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Добірка наукової літератури з теми "Hue classification"

Автор: Grafiati
Опубліковано: 23 вересня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Hue classification"

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.

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Анотація:
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.
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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.

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Анотація:
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.
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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.

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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.
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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.

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Анотація:
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.
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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.

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Анотація:
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.
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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.

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Анотація:
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.
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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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10

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.

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Анотація:
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.
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Більше джерел

Дисертації з теми "Hue classification"

1

Morrison, Jeffrey Glenn. "The effects of hue and contrast on binary classification." Thesis, Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/28758.

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2

Camargo, Marcel Pinton de. "Aerial machine vision, geographical information system and hue for pattern classification in agriculture." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-17012019-180101/.

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Анотація:
In this research we aim to achieve cybernetic cohesion information flow in precision agriculture, integrating machine learning methods, computer vision, geographical information system and UAV-photogrammetry in an irrigated area with slaughterhouse wastewater, under five treatments (W100 - irrigation with superficial water and 100% of nitrogen mineral fertilization, E0, E33, E66 and E100 - irrigation with treated effluent from slaughterhouse and addition of 0, 33, 66 and 100% of nitrogen mineral fertilization, respectively) and four replications on grassland (Cynodon dactylon (L.) Pers.). Several images (between one hundred and two hundred) with red, green, blue (RGB) color model were captured using a quadcopter flying at 20 meter altitude and obtaining spatial resolution of 1 centimeter on a surface of approximately 0.5 ha. The images were orthorectified together with nine ground control points done by differential global positioning system (GPS), both processed in the Agisoft PhotoScan software. Thirteen photogrammetric projects were done over time with 30-day revisit, the root mean squared error (RMSE) was used as accuracy measurement, and reached values lower than 5 centimeters for x, y and z axis. The orthoimage obtained with unmanned aerial vehicle (UAV) photogrammetry was changed from RGB to hue, saturation, value (HSV) color model, and the hue color space was chosen due to independence of illumination, beyond it has a good description of exposure of soil and vegetation, but it is dependent of light source temperature, so difficult to estabilish a static threshold, so we selected an unsupervised classification method, K-Means, to classify the unknown patterns along the area. Polygons were drawn delimiting the area represented by each portion and a supervised classification method based on entropy was used, the decision tree, to explore and find patterns that recognize each treatment. These steps are also displayed in forms of georeferenced thematic maps and were executed in the open source softwares Python, QGIS and Weka. The rules defined on the hue color space reached an accuracy of 100% on the training set, and provided a better understanding about the distribution of soil and vegetation on the parcels. This methodology shows a great potential for analysis of spectral data in precision agriculture.
Nesta pesquisa pretendemos alcançar a coesão cibernética no fluxo de informações dentro da agricultura de precisão, integrando métodos de aprendizagem de máquinas, visão computacional, sistema de informação geográfica e aerofotogrametria em uma área irrigada com efluente de matadouro, sob cinco tratamentos (W100 - irrigação com água superficial e 100 % de adubação mineral nitrogenada, E0, E33, E66 e E100 - irrigação com efluente tratado de abatedouro e adição de 0, 33, 66 e 100% de adubação mineral nitrogenada, respectivamente) e quatro repetições em pastagem (Cynodon dactylon (L.) Pers.) Várias imagens (entre cem e duzentas) com modelo de cor vermelho, verde e azul (RGB) foram capturadas por um quadricóptero voando a 20 metros de altitude, e obtendo resolução espacial de 1 centímetro em uma superfície de aproximadamente 0.5 ha. As imagens foram ortorretificadas juntamente com nove pontos de controle, realizados pelo sistema de posicionamento global diferencial (GPS), ambos processados no software Agisoft PhotoScan. Treze projetos fotogramétricos foram realizados ao longo do tempo com revisita de 30 dias, a raiz do erro quadrático médio (RMSE) foi usada como medida de acurácia e atingiu valores menores que 5 centímetros para os eixos x, y e z. A ortoimagem obtida com a fotogrametria do veículo aéreo não tripulado (UAV) foi alterada de RGB para matiz, saturação, valor (HSV) e o espaço de cor matiz foi escolhido devido a independência da iluminação, além de ter boa descrição da exposição do solo e vegetação. Entretanto este é dependente da temperatura da fonte de luz, portanto difícil de se estabelecer um limiar estático, logo selecionamos um método de classificação não supervisionado, o K-Means, para classificar os padrões desconhecidos ao longo da área. Polígonos foram traçados delimitando a área representada por cada parcela e um método supervisionado de classificação baseado na entropia foi utilizado, a árvore de decisão, para explorar e encontrar padrões que reconheçam cada tratamento. Essas etapas também são exibidas em formas de mapas temáticos georeferenciados e foram executadas nos softwares de código aberto Python, QGIS e Weka. As regras definidas no espaço de cor matiz atingiram uma acurácia de 100% no conjunto de treinamento e proporcionaram um melhor entendimento sobre a distribuição do solo e da vegetação nas parcelas. Esta metodologia mostra um grande potencial para análise de dados na agricultura de precisão.
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3

Braun, Andreas, Volker Hochschild, Gia Tung Pham, Linh Hoang Khanh Nguyen, and Felix Bachofer. "Linking land subsidence to soil types within Hue city in Central Vietnam." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A70833.

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Анотація:
Coastal areas of Southeast Asia are progressively threatened by flooding as a consequence of more frequent precipitation extremes and rising sea levels. Especially urban areas are affected by flood risk which is additionally increased by surface subsidence related to building activities and groundwater extraction. However, the severity of subsidence as well as its triggers and environmental interrelations are only little understood. This study measures surface subsidence for Hue city by using persistent scatterer radar interferometry (PS-InSAR). A series of 53 images acquired by the Sentinel-1 radar satellite between 2018 and 2019 was analyzed to reliably retrieve surface changes at the millimeter scale. The overall displacement ranges between -25 and +10 millimeters per year. Its spatial distribution was then compared to the extent of different soil types in the study area to conduct an analysis of variance (ANOVA). The results confirmed a significant difference between the soil types with Plinthic Acrisols as the soil type having the largest negative average surface velocity. Possible triggers are the intrusion of slack water from the surrounding rice cultivation areas and construction activities which lead to increasing weight and soil compaction. The findings shall raise awareness for the topic and underline the demand for further research.
Mưa lớn và nước biển dâng là những nguyên nhân gây lũ lụt ngày càng nghiêm trọng ở các khu vực ven biển Đông Nam Á. Đặc biệt việc gia tăng công trình xây dựng và khai thác nước ngầm gây sụt lún bề mặt dẫn đến ngập lụt ở các vùng đô thị. Tuy nhiên, các nghiên cứu về mối tương quan giữa sụt lún bề mặt với các hiện tượng môi trường chưa được chú trọng nhiều. Trong nghiên cứu này, độ lún bề mặt của thành phố Hue được đo bằng phương pháp giao thoa radar tán xạ liên tục (PS-InSAR). Phân tích 53 ảnh vệ tinh Sentinel-1 từ năm 2018-2019 cho thấy sự thay đổi tổng thể bề mặt dao động từ -25mm đến 10mm mỗi năm. Phân tích phương sai (ANOVA) cho thấy sự thay đổi bề mặt khác nhau tùy từng loại đất, trong đó đất đỏ vàng (Plinthic Acrisols) có tốc độ sụt lún trung bình cao nhất. Các tác nhân có thể là do sự xâm nhập của nước từ các vùng trồng lúa xung quanh và các hoạt động xây dựng dẫn đến tăng trọng lượng và nén đất. Những phát hiện này là cơ hội nâng cao nhận thức về sự sụt lún bề mặt và cần được nghiên cứu thêm.
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4

Van, Thi Yen, Hoang Khanh Linh Nguyen, Bich Ngoc Nguyen, and Quy Tai Le. "Study on biomass and carbon stock of woody floor at several forests in Bach Ma national park, Thua Thien Hue province." Technische Universität Dresden, 2016. https://tud.qucosa.de/id/qucosa%3A32415.

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Анотація:
The aims of the research are to identify biomass and estimate carbon stock of woody floor at some forest status in Bach Ma National Park and to recommend some solutions in order to improve the effectiveness of forest management toward sustainable development. Collecting the relative data through articles, books, internet information and organizations and inheriting the data from previous research on biomass and carbon stock. Moreover, the research implemented field surveys by designing 31 circular plots (1,256 m2) with three radii including 4 m, 14 m and 20 m for measuring trees from 5 to 20 cm, 20-50 cm and > 50 cm DBH (diameter at breast height), respectively. Correspondingly, D1.3, H were measured. The results showed that biomass of rich, medium and restoration forests are 144.16, 43.17 and 20.31 ton/ ha, respectively. The total average C-stock is calculated as follows: rich forest 264.53 (ton/ha), medium forest (79.21 ton/ha) and restoration forest (37.27 ton/ha). Therefore, the rich forest has the highest CO2 absorption (399.78 ton/ha). Meanwhile, CO2 absorption of medium and restoration forests are 133.13 ton/ha and 46.81 ton/ha.
Mục tiêu của đề tài là nghiên cứu sinh khối và trữ lượng carbon của tầng cây gỗ tại một số trạng thái rừng thuộc vườn quốc gia Bạch Mã, tỉnh Thừa Thiên Huế. Để thực hiện được mục tiêu đó, đề tài cần xác định sinh khối của tầng cây gỗ ở một số trạng thái rừng tại vườn quốc gia Bạch Mã, đánh giá trữ lượng carbon của tầng cây gỗ ở một số trạng thái rừng tại vườn quốc gia Bạch Mã, đề xuất một số giải pháp nhằm nâng cao hiệu quả quản lý rừng theo hướng phát triển bền vững. Đề tài tiến hành thu thập các số liệu có liên quan bằng cách tham khỏa tài liệu từ sách báo, internet, tìm kiếm thông tin từ các cơ quan, ban ngành, kế thừa số liệu từ các công trình nghiên cứu sinh khối và trữ lượng carbon. Bên cạnh đó, đề tài còn thực hiện quá trình đi điều tra thực địa lập 31 ô tiêu chuẩn, mỗi ô tiêu chuẩn có diện tích là 1256m2, ô tiêu chuẩn hình tròn với 3 cấp bán kính là 4m, 14m, 20m tương ứng với đường kính cây lần lượt là 5-20cm, 20-50cm và lớn hơn 50 cm. Tiến hành đo các chỉ số sinh trưởng của cây là D1.3, Hvn. Kết quả nghiên cứu cho thấy sinh khối trạng thái rừng giàu chiếm khối lượng lớn nhất khoảng 144,16 tấn/ha, tiếp đến là trạng thái rừng trung bình chiếm 43,17 tấn/ha và rừng phục hồi là 20,31 tấn/ha. Đối với trữ lượng carbon thì rừng giàu là 264,53 tấn/ha, rừng trung bình là 79,21 tấn/ha, rừng phục hồi là 37,27 tấn/ha. Xét về khả năng hấp thụ CO2 thì trạng thái rừng giàu là hấp thụ nhiều nhất khoảng 399,78 tấn/ha, thấp nhất là rừng phục hồi với 46,81 tấn/ha, còn rừng trung bình là 133,13 tấn/ha.
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5

Nguyen, Hoang Khanh Linh, and Bich Ngoc Nguyen. "Mapping biomass and carbon stock of forest by remote sensing and GIS technology at Bach Ma National Park, Thua Thien Hue province." Technische Universität Dresden, 2016. https://tud.qucosa.de/id/qucosa%3A32416.

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Анотація:
The objective of this study is to build biomass and carbon stock map at several type of forest in Bach Ma National Park, Thua Thien Hue province. To accomplish that goal, field survey was accompanied with the interpretation result of remote satellite imagery in the GIS to determine biomass and carbon stock accumulation of forest. Landsat 8 satellite image year 2014 at 15 meters resolution was used for the analysis and classification of forest status. The results showed that the rich forest had the biggest amount biomass reserve in comparison with medium and restoration forest. It indicated that estimated biomass of rich forest was 144.16 tons/ha; meanwhile, biomass of medium and restoration forest was reached at 43.17 tons/ha and 20.31 tons/ha, respectively. It means that the total biomass of rich forest was approximately three times as the total biomass of medium forest and seven times compared to restoration forest. Based on estimated biomass map, carbon stock map at Bach Ma National Park was calculated. The carbon stock reserve of differential forest types was unequal and considerable disparity between the rich forest and the rest. Carbon stock of rich forest was 264.53 tons/ha, which was higher nearly three times than medium forest and nearly seven times than restoration forest. The determination of biomass and carbon stock map from tree layer not only contributes to understand the status of forest conditions, but also provide a strategy in reducing emissions and adaptation to climate change. In addition, the research results could be the scientific reference for trade sell carbon certificates in the commercial market within the country and globally.
Mục tiêu của nghiên cứu này là xây dựng được bản đồ sinh khối và trữ lượng cacbon của tầng cây gỗ tại một số trạng thái rừng thuộc vườn quốc gia Bạch Mã, tỉnh Thừa Thiên Huế. Để thực hiện được mục tiêu đó, nghiên cứu đã phối hợp kết quả phân loại ảnh với số liệu điều tra thực địa trong GIS để xác định sinh khối rừng, trữ lượng carbon tích lũy của tầng cây gỗ ở các trạng thái rừng thuộc vườn quốc gia Bạch Mã, tỉnh Thừa Thiên Huế. Ảnh vệ tinh landsat 8 năm 2014 có độ phân giải 15 mét được sử dụng để phân tích và phân loại hiện trạng rừng. Kết quả nghiên cứu cho thấy, trạng thái rừng giàu có sinh khối lớn hơn nhiều so với 2 trạng thái rừng còn lại, tổng sinh khối ước tính là144,16 tấn/ha. Sinh khối rừng trung bình đạt 43,17 tấn/ha, còn sinh khối rừng của rừng phục hồi là 20,31 tấn/ha. Tổng sinh khối rừng giàu xấp xỉ gấp 3 lần so với rừng trung bình và gấp 7 lần so với rừng phục hồi. Đối với trữ lượng carbon của các trạng thái rừng không đồng đều nhau và có sự chênh lệch khá lớn giữa rừng giàu và các trạng rừng còn lại. Trữ lượng carbon của rừng giàu là 264,53 tấn/ha cao hơn gần gấp 3 so với rừng trung bình và gấp gần 7 lần so với rừng phục hồi. Kết quả việc xác định sinh khối và trữ lượng carbon tầng cây gỗ của các trạng thái rừng cung cấp cơ sở khoa học và tạo điều kiện thuận lợi cho việc buôn bán chứng chỉ carbon trên thị trường thương mại trong phạm vi quốc gia và toàn cầu.
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6

Eldrogi, Nawal. "Vision par ordinateur poursuivi automatique et caracterisation decomportement de civelles." Thesis, Pau, 2021. http://www.theses.fr/2021PAUU3063.

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Le suivi des organismes vivants permet de comprendre les dangers qui les guettent et qui peuvent avoir des effets désastreux sur la biodiversité. Dans le cadre de la fédération de recherche MIRA (Milieux et Ressources Aquatiques) de l'université de Pau et des Pays de l'Adour, les laboratoires de biologie et d'informatique collaborent pour l'étude des poissons migrateurs du bassin de l'Adour. L'une des actions de recherche concerne l'acquisition de connaissances sur lecomportement migratoire d'une espèce menacée : la civelle d''anguille. Une hypothèse, à vérifier, est que le comportement migratoire des civelles est lié à leurs réserves énergétiques et au rythme auquel elles utilisent ces réserves, car elles ne s'alimentent pas durant leur migration estuarienne. Pour cette étude, des civelles sont marquées avec un élastomère coloré, puis introduites dans un milieu expérimental simulant les marées, et filmées pendant plusieurs semaines afin de suivre leurs déplacements. L'objectif du présent travail est d'exploiter des techniques de vision par ordinateur permettant un suivi automatique de civelles dans un aquarium afin d'extraire les informations répondant aux besoins des biologistes telles que le nombre de passages en fonction du sens de marée, la vitesse et la direction de nage. Les techniques actuelles de dépouillement des données vidéo sont soit entièrement manuelles, soit basées sur du traitement d'image élémentaire, qui n'exploite pas explicitement la dimension temporelle des séquences vidéo collectées, et qui ne donne que des informations sommaires. La problématique de la thèse concerne donc l'apport d'un traitement temporel des vidéos pour la détection de mouvement, l'estimation de mouvement et un suivi des civelles robuste aux occultations. Par ailleurs, une technique non supervisée (sans apprentissage lourd sur une grosse base de données) est souhaitée. Enfin l'obtention d'informations complémentaires plus fines non accessibles à l'observation par l'opérateur humain est envisagée (telle que l'ondulation de nage). Dans ce mémoire de thèse, les étapes d'un algorithme permettant de détecter et suivre les civelles dans des séquences vidéo sont développées. La détection de civelle utilise une technique de soustraction du fond, suivie de l'analyse des composantes connexes sur l'image résultante afin d'extraire les informations géométriques des boîtes englobantes des marquages. Pour la classification couleur, le choix d'un système teinte-luminance-saturation, combiné à une classification non supervisée par l'algorithme des K-moyennes sur la composante de teinte, permet un apprentissage des plages de couleur des classes. Ces plages sont utilisées pour identifier la couleur de chaque marquage détecté. L'algorithme de suivi utilise un filtre de Kalman et une méthode d'association des données couleur. Une technique de couplage des marquages permet enfin l'identification individuelle de chaque civelle. Ceci permet d'obtenir leur trajectoire dans l'aquarium, ainsi que les informations dynamiques (vitesse moyenne, sens de déplacement, nombre de passages). En parallèle, l'intérêt d'une estimation du mouvement par le flux optique pour extraire des informations plus précises est investigué. Il s'agit du champ de vecteurs-vitesses obtenu par des méthodes différentielles (algorithme de Horn et Schunck et de Lucas et Kanade), qui permet d'obtenir des renseignements actuellement non disponibles pour les biologistes, tels que l'ondulation de nage des civelles, se traduisant par la divergence et la convergence du champ de vecteurs-vitesses. Finalement, on présente le système d'information complet avec l'interface homme-machine de suivi automatique des civelles, développé pour répondre aux besoins des biologistes. Il permet d'identifier les civelles, de déterminer leur sens de passage (avec ou contre le courant), de les compter, réduisant ainsi le temps de travail d'observation fastidieuse de l'opérateur humain
Monitoring living organisms makes it possible to understand the dangers that threaten hem and that might have disastrous effects on biodiversity. As part of the MIRA research federation (Milieux and Aquatic Resources) at the University of Pau et Pays de l'Adour, the biology and computer laboratories work together to study migratory fishes in the Adour basin.One of the research actions concerns the acquisition of knowledge about the migratory behavior of an endangered species : the glass eel. One hypothesis to be verified is that the migratory behavior of glass eels is linked to their energy reserves and to the rate at which they use these reserves, because they do not feed during their estuarine migration. For this study, glass eels are marked with a colored elastomer, and then introduced into an experimental medium simulating the tides, and filmed for several weeks in order to follow their movements. The objective of this work is to exploit computer vision techniques allowing automatic tracking of glass eels in an aquarium in order to extract information meeting the needs of biologists, such as counting the number of passages according to the direction of the tide, and measuring the swimming speed and direction. The current techniques for analyzing video data are either entirely manual, or based on elementary image processing that does not explicitly exploit the temporal dimension of the video sequences collected and gives only summary information. The problem of the thesis therefore concerns the contribution of a temporal processing of videos for motion detection, motion estimation and monitoring of glass eels robust to occlusions. Moreover, an unsupervised technique (without heavy training on a large dataset) is desirable, and also obtaining more detailed additional information not accessible to observation by the human operator is envisioned (such as the swimming undulation behavior). In this thesis, the steps of an algorithm allowing to detect and follow glass eels in video sequences are developed. Elver detection uses a background subtraction technique followed by a connected-component analysis of the resulting image in order to extract the geometric information from the bounding boxes of the markings. For color classification, the choice of a hue-luminance-saturation system, combined with an unsupervised classification by the K-means algorithm on the hue component, allows learning of the color range of the classes. These ranges are used to identify the color of each marking detected. The tracking algorithm uses a Kalman filter and a color data association method. Thereafter, a tagging coupling technique allows the individual identification of each glass eel. This makes it possible to obtain their trajectory in the aquarium, as well as dynamic information (average speed, motion direction, number of passages). In parallel, the potential interest of motion estimation by optical flow techniques to extract more precise information is investigated. The velocity-vector field obtained by differential methods (algorithms of Horn & Schunck and of Lucas & Kanade) makes it possible to obtain information currently not available to biologists, such as the swimming undulation of the elvers resulting in the divergence and convergence of the velocity-vector field. Finally, we present the complete information system with the human-machine interface for automatic monitoring of glass eels developed to meet the needs of biologists. This makes it possible to identify glass eels, to determine their direction of passage (with or against the water current) and to count them, thus reducing the tedious observation work time of a human operator
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Schneider, Anke. "Farbeinflussfaktoren zur emotionalen Bildwirkung und ihre Bedeutung für das Retrieval von Tourismusbildern." Universitätsverlag Chemnitz, 2015. https://monarch.qucosa.de/id/qucosa%3A20550.

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Der Einsatz von Bildern in den unterschiedlichsten Bereichen ist enorm gestiegen, da Bilder die Fähigkeit haben Erlebnisse, sowie Emotionen zu erzeugen und die Phantasie anzuregen. Zudem lässt die rasante Entwicklung im Multimediabereich die Anzahl der fotografierten und gespeicherten Bilder steigen. Die Suche nach dem „besten Bild“ für z.B. eine Kampagne gestaltet sich schwierig, da die Inhalte mehrerer Bilder zu einem Thema nicht selten eine hohe Ähnlichkeit aufweisen. Die Bilder können sich allerdings sehr deutlich in ihren Low-Level Features, wie Farbton, Sättigung und Helligkeit, unterscheiden. Jedoch ist der Emotional Gap zwischen diesen Low-Level Features und der dahinter steckenden High-Level-Semantik im inhaltsbasierten Image Retrieval nur marginal untersucht. Im Fokus dieser Arbeit steht die Analyse des Einflusses der emotionalen Wirkung eines Bildes auf die Qualität der Image Retrieval Ergebnisse. Dies umfasst zum einen die Untersuchung der von Farbeigenschaften eines Bildes ausgelösten Emotionen, sowie die Evaluation der Ergebnisse einer emotionalen Bildsuche. Durch verschiedene Experimente kann gezeigt werden, dass die Helligkeit und der Farbton die emotionale Wahrnehmung eines Bildes maßgeblich beeinflussen. Anhand der Ergebnisse konnte eine emotionale Annotation von Bildern und somit die Einbindung von Emotionen in den Suchprozess ermöglicht werden. Die anschließende Evaluierung der Suchergebnisse zeigt, dass die Qualität der Ergebnisse des Image Retrievals verbessert werden konnte.
The use of pictures in a variety of areas has increased tremendously in recent years, as they stimulate a person’s imagination and help to create first experiences and emotions. Furthermore, the rapid developments in multimedia have led to an escalation of the number of digitally stored pictures and photographs. Consequently, finding the ‘best picture’ for a convincing advertising campaign has been becoming increasingly difficult due to the abundance of available pictures. To further complicate this search process, a lot of pictures related to a specific topic are very similar with regard to their content. However, their low-level features, such as hue, saturation, and luminance, might differ considerably. Therefore, this work focusses on the influence of emotional characteristics on the image retrieval process. This includes the study of emotions caused by the color properties of a picture, as well as the evaluation of the results of an emotional image retrieval processes. Results of different experiments show that a picture’s luminance and color have the power to influence emotion. The subsequent evaluation of the results shows an improvement of emotional image retrieval processes. Consequently, one can conclude that the consideration of emotions for ranking affects the quality of the results of the Image Retrieval positively.
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8

Chen, Yen-Lun. "Margin and Domain Classifications for Target Detection over Huge Population of Outliers." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1269539889.

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9

Steinhauser, Dominik. "Detekce a rozpoznání hub v přirozeném prostředí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363798.

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In this thesis is handled the problem of mushroom detection and recognition in natural environment. Convolutional neural networks are used. The beginning of this thesis is dedicated to the theory of neural networks. Further is solved the problem of object detection and classification. Using neural network trained for classification is solved also the task of localization. Results of trained CNNs are analised.
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10

Drude, Matthias. "Balduin und das Heu." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1237145119116-68508.

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Книги з теми "Hue classification"

1

Zhong hua zang jia. Beijing Shi: Zhongguo lin ye chu ban she, 2012.

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2

Zhongguo lan hua ming pin zhen pin jian shang tu dian. Fuzhou Shi: Fujian ke xue ji shu chu ban she, 2003.

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3

Shu wen hua jiu jiang. Taiyuan Shi: San Jin chu ban she, 2012.

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4

Zhong zi zhi wu hua fen dian jing tu zhi. Kunming Shi: Yunnan ke ji chu ban she, 2003.

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5

Zhongguo Yunnan Hengduan Shan ye sheng hua hui: The wild flowers in Hengduan Mountains in Yunnan. Kunming Shi: Yunnan ren min chu ban she, 1993.

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6

author, Guo Zhilong 1989, ed. Xin xi shi dai fan zui ding liang biao zhun de ti xi hua gou jian: XINXI SHIDAI FANZUI DINGLIANG BIAOZHUN DE TIXIHUA GOUJIAN. Beijing: Zhongguo fa zhi chu ban she, 2013.

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7

Tyttl, František. Historický atlas hub: Obrazy Františka Tyttla = Historischer Atlas der Pilze : Bilder von František Tyttl = A historical atlas of fungi : pictures by František Tyttl. Praha: Academia, 2008.

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8

Han yu ci lei de ren zhi yan jiu he mo hu hua fen: A cognitive investigation and fuzzy classification of word-class in mandarin Chinese. Shanghai: Shanghai jiao yu chu ban she, 2010.

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9

Chen, Xinqi. Zhongguo lan hua. Xin hua shu dian Beijing fa xing suo fa xing, 1998.

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10

Yin erh mu ho hua erh mu. Pei-ching: Kʻo hsüeh chʻu pan she, 1992.

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Частини книг з теми "Hue classification"

1

O’Hare, Cerwyss. "Folk Classification in the HTE ‘Plants’ category." In Categorization in the History of English, 179. Amsterdam: John Benjamins Publishing Company, 2004. http://dx.doi.org/10.1075/cilt.261.10oha.

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2

Lelu, Alain. "Clusters and factors: neural algorithms for a novel representation of huge and highly multidimensional data sets." In New Approaches in Classification and Data Analysis, 241–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-51175-2_27.

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3

Tomašev, Nenad, and Dunja Mladenić. "Hub Co-occurrence Modeling for Robust High-Dimensional kNN Classification." In Advanced Information Systems Engineering, 643–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40991-2_41.

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4

Chittora, Anshu, and Hemant A. Patil. "Modified Group Delay Based Features for Asthma and HIE Infant Cries Classification." In Text, Speech, and Dialogue, 595–602. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24033-6_67.

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5

Lin, Xiaoli, Xiaolong Zhang, and Jing Hu. "Classification of Hub Protein and Analysis of Hot Regions in Protein-Protein Interactions." In Intelligent Computing Theories and Application, 365–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63312-1_32.

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6

Cuevas, Erik, Daniel Zaldivar, Marco Perez-Cisneros, and Marco Block. "LVQ Neural Networks in Color Segmentation." In Soft Computing Methods for Practical Environment Solutions, 45–63. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-893-7.ch004.

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Segmentation in color images is a complex and challenging task in particular to overcome changes in light intensity caused by noise and shadowing. Most of the segmentation algorithms do not tolerate variations in color hue corresponding to the same object. By means of the Learning Vector Quantization (LVQ) networks, neighboring neurons are able to learn how to recognize close sections of the input space. Neighboring neurons would thus correspond to color regions illuminated in different ways. This chapter presents an image segmentator approach based on LVQ networks which considers the segmentation process as a color-based pixel classification. The segmentator operates directly upon the image pixels using the classification properties of the LVQ networks. The algorithm is effectively applied to process sampled images showing its capacity to satisfactorily segment color despite remarkable illumination differences.
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7

Lunney, Mark, Donal Nolan, and Ken Oliphant. "1. General Introduction." In Tort Law: Text and Materials. Oxford University Press, 2017. http://dx.doi.org/10.1093/he/9780198745525.003.0001.

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This chapter first discusses the historical development of tort law, covering the origins of tort law; the forms of action; the development of fault-based liability; eighteenth-century developments; the classification of obligations; and the modern pre-eminence of negligence. It then turns to theories of tort, covering the aims of the law of tort and doctrinal classifications. Finally, the chapter considers modern influences on tort law, covering the influence of insurance; the influence of human rights; and concerns about ‘compensation culture’.
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8

Koffman, Laurence, and Elizabeth Macdonald. "Classification of terms." In The Law of Contract, 142–56. Oxford University Press, 2010. http://dx.doi.org/10.1093/he/9780199570201.003.0573.

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"Chapter 5 The complex noun: compounding, possession, and noun classification." In A Grammar of Hup, 213–83. Berlin, New York: Mouton de Gruyter, 2008. http://dx.doi.org/10.1515/9783110199079.213.

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Macdonald, Elizabeth, Ruth Atkins, and Jens Krebs. "8. Classification of terms." In Koffman & Macdonald's Law of Contract. Oxford University Press, 2018. http://dx.doi.org/10.1093/he/9780198752844.003.0008.

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This chapter distinguishes conditions, warranties and innominate terms in relation to the different consequences of their breach; the availability, or not, of the right to terminate for breach. It identifies the test for determining which classification applies to a particular term, relating it to the benefits and drawbacks of the condition and the innominate term categorizations: certainty/inflexibility in relation to conditions and flexibility/certainty in relation to innominate terms. Significant cases, such as Hong Kong Fir and Bunge v Tradax are looked at in detail.
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Тези доповідей конференцій з теми "Hue classification"

1

Xu, Feng, and Yujin Zhang. "Atmosphere-based image classification through luminance and hue." In Visual Communications and Image Processing 2005. SPIE, 2005. http://dx.doi.org/10.1117/12.631579.

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2

Trivedi, Vijay Kumar, Piyush Kumar Shukla, and Anjana Pandey. "Hue based Plant Leaves Disease Detection and Classification Using Machine Learning Approach." In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2021. http://dx.doi.org/10.1109/csnt51715.2021.9509667.

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3

Sari, Yuslena, Muhammad Alkaff, and Muti'a Maulida. "Classification of Rice Leaf using Fuzzy Logic and Hue Saturation Value (HSV) to Determine Fertilizer Dosage." In 2020 Fifth International Conference on Informatics and Computing (ICIC). IEEE, 2020. http://dx.doi.org/10.1109/icic50835.2020.9288585.

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Romani, S., P. Sobrevilla, and E. Montseny. "On the Reliability Degree of Hue and Saturation Values of a Pixel for Color Image Classification." In Proceedings of the IEEE International Conference on Fuzzy Systems. IEEE, 2005. http://dx.doi.org/10.1109/fuzzy.2005.1452411.

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5

Fitriyah, Hurriyatul, Edita Rosana Widasari, and Gembong Edhi Setyawan. "Traffic sign recognition using edge detection and eigen-face: Comparison between with and without color pre-classification based on Hue." In 2017 International Conference on Sustainable Information Engineering and Technology (SIET). IEEE, 2017. http://dx.doi.org/10.1109/siet.2017.8304127.

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6

Papa, Joao P., Fabio A. M. Cappabianco, and Alexandre Xavier Falcao. "Optimizing Optimum-Path Forest Classification for Huge Datasets." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.1012.

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7

Gurchenko, V., and S. Mihalev. "CARGO TRUCKS: ALL KINDS OF THE FOREIGN TRANSPORT." In Manager of the Year. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2022. http://dx.doi.org/10.34220/my2021_29-32.

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This article describes the types and classification of trucks, where they are used, their examples and characteristics. Due to the huge range of transported goods, trucks have a rather broad and complex classification. The article reveals the specifics of rational use of trucks. It should be noted that both the size and volume of cargo is of great importance when transporting it. Road freight transport is much cheaper than transporting the same cargo by air, but it is also more expensive than similar transport by water or rail.
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8

Abdelkhalek, Rihab. "Towards New Optimized Artificial Immune Recognition Systems under the Belief Function Theory." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/821.

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Artificial Immune Recognition Systems (AIRS) are powerful machine learning techniques, which aim to solve real world problems. A number of AIRS versions have produced successful prediction results. Nevertheless, these methods are unable to handle the uncertainty that could spread out at any stage of the AIRS approach. This issue is considered as a huge obstacle for having accurate and effective classification outputs. Therefore, our main objective is to handle this uncertainty using the belief function theory. We opt also in this article for an optimization over the classical AIRS approaches in order to enhance the classification performance.
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He, Fan, Jiangwei Ji, Jiaoshen Xu, Zengpei Zhu, and Kewei Cai. "Practice and Discussion on Safety Categorisation and Classification of Defence in Depth Level 1/2 Functions Based on IAEA-SSG-30." In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-93238.

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Abstract The safety classification of structures, systems and components (SSC) in nuclear power plants (SSG-30) was issued in 2014 by the International Atomic Energy Agency (IAEA). Both the Hua-long Pressurized Reactor (HPR1000) and UK version of the Hua-long Pressurized Reactor (UK HPR1000) Safety Function Categorisation and System, Structure and Component (SSC) Classification methodology have been developed based on IAEA SSG-30 with consideration of different regulatory requirement. The SSG-30 is a high level guidance to perform the safety categorization and classification and there are still some issues that need to be analyzed in specific application such as failure consequence severity criteria and consequence analysis hypothesis. With the expansion of Defence in Depth (DiD) levels in nuclear power plant design, there are differences about function categorization requirements between different countries. This paper analyzes the differences of definition about DiD levels and the function categorisation requirements in different international standards. The IAEA SSG-30 methodology for application is evaluated to meet different regulatory requirements and the alignment or expansion is needed for application have been identified to determine the function categorisation in DID 1/2 levels. The principles of the HPR1000 and UK HPR1000 on DID 1/2 functions categorisation have been summarized and the subsequent introduce and discussion are about practices to apply these principles on special safety functions and safety features.
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Guiying Wei, Xuedong Gao, and Sen Wu. "Study of text classification methods for data sets with huge features." In 2010 2nd International Conference on Industrial and Information Systems (IIS 2010). IEEE, 2010. http://dx.doi.org/10.1109/indusis.2010.5565817.

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Звіти організацій з теми "Hue classification"

1

Ander, Kjell. An abdominal stridulation organ in Cyphoderris (Prophalangopsidae) and concerning the systematic classification of the Ensifera (Saltatoria). MacEwan University Library, January 2021. http://dx.doi.org/10.31542/r.gm:2687.

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Ensiferan insects (crickets, katydids, grigs and allies) are well known for rubbing parts of their cuticle together to produce sound: a process called stridulation. In this article Swedish entomologist Kjell Ander describes a novel (at the time) stridulatory apparatus in the great grig, Cyphoderris monstrosa (Prophalangopsidae), a relict ensiferan found in the mountainous regions of western North America. Ander used preserved specimens to predict the sound-producing function of a pair of abdominal file-scraper apparatuses, although he was never able to directly test his proposed mechanism nor did he speculate as to the adaptive significance of the structures. The article concludes with a review of the systematic placement of various higher level taxa within the order Orthoptera, of which Ensifera is one suborder.
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2

Ander, Kjell. An abdominal stridulation organ in Cyphoderris (Prophalangopsidae) and concerning the systematic classification of the Ensifera (Saltatoria). MacEwan University Library, January 2021. http://dx.doi.org/10.31542/r.gm:2687.

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Анотація:
Ensiferan insects (crickets, katydids, grigs and allies) are well known for rubbing parts of their cuticle together to produce sound: a process called stridulation. In this article Swedish entomologist Kjell Ander describes a novel (at the time) stridulatory apparatus in the great grig, Cyphoderris monstrosa (Prophalangopsidae), a relict ensiferan found in the mountainous regions of western North America. Ander used preserved specimens to predict the sound-producing function of a pair of abdominal file-scraper apparatuses, although he was never able to directly test his proposed mechanism nor did he speculate as to the adaptive significance of the structures. The article concludes with a review of the systematic placement of various higher level taxa within the order Orthoptera, of which Ensifera is one suborder.
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3

Ross, Andrew, David Johnson, Hai Le, Danny Griffin, Carl Mudd, and David Dawson. USACE Advanced Modeling Object Standard : Release 1.0. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42152.

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The U.S. Army Corps of Engineers (USACE) Advanced Modeling Object Standard (AMOS) has been developed by the CAD/BIM Technology Center for Facilities, Infrastructure, and Environment to establish standards for support of the Advanced Modeling process within the Department of Defense (DoD) and the Federal Government. The critical component of Advanced Modeling is the objects themselves- and either make the modeling process more difficult or more successful. This manual is part of an initiative to develop a nonproprietary Advanced Modeling standard that incorporates both vertical construction and horizontal construction objects that will address the entire life cycle of facilities within the DoD. The material addressed in this USACE Advanced Modeling Object Standard includes a classification organization that is needed to identify models for specific use cases. Compliance with this standard will allow users to know whether the object model they are getting is graphically well developed but data poor or if it does have the data needed for creating contract documents. This capability will greatly reduce the designers’ efforts to either build an object or search/find/edit an object necessary for the development of their project. Considering that an advanced model may contain hundreds of objects this would represent a huge time savings and improve the modeling process.
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4

More precise classification of risk in prostate cancer reveals a huge variation in treatment. National Institute for Health Research, September 2020. http://dx.doi.org/10.3310/alert_41227.

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