Curve matching for open 2D curves

Cui, Meiying; Femiani, John; Hu, Jing; Wonka, Peter; Razdan, Anshuman

doi:10.1016/j.patrec.2008.08.013

Cited by 82 publications

(56 citation statements)

References 16 publications

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“…In order to match the optical paths through all arms of the device, an iterative mathematical routine, based on adjusting the curvature and direction of each route individually, has been developed. To date few such algorithms have been described in the literature, with some of the most developed work appearing in the field of machine vision where they were used in a completely different context for image/shape recognition [11][12][13]. Nevertheless, these concepts spring from the same foundation as the path length matching algorithm for photonic devices presented here.…”

Section: Introductionmentioning

confidence: 99%

Design of optically path-length-matched, three-dimensional photonic circuits comprising uniquely routed waveguides

Charles¹,

Jovanović²,

Gross³

et al. 2012

Appl. Opt.

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A method for designing physically path length matched, three-dimensional photonic circuits is described. We focus specifically on the case where all the waveguides are uniquely routed from the input to output; a problem which has not been addressed to date and allows for the waveguides to be used in interferometric measurements. Circuit elements were fabricated via the femtosecond laser direct-write technique. We demonstrate via interferometric methods that the fabricated circuits were indeed optically path length matched to within 45 μm which is within the coherence length required for many applications.

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Section: Introductionmentioning

confidence: 99%

Design of optically path-length-matched, three-dimensional photonic circuits comprising uniquely routed waveguides

Charles¹,

Jovanović²,

Gross³

et al. 2012

Appl. Opt.

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“…He presented a method that transforms the rastor of each shape into a one dimensional signal according to the occurance of the shape points on the Hilbert curve. In some case, the best matching of an open contour with part of a closed contour needs to be established [2] but all of the above complicating factors contribute collectively to increasing the complexity of the matching problem. Shape matching including wide range of applications such as, object detection and recognition, content based retrieval of images and image registration.To perform shape matching, most of the existing methods [1] define shape representations and descriptors which are the compared through appropriately selected methods and metrics.…”

Section: Related Workmentioning

confidence: 99%

Scale Invariant Fractional Shape matching for computerized images

Ghongade¹

2014

IOSRJEN

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-Shape Matching is an important aspect in image processing . It is an important step whenever there arouses a need to match a certain image with another . There are a lot of applications wherein the image matching is the key step towards the fulfillment of the application. So in such cases Partial image matcing usually becomes a crucial part of the whole process. Hence here we present a novel approach of partial shape matching for the scale invariant images. The problem of establishing a match between an open contour and a closed contour is tried to resolve in this paper. The computation of the shape descriptors is alo done along open or closed contours. The net ordered collections of shape descriptors constitute the global shape representation. Here we propose an alternate technique to an existing Dynamic time warping matching technique for the matching of the shape representation. Use of the properties of the employed shape descriptor, sampling scheme and matching procedure, the proposed approach performs partial shape matching that is invariant to Euclidean transformations. Also the problem of matching the closed to closed contours is automatically treated as a special case. The proposed method gives a better vision and a better result as compared to that of existing system because of the extensive dataset used in the proposed application. And thus the proposed system is a better solution of partial shape matching and the problems related to partial shape matching

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“…There are several techniques in the literature that can be used to match curves, especially in image processing and pattern recognition [149], but because we project both Capacity and demand surfaces into 2-D curves, matching 2-D curves is graphically applicable.…”

Section: Curves Match Fitmentioning

confidence: 99%

Capacity metric for chip heterogeneous multiprocessors

Otoom

Paul

2011

Proceedings of the Seventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis

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The primary contribution of this thesis is the development of a new performance metric, Capacity, which evaluates the performance of Chip Heterogeneous Multiprocessors (CHMs) that process multiple heterogeneous channels. Performance metrics are required in order to evaluate any system, including computer systems. A lack of appropriate metrics can lead to ambiguous or incorrect results, something discovered while developing the secondary contribution of this thesis, that of workload modes for CHMsor Workload Specific Processors (WSPs).For many decades, computer architects and designers have focused on techniques that reduce latency and increase throughput. The change in modern computer systems built around CHMs that process multi-channel communications in the service of single users calls this focus into question. Modern computer systems are expected to integrate tens to hundreds of processor cores onto single chips, often used in the service of single users, potentially as a way to access the Internet. Here, the design goal is to integrate as much functionality as possible during a given time window. Without the ability to correctly identify optimal designs, not only will the best performing designs not be found, but resources will be wasted and there will be a lack of insight to what leads to better performing designs. To address performance evaluation challenges of the next generation of computer systems, such as multicore computers inside of cell phones, we found that a structurally different metric is needed and proceeded to develop such a metric.In contrast to single-valued metrics, Capacity is a surface with dimensionality related to the number of input streams, or channels, processed by the CHM. We develop some fundamental Capacity curves in two dimensions and show how Capacity shapes reveal interaction of not only programs and data, but the interaction of multiple data streams as they compete for access to resources on a CHM as well. For the analysis of Capacity surface shapes, we propose the development of a demand characterization method in which its output is in the form of a surface. By overlaying demand surfaces over Capacity iii surfaces, we are able to identify when a system meets its demands and by how much.Using the Capacity metric, computer performance optimization is evaluated against workloads in the service of individual users instead of individual applications, aggregate applications, or parallel applications. Because throughput was originally derived by drawing analogies between processor design and pipelines in the automobile industry, we introduce our Capacity metric for CHMs by drawing an analogy to automobile production, signifying that Capacity is the successor to throughput. By developing our Capacity metric, we illustrate how and why different processor organizations cannot be understood as being better performers without both magnitude and shape analysis in contrast to other metrics, such as throughput, that consider only magnitude.In this work, we make the following major ...

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Curve matching for open 2D curves

Cited by 82 publications

References 16 publications

Design of optically path-length-matched, three-dimensional photonic circuits comprising uniquely routed waveguides

Design of optically path-length-matched, three-dimensional photonic circuits comprising uniquely routed waveguides

Scale Invariant Fractional Shape matching for computerized images

Capacity metric for chip heterogeneous multiprocessors

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