Processing method study for slide-scar of sliding fingerprint based on quality evaluation and direction judgment

2010 ◽  
Author(s):  
Jie Lu ◽  
Xuxiao Hu ◽  
Puyu Wang
Keyword(s):  
Quality Evaluation ◽  
Processing Method ◽  
Direction Judgment ◽  
Slide Scar

Rice Kernel Separations Using Contour Analysis and Skeleton

2015 ◽  
Vol 781 ◽  
pp. 515-518
Author(s):  
Chaladchai Siriwongkul ◽  
Pattarawit Polpinit
Keyword(s):  
Quality Evaluation ◽  
Median Filter ◽  
Processing Method ◽  
Separation Process ◽  
Contour Analysis ◽  
Separation Algorithm ◽  
Rice Quality ◽  
Binary Images ◽  
Separation Line ◽  
Broken Rice

Determine the percentage of broken rice kernel is crucial for rice quality evaluation. This paper studies a digital image processing method that can effectively separate touching rice kernels in an image of rice used for quality evaluation. An alternative separation algorithm based on contour analysis and skeleton is proposed to separate touching rice kernels. The proposed algorithm can be divided into three parts, namely, pre-processing, obtaining the candidates for separation line endpoints, and analysis for separation process. In the pre-processing, the images are converted into grayscale images. Then the median filter is applied in order to remove noise. Finally the binary images are obtained using Otsu’s algorithm. The next step is to obtain the candidates for separation line endpoints from concave points on the contour of rice kernels. The final step is to draw a separation lines among the candidates using several categories based on concave analysis and skeleton. The experimental results show that the proposed algorithm can accurately separate touching rice kernels and as a result the accurate percentage of broken rice can be obtained.

Quality Classification Model of Material Products Based on Ordinal Logistic Regression

2012 ◽  
Vol 460 ◽  
pp. 393-397 ◽  
Author(s):  
Peng Fei Mu ◽  
Dong Ling Zhang ◽  
Xiao Mei Xu ◽  
Yang Liu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Quality Evaluation ◽  
Ordinal Logistic Regression ◽  
Processing Method ◽  
Classification Model ◽  
Classification Result ◽  
Quality Classification ◽  
Ordinal Logistic Regression Model

It presents a proposed method for the development of quality evaluation and classification for material products, and shows the application of the ordinal logistic regression model and its advantages. It involved several steps: applying the linguistic information processing method, building the ordinal logistic regression model, differentiating and analyzing the quality evaluation to reach the quality classification result

scholarly journals Study on Standardisation and Quality Evaluation of Peanut Milk by Different Processing Methods

2020 ◽  
pp. 60-72 ◽  
Author(s):  
T. Siva Sakthi ◽  
V. Meenakshi ◽  
S. Kanchana ◽  
S. Vellaikumar
Keyword(s):  
Quality Evaluation ◽  
Lactose Intolerance ◽  
Processing Method ◽  
Lifestyle Changes ◽  
Oilseed Crop ◽  
Processing Methods ◽  
Physiochemical Properties ◽  
Local Variety ◽  
Medical Issues ◽  
Peanut Production

Peanut (Araches hypogea) is an important oilseed crop originated from South Africa, while India representing one of its leading producer nearly 14% of world peanut production. Now a days some of the lifestyle changes and medical issues like cow’s milk allergy, lactose intolerance and hypercholesterolemia people were shifted into plant based nondairy beverages. Peanut milk was developed from two different peanut varieties viz., TNAU CO-6 and local variety. Proximate composition of local and TNAU CO 6 variety of peanut was analyzed and the carbohydrates, protein, fat was high in TNAU CO 6 peanut variety and its value was 26.7 (g/100g), 27.8 (g/100g), 38(g/100g) when compared to local variety, its value was 25.2 (g/100g), 24.7 (g/100g), 39(g/100g) respectively. Peanut milk was extracted by five different processing method: fresh soaking, blanching, roasting and germination methods in both local and TNAU CO 6 variety. Peanut milk prepared without any treatments and processing was considered as a control peanut milk. Among the different treatment, the best treatment was selected based on sensory scores in each processing methods of both peanut varieties. Among these different treatments blanching method B1 (2mins) was best in local variety, B2(3 mins) was best in CO 6 variety. In roasting method R2 (roasting for 5 mins and soaking for 3 hrs), soaking method S2(3 hrs soaking), germination method G1(8 hrs germinated) was best treatment in both the selected peanut varieties. Based on the observation and sensory evaluation, the result showed that among these five processing methods roasting method was the best method for the peanut milk extraction and its physiochemical properties were analyzed in both the varieties of prepared peanut milk among these CO 6 variety peanut milk had good result and better acceptability.

scholarly journals Measurement of the Uniformity of Thermally Bonded Points in Polypropylene Spunbonded Non-Wovens Using Image Processing and its Relationship With Their Tensile Properties

2018 ◽  
Vol 18 (4) ◽  
pp. 405-418 ◽  
Author(s):  
Mina Emadi ◽  
Mohammad Ali Tavanaie ◽  
Pedram Payvandy
Keyword(s):  
Image Processing ◽  
Tensile Properties ◽  
Quality Evaluation ◽  
Clustering Algorithm ◽  
Evaluation Method ◽  
Signal To Noise Ratio ◽  
Tensile Tests ◽  
Processing Method ◽  
Image Processing Method ◽  
Quadrant Method

Abstract This article aims at the image processing of surface uniformity and thermally bonded points uniformity in polypropylene spunbonded non-wovens. The investigated samples were at two different weights and three levels of non-uniformity. An image processing method based on the k-means clustering algorithm was applied to produce clustered images. The best clustering procedure was selected by using the lowest Davies-Bouldin index. The peak signal-to-noise ratio (PSNR) image quality evaluation method was used to choose the best binary image. Then, the non-woven surface uniformity was calculated using the quadrant method. The uniformity of thermally bonded points was calculated through an image processing method based on morphological operators. The relationships between the numerical outcomes and the empirical results of tensile tests were investigated. The results of image processing and tensile behavior showed that the surface uniformity and the uniformity of thermally bonded points have great impacts on tensile properties at the selected weights and non-uniformity levels. Thus, a sample with a higher level of uniformity and, consequently, more regular bonding points with further bonding percentage depicts the best tensile properties.

Schungite raw material quality evaluation using image processing method

2017 ◽  
Author(s):  
Aleksandr N. Chertov ◽  
Elena V. Gorbunova ◽  
Roman V. Sadovnichii ◽  
Natalia N. Rozhkova
Keyword(s):  
Image Processing ◽  
Quality Evaluation ◽  
Processing Method ◽  
Raw Material ◽  
Material Quality ◽  
Image Processing Method

Low-temperature Mo-catalyzed growth of crystalline Si3N4 by CVD

1993 ◽  
Vol 51 ◽  
pp. 918-919
Author(s):  
K.L. More ◽  
R.A. Lowden ◽  
T.M. Besmann
Keyword(s):  
Vapor Deposition ◽  
Hot Isostatic Pressing ◽  
Chemical Vapor ◽  
Processing Method ◽  
Crystalline Material ◽  
Structural Ceramic ◽  
Alternative Processing ◽  
Strong Candidate ◽  
Hot Pressing Sintering ◽  
Processing Techniques

Silicon nitride possesses an attractive combination of thermo-mechanical properties which makes it a strong candidate material for many structural ceramic applications. Unfortunately, many of the conventional processing techniques used to produce Si3N4, such as hot-pressing, sintering, and hot-isostatic pressing, utilize significant amounts of densification aids (Y2O3, Al2O3, MgO, etc.) which ultimately lowers the utilization temperature to well below that of pure Si3N4 and also decreases the oxidation resistance. Chemical vapor deposition (CVD) is an alternative processing method for producing pure Si3N4. However, deposits made at temperatures less than ~1200°C are usually amorphous and at slightly higher temperatures, the deposition of crystalline material requires extremely low deposition rates (~5 μm/h). Niihara and Hirai deposited crystalline α-Si3N4 at 1400°C at a deposition rate of ~730 μm/h. Hirai and Hayashi successfully lowered the CVD temperature for the growth of crystalline Si3N4 by adding TiCl4 vapor to the SiCl4, NH3, and H2 reactants. This resulted in the growth of α-Si3N4 with small amounts of TiN at temperatures as low as 1250°C.

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