نتایج جستجو برای: manhattan and euclidean distance
تعداد نتایج: 16850583 فیلتر نتایج به سال:
introduction: appropriate definition of the distance measure between diffusion tensors has a deep impact on diffusion tensor image (dti) segmentation results. the geodesic metric is the best distance measure since it yields high-quality segmentation results. however, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. the main goal of this ...
Bibit padi yang mempunyai kualitas unggul memiliki peran penting dalam peningkatan produktivitas pada sektor pertanian. Banyaknya bibit dikembangkan oleh Balai Besar Penelitian Tanaman Padi menghasilkan karakteristik baru serta kemiripan hampir sama. berdasarkan karakteristiknya dapat dikelompokkan dengan menggunakan metode Clustering dimana proses perhitungannya pengukuran jarak. Pada peneliti...
A new general algorithm for computing distance transforms of digital images is presented. The algorithm consists of two phases. Both phases consist of two scans, a forward and a backward scan. The first phase scans the image column-wise, while the second phase scans the image row-wise. Since the computation per row (column) is independent of the computation of other rows (columns), the algorith...
We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. In this space, the Euclidean distance between any two nodes approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed using A* search with the Euclidean distances as heurist...
Choosing which fast Nearest Neighbour search algorithm to use depends on the task we face. Usually kd-tree search algorithm is selected when the similarity function is the Euclidean or the Manhattan distances. Generic fast search algorithms (algorithms that works with any distance function) are only used when there is not specific fast search algorithms for the involved distance function. In th...
Introduction: Appropriate definition of the distance measure between diffusion tensors has a deep impact on Diffusion Tensor Image (DTI) segmentation results. The geodesic metric is the best distance measure since it yields high-quality segmentation results. However, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. The main goal of this ...
This paper proposes two unconventional metrics as an important tool for assessment research: the Manhattan (L1) and the Euclidean (L2) distance measures. We used them to evaluate the results of a Latent Semantic Analysis (LSA) system to assess short answers to two questions about HTML in an introductory computer science class. This is the only study, as far as we know, that addresses the questi...
Motivation: Many analyses of metabolomic data depend on the choice of distance measure, but it is unclear how to make an appropriate choice. The choice is especially unclear when the variance in metabolite abundance is not constant. Methods: We describe a class of weighted distance measures that account for non-constant variance in metabolite abundance by using a model of the relationship betwe...
Cryptography facilitates selective communication through encryption of messages and or data. Block-cipher processing is one the prominent methods for modern cryptographic symmetric schemes. The rise in attacks on block-ciphers led to development more difficult However, attackers decrypt generic given sufficient time computing. Recent research had applied machine learning classification algorith...
A distance based classification is one of the popular methods for classifying instances using a point-to-point distance based on the nearest neighbour or k-NEAREST NEIGHBOUR (k-NN). The representation of distance measure can be one of the various measures available (e.g. Euclidean distance, Manhattan distance, Mahalanobis distance or other specific distance measures). In this paper, we propose ...
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