Rancang Bangun Editor Kurva Polyline Dengan Metode Curve Analogies

Model-model kurva banyak digunakan untuk pembuatan sketsa. Untuk merancang model kurva untuk sketsa tersebut dapat dilakukan dengan berbagai macam cara. Salah satunya adalah menyusun program secara manual dalam membentuk model-model kurva untuk menggambar sketsa yang diinginkan. Pendekatan ini memberi kontrol yang besar ke programmer. Pendekatan lain yaitu mengambil detail kurva yang dimasukkan pengguna. Salah satu metode dalam pendekatan ini adalah dengan mempelajari style-style garis dari contoh - contoh. Pendekatan ini disebut dengan pendekatan curve analogies. Penelitian ini bertujuan untuk menerapkan metode curve analogies dalam membuat editor kurva polyline. Curve Analogies bertujuan membentuk kurva baru dari kurva contoh. Inputan untuk kurva analogies ada 3 macam yaitu dua kurva garis dan satu kurva detil (kurva contoh). Dua kurva garis tersebut adalah kurva inputan dari pengguna dan kurva yang mengikuti kurva detil. Dua kurva garis ini digunakan untuk mencari nilai transformasi. Sedangkan untuk membuat kurva baru dilakukan proses synthesis dengan algoritma synthesis. Algoritma synthesis membentuk kurva baru berdasarkan style dari kurva contoh. Kurva hasil proses synthesis di transformasi sesuai dengan nilai transformasinya. Kurva baru yang dihasilkan harus selalu melalui titik kontrol yang pertama dan terakhir dari kurva garis yang diinputkan penguna. Uji coba perangkat lunak ini dilakukan dengan menjalankan beberapa skenario. Skenario pertama dengan memasukkan satu obyek gambar, kedua memasukkan lebih dari satu obyek gambar, ketiga membuat kurva contoh baru dan yang keempat melakukan sintesa kurva. Dari hasil beberapa skenario tersebut dapat disimpulkan metode curve analogies dapat digunakan untuk membuat editor kurva polyline

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

Ergoregion in Metamaterials Mimicking a Kerr Spacetime

We propose a simple singularity-free coordinate transformation that could be implemented in Maxwell's equations in order to simulate one aspect of a Kerr black hole. Kerr black holes are known to force light to rotate in a predetermined direction inside the ergoregion. By making use of cosmological analogies and the theoretical framework of transformation optics, we have designed a metamaterial that can make light behave as if it is propagating around a rotating cosmological massive body. We present numerical simulations involving incident Gaussian beams interacting with the materials to verify our predictions. The ergoregion is defined through the dispersion curve of the off-axis permittivities components.Comment: 10 pages, 4 figures, presented at FiO 201

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and for high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but with performance analysis and optimization impeded by speed of computation. We have developed new feature extraction techniques on notional use of physical paradigms, with parametrization aimed to be more familiar to a scientifically trained user, aiming to make best use of computational resource. This paper is the first unified description of these new approaches, outlining the basis and results that can be achieved. We describe how gravitational force can be used for low-level analysis, while analogies of water flow and heat can be deployed to achieve high-level smooth shape detection, by determining features and shapes in a selection of images, comparing results with those by stock approaches from the literature. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

Ranking relations using analogies in biological and information networks

Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects $\mathbf{S}=\{A^{(1)}:B^{(1)},A^{(2)}:B^{(2)},\ldots,A^{(N)}:B ^{(N)}\}$, measures how well other pairs A:B fit in with the set $\mathbf{S}$. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS321 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org