In this paper, we consider iterative methods based on sampling for computing solutions to separable nonlinear inverse problems where the entire dataset cannot be accessed or is not available all-at-once. In such scena...
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ISBN:
(纸本)9783030223687;9783030223670
In this paper, we consider iterative methods based on sampling for computing solutions to separable nonlinear inverse problems where the entire dataset cannot be accessed or is not available all-at-once. In such scenarios (e.g., when massive amounts of data exceed memory capabilities or when data is being streamed), solving inverse problems, especially nonlinear ones, can be very challenging. We focus on separable nonlinear problems, where the objective function is nonlinear in one (typically small) set of parameters and linear in another (larger) set of parameters. For the linear problem, we describe a limited-memory sampled Tikhonov method, and for the nonlinear problem, we describe an approach to integrate the limited-memory sampled Tikhonov method within a nonlinear optimization framework. the proposed method is computationally efficient in that it only uses available data at any iteration to update both sets of parameters. Numerical experiments applied to massive super-resolution image reconstruction problems show the power of these methods.
this paper is concerned with a problem arising when editing color images, namely the Luminance-Hue Specification. this problem often occurs when converting an edited image in a given color-space to RGB. Indeed, the co...
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People often recognize 3D objects by their boundary shape. Designing an algorithm for such a task is interesting and useful for retrieving objects from a shape database. In this paper, we present a fast 2-stage algori...
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ISBN:
(纸本)9783540728221
People often recognize 3D objects by their boundary shape. Designing an algorithm for such a task is interesting and useful for retrieving objects from a shape database. In this paper, we present a fast 2-stage algorithm for recognizing 3D objects using a new feature space, built from curvature scalespace images, as a shape representation that is scale, translation, rotation and reflection invariant. As well, the new shape representation removes the inherent ambiguity of the zero position of arc length for a scalespace image. the 2-stage matching algorithm, conducted in the eigenspaces of the feature space, is analogous to the way people recognize an object: first identifying the type of object, and then determining the actual object. We test the new algorithm on a 3D database comprising 209 colour objects in 2926 different view poses, and achieve a 97% recognition rate for the object type and 95% for the object pose.
the two-volume set LNCS 15667 and 15668 constitutes the proceedings of the 10th international conference on scale space and variational methods in computer vision, ssvm 2025, which took place in Dartington, UK, in May...
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ISBN:
(数字)9783031923661
ISBN:
(纸本)9783031923654
the two-volume set LNCS 15667 and 15668 constitutes the proceedings of the 10th international conference on scale space and variational methods in computer vision, ssvm 2025, which took place in Dartington, UK, in May 2025.
the total of 63 full papers accepted in the proceedings were carefully reviewed and selected from 81 submissions. they were organized in topical sections as follows:
Part I: Inverse Problems in Imaging; machine and deep learning in imaging;
Part II: Optimization for imaging: theory and methods; scalespace, PDES, flow, motion and registration.
the two-volume set LNCS 15667 and 15668 constitutes the proceedings of the 10th international conference on scale space and variational methods in computer vision, ssvm 2025, which took place in Dartington, UK, in May...
详细信息
ISBN:
(数字)9783031923692
ISBN:
(纸本)9783031923685
the two-volume set LNCS 15667 and 15668 constitutes the proceedings of the 10th international conference on scale space and variational methods in computer vision, ssvm 2025, which took place in Dartington, UK, in May 2025.
the total of 63 full papers accepted in the proceedings were carefully reviewed and selected from 81 submissions. they were organized in topical sections as follows:
Part I: Inverse Problems in Imaging; machine and deep learning in imaging;
Part II: Optimization for imaging: theory and methods; scalespace, PDES, flow, motion and registration.
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