The application of an oblique-projected Landweber method to a model of supervised learning

Johansson, Björn; Elfving, Tommy; Kozlov, Vladimir; Censor, Yair; Forssén, Per-Erik; Granlund, Gösta H.

doi:10.1016/j.mcm.2005.12.010

Cited by 39 publications

(32 citation statements)

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“…In [25] linear convergence of the projected Landweber method was shown, and in [23] an expression for the rate constant was derived.…”

Section: Projected Sirt Methodsmentioning

confidence: 99%

“…Such constraints incorporate prior physical knowledge about the solution, and therefore they typically lead to smaller reconstruction errors (see Figure 2.1 for an example). Some applications of projected iterative methods in seismology, image restoration, nonnegative matrix factorization, matrix completion, and supervised learning can be found in [1], [2], [3], [7], [23], [28], [31], [32]. We focus on regularizing iterations with semiconvergence, where the iteration number plays the role of the regularizing parameter.…”

Section: Introductionmentioning

confidence: 99%

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Semiconvergence and Relaxation Parameters for Projected SIRT Algorithms

Elfving¹,

Hansen²,

Nikazad³

2012

SIAM J. Sci. Comput.

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Abstract. We give a detailed study of the semiconvergence behavior of projected nonstationary simultaneous iterative reconstruction technique (SIRT) algorithms, including the projected Landweber algorithm. We also consider the use of a relaxation parameter strategy, proposed recently for the standard algorithms, for controlling the semiconvergence of the projected algorithms. We demonstrate the semiconvergence and the performance of our strategies by examples taken from tomographic imaging.

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“…In [25] linear convergence of the projected Landweber method was shown, and in [23] an expression for the rate constant was derived.…”

Section: Projected Sirt Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semiconvergence and Relaxation Parameters for Projected SIRT Algorithms

Elfving¹,

Hansen²,

Nikazad³

2012

SIAM J. Sci. Comput.

Self Cite

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show abstract

“…The strongest mode is defined as the decoding window with largest evidence [5]. [7,5], however, coherence has not previously been suggested, although it has been suggested for the structure tensor [1]. For notational and conceptual clarity and without loss of generality, basis functions are assumed to be centered at integer positions in this section (h = 3).…”

Section: Coherencementioning

confidence: 99%

Weighted Update and Comparison for Channel-Based Distribution Field Tracking

Öfjäll

Felsberg

2015

Computer Vision - ECCV 2014 Workshops

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Abstract. There are three major issues for visual object trackers: model representation, search and model update. In this paper we address the last two issues for a specific model representation, grid based distribution models by means of channel-based distribution fields. Particularly we address the comparison part of searching. Previous work in the area has used standard methods for comparison and update, not exploiting all the possibilities of the representation. In this work we propose two comparison schemes and one update scheme adapted to the distribution model. The proposed schemes significantly improve the accuracy and robustness on the Visual Object Tracking (VOT) 2014 Challenge dataset.

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“…The channel representation has previously been applied for associative learning [25] or statistically robust smoothing [10].…”

Section: Relation To Density Estimationmentioning

confidence: 99%

Real-time view-based pose recognition and interpolation for tracking initialization

Felsberg

Hedborg

2007

J Real-Time Image Proc

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In this paper we propose a new approach to realtime view-based pose recognition and interpolation. Pose recognition is particularly useful for identifying camera views in databases, video sequences, video streams, and live recordings. All of these applications require a fast pose recognition process, in many cases video real-time. It should further be possible to extend the database with new material, i.e., to update the recognition system online.The method that we propose is based on P-channels, a special kind of information representation which combines advantages of histograms and local linear models. Our approach is motivated by its similarity to information representation in biological systems but its main advantage is its robustness against common distortions such as clutter and occlusion. The recognition algorithm consists of three steps:1. low-level image features for color and local orientation are extracted in each point of the image 2. these features are encoded into P-channels by combining similar features within local image regions 3. the query P-channels are compared to a set of prototype P-channels in a database using a least-squares approach.The algorithm is applied in two scene registration experiments with fish-eye camera data, one for pose interpolation from synthetic images and one for finding the nearest view in a set of real images. The method compares favorable to SIFT-based methods, in particular concerning interpolation. The method can be used for initializing pose-tracking systems, either when starting the tracking or when the tracking has failed and the system needs to re-initialize. Due to its This work has been supported by the CENIIT project CAIRIS (http://www.cvl.isy.liu.se/Research/Object/CAIRIS), EC Grants IST-2003-004176 COSPAL and IST-2002-002013 MATRIS. This paper does not represent the opinion of the European Community, and the European Community is not responsible for any use which may be made of its contents.M. Felsberg · J. Hedborg Computer Vision Laboratory Linköping University, Sweden Tel.: +46-13-282460 Fax: +46-13-138526 E-mail: mfe@isy.liu.se real-time performance, the method can also be embedded directly into the tracking system, allowing a sensor fusion unit choosing dynamically between the frame-by-frame tracking and the pose recognition.

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The application of an oblique-projected Landweber method to a model of supervised learning

Cited by 39 publications

References 10 publications

Semiconvergence and Relaxation Parameters for Projected SIRT Algorithms

Semiconvergence and Relaxation Parameters for Projected SIRT Algorithms

Weighted Update and Comparison for Channel-Based Distribution Field Tracking

Real-time view-based pose recognition and interpolation for tracking initialization

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