scholarly journals Pembentua Pola Desain Motif Karawo Gorontalo Menggunakan K-Means Color Quantization dan Structured Forest Edge Detecion

2021 ◽  
Vol 8 (3) ◽  
pp. 625
Author(s):  
Syahrial Syahrial ◽  
Rizal Lamusu
Keyword(s):  
Design Pattern ◽  
Color Image ◽  
Threshold Value ◽  
Forest Edge ◽  
Gaussian Filter ◽  
Color Quantization ◽  
Edge Image ◽  
Pattern Design ◽  
Testing Parameter ◽  
Parameter Values

<p class="Abstrak">Sulaman Karawo merupakan kerajinan tangan berupa sulaman khas dari daerah Gorontalo. Motif sulaman diterapkan secara detail berdasarkan suatu pola desain tertentu. Pola desain digambarkan pada kertas dengan berbagai panduannya. Gambar yang diterapkan pada pola memiliki resolusi sangat rendah dan harus mempertahankan bentuknya. Penelitian ini mengembangkan metode pembentukan pola desain motif Karawo dari citra digital. Proses dilakukan dengan pengolahan awal menggunakan <em>k-means color quantization (KMCQ)</em> dan deteksi tepi <em>structured forest</em>. Proses selanjutnya melakukan pengurangan resolusi menggunakan metode <em>pixelation</em> dan <em>binarization</em>. Luaran dari algoritma menghasilkan 3 citra berbeda dengan ukuran yang sama, yaitu: citra tepi, citra biner, dan citra berwarna. Ketiga citra tersebut selanjutnya dilakukan proses pembentukan pola desain motif Karawo dengan berbagai petunjuk pola bagi pengrajin. Hasil menunjukkan bahwa pola desain motif dapat digunakan dan dimengerti oleh para pengrajin dalam menerapkannya di sulaman Karawo. Pengujian nilai-nilai parameter dilakukan pada metode <em>k-means</em>, <em>gaussian filter</em>, <em>pixelation</em>, dan <em>binarization.</em> Parameter-parameter tersebut yaitu: k pada <em>k-means</em>, <em>kernel</em> pada <em>gaussian filter</em>, lebar piksel pada <em>pixelation</em>, dan nilai <em>threshold</em> pada <em>binarization</em>. Pengujian menunjukkan nilai terendah tiap parameter adalah k=4, kernel=3x3, lebar piksel=70, dan <em>threshold</em>=20. Hasil memperlihatkan makin tinggi nilai-nilai tersebut maka semakin baik pola desain motif yang dihasilkan. Nilai-nilai tersebut merupakan nilai parameter terendah dalam pembentukan pola desain motif berkualitas baik berdasarkan indikator-indikator dari desainer.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Karawo embroidery is a unique handicraft from Gorontalo. The embroidery motif is applied in detail based on a certain design pattern. These patterns are depicted on paper with various guides. The image applied to the pattern is very low resolution and retains its shape. This study develops a method to generate a Karawo design pattern from a digital image. The process begins by using k-means color quantization (KMCQ) to reduce the number of colors and edge detection of the structured forest. The next process is to change the resolution using pixelation and binarization methods. The output algorithm produces 3 different state images of the same size, which are: edge image, binary image, and color image. These images are used in the formation of the Karawo motif design pattern. The motif contains various pattern instructions for the craftsman. The results show that it can be used and understood by the craftsmen in its application in Karawo embroidery. Testing parameter values on the k-means method, Gaussian filter, pixelation, and binarization. These parameters are k on KMCQ, the kernel on a gaussian filter, pixel width in pixelation, and threshold value in binarization. The results show that the lowest value of each parameter is k=4, kernel=3x3, pixel width=70, and threshold=20. The results show that the higher these values, the better the results of the pattern design motif. Those values are the lower input to generate a good quality pattern design based on the designer’s indicators.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Mean-Based Breakpoint Selection on Circular Histogram

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiulun Fan ◽  
Jipeng Yang
Keyword(s):  
Selection Criteria ◽  
Color Image ◽  
Selection Criterion ◽  
Threshold Value ◽  
Simple Expression ◽  
Circular Data ◽  
Cumulative Distribution ◽  
Color Model ◽  
Hsi Color Model ◽  
Selection Of

Circular histogram represents the statistical distribution of circular data; the H component histogram of HSI color model is a typical example of the circular histogram. When using H component to segment color image, a feasible way is to transform the circular histogram into a linear histogram, and then, the mature gray image thresholding methods are used on the linear histogram to select the threshold value. Thus, the reasonable selection of the breakpoint on circular histogram to linearize the circular histogram is the key. In this paper, based on the angles mean on circular histogram and the line mean on linear histogram, a simple breakpoint selection criterion is proposed, and the suitable range of this method is analyzed. Compared with the existing breakpoint selection criteria based on Lorenz curve and cumulative distribution entropy, the proposed method has the advantages of simple expression and less calculation and does not depend on the direction of rotation.

iPattern

2017 ◽  
Vol 6 (2) ◽  
pp. 12-27
Author(s):  
Youmna Bassiouny ◽  
Rimon Elias ◽  
Philipp Paulsen
Keyword(s):  
Design Pattern ◽  
Design Patterns ◽  
Three Dimensional ◽  
Computational Design ◽  
Pattern Design ◽  
Design Problems ◽  
Innovative Design ◽  
Rule Based ◽  
Science And Art ◽  
Design Software

Computational design takes a computer science view of design, applying both the science and art of computational approaches and methodologies to design problems. This article proposes to convert design methodologies studied by designers into rule-based computational design software and help them by providing suggestions for designs to build upon given a set of primitive shapes and geometrical rules. iPattern is a pattern-making software dedicated to designers to generate innovative design patterns that can be used in a decorative manner. They may be applied on wallpapers, carpets, fabric textiles, three-dimensional lanterns, tableware, etc. The purpose is to create a modern pattern design collection that adds a new essence to the place. In order to generate creative design patterns, primitive shapes and geometrical rules are used. The generated design pattern is constructed based on the grid of the Flower of Life of the sacred geometry or similar grids constructed using primitive shapes (rectangles, squares and triangles) combined in the layout of the Flower of Life.

Analysis of Segmentation Parameters Effect towards Parallel Processing Time on Fuzzy C Means Algorithm

2018 ◽  
Vol 1 (4) ◽  
pp. 99
Author(s):  
Cepi Ramdani ◽  
Indah Soesanti ◽  
Sunu Wibirama
Keyword(s):  
Parallel Processing ◽  
Processing Time ◽  
Color Image ◽  
Clustering Algorithms ◽  
Fuzzy C Means ◽  
Sequential Processing ◽  
Processing Times ◽  
Parameter Values ◽  
Fuzzy C Means Algorithm ◽  
Segmentation Parameters

Fuzzy C Means algorithm or FCM is one of many clustering algorithms that has better accuracy to solve problems related to segmentation. Its application is almost in every aspects of life and many disciplines of science. However, this algorithm has some shortcomings, one of them is the large amount of processing time consumption. This research conducted mainly to do an analysis about the effect of segmentation parameters towards processing time in sequential and parallel. The other goal is to reduce the processing time of segmentation process using parallel approach. Parallel processing applied on Nvidia GeForce GT540M GPU using CUDA v8.0 framework. The experiment conducted on natural RGB color image sized 256x256 and 512x512. The settings of segmentation parameter values were done as follows, weight in range (2-3), number of iteration (50-150), number of cluster (2-8), and error tolerance or epsilon (0.1 – 1e-06). The results obtained by this research as follows, parallel processing time is faster 4.5 times than sequential time with similarity level of image segmentations generated both of processing types is 100%. The influence of segmentation parameter values towards processing times in sequential and parallel can be concluded as follows, the greater value of weight parameter then the sequential processing time becomes short, however it has no effects on parallel processing time. For iteration and cluster parameters, the greater their values will make processing time consuming in sequential and parallel become large. Meanwhile the epsilon parameter has no effect or has an unpredictable tendency on both of processing time.

scholarly journals The effect of the signalling scheme on the robustness of pattern formation in development

2012 ◽  
Vol 2 (4) ◽  
pp. 465-486 ◽  
Author(s):  
Hye-Won Kang ◽  
Likun Zheng ◽  
Hans G. Othmer
Keyword(s):  
Pattern Formation ◽  
Reaction Diffusion ◽  
Threshold Value ◽  
Cell Types ◽  
State Variable ◽  
Complex Process ◽  
Spatially Distributed ◽  
Stochastic Reaction ◽  
Different Cell Types ◽  
Parameter Values

Pattern formation in development is a complex process which involves spatially distributed signals called morphogens that influence gene expression and thus the phenotypic identity of cells. Usually different cell types are spatially segregated, and the boundary between them may be determined by a threshold value of some state variable. The question arises as to how sensitive the location of such a boundary is to variations in properties, such as parameter values, that characterize the system. Here, we analyse both deterministic and stochastic reaction-diffusion models of pattern formation with a view towards understanding how the signalling scheme used for patterning affects the variability of boundary determination between cell types in a developing tissue.

Three-Dimensional Pattern Blending Design of Five-Surface Plane Developed Drawing

2014 ◽  
Vol 666 ◽  
pp. 371-374
Author(s):  
Qi Jia ◽  
Xu Liang Lv ◽  
Wei Dong Xu ◽  
Jiang Hua Hu ◽  
Xian Hui Rong
Keyword(s):  
Design Pattern ◽  
Computer Programming ◽  
Three Dimensional ◽  
Processing Method ◽  
Compression Method ◽  
Pattern Design ◽  
Target Pattern ◽  
Surface Plane ◽  
Processing Steps

The blending effect of design pattern of five-surface plane developed drawing is important to the design pattern. The blending of three-dimensional pattern design can realize the automation of pattern design. The target pattern design of air brushing is based on the square design picture. Therefore, the effect is bad, especially in the edges between surfaces. Edge processing method is developed to solve this problem. The pixels are compressed in accordance with the image pixel compression method. According to the processing steps, the computer programming is performed. The results shows that, the floral drawing in the blending design figure can extend to the adjacent surfaces naturally, and the blending effect is great after the three-dimensional blending perform.

scholarly journals Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1641 ◽  
Author(s):  
Phuong Cu Thi ◽  
James Ball ◽  
Ngoc Dao
Keyword(s):  
Threshold Value ◽  
Uncertainty Estimation ◽  
Design Flow ◽  
Design Flood ◽  
Bayesian Techniques ◽  
Flood Estimation ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Parameter Values ◽  
The Impact

In the last few decades tremendous progress has been made in the use of catchment models for the analysis and understanding of hydrologic systems. A common application involves the use of these models to predict flows at catchment outputs. However, the outputs predicted by these models are often deterministic because they focused only on the most probable forecast without an explicit estimate of the associated uncertainty. This paper uses Bayesian and Generalized Likelihood Uncertainty Estimation (GLUE) approaches to estimate uncertainty in catchment modelling parameter values and uncertainty in design flow estimates. Testing of join probability of both these estimates has been conducted for a monsoon catchment in Vietnam. The paper focuses on computational efficiency and the differences in results, regardless of the philosophies and mathematical rigor of both methods. It was found that the application of GLUE and Bayesian techniques resulted in parameter values that were statistically different. The design flood quantiles estimated by the GLUE method were less scattered than those resulting from the Bayesian approach when using a closer threshold value (1 standard deviation departed from the mean). More studies are required to evaluate the impact of threshold in GLUE on design flood estimation.

The Padding of Vein Image Features and Hardware Designs in M-Health Environments

2021 ◽  
pp. 125-158
Keyword(s):  
Cloud Computing ◽  
Median Filter ◽  
Threshold Value ◽  
Image Features ◽  
Moving Window ◽  
Gaussian Filter ◽  
Timing Diagrams ◽  
Segmentation Task ◽  
Hardware Designs ◽  
Distributed Cloud

This chapter describes the timing diagrams of padding features and hardware designs of segmentation, controllers, and filters. Further, the authors have described that the hardware design concept of segmentation task can be performed online in a distributed cloud computing m-health environment. The segmentation phase uses two Gaussian filter functions with different sizes of filter masks and standard deviation with a threshold value to make a distinction between veins image patterns and the corresponding backgrounds in the cloud IoT-based m-health environment. In order to design the hardware architecture of the median filter, the superior moving window architecture is used by researchers to accommodate a larger size median filter in the cloud IoT-based m-health environment.

Color Quantization Based on Gaussian Mixture Mode

2012 ◽  
Vol 457-458 ◽  
pp. 650-654
Author(s):  
Qiu Chun Jin ◽  
Xiao Li Tong
Keyword(s):  
Image Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Image ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Color Quantization ◽  
Quantization Method ◽  
Color Palette ◽  
Image Quantization

Color quantization is an important technique for image analysis that reduces the number of distinct colors for a color image. A novel color image quantization algorithm based on Gaussian mixture model is proposed. In the approach, we develop a Gaussian mixture model to design the color palette. Each component in the GMM represents a type of color in the color palette. The task of color quantization is to group pixels into different component. Experimental results show that our quantization method can obtain better results than other methods.

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