A TIME-OPTIMAL MULTIPLE-QUERY NEAREST-NEIGHBOR ALGORITHM ON MESHES WITH MULTIPLE BROADCASTING

1995 ◽  
Vol 09 (04) ◽  
pp. 663-677 ◽  
Author(s):  
ION STOICA
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Graphics ◽  
Nearest Neighbor ◽  
Optimal Algorithm ◽  
Theoretical Interest ◽  
Nearest Neighbor Algorithm ◽  
Practical Applications ◽  
Time Optimal ◽  
Multiple Query

The multiple-query nearest-neighbor (MQNN) problem is stated as follows: given a set S of n points in plane and a set Q of m(1≤m≤n) query points, determine for every point in Q its closest neighbor in S. Besides the pure theoretical interest, this problem has many practical applications in various areas such as: computer graphics, pattern recognition and image processing. First, this paper proposes a new time-optimal algorithm to solve the all nearest-neighbor (ANN) problem in [Formula: see text] time on a mesh-connected computer of size [Formula: see text]. Next, using this result in conjunction with the generalized multiple search (GMS) paradigm of Bokka et al.3,5 we devise a time-optimal algorithm that solves the MQNN problem in [Formula: see text] time on a mesh with multiple broadcasting (MMB) of size [Formula: see text].

Analisis Kualitas Interpolasi Terhadap Fitur Statistik pada Citra

2017 ◽  
Vol 8 (2) ◽  
Author(s):  
Meirista Wulandari
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Statistical Methods ◽  
Nearest Neighbor ◽  
Input Image ◽  
Bilinear Interpolation ◽  
Interpolation Technique ◽  
Image Density ◽  
Index Terms ◽  
Bicubic Interpolation

There are a lot of applications of pattern recognition which need input image with a certain size. The size effect the result of pattern recognition. Determining size of image adopts interpolation technique. Interpolated image’s quality depends on interpolation technique. Texture is the main feature which is used in image processing and computer vision to classify object. One of some methods that are used to characterize texture is statistical methods. Statistical methods characterize texture by the statistical distribution of the image density. This research compared 4 interpolation methods (Nearest Neighbor Interpolation, Bilinear Interpolation, Bicubic Interpolation and Nearest Neighbor Value Interpolation) and 6 features of 10 test images. Based on 6 features which are researched, skewness changes upto 800%, energy 90%, entropy 75%, smoothness 18%, standard deviation 10% and mean 0,9%. Index Terms—Interpolation, Statistical feature, NNI, Bilinear Interpolation, NNV, Bicubic Interpolation

scholarly journals On Some Fuzzy Classification Algorithms and the AEC Model

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Pattern Recognition ◽  
Decision Rule ◽  
Nearest Neighbor ◽  
Fuzzy Classification ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Recognition Algorithms ◽  
K Nearest Neighbor Algorithm

In the paper some fuzzy classification algorithms based upon a nearest neighbor decision rule areconsidered in terms of the pattern recognition algorithms which are based on the computation of estimates (theso-called AEC model). It is shown that the fuzzy K nearest neighbor algorithm can be assigned to the AECclass. In turn, it is found that some standard AEC algorithms, which depend on a number of numericalparameters, can be used as fuzzy classification algorithms. Yet among them there exist algorithms extremalwith respect to these parameters. Such algorithms provide maximum values of the associated performancemeasures.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Sign in / Sign up

Export Citation Format

Share Document