• Title, Summary, Keyword: texture analysis

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Texture Comparison with an Orientation Matching Scheme

  • Nguyen, Cao Truong Hai;Kim, Do-Yeon;Park, Hyuk-Ro
    • Journal of Information Processing Systems
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    • v.8 no.3
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    • pp.389-398
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    • 2012
  • Texture is an important visual feature for image analysis. Many approaches have been proposed to model and analyze texture features. Although these approaches significantly contribute to various image-based applications, most of these methods are sensitive to the changes in the scale and orientation of the texture pattern. Because textures vary in scale and orientations frequently, this easily leads to pattern mismatching if the features are compared to each other without considering the scale and/or orientation of textures. This paper suggests an Orientation Matching Scheme (OMS) to ease the problem of mismatching rotated patterns. In OMS, a pair of texture features will be compared to each other at various orientations to identify the best matched direction for comparison. A database including rotated texture images was generated for experiments. A synthetic retrieving experiment was conducted on the generated database to examine the performance of the proposed scheme. We also applied OMS to the similarity computation in a K-means clustering algorithm. The purpose of using K-means is to examine the scheme exhaustively in unpromising conditions, where initialized seeds are randomly selected and algorithms work heuristically. Results from both types of experiments show that the proposed OMS can help improve the performance when dealing with rotated patterns.

Nondestructive Measurement of Cheese Texture using Noncontact Air-instability Compensation Ultrasonic Sensors

  • Baek, In Suck;Lee, Hoonsoo;Kim, Dae-Yong;Lee, Wang-Hee;Cho, Byoung-Kwan
    • Journal of Biosystems Engineering
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    • v.37 no.5
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    • pp.319-326
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    • 2012
  • Purpose: Cheese texture is an important sensory attribute mainly considered for consumers' acceptance. The feasibility of nondestructive measurements of cheese texture was explored using non-contact ultrasonic sensors. Methods: A novel non-contact air instability compensation ultrasonic technique was used for five varieties of hard cheeses to measure ultrasonic parameters, such as velocity and attenuation coefficient. Five texture properties, such as fracturability, hardness, springiness, cohesiveness, and chewiness were assessed by a texture profile analysis (TPA) and correlated with the ultrasonic parameters. Results: Texture properties of five varieties of hard cheese were estimated using ultrasonic parameters with regression analysis models. The most effective model predicted the fracturability, hardness, springiness, and chewiness, with the determination coefficients of 0.946 (RMSE = 21.82 N), 0.944 (RMSE = 63.46 N), 0.797 (RMSE = 0.06 ratio), and 0.833 (RMSE = 17.49 N), respectively. Conclusions: This study demonstrated that the non-contact air instability compensation ultrasonic sensing technique can be an effective tool for rapid and non-destructive determination of cheese texture.

Multiple Texture Objects Extraction with Self-organizing Optimal Gabor-filter (자기조직형 최적 가버필터에 의한 다중 텍스쳐 오브젝트 추출)

  • Lee, Woo-Beom;Kim, Wook-Hyun
    • The KIPS Transactions:PartB
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    • v.10B no.3
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    • pp.311-320
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    • 2003
  • The Optimal filter yielding optimal texture feature separation is a most effective technique for extracting the texture objects from multiple textures images. But, most optimal filter design approaches are restricted to the issue of supervised problems. No full-unsupervised method is based on the recognition of texture objects in image. We propose a novel approach that uses unsupervised learning schemes for efficient texture image analysis, and the band-pass feature of Gabor-filter is used for the optimal filter design. In our approach, the self-organizing neural network for multiple texture image identification is based on block-based clustering. The optimal frequency of Gabor-filter is turned to the optimal frequency of the distinct texture in frequency domain by analyzing the spatial frequency. In order to show the performance of the designed filters, after we have attempted to build a various texture images. The texture objects extraction is achieved by using the designed Gabor-filter. Our experimental results show that the performance of the system is very successful.

Effect of Weft Knit Structural Characteristics on the Subjective Texture and Sensibility (위편성물 소재의 구성특성이 주관적 질감 및 감성에 미치는 영향)

  • 주정아;유효선
    • Journal of the Korean Society of Clothing and Textiles
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    • v.28 no.11
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    • pp.1516-1523
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    • 2004
  • The purpose of this study were to analyze the effect of weft knit structural characteristics on the subjective texture and sensibility. For this, the material was knitted into 8 kinds of weft plain knit fabrics with four kinds of fiber components such as wool, acryl, rayon, and nylon, 3 steps of densities and 3 steps of twist numbers to ply two yarns. The data were analyzed by factor analysis, ANOVA and multidimensional scaling. From factor analysis, subjective textures were categorized as 'bulk/resilience', 'surface/density' and 'soft/drape', and subjective sensibilities were categorized as 'natural/comfortable', 'feminine/elegance' and 'stable/neat' Among the knit structural characteristics, the component of fibers and the density of fabrics were the important factors to give variations in texture and sensibility : In comparison with wool knit of medium density, the knit fabrics of other components and different densities each showed a unique texture and sensibility. But twist number to ply two yams had a few influence on subjective properties. As a result of MDS analysis, the texture and sensibility of plain weft knit fabrics was classified as 'thin-full', 'hard-soft', 'young-old' and 'warm-cool'.

Evaluation of the Texture Image and Preference according to Wool Fiber Blending Ratios and the Characteristics of Men's Suit Fabrics (모섬유의 혼방비율과 직물 특성에 따른 남성 정장용 소재의 질감이미지와 선호도 평가)

  • Kim, Hee-Sook;Na, Mi-Hee
    • Korean Journal of Human Ecology
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    • v.20 no.2
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    • pp.413-426
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    • 2011
  • This research was designed to compare the subjective evaluation of texture image and preference according to fiber blending ratio of men's suit fabrics. 110 subjects evaluated the texture image and preference of various fabrics. For statistical analysis, factor analysis, MDS, pearson correlation and ANOVA were used. The results were as follows: Sensory image factors of suit fabrics were 'smoothness', 'bulkiness', 'stiffness', 'elasticity', 'moistness' and 'weight sensation'. Sensibility image factors were 'classic', 'practical', 'characteristic' and 'sophisticated'. 'Bulkiness' and 'elasticity' sensory images showed high correlations with sensibility images. Fabrics with high wool blending ratio showed as 'classic' and 'sophisticated', 'bulkiness' and 'elasticity' texture images and fabrics with low wool blending ratio showed texture images of 'characteristic', 'surface character', 'stiffness', 'moistness' and 'weight sensation'. Wool fiber blending ratio affected on the purchase preference and tactile preference. Using regression analysis, it was shown that sensibility images had more of an effect on preference than sensory images. The thickness and pattern type showed positive effects and fiber blending ratio showed negative effects on the preference.

Analysis of Texture Information of forest stand on High Resolution Satellite Imagery (임분 특성에 따른 고해상도 위성영상의 Texture 정보 분석)

  • 김태근;이규성
    • Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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    • pp.145-150
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    • 2003
  • 고해상도 위성영상을 이용한 산림의 분석은 기존의 중ㆍ저해상도 영상의 분석과 다른 접근이 필요하다. 본 연구는 임분 특성을 해석하는데 중요한 판독기준인 texture를 이용하여 영상 안에서 임상, 임목직경급, 수관울폐도 등에 따른 Texture 정보를 비교 분석하고자 한다. 울산 일부 산림지역을 대상으로 3개의 가시광선 밴드와 1개의 근적외선 밴드의 1m IKONOS 영상을 이용하여 Texture 정보를 추출하는데 일반적으로 사용되는 통계적인 방법 중에 하나인 GLCM(Gray-Level Co-occurrence matrix)을 통해 Texture 분석을 하였다. 또한 1996년도에 제작된 4차 임상도를 통해 추출된 산림 특성별 Texture 정보를 비교 검토하여 고해상도 위성영상을 활용하여 산림 특성을 해석하는데 최적의 Texture 정보를 제시하고자 하였다. 고해상도 영상에서 나타나는 임분의 특성별 질감정보는 임상, 직경, 임목밀도에 따라 다양하게 나타났다.

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Video Segmentation Using DCT and Guided Filter in real time (DCT와 Guided 필터를 이용한 실시간 영상 분류)

  • Shin, Hyunhak;Lee, Zucheul;Kim, Wonha
    • Journal of Broadcast Engineering
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    • v.20 no.5
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    • pp.718-727
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    • 2015
  • In this paper, we present a novel segmentation method that can extract new foreground objects from a current frame in real-time. It is performed by detecting differences between the current frame and reference frame taken from a fixed camera. We minimize computing complexity for real-time video processing. First DCT (Discrete Cosine Transform) is utilized to generate rough binary segmentation maps where foreground and background regions are separated. DCT shows better result of texture analysis than previous methods where texture analysis is performed in spatial domain. It is because texture analysis in frequency domain is easier than that in special domain and intensity and texture in DCT are taken into account at the same time. We maximize run-time efficiency of DCT by considering color information to analyze object region prior to DCT process. Last we use Guided filter for natural matting of the generated binary segmentation map. In general, Guided filter can enhance quality of intermediate result by incorporating guidance information. However, it shows some limitations in homogeneous area. Therefore, we present an additional method which can overcome them.

Estrus Detection in Sows Based on Texture Analysis of Pudendal Images and Neural Network Analysis

  • Seo, Kwang-Wook;Min, Byung-Ro;Kim, Dong-Woo;Fwa, Yoon-Il;Lee, Min-Young;Lee, Bong-Ki;Lee, Dae-Weon
    • Journal of Biosystems Engineering
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    • v.37 no.4
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    • pp.271-278
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    • 2012
  • Worldwide trends in animal welfare have resulted in an increased interest in individual management of sows housed in groups within hog barns. Estrus detection has been shown to be one of the greatest determinants of sow productivity. Purpose: We conducted this study to develop a method that can automatically detect the estrus state of a sow by selecting optimal texture parameters from images of a sow's pudendum and by optimizing the number of neurons in the hidden layer of an artificial neural network. Methods: Texture parameters were analyzed according to changes in a sow's pudendum in estrus such as mucus secretion and expansion. Of the texture parameters, eight gray level co-occurrence matrix (GLCM) parameters were used for image analysis. The image states were classified into ten grades for each GLCM parameter, and an artificial neural network was formed using the values for each grade as inputs to discriminate the estrus state of sows. The number of hidden layer neurons in the artificial neural network is an important parameter in neural network design. Therefore, we determined the optimal number of hidden layer units using a trial and error method while increasing the number of neurons. Results: Fifteen hidden layers were determined to be optimal for use in the artificial neural network designed in this study. Thirty images of 10 sows were used for learning, and then 30 different images of 10 sows were used for verification. Conclusions: For learning, the back propagation neural network (BPN) algorithm was used to successful estimate six texture parameters (homogeneity, angular second moment, energy, maximum probability, entropy, and GLCM correlation). Based on the verification results, homogeneity was determined to be the most important texture parameter, and resulted in an estrus detection rate of 70%.

Discriminatory Projection of Camouflaged Texture Through Line Masks

  • Bhajantri, Nagappa;Pradeep, Kumar R.;Nagabhushan, P.
    • Journal of Information Processing Systems
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    • v.9 no.4
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    • pp.660-677
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    • 2013
  • The blending of defective texture with the ambience texture results in camouflage. The gray value or color distribution pattern of the camouflaged images fails to reflect considerable deviations between the camouflaged object and the sublimating background demands improved strategies for texture analysis. In this research, we propose the implementation of an initial enhancement of the image that employs line masks, which could result in a better discrimination of the camouflaged portion. Finally, the gray value distribution patterns are analyzed in the enhanced image, to fix the camouflaged portions.

Textural Properties of Gluten-free Rice Pasta Prepared Employing Various Starches (전분을 첨가한 글루텐 프리 쌀 파스타의 텍스처 특성)

  • Jung, Jin Hyuck;Yoon, Hye Hyun
    • Korean journal of food and cookery science
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    • v.33 no.1
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    • pp.28-36
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    • 2017
  • Purpose: This study was conducted to understand the factors that affect the texture of gluten-free rice pasta prepared buckwheat, mung bean, and acorn starches and to compare textural properties of samples 100% semolina. Methods: The moisture content, weight and water absorption test investigated and texture profile analysis measured by texture analyzer. Results: 100% semolina sample's value was lower than gluten-free rice pasta moisture content, weight and water absorption test. moisture content weight was in pasta with mung bean starchin pasta with buckwheat starch. Texture profile analysis showed that increasing amount of buckwheat, mung bean, and acorn starches increased hardness, chewiness, cohesiveness and springiness, and decreased adhesiveness of gluten free rice pasta. Conclusion: This study suggested that adding buckwheat, mungbean and acorn starches could improve texture properties of gluten-free rice pasta.