manipulation of synchronous (simultaneously captured) frames. This paper is concerned with the automated determination of this synchrony when the temporal alignment of sequences is unknown. A cost function characterising departure from synchrony is first evolved for the case in which two videos are generated by cameras that may be moving. A novel voting method is then presented for minimising the cost function in the case where the ratio of the cameras' frame rates is unknown. Experimental results indicate this relatively general approach holds promise.Pooley, D.W.; Brooks, M.J.; van den Hengel, A.J.; Chojnacki, W

On the fitting of surfaces to data with covariances

Copyright © 2000 IEEEWe consider the problem of estimating parameters of a model described by an equation of special form. Specific models arise in the analysis of a wide class of computer vision problems, including conic fitting and estimation of the fundamental matrix. We assume that noisy data are accompanied by (known) covariance matrices characterizing the uncertainty of the measurements. A cost function is first obtained by considering a maximum-likelihood formulation and applying certain necessary approximations that render the problem tractable. A Newton-like iterative scheme is then generated for determining a minimizer of the cost function. Unlike alternative approaches such as Sampson's method or the renormalization technique, the new scheme has as its theoretical limit the minimizer of the cost function. Furthermore, the scheme is simply expressed, efficient, and unsurpassed as a general technique in our testing. An important feature of the method is that it can serve as a basis for conducting theoretical comparison of various estimation approaches.Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel and Darren Gawle

What value covariance information in estimating vision parameters?

©2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Many parameter estimation methods used in computer vision are able to utilise covariance information describing the uncertainty of data measurements. This paper considers the value of this information to the estimation process when applied to measured image point locations. Covariance matrices are first described and a procedure is then outlined whereby covariances may be associated with image features located via a measurement process. An empirical study is made of the conditions under which covariance information enables generation of improved parameter estimates. Also explored is the extent to which the noise should be anisotropic and inhomogeneous if improvements are to be obtained over covariance-free methods. Critical in this is the devising of synthetic experiments under which noise conditions can be precisely controlled. Given that covariance information is, in itself, subject to estimation error tests are also undertaken to determine the impact of imprecise covariance information upon the quality of parameter estimates. Finally, an experiment is carried out to assess the value of covariances in estimating the fundamental matrix from real imagesBrooks, M.J. Chojnacki, W. Gawley, D. van den Hengel, A

Revisiting Hartley's normalized eight-point algorithm

Copyright © 2003 IEEEHartley's eight-point algorithm has maintained an important place in computer vision, notably as a means of providing an initial value of the fundamental matrix for use in iterative estimation methods. In this paper, a novel explanation is given for the improvement in performance of the eight-point algorithm that results from using normalized data. It is first established that the normalized algorithm acts to minimize a specific cost function. It is then shown that this cost function I!; statistically better founded than the cost function associated with the nonnormalized algorithm. This augments the original argument that improved performance is due to the better conditioning of a pivotal matrix. Experimental results are given that support the adopted approach. This work continues a wider effort to place a variety of estimation techniques within a coherent framework.Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel and Darren Gawle

A statistical rationalisation of Hartley's normalised eight-point algorithm

©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.The eight-point algorithm of Hartley occupies an important place in computer vision, notably as a means of providing an initial value of the fundamental matrix for use in iterative estimation methods. In this paper, a novel explanation is given for the improvement in performance of the eight-point algorithm that results from using normalised data. A first step is singling out a cost function that the normalised algorithm acts to minimise. The cost function is then shown to be statistically better founded than the cost function associated with the non-normalised algorithm. This augments the original argument that improved performance is due to the better conditioning of a pivotal matrix. Experimental results are given that support the adopted approach. This work continues a wider effort to place a variety of estimation techniques within a coherent framework.Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel, Darren Gawle

Characterness: An indicator of text in the wild

Text in an image provides vital information for interpreting its contents, and text in a scene can aid a variety of tasks from navigation to obstacle avoidance and odometry. Despite its value, however, detecting general text in images remains a challenging research problem. Motivated by the need to consider the widely varying forms of natural text, we propose a bottom-up approach to the problem, which reflects the characterness of an image region. In this sense, our approach mirrors the move from saliency detection methods to measures of objectness. In order to measure the characterness, we develop three novel cues that are tailored for character detection and a Bayesian method for their integration. Because text is made up of sets of characters, we then design a Markov random field model so as to exploit the inherent dependencies between characters. We experimentally demonstrate the effectiveness of our characterness cues as well as the advantage of Bayesian multicue integration. The proposed text detector outperforms state-of-the-art methods on a few benchmark scene text detection data sets. We also show that our measurement of characterness is superior than state-of-the-art saliency detection models when applied to the same task. © 2013 IEEE

FRET Imaging of Diatoms Expressing a Biosilica-Localized Ribose Sensor

Future materials are envisioned to include bio-assembled, hybrid, three-dimensional nanosystems that incorporate functional proteins. Diatoms are amenable to genetic modification for localization of recombinant proteins in the biosilica cell wall. However, the full range of protein functionalities that can be accommodated by the modified porous biosilica has yet to be described. Our objective was to functionalize diatom biosilica with a reagent-less sensor dependent on ligand-binding and conformational change to drive FRET-based signaling capabilities. A fusion protein designed to confer such properties included a bacterial periplasmic ribose binding protein (R) flanked by CyPet (C) and YPet (Y), cyan and yellow fluorescent proteins that act as a FRET pair. The structure and function of the CRY recombinant chimeric protein was confirmed by expression in E. coli prior to transformation of the diatom Thalassiosira pseudonana. Mass spectrometry of the recombinant CRY showed 97% identity with the deduced amino acid sequence. CRY with and without an N-terminal Sil3 tag for biosilica localization exhibited characteristic ribose-dependent changes in FRET, with similar dissociation constants of 123.3 µM and 142.8 µM, respectively. The addition of the Sil3 tag did not alter the affinity of CRY for the ribose substrate. Subsequent transformation of T. pseudonana with a vector encoding Sil3-CRY resulted in fluorescence localization in the biosilica and changes in FRET in both living cells and isolated frustules in response to ribose. This work demonstrated that the nano-architecture of the genetically modified biosilica cell wall was able to support the functionality of the relatively complex Sil3-CyPet-RBP-YPet fusion protein with its requirement for ligand-binding and conformational change for FRET-signal generation