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1
Shewfelt, R. L., J. K. Brecht, and C. N. Thai. "CLASSIFICATION OF TOMATO RIPENESS AND MATURITY BY FOOD COLORIMETRY." HortScience 27, no. 6 (June 1992): 651b—651. http://dx.doi.org/10.21273/hortsci.27.6.651b.
Повний текст джерелаАнотація:
Tomato ripeness is currently assessed by a subjective visual classification scheme based on external color while maturity of green fruit is based on a destructive evaluation of internal locule development. In an effort to develop an objective method of tomato maturity and ripeness classification, external color measurements were performed on fresh, sized (6×7) `mature-green' tomatoes (cv “Sunny') initially and through ripening using a Gardner XL-845 colorimeter. Hue angle (tan-1 b/a, designated θ) provided the best objective means of ripeness classification with proposed ranges for mature-green (θ>114), breaker (101<θ<114), turning (85<θ<101), pink (64<θ<85), light red (36<θ<64) and red (θ<36) classes using average hue at the circumference. Hue angle at the blossom end was 2-12° lower than at the circumference due to initiation of color development at the blossom end. Colorimetry was not able to distinguish differences in physiological maturity of mature-green tomatoes as determined by the length of time required to develop from mature-green to breaker which varied from 1 to 22 days in the test.
2
Zhao, Yan, and Shuai Liu. "Robust Image Hashing Based on Cool and Warm Hue and Space Angle." Security and Communication Networks 2021 (July 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/3803481.
Повний текст джерелаАнотація:
Image hashing has attracted more and more attention in the field of information security. In this paper, a novel hashing algorithm using cool and warm hue information and three-dimensional space angle is proposed. Firstly, the original image is preprocessed to get the opposite color component and the hue component H in HSV color space. Then, the distribution of cool and warm hue pixels is extracted from hue component H. Blocks the hue component H, according to the proportion of warm hue and cool hue pixels in each small block, combined with the quaternion and opposite color component, constructed the cool and warm hue opposite color quaternion (CWOCQ) feature. Then, three-dimensional space, opposite color, and cool and warm hue are combined to obtain the three-dimensional space angle (TDSA) feature. The CWOCQ feature and the TDSA feature are connected and disturbed to obtain the final hash sequence. Experimental results show that the proposed algorithm has good security and has better image classification performance and shorter computation time compared with some advanced algorithms.
3
Bible, Bernard B., and Richard J. McAvoy. "A CIELAB Color Classification Scheme for Poinsettias." HortScience 32, no. 3 (June 1997): 456F—456. http://dx.doi.org/10.21273/hortsci.32.3.456f.
Повний текст джерелаАнотація:
Forty-two poinsettia cultivars were grown as a 15-cm single-plant pinched crop at 21/16.5°C (day/night) temperatures during Fall 1995 with standard commercial practices for irrigating, fertilizing, and pest control. On 7 Dec., 156 consumers rated the cultivars for their overall appeal. On 11 Dec., color coordinate (CIELAB) readings for bracts and leaves were taken with a Minolta 200b colorimeter. The colorimeter was set to illuminate C and has a 8-mm aperture. Bracts and leaves were placed on a white tile background for colorimetric readings. In 1996, a similar evaluation was conducted with 55 poinsettia cultivars. Using the L-value of leaves as a criterion, cultivars were separated into medium green-leafed and dark green-leafed groupings. For bracts among the red types, hue angle values were used to separate cultivars into cool red types (hue angle ≈20–22°) and warm red types (hue angle ≈24–25°). Based on the 1995 study, cultivars within the cool red bracts and dark green foliage group—those that were darker, duller red (lower L and chroma)—were less attractive (lower consumer ratings) than lighter, more-vivid red cultivars. For cultivars within the cool red bracts and medium green foliage group, consumers preferred the darker duller red cultivars. Perhaps dark foliage gives a more pleasing contrast with the more vivid cool reds than does the medium green foliage. In general, consumers rated red cultivars hire than non-red cultivars.
4
Miller, David L. "Over the rainbow: The classification of unique hues." Behavioral and Brain Sciences 20, no. 2 (June 1997): 204–5. http://dx.doi.org/10.1017/s0140525x97431424.
Повний текст джерелаАнотація:
Saunders & van Brakel's analysis of the phenomenal categorization and subsequent experimental research in unique hues fails to include contemporary methodological improvements. Alternative strategies are offered from the author's research that rely less on language and world knowledge and provide strong evidence for the general theoretical constructs of elemental hue, nonbasic, and basic color terms.
5
Park, DaEun, HaeRyung Hong, and YungKyung Park. "Fine Classification of Korean Skin Color by Tone and Hue." Journal of Korea Society of Color Studies 33, no. 3 (August 31, 2019): 36–44. http://dx.doi.org/10.17289/jkscs.33.3.201908.36.
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6
Kim, Taehyeong, Dae-Hyun Lee, Kyoung-Chul Kim, Taeyong Choi, and Jun Myoung Yu. "Tomato Maturity Estimation Using Deep Neural Network." Applied Sciences 13, no. 1 (December 28, 2022): 412. http://dx.doi.org/10.3390/app13010412.
Повний текст джерелаАнотація:
In this study, we propose a tomato maturity estimation approach based on a deep neural network. Tomato images were obtained using an RGB camera installed on a monitoring robot and samples were cropped to generate a dataset with which to train the classification model. The classification model is trained using cross-entropy loss and mean–variance loss, which can implicitly provide label distribution knowledge. For continuous maturity estimation in the test stage, the output probability distribution of four maturity classes is calculated as an expected (normalized) value. Our results demonstrate that the F1 score was approximately 0.91 on average, with a range of 0.85–0.97. Furthermore, comparison with the hue value—which is correlated with tomato growth—showed no significant differences between estimated maturity and hue values, except in the pink stage. From the overall results, we found that our approach can not only classify the discrete maturation stages of tomatoes but can also continuously estimate their maturity. Furthermore, it is expected that with higher accuracy data labeling, more precise classification and higher accuracy may be achieved.
7
Kim, Dong Sub, Da Uhm Lee, Jeong Ho Lim, Steven Kim, and Jeong Hee Choi. "Agreement Between Visual and Model-Based Classification of Tomato Fruit Ripening." Transactions of the ASABE 63, no. 3 (2020): 667–74. http://dx.doi.org/10.13031/trans.13812.
Повний текст джерелаАнотація:
Highlights Human visual classification and predictive models often disagree when only color indices are used. The degree of agreement is improved significantly when predictive models are cultivar-specific. The degree of agreement can be improved when firmness and carotenoid contents are considered. Abstract. Traditionally, the ripening stage of tomato fruit is determined by the observed percentage of red color on the fruit surface based on color charts provided by USDA standards. However, multiple observers can assign different ripening stages to the same tomato fruit due to subjectivity and/or inaccurate evaluations. This practical challenge has not been extensively discussed in the literature, so we assessed the degree of agreement between human visual classification and model-based prediction using physicochemical properties such as color (L*, a*, b*, hue, and chroma), firmness, and carotenoid contents. In our exploratory data analyses, we clearly observed increasing a* and decreasing L*, hue, and firmness with respect to ripening stage, but the rate of change seemed different from cultivar to cultivar. To assess the degree of agreement, cross-validations were used to compare thirty linear regression models with various combinations of the predictors. The cross-validations indicated that predictions from a cultivar-specific model agreed well with human visual classifications. When the cultivar-specific model was considered with the color indices, we achieved up to 95.5% accuracy. When firmness, lycopene, and ß-carotene were added to the model, the accuracy increased to 96.8%. These results suggest the reliability of non-destructive methods for auto-sorting systems. Keywords: ß-carotene, Color index, Firmness, Fruit color, Lycopene, Ripening, Tomato.
8
Smith, Stacey D. "Quantifying Color Variation: Improved Formulas for Calculating Hue with Segment Classification." Applications in Plant Sciences 2, no. 3 (March 2014): 1300088. http://dx.doi.org/10.3732/apps.1300088.
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9
Nguyen, Thi Hong Hai, and Catherine Cheung. "The classification of heritage tourists: a case of Hue City, Vietnam." Journal of Heritage Tourism 9, no. 1 (July 17, 2013): 35–50. http://dx.doi.org/10.1080/1743873x.2013.818677.
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Thoriq, Adhe Irham, Muhamad Haris Zuhri, Purwanto Purwanto, Pujiono Pujiono, and Heru Agus Santoso. "Classification of Banana Maturity Levels Based on Skin Image with HSI Color Space Transformation Features Using the K-NN Method." Journal of Development Research 6, no. 1 (May 31, 2022): 11–15. http://dx.doi.org/10.28926/jdr.v6i1.200.
Повний текст джерелаАнотація:
Banana or Musa Paradisiaca is one type of fruit that is often found in Southeast Asia. The most popular is the Raja banana (Musa paradisiaca L.). The advantage of the plantain is that it has a fragrant aroma and is of medium size and has a very sweet taste that is appetizing when it is fully ripe. While the drawback of plantains is that they ripen quickly, if not handled properly, it can change the nutritional value and nutrients contained in plantains. In this study, the author focuses on identifying the level of ripeness of bananas using the image of a plantain fruit that is still intact and its skin. Processing of the image of the plantain fruit using HSI (Hue Saturation Intensity) color space transformation feature extraction. The tool used to extract the HSI (Hue Saturation Intensity) color space transformation feature is Matlab. The attribute values obtained from the extraction are the Red, Green, Blue values obtained from the RGB values. Hue, saturation and intensity attributes were obtained from HSI extraction. Classification of the level of ripeness of plantain fruit is done with the help of the rapidminer tool. The method used is K-NN. The results obtained from this test are the accuracy value of 91.33% with a standard deviation value of+/- 4.52% with a value of k=4. The RMSE value obtained is 0.276.
11
Lazaro, Antonio, Marti Boada, Ramon Villarino, and David Girbau. "Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor." Sensors 19, no. 7 (April 11, 2019): 1741. http://dx.doi.org/10.3390/s19071741.
Повний текст джерелаАнотація:
This paper presents a color-based classification system for grading the ripeness of fruit using a battery-less Near Field Communication (NFC) tag. The tag consists of a color sensor connected to a low-power microcontroller that is connected to an NFC chip. The tag is powered by the energy harvested from the magnetic field generated by a commercial smartphone used as a reader. The raw RGB color data measured by the colorimeter is converted to HSV (hue, saturation, value) color space. The hue angle and saturation are used as features for classification. Different classification algorithms are compared for classifying the ripeness of different fruits in order to show the robustness of the system. The low cost of NFC chips means that tags with sensing capability can be manufactured economically. In addition, nowadays, most commercial smartphones have NFC capability and thus a specific reader is not necessary. The measurement of different samples obtained on different days is used to train the classification algorithms. The results of training the classifiers have been saved to the cloud. A mobile application has been developed for the prediction based on a table-based method, where the boundary decision is downloaded from a cloud service for each product. High accuracy, between 80 and 93%, is obtained depending on the kind of fruit and the algorithm used.
12
Al-Windi, Basim K. M. A., Amel H. Abbas, and Mohammed Shakir Mahmood. "Using Texture Analyses and Statistical Classification for Detection Plant Leaf Diseases." Al-Mustansiriyah Journal of Science 32, no. 5 (December 15, 2021): 1–4. http://dx.doi.org/10.23851/mjs.v32i5.1115.
Повний текст джерелаАнотація:
The proposed method is based on classifying 15 types of plant leaf disease. Hue saturation value was used to delete the background and the healthy areas to show only the affected area in the image. Texture analyses adopted in image features extractions from the R component &G component &B component and creating 3 components which are RG and RB and GB color of the RGB color space images of diseased leaves. Building image classifier using statistical method for classification.
13
ROKUTANDA, Chie, and Takeshi NAKAGAWA. "THE CLASSIFICATION OF THE ARCHITECTURAL STYLES IN HUE NGUYEN DYNASTY ARCHITECTURAL REMAINS." Journal of Architecture and Planning (Transactions of AIJ) 78, no. 688 (2013): 1409–14. http://dx.doi.org/10.3130/aija.78.1409.
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14
Granados-López, D., A. García-Rodríguez, S. García-Rodríguez, A. Suárez-García, M. Díez-Mediavilla, and C. Alonso-Tristán. "Pixel-Based Image Processing for CIE Standard Sky Classification through ANN." Complexity 2021 (December 20, 2021): 1–15. http://dx.doi.org/10.1155/2021/2636157.
Повний текст джерелаАнотація:
Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image-processing methods are applied. While the use of the traditional RGB color space for image-processing yielded good results (ANN accuracy equal to 86.6%), other color spaces, such as Hue Saturation Value (HSV), which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image-processing methods could improve ANN-algorithm accuracy, depending on the image information required for the classification problem.
15
Abadi, F. R., R. E. Masithoh, L. Sutiarso, and S. Rahayoe. "Effect of size reduction on yellow soybean seed characterization based on colorimetry." IOP Conference Series: Earth and Environmental Science 1116, no. 1 (December 1, 2022): 012063. http://dx.doi.org/10.1088/1755-1315/1116/1/012063.
Повний текст джерелаАнотація:
Abstract Soybean seed, which is generally yellow in color, is the primary product of soybean plants sold in Indonesian market. To characterize non-destructively, it is necessary to understand the extent to which physical treatment, including size reduction, may affects the color characteristics. Therefore, this study aimed to determine the effect of size reduction of soybean seeds on its color parameters. A completely randomized design was performed with particle size factor with five levels and variety factor with four levels. Particle size included: intact seed; >595; 595-250 μm; 250-145 μm; and <145 μm of particle size, while variety included Anjasmoro; Argomulyo; Grobogan and soybean seed obtained from local market. Color parameters which used were L, a*, b*, Hue and C*. The ANOVA with Duncan multiple range test (α=0.05) and PCA were performed to analyze the effect of color parameters to sample classification. The results showed that the L and Hue value was significantly different (p<0.05) for all particle size. The smaller the particle size, the greater the L value and the smaller the Hue value. The L value was also significantly different (p<0.05) for all varieties and was able to classify all varieties. The PCA analysis result in up to 96% of PC-1 and PC-2 showing that size reduction was able to classify all samples based on all parameters; distinct classification of Anjasmoro and local market soybean can be observed. The yellowish color that represented by a* value, showed the higher distance than other parameters.
16
Mausfeld, Rainer J. "Why bother about opponency? Our theoretical ideas on elementary colour coding have changed our language of experience." Behavioral and Brain Sciences 20, no. 2 (June 1997): 203. http://dx.doi.org/10.1017/s0140525x97411421.
Повний текст джерелаАнотація:
There is no natural and pretheoretical classification of colour appearances into hue, saturation, brightness, unique hues, and so on. Rather, our theoretical insights into the coding of colour have reciprocally shaped the way we talk about colour appearances. Opponency is only one of many fundamental aspects of colour coding, and we are hardly justified in ascribing some theoretical prominance to it.
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Przybył, Krzysztof, Piotr Boniecki, Krzysztof Koszela, Łukasz Gierz, and Mateusz Łukomski. "Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process." Czech Journal of Food Sciences 37, No. 2 (May 10, 2019): 135–40. http://dx.doi.org/10.17221/427/2017-cjfs.
Повний текст джерелаАнотація:
The research methodology consists of several stages to develop a noninvasive method of identifying the turgor of potato tubers during the storage. During the first stage, a graphic database (set of training data) has been created for selected varieties of potatoes. As a next step, special proprietary software called ’PID system’ was used together with a commercial MATLAB package to extract parameters defining the digital image descriptors. This included: hue space models, shape coefficient and image texture. Thirdly, Artificial Neural Network (ANN) training was conducted with the use of Statistica and MATLAB tools. As a result of the analysis, a neural model has been obtained, which had the greatest classification features.
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Escuredo, Olga, María Shantal Rodríguez-Flores, Sergio Rojo-Martínez, and María Carmen Seijo. "Contribution to the Chromatic Characterization of Unifloral Honeys from Galicia (NW Spain)." Foods 8, no. 7 (June 29, 2019): 233. http://dx.doi.org/10.3390/foods8070233.
Повний текст джерелаАнотація:
Honey color and other physicochemical characteristics depend mainly on the botanical and geographical origin. The study of these properties could make easier a correct classification of unifloral honey. This work determined the palynological characteristics and some physicochemical properties such as pH, electrical conductivity, and color (Pfund scale and the CIELa*b* coordinates), as well as the total content of the bioactive compounds phenols and flavonoids of ninety-three honey samples. Samples were classified as chestnut, blackberry, heather, eucalyptus, and honeydew honey. The study showed a close relationship between the physicochemical variables and the botanical origin. The five types of honey presented different physicochemical properties among them. A principal component analysis showed that Hue, lightness, b*, and Chroma variables were important for the honey types classification, followed by Erica pollen, pH, Cytisus, and Castanea variables. A forward stepwise regression analysis was performed introducing as dependent variables the color (mm Pfund) and the Chroma and the Hue variables. The regression models obtained explained 86%, 74%, and 86% of the variance of the data, respectively. The combination of the chromatic and physicochemical and pollen variables through the use of multivariable methods was optimal to characterize and group the honey samples studied.
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Song, Lin, Huixuan Zhao, Zongfang Ma, and Qi Song. "A new method of construction waste classification based on two-level fusion." PLOS ONE 17, no. 12 (December 27, 2022): e0279472. http://dx.doi.org/10.1371/journal.pone.0279472.
Повний текст джерелаАнотація:
The automatic sorting of construction waste (CW) is an essential procedure in the field of CW recycling due to its remarkable efficiency and safety. The classification of CW is the primary task that guides automatic and precise sorting. In our work, a new method of CW classification based on two-level fusion is proposed to promote classification performance. First, statistical histograms are used to obtain global hue information and local oriented gradients, which are called the hue histogram (HH) and histogram of oriented gradients (HOG), respectively. To fuse these visual features, a bag-of-visual-words (BoVW) method is applied to code HOG descriptors in a CW image as a vector, and this process is named B-HOG. Then, based on feature-level fusion, we define a new feature to combine HH and B-HOG, which represent the global and local visual characteristics of an object in a CW image. Furthermore, two base classifiers are used to learn the information from the color feature space and the new feature space. Based on decision-level fusion, we propose a joint decision-making model to combine the decisions from the two base classifiers for the final classification result. Finally, to verify the performance of the proposed method, we collect five types of CW images as the experimental data set and use these images to conduct experiments on three different base classifiers. Moreover, we compare this method with other extant methods. The results demonstrate that our method is effective and feasible.
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Jirsa, Ondřej, and Ivana Polišenská. "Identification of Fusarium damaged wheat kernels using image analysis." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59, no. 5 (2011): 125–30. http://dx.doi.org/10.11118/actaun201159050125.
Повний текст джерелаАнотація:
Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON) content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue), very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance). The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.
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Mandal, Satyendra Nath, Sanket Dan, Pritam Ghosh, Subhranil Mustafi, Kunal Roy, Kaushik Mukherjee, Dilip Kumar Hajra, and Santanu Banik. "Pig Breeds Classification using Neuro-Statistic Model." Science & Technology Journal 7, no. 2 (July 1, 2019): 78–88. http://dx.doi.org/10.22232/stj.2019.07.02.10.
Повний текст джерелаАнотація:
Image classification using fully connected neural network is not efficient due to huge number of parameters in each layer. In this paper, we propose a Neuro-Statistic model for classification of five different pig breeds from pig images. The model consists of four sub modules which work together as a layered structure. We captured multiple individual pig images of five different pig breeds from different organized farms to conduct this research, segmented the captured pig images using hue based segmentation algorithm and then calculated the statistical properties like entropy, standard deviation, variance, mean, median, mode and color properties like H.S.V from the content of the individual segmented images. We fed all the extracted properties into Neural Network for Pig Breed (NNPB) to perform pig breed prediction with the classification module and analyzed the best performance, regression error plot, Error histogram and training state of NNPB. The performance of NNPB network was accepted based on error analysis and finally, we used the trained model to predict the breed of 50 pig images and achieved the prediction accuracy of 90%.
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Lee, Sang Hwa, and Jung-Yoon Kim. "Classification of the Era Emotion Reflected on the Image Using Characteristics of Color and Color-Based Classification Method." International Journal of Software Engineering and Knowledge Engineering 29, no. 08 (August 2019): 1103–23. http://dx.doi.org/10.1142/s0218194019400114.
Повний текст джерелаАнотація:
Paintings convey the composition and characteristics of artists; therefore, it is possible to feel the intended style of painting and emotion of each artist through their paintings. In general, basic elements that constitute traditional paintings are color, texture, and composition (formative elements constituting the paintings are color and shape); however, color is the most crucial element expressing the emotion of a painting. In particular, traditional colors manifest the color containing historicity of the era, so the color shown in painting images is considered a representative color of the culture to which the painting belongs. This study constructed a color emotional system by analyzing colors and rearranged color emotion adjectives based on color combination techniques and clustering algorithm proposed by Kobayashi as well as I.R.I HUE & TONE 120 System. Based on the embodied color emotion system, this study confirmed classified emotions of images by extracting and classifying emotions from traditional Korean painted images, focusing on traditional painted images of the late Joseon Dynasty. Moreover, it was possible to verify the cultural traits of the era through the classified emotion images.
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Memon, Mehak Maqbool, Manzoor Ahmed Hashmani, Aisha Zahid Junejo, Syed Sajjad Rizvi, and Adnan Ashraf Arain. "A Novel Luminance-Based Algorithm for Classification of Semi-Dark Images." Applied Sciences 11, no. 18 (September 18, 2021): 8694. http://dx.doi.org/10.3390/app11188694.
Повний текст джерелаАнотація:
Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sophisticated hardware and inadequate image classifier training time remain significant problems to be handled. These techniques fail to classify the visual scene-based images with high accuracy. The other alternative is computer vision (CV) methods, which also have major issues. CV methods do provide some basic procedures which may assist in such classification but, to the best of our knowledge, no CV algorithm exists to perform such classification, i.e., these do not account for semi-dark images in the first place. Moreover, these methods do not provide a well-defined protocol to calculate images’ content visibility and thereby classify images. One of the key algorithms for calculation of images’ content visibility is backed by the HSL (hue, saturation, lightness) color model. The HSL color model allows the visibility calculation of a scene by calculating the lightness/luminance of a single pixel. Recognizing the high potential of the HSL color model, we propose a novel framework relying on the simple approach of the statistical manipulation of an entire image’s pixel intensities, represented by HSL color model. The proposed algorithm, namely, Relative Perceived Luminance Classification (RPLC) uses the HSL (hue, saturation, lightness) color model to correctly identify the luminosity values of the entire image. Our findings prove that the proposed method yields high classification accuracy (over 78%) with a small error rate. We show that the computational complexity of RPLC is much less than that of the state-of-the-art ML algorithms.
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Wang, Fu Juan. "Chinese Date Grading Based on Computer Vision." Advanced Materials Research 838-841 (November 2013): 3283–86. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.3283.
Повний текст джерелаАнотація:
In order to implement the accuracy and robust classification of Chinese dates according to size and color based on computer vision techniques on line, the method of classification according to size and color for Chinese date was studied. Taking the black rollers as background, the Chinese date images were pre-segmented by double thresholds in RGB color space. Through morphological operation, contour trace and region fill, the whole Chinese date target was obtained. the maximum diameter value was used to be the character value for size classification. The difference of saturation and hue of pericarp area in HIS color space was the color grading criteria. The results indicated that the accuracy of diameter measurement is 1.92mm, Experiment results proved the methods is effective to classify Chinese date by size and shape.
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Lezoray, Olivier, and Michel Lecluse. "AUTOMATIC SEGMENTATION AND CLASSIFICATION OF CELLS FROM BRONCHO ALVEOLAR LAVAGE." Image Analysis & Stereology 26, no. 3 (May 3, 2011): 111. http://dx.doi.org/10.5566/ias.v26.p111-119.
Повний текст джерелаАнотація:
Broncho alveolar lavage is the most commonly used diagnostic tool for confirming alveolar hemorrhage. Golde has introduced a ranking score, based on the hemosiderin content of macrophages which enables ranking cells from 0 to 4 based on the degree of Prussian blue stain. We propose a complete image analysis scheme to automatically perform both the extraction of the cellular objects and the ranking of each cell according to the Golde score. The image analysis techniques used mainly involve clustering and mathematical morphology. A 2D histogram is clustered to extract the main cellular components, a color watershed is used to determine and refine the regions. Finally, the cellular components of interest are firstly classified according to their hue and secondly according to their staining repartition. The proposed image analysis technique is very fast and produces reliable and accurate results.
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Wandi, Dede, Fauziah Fauziah, and Nur Hayati. "Deteksi Kelayuan Pada Bunga Mawar dengan Metode Transformasi Ruang Warna Hue Saturation Intensity (HSI) dan Hue Saturation Value (HSV)." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 1 (January 22, 2021): 308. http://dx.doi.org/10.30865/mib.v5i1.2562.
Повний текст джерелаАнотація:
The rose is a plant of the genus Rosa. The rose consists of more than 100 species with various colors. In selecting and sorting roses, roses are often found that are still fresh and wilted. Based on the problems faced in roses, a system design is carried out that can detect the wilting condition of roses. By applying the HSI and HSV methods to image processing applications, it is hoped that it can help in choosing the condition of roses. With research methods through observation and literature study. To see the conditions, roses can be divided into wilted flowers and fresh flowers. In its implementation and classification, by detecting the color of roses in the HSI and HSV color space, from a total of 230 images of red and white roses that tested 200 images using HSI and HSV, the value of Range was obtained on the HSI, H = 0.240634 - 0.5, S = 0.781818 - 1, and I = 0.477124 - 1 in the Fresh category, while the HSI Wilt Category, H = 0.170495 - 0.5, S = 0.40239 - 1, I = 0.562092 - 1. and also obtained the value of Range with HSV with Fresh category H = 0.240634 - 0.5, S = 0 - 0.988235, V = 0 - 0.988235, and Wilt category H = 0.170495-0.5, S = 0 - 0.996078, V = 0 - 0.996078. With an accuracy value of the HSI and HSV of 86.9%. Therefore, it can be concluded that the detection of wilting in roses using the HSI and HSV methods is the fastest in the process using the HSI method because it reads all the min-max values.
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Huan, Vu Phan, Le Kim Hung, and Nguyen Hoang Viet. "Fault Classification and Location on 220kV Transmission line Hoa Khanh – Hue Using Anfis Net." Journal of Automation and Control Engineering 3, no. 2 (2015): 98–104. http://dx.doi.org/10.12720/joace.3.2.98-104.
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López Camelo, Andrés F., and Perla A. Gómez. "Comparison of color indexes for tomato ripening." Horticultura Brasileira 22, no. 3 (September 2004): 534–37. http://dx.doi.org/10.1590/s0102-05362004000300006.
Повний текст джерелаАнотація:
Color in tomato is the most important external characteristic to assess ripeness and postharvest life, and is a major factor in the consumer's purchase decision. Degree of ripening is usually estimated by color charts. Colorimeters, on the other hand, express colors in numerical terms along the L*, a* and b* axes (from white to black, green to red and blue to yellow, respectively) within the CIELAB color sphere which are usually mathematically combined to calculate the color indexes. Color indexes and their relationship to the visual color classification of tomato fruits vine ripened were compared. L*, a* and b* data (175 observations from eleven cultivars) from visually classified fruits at harvest in six ripening stages according to the USDA were used to calculate hue, chroma, color index, color difference with pure red, a*/b* and (a*/b*)². ANOVA analysis were performed and means compared by Duncan's MRT. Color changes throughout tomato ripening were the result of significant changes in the values of L*, a* and b*. Under the conditions of this study, hue, color index, color difference and a*/b* expressed essentially the same, and the color categories were significantly different in terms of human perception, with hue showing higher range of values. Chroma was not a good parameter to express tomato ripeness, but could be used as a good indicator of consumer acceptance when tomatoes are fully ripened. The (a*/b*)² relationship had the same limitations as chroma. For vine ripened fruits, hue, color index, color difference and a*/b* could be used as objective ripening indexes. It would be interesting to find out what the best index would be if ripening took place under inadequate conditions of temperature and ilumination.
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Harel, B., P. Kurtser, Y. Parmet, and Y. Edan. "Sweet pepper maturity evaluation." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 167–71. http://dx.doi.org/10.1017/s2040470017001236.
Повний текст джерелаАнотація:
This paper focuses on maturity evaluation derived by a color camera for a sweet pepper robotic harvester. Different color and morphological features for sweet pepper maturity were evaluated. Side view and bottom view of sweet paper were analyzed and compared for their ability to classify into 4 maturity classes. The goal of this study was to differentiate between the two center classes which are difficult to separate. Statistical analysis of 13 different features in reliance to the maturity classification and the views indicated the best features for classification. The results show that the features that can be used for classification between the two central classes from both bottom and side views are: Hue range, Equal2Real – the ratio between the equivalent equal sized circle perimeter to the shape perimeter and Area2Peri – the ratio between the area to the perimeter.
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Aravena, Ricardo A., Mitchell B. Lyons, Adam Roff, and David A. Keith. "A Colourimetric Approach to Ecological Remote Sensing: Case Study for the Rainforests of South-Eastern Australia." Remote Sensing 13, no. 13 (June 29, 2021): 2544. http://dx.doi.org/10.3390/rs13132544.
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To facilitate the simplification, visualisation and communicability of satellite imagery classifications, this study applied visual analytics to validate a colourimetric approach via the direct and scalable measurement of hue angle from enhanced false colour band ratio RGB composites. A holistic visual analysis of the landscape was formalised by creating and applying an ontological image interpretation key from an ecological-colourimetric deduction for rainforests within the variegated landscapes of south-eastern Australia. A workflow based on simple one-class, one-index density slicing was developed to implement this deductive approach to mapping using freely available Sentinel-2 imagery and the super computing power from Google Earth Engine for general public use. A comprehensive accuracy assessment based on existing field observations showed that the hue from a new false colour blend combining two band ratio RGBs provided the best overall results, producing a 15 m classification with an overall average accuracy of 79%. Additionally, a new index based on a band ratio subtraction performed better than any existing vegetation index typically used for tropical evergreen forests with comparable results to the false colour blend. The results emphasise the importance of the SWIR1 band in discriminating rainforests from other vegetation types. While traditional vegetation indices focus on productivity, colourimetric measurement offers versatile multivariate indicators that can encapsulate properties such as greenness, wetness and brightness as physiognomic indicators. The results confirmed the potential for the large-scale, high-resolution mapping of broadly defined vegetation types.
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Nasution, Aulia Muhammad Taufiq, and Syakir Almas Amrullah. "Simple Vision System for Apple Varieties Classification." Industria: Jurnal Teknologi dan Manajemen Agroindustri 11, no. 1 (April 30, 2022): 51–63. http://dx.doi.org/10.21776/ub.industria.2022.011.01.6.
Повний текст джерелаАнотація:
Every variety of apple has its particular physical characteristics, which are affected by different pre-harvest factors. Manual classification of these varieties by human labor has several weaknesses, such as the inconsistency, subjectivity, fatigue and different accuracy due to different level of experience of the inspector. This study was aimed to design and evaluate a simple computer-based vision system for recognizing and grading several varieties of apples based on their physical characteristics. Images of apples were taken and were used as training data with different algorithms to extract the particular characteristics of each variety, such as color and shape. The extracted Hue color channels and contour vector were recorded as the reference data and were used to recognize the similar characteristic of those images from the testing data group. The k-nearest neighbors algorithm was used to decide whether an apple belongs to a particular variety. The results show that the recognition rate based on color only was between 84–97% and it was between 5–77% it is based on the shape only. Rotating the image significantly increases the recognition rate (to be between 5 - 69% based on the shape only). Moreover, combining both color and shape characteristics significantly improves the recognition rate.
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Areni, Intan Sari, Indrabayu Amirullah, and Nurhikma Arifin. "Klasifikasi Kematangan Stroberi Berbasis Segmentasi Warna dengan Metode HSV." Jurnal Penelitian Enjiniring 23, no. 2 (November 30, 2019): 113–16. http://dx.doi.org/10.25042/jpe.112019.03.
Повний текст джерелаАнотація:
Classification of Strawberry Maturity Based on Color Segmentation using HSV Method. Manual fruit maturity classification has many limitations because it is influenced by human subjectivity. Hence, the application of digital image processing and artificial intelligence becomes more effective and efficient. This study aims to create a classification system that automatically divides strawberry maturity into three categories, namely not ripe, half-ripe, and ripe. The process of identifying the level of fruit maturity is based on the color characteristics Red, Green, Blue (RGB) value of the image. The method used for color segmentation is Hue, Saturation, Value (HSV) and for the classification of strawberry maturity using the Multi-Class Support Vector Machine (SVM) algorithm with a Radial Basic Function (RBF) kernel. Strawberry image data was retrieved using the Logitech C920 camera. The dataset consisted of 158 images of strawberries. The results showed that the classification of strawberry maturity using the multi-class SVM algorithm with kernel parameters RBF cost (C) = 10 and gamma (γ) = 10-3 produced the highest accuracy of 97%.
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Ahmad, Hanaa M., and Shrooq R. Hameed. "Eye Diseases Classification Using Back Propagation Artificial Neural Network." Engineering and Technology Journal 39, no. 1B (March 25, 2021): 11–20. http://dx.doi.org/10.30684/etj.v39i1b.1363.
Повний текст джерелаАнотація:
A human eye is a vital organ responsible for a person's vision. So, the early detection of eye diseases is essential. The objective of this paper deals with diagnosing of seven different external eye diseases that can be recognized by a human eye. These diseases cause problems either in eye pupil, in sclera of eye or in both or in eyelid. Color histogram and texture features extraction techniques with classification technique are used to achieve the goal of diagnosing external eye diseases. Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification. The comparative study states that the features extracted from HMMD color space is better than other features like Histogram of Oriented Gradient (HOG) features and give the same accuracy as features extracted directly from medical expert recorded symptoms. The proposed method is applied on external eye diseases data set consisting of 416 images with an accuracy rate of 85.26315%, which is the major result that was achieved in this study.
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Solís, Martín, Erick Muñoz-Alvarado, and María Carmen Pegalajar. "The Transformation of RGB Images to Munsell Soil-Color Charts." Uniciencia 36, no. 1 (June 1, 2022): 1–10. http://dx.doi.org/10.15359/ru.36-1.36.
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[Objective] The transformation from RGB to Munsell color space is a relevant issue for different tasks, such as the identification of soil taxonomy, organic materials, rock materials, skin types, among others. This research aims to develop alternatives based on feedforward networks and the convolutional neural networks to predict the hue, value, and chroma in the Munsell soil-color charts (MSCCs) from RGB images. [Methodology] We used images of Munsell soil-color charts from 2000 and 2009 versions taken from Millota et al. (2018) to train and test the models. A division of 2856 images in 10% for testing, 20% for validation, and 70% for training was used to build the models. [Results] The best approach was the convolutional neural networks for classification with 93% of total accuracy of hue, value, and chroma combination; it comprises three CNN, one for the hue prediction, another for value prediction, and the last one for chroma prediction. However, the three best models show closeness between the prediction and real values according to the CIEDE2000 distance. The cases classified incorrectly with this approach had a CIEDE2000 average of 0.27 and a standard deviation of 1.06. [Conclusions] The models demonstrated better color recognition in uncontrolled environments than the Transformation of Centore, which is the classical method to transform from RGB to HVC. The results were promising, but the model should be tested with real images at different applications, such as soil real images, to classify the soil color.
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Garcia-Lamont, Farid, Matias Alvarado, and Jair Cervantes. "Systematic segmentation method based on PCA of image hue features for white blood cell counting." PLOS ONE 16, no. 12 (December 31, 2021): e0261857. http://dx.doi.org/10.1371/journal.pone.0261857.
Повний текст джерелаАнотація:
Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.
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Zhang, Xuanhan. "Research on Colour Matching in Art Design Based on Neural Network Mathematics Models." Mathematical Problems in Engineering 2022 (March 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/3873213.
Повний текст джерелаАнотація:
Colour, an art term, is an important formal element that can influence our changing feelings, and colour matching has a very important place in art. Colour is an important artistic language in the study of art, and colour is also a more attractive representation of our real world. In this paper, we fine-tune an existing mathematics model to analyze the effect of hue, luminance, saturation, and contrast on the emotion classification of art paintings and achieve an accuracy improvement of 3.4% over the current state of the art on the public dataset Twitter image dataset. Finally, we propose a pretraining strategy for a related task that significantly improves the sentiment classification task of paintings and analyze the experimental results through visual structures.
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Barton, Franca B., Donald S. Fong, and Genell L. Knatterud. "Classification of Farnsworth-Munsell 100-hue test results in the early treatment diabetic retinopathy study." American Journal of Ophthalmology 138, no. 1 (July 2004): 119–24. http://dx.doi.org/10.1016/j.ajo.2004.02.009.
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Daul, Christian, Ronald Rösch, and Bernhard Claus. "Building a color classification system for textured and hue homogeneous surfaces: system calibration and algorithm." Machine Vision and Applications 12, no. 3 (October 1, 2000): 137–48. http://dx.doi.org/10.1007/s001380050132.
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Tuan, Tran Anh. "ASSESSMENT OF URBAN ENVIRONMENTAL SUSTAINABILITY IN HUE CITY AS A CASE STUDY." Vietnam Journal of Science and Technology 54, no. 2A (March 19, 2018): 195. http://dx.doi.org/10.15625/2525-2518/54/2a/11930.
Повний текст джерелаАнотація:
Hue city was chosen for a pilot application of indicators on environmentally sustainable city, which were developed by Vietnam Ministry of Natural Resources and Environment. The indicator set is composed of four key categories (water, air, solid waste and climate change response), which are broken down into 16 underlying performance indicators. In reality, there has not been any assessment process in place to guide cities in Vietnam in doing their own assessment. In this research, an assessment process including 5 steps was built up; and a barometer with 5 classification bands ranging from 0 to 100 was recommended to use for both category and overall assessment. The weighting of 4 urban environmental categories was undertaken based upon a Delphi method with informed inputs from an expert panel. The 5 - step analysis process showed that Hue city was ranked “medium” with the score between 41 - 60 for water and solid waste, “fairly good” in terms of air (score of 85) and “poor” as to climate change response (score of 40). The sum of all category scores of Hue city, which is also rebased to 100, is valued at 60. Thus, the city was ranked “fairly good” in the overall. Such assessment results are much expected to provide assistance in decision-making at various levels of local authority and help them set forth some appropriate improvement measures on urban environmental sustainability issues. As such, the city would soon meet some concerned requirements to become a leading city of Vietnam in urban environmental sustainability.
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Hamdini, Rabah, Nacira Diffellah, and Abderrahmane Namane. "Color Based Object Categorization Using Histograms of Oriented Hue and Saturation." Traitement du Signal 38, no. 5 (October 31, 2021): 1293–307. http://dx.doi.org/10.18280/ts.380504.
Повний текст джерелаАнотація:
In the last few years, there has been a lot of interest in making smart components, e.g. robots, able to simulate human capacity of object recognition and categorization. In this paper, we propose a new revolutionary approach for object categorization based on combining the HOG (Histograms of Oriented Gradients) descriptors with our two new descriptors, HOH (Histograms of Oriented Hue) and HOS (Histograms of Oriented Saturation), designed it in the HSL (Hue, Saturation and Luminance) color space and inspired by this famous HOG descriptor. By using the chrominance components, we have succeeded in making the proposed descriptor invariant to all lighting conditions changes. Moreover, the use of this oriented gradient makes our descriptor invariant to geometric condition changes including geometric and photometric transformation. Finally, the combination of color and gradient information increase the recognition rate of this descriptor and give it an exceptional performance compared to existing methods in the recognition of colored handmade objects with uniform background (98.92% for Columbia Object Image Library and 99.16% for the Amsterdam Library of Object Images). For the classification task, we propose the use of two strong and very used classifiers, SVM (Support Vector Machine) and KNN (k-nearest neighbors) classifiers.
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Saputra, Wanvy Arifha, and Agus Zainal Arifin. "Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna." JURNAL INFOTEL 9, no. 1 (February 4, 2017): 56. http://dx.doi.org/10.20895/infotel.v9i1.164.
Повний текст джерелаАнотація:
The image of the tuna before entering process classification, it must have a good segmentation results. The result of good segmentation is object and background separate clearly. The image of tuna which has a distribution of light that is uneven and has a complex texture will produce an error segmentation. One method of image segmentation was seeded region growing and parameters that used only two, namely seed and threshold. This research proposed method seeded region growing in the HSI color space for image segmentation of tuna. The Color space of RGB (red green blue) on image of tuna transformed into a color space HSI (hue saturation intensity) then only the hue color space used as segmentation by using seeded region growing. Determination of seed and threshold parameters can do manually and the result of the segmentation do refinement with mathematical morphology. The experiment using 30 image of tuna to segmentation and evaluation methods using RAE (relative foreground area error), MAE (missclassification error) and the MHD (modified Hausdroff distance). The image of the tuna successfully performed segmentation evidenced by a value RAE, ME and MHD respectively are 5,40%, 1,53% dan 0,41%.
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Warmund, Michele Renee. "Kernel Color of Three Black Walnut Cultivars after Delayed Hulling at Five Successive Harvest Dates." HortScience 43, no. 7 (December 2008): 2256–58. http://dx.doi.org/10.21273/hortsci.43.7.2256.
Повний текст джерелаАнотація:
‘Emma K’, ‘Kwik Krop’, and ‘Sparrow’ black walnuts (Juglans nigra L.) were collected weekly in Sept. and Oct. 2007 to determine the effect of delayed hulling of fruits on kernel color at successive harvest dates. Delayed hulling of fruits resulted in lower kernel color values, including L*, chroma, hue angle, and LCH sum (L* + chroma + hue angle values) than those of fruits that were immediately hulled after harvest. ‘Sparrow’ kernels were visually the darkest brown color after delayed hulling. However, the effect of delayed hulling (i.e., change in kernel LCH sum values) over all harvest dates was greatest for ‘Emma K’. LCH sums of kernels generally decreased as the time of harvest was delayed. For ‘Sparrow’, mean kernel LCH sums from immediately hulled fruits decreased sharply from the third week of harvest on 20 Sept. (i.e., the “normal” date of harvest) to the next week. This decrease in LCH sums represented a change in kernel color classification from medium brown at Week 3 to dark brown in Week 4. Visual color changes for ‘Kwik Krop’ were less apparent as a result of the narrow range of color over harvest dates.
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Barbon, Sylvio, Ana Paula Ayub da Costa Barbon, Rafael Gomes Mantovani, and Douglas Fernandes Barbin. "Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification." Journal of Spectroscopy 2018 (August 7, 2018): 1–12. http://dx.doi.org/10.1155/2018/8949741.
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Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.
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Saeed, Sajjad. "Fuzzy-Based Multi-Crop Classification Using High Resolution UAV Imagery." Quaid-e-Awam University Research Journal of Engineering Science & Technology 19, no. 1 (June 30, 2021): 1–8. http://dx.doi.org/10.52584/qrj.1901.01.
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Accurate information regarding crop classification is important to estimation crop yield. It is used to depict the relationship between exponentially growing world population and food demand. The purpose of this research is to recognize multiple crops in a single UAV-based image. The task itself is chaotic as every crop exhibits similar hue, color and other plant characteristics. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to classify 17 different crops based on their high spatial and temporal signatures of normalized difference vegetation index (NDVI) values acquired through multispectral sensor onboard a quadrotor. The multispectral images were classified into two classes (soil and crop) and NDVI signatures for each crop were extracted from images. Detailed dataset was prepared as a timeline through sampling, covering almost all phenological phases of the crops. The NDVI dataset was passed through ANFIS to classify NDVI vectors. ANFIS had only one output variable: the crop type that was formulated from 8 input variables. ANFIS used 2 membership functions for one input variable and formulated 256 fuzzy rules for the classification. The results show a high level of classification accuracy.
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He, Yanhu, Rongyang Wang, Yanfeng Wang, and Chuanyu Wu. "Fourier Descriptors Based Expert Decision Classification of Plug Seedlings." Mathematical Problems in Engineering 2019 (January 23, 2019): 1–10. http://dx.doi.org/10.1155/2019/5078735.
Повний текст джерелаАнотація:
To enable automatic transplantation of plug seedlings and improve identification accuracy, an algorithm to identify ideal seedling leaf sets based on Fourier descriptors is developed, and a classification method based on expert system is adopted to improve the identification rate of the plug seedlings. First, the image of the plug seedlings is captured by image acquisition system, followed by application of K-means clustering for image segmentation and binary processing and identification of the ideal seedling leaf set by Fourier descriptors. Then we obtain feature vectors, such as gray scale (R+B+G)/3, hue H, and rectangularity. After that the knowledge model of the plug seedlings is defined, and the inference engine based on knowledge is designed. Finally, the recognizing test is carried out. The success rate of the identification of 10 varieties of plug seedlings from 190 plates is 98.5%. For the same sample, the recognizing rate of support vector machine (SVM) is 85%, the recognizing rate of particle-swarm optimization SVM (PSOSVM) is 87%, the recognizing rate of back propagation neural network (BP) is 63%, and the recognizing rate of Fourier descriptors SVM (FDSVM) is 87%. These results show that our recognition method based on an expert system satisfies the requirements of automatic transplanting.
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Mukherjee, Rashmi, Dhiraj Dhane Manohar, Dev Kumar Das, Arun Achar, Analava Mitra, and Chandan Chakraborty. "Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/851582.
Повний текст джерелаАнотація:
The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed intoHSI(hue, saturation, and intensity) color space and subsequently the “S” component ofHSIcolor channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793).
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Musliman, Anwar Siswanto, Abdul Fadlil, and Anton Yudhana. "Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction." Jurnal Online Informatika 6, no. 1 (June 17, 2021): 63. http://dx.doi.org/10.15575/join.v6i1.704.
Повний текст джерелаАнотація:
In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells.
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Sanjaya, Suwanto, Morina Lisa Pura, Siska Kurnia Gusti, Febi Yanto, and Fadhilah Syafria. "K-Nearest Neighbor for Classification of Tomato Maturity Level Based on Hue, Saturation, and Value Colors." Indonesian Journal of Artificial Intelligence and Data Mining 2, no. 2 (November 17, 2019): 101. http://dx.doi.org/10.24014/ijaidm.v2i2.7975.
Повний текст джерелаАнотація:
The selection of tomatoes can use several indicators. One of the indicators is the fruit color. In digital image processing, one of the color information that could be used in Hue, Saturation, and Value (HSV). In this research, HSV is proposed as a color model feature for information on the ripeness of tomatoes. The total data of tomato images used in this research were 400 images from four sides. The maturity level of tomatoes uses five levels, namely green, turning, pink, light red, and red. The process of divide data uses K-Fold Cross Validation with ten folds. The method used for classification is k-Nearest Neighbor (kNN). The scenario of the test performed is to combine the image size with the parameter value of the neighbor (k). The image sizes tested are 100x100 pixels, 300x300 pixels, 600x600 pixels and 1000x1000 pixels. The “k” values tested were 1, 3, 5, 7, 9, 11, and 13. The highest accuracy reached 92.5% in the image size 1000x1000 pixels with a parameter “k” is 3. The result of the experiment showed that the image size has a significant influence of accuracy, but the parameter value of neighbor (k) has an influence that is not too significant.
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Mao, Peng Jun, Lu Liu, Jun Wang, and Chun Yan Hu. "A Study on Gray Relational Analysis of Many Factor Weights in Tobacco Leaves Classification." Advanced Materials Research 139-141 (October 2010): 1728–31. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1728.
Повний текст джерелаАнотація:
For the nine characteristic factors of tobacco leaf grading standards have different degree of influence on final grading results and lack of objective evaluation method, in this paper, we applied the gray relational analysis method to determine the weight of tobacco leaf factors in every grade, which calculate the gray relational analysis of nine characteristic factors: such as hue, lightness value, chroma, length, leaf structure, waste, oil, maturity and body. The gray relation was normalized to get the weight of the nine factors in tobacco leaf classification. By contrasted with the subjective evaluation of five experts in tobacco field, the calculation results are basically consistent with the experts’ recommendation. It illustrates that the application of Grey relational method to calculate influence ability of flue-cured tobacco grading factors is feasible. This method eliminates the subjectivity of the weight of each factor and can make the results more realistic.
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Nguyen, Thanh-Hai, Thanh-Nghia Nguyen, and Ba-Viet Ngo. "A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease." AgriEngineering 4, no. 4 (October 5, 2022): 871–87. http://dx.doi.org/10.3390/agriengineering4040056.
Повний текст джерелаАнотація:
Tomato leaves can have different diseases which can affect harvest performance. Therefore, accurate classification for the early detection of disease for treatment is very important. This article proposes one classification model, in which 16,010 tomato leaf images obtained from the Plant Village database are segmented before being used to train a deep convolutional neural network (DCNN). This means that this classification model will reduce training time compared with that of the model without segmenting the images. In particular, we applied a VGG-19 model with transfer learning for re-training in later layers. In addition, the parameters such as epoch and learning rate were chosen to be suitable for increasing classification performance. One highlight point is that the leaf images were segmented for extracting the original regions and removing the backgrounds to be black using a hue, saturation, and value (HSV) color space. The segmentation of the leaf images is to synchronize the black background of all leaf images. It is obvious that this segmentation saves time for training the DCNN and also increases the classification performance. This approach improves the model accuracy to 99.72% and decreases the training time of the 16,010 tomato leaf images. The results illustrate that the model is effective and can be developed for more complex image datasets.