Готові списки джерел за темами / Classifications des images

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Добірка наукової літератури з теми "Classifications des images"

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

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Статті в журналах з теми "Classifications des images"

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FURTADO, Luiz Felipe de Almeida, Thiago Sanna Freire SILVA, Pedro José Farias FERNANDES, and Evelyn Márcia Leão de Moraes NOVO. "Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques." Acta Amazonica 45, no. 2 (June 2015): 195–202. http://dx.doi.org/10.1590/1809-4392201401439.

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Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.
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Shi, Li Jun, Xian Cheng Mao, and Zheng Lin Peng. "Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination." Applied Mechanics and Materials 263-266 (December 2012): 2561–65. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2561.

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Анотація:
This paper presents a new method for classification of remote sensing image based on multiple classifiers combination. In this method, three supervised classifications such as Mahalanobis Distance, Maximum Likelihood and SVM are selected to sever as the sub-classifications. The simple vote classification, maximum probability category method and fuzzy integral method are combined together according to certain rules. And adopted color infrared aerial images of Huairen country as the experimental object. The results show that the overall classification accuracy was improved by 12% and Kappa coefficient was increased by 0.12 compared with SVM classification which has the highest accuracy in single sub-classifications.
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Ghimire, Santosh. "On the Image Pixels Classification Methods." Journal of the Institute of Engineering 15, no. 2 (July 31, 2019): 202–9. http://dx.doi.org/10.3126/jie.v15i2.27667.

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Анотація:
In this article, we first discuss about the images and image pixels classifications. Then we briefly discuss the importance of classification of images and finally focus on various methods of classification which can be implemented to classify image pixels.
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Klose, Christian D., Alexander D. Klose, Uwe Netz, Juergen Beuthan, and Andreas H. Hielscher. "Multiparameter classifications of optical tomographic images." Journal of Biomedical Optics 13, no. 5 (2008): 050503. http://dx.doi.org/10.1117/1.2981806.

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A.khalil and Almas a.Khalil, Turkan. "Fuzzy rule Base-Multispectral Images Classifications." Iraqi National Journal of Earth Sciences 5, no. 2 (December 28, 2005): 32–40. http://dx.doi.org/10.33899/earth.2005.41243.

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Li, Mengmeng, and Alfred Stein. "Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects." Remote Sensing 12, no. 24 (December 18, 2020): 4158. http://dx.doi.org/10.3390/rs12244158.

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Анотація:
Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.
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Molina, P. C., M. P. Castro, and C. S. Anjos. "ASSESSMENT OF PCA AND MNF INFLUENCE IN THE VHR SATELLITE IMAGE CLASSIFICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 4, 2020): 55–60. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-55-2020.

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Анотація:
Abstract. Orbital images have been increasingly refined spatially as spectrally as that is the case with those provided by satellite Earth observation WorldView-3 used in this paper. However, the images are very susceptible to noise interference, so it is difficult to identify and characterize objects. Therefore, it is essential to use techniques to minimize them. Thus, through increasingly innovative processing, it is possible to carry out detailed characterization mainly of urban areas. This work aims to perform the classification of images Worldview-3 using the advanced methods of classification Random Forest and Deep Learning for the region of Botafogo in the municipality of Rio de Janeiro, Brazil. Such classifications were performed for four different data sets, including the spectral bands and transformations (MNF and PCA) resulting from the original images. The results demonstrate that the use of transformations resulting from the original images as input data for the extraction of attributes in conjunction with the spectral bands improves the accuracy of the classifications generated by the Random Forest and Deep Learning method.
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Kijima, Hiroaki, Shin Yamada, Natsuo Konishi, Hitoshi Kubota, Hiroshi Tazawa, Takayuki Tani, Norio Suzuki, et al. "The Reliability of Classifications of Proximal Femoral Fractures with 3-Dimensional Computed Tomography: The New Concept of Comprehensive Classification." Advances in Orthopedics 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/359689.

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Анотація:
The reliability of proximal femoral fracture classifications using 3DCT was evaluated, and a comprehensive “area classification” was developed. Eleven orthopedists (5–26 years from graduation) classified 27 proximal femoral fractures at one hospital from June 2013 to July 2014 based on preoperative images. Various classifications were compared to “area classification.” In “area classification,” the proximal femur is divided into 4 areas with 3 boundary lines: Line-1 is the center of the neck, Line-2 is the border between the neck and the trochanteric zone, and Line-3 links the inferior borders of the greater and lesser trochanters. A fracture only in the first area was classified as a pure first area fracture; one in the first and second area was classified as a 1-2 type fracture. In the same way, fractures were classified as pure 2, 3-4, 1-2-3, and so on. “Area classification” reliability was highest when orthopedists with varying experience classified proximal femoral fractures using 3DCT. Other classifications cannot classify proximal femoral fractures if they exceed each classification’s particular zones. However, fractures that exceed the target zones are “dangerous” fractures. “Area classification” can classify such fractures, and it is therefore useful for selecting osteosynthesis methods.
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Cracknell, Matthew, Anita Parbhakar-Fox, Laura Jackson, and Ekaterina Savinova. "Automated Acid Rock Drainage Indexing from Drill Core Imagery." Minerals 8, no. 12 (December 4, 2018): 571. http://dx.doi.org/10.3390/min8120571.

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Анотація:
The automated classification of acid rock drainage (ARD) potential developed in this study is based on a manual ARD Index (ARDI) logging code. Several components of the ARDI require accurate identification of sulfide minerals that hyperspectral drill core scanning technologies cannot yet report. To overcome this, a new methodology was developed that uses red–green–blue (RGB) true color images generated by Corescan® to determine the presence or absence of sulfides using supervised classification. The output images were then recombined with Corescan® visible to near infrared-shortwave infrared (VNIR-SWIR) mineral classifications to obtain information that allowed an automated ARDI (A-ARDI) assessment to be performed. To test this, A-ARDI estimations and the resulting acid-forming potential classifications for 22 drill core samples obtained from a porphyry Cu–Au deposit were compared to ARDI classifications made from manual observations and geochemical and mineralogical analyses. Results indicated overall agreement between automated and manual ARD potential classifications and those from geochemical and mineralogical analyses. Major differences between manual and automated ARDI results were a function of differences in estimates of sulfide and neutralizer mineral concentrations, likely due to the subjective nature of manual estimates of mineral content and automated classification image resolution limitations. The automated approach presented here for the classification of ARD potential offers rapid and repeatable outcomes that complement manual and analyses derived classifications. Methods for automated ARD classification from digital drill core data represent a step-change for geoenvironmental management practices in the mining industry.
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Cherici, Céline. "Dossier thématique : images et classifications du vivant." Bulletin d’histoire et d’épistémologie des sciences de la vie Volume 23, no. 2 (2016): 119. http://dx.doi.org/10.3917/bhesv.232.0119.

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Більше джерел

Дисертації з теми "Classifications des images"

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Sonoda, Luke Ienari. "Classifications of lesions in magnetic resonance images of the breast." Thesis, King's College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406934.

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Thompson, J. Paul. "Classifications of gross morphologic and magnetic resonance images of human intervertebral discs." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26647.

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Анотація:
The pathogenesis of low back pain is complex but likely involves the intervertebral disc (Nachemson, 1976). Direct evidence for its importance is lacking because an accurate in vivo method of imaging the lumbar intervertebral disc has not been established. The objective of this research was to develop classifications of gross morphologic appearance and magnetic resonance image (MRI) of the disc that describe the changes associated with aging and degeneration, thereby permitting interpretation of the MRI in terms of gross morphology and allowing correlation of morphologic, chemical, mechanical, radiologic and epidemiologic data with a standard reference of disc aging and degeneration. The classifications were developed on the basis of literature review, detailed examination of 55 discs and expert advice. Two sets of three observers, one for the morphologic classification and one for the MRI classification evaluated 68 life size randomized duplicates of discs making detailed observations about overall category and 17 regional morphologic parameters and 11 regional MRI parameters. The data was tested to demonstrate the validity of the classifications using established criteria (Tugwell & Bombardier, 1982; Guyatt 4 Kirschner, 1985; Feinstein, 1985). The consistency with which the classifications could be applied was evaluated by calculating weighted kappa, a statistical test of agreement that corrects for agreement by chance; the ability of the classifications to distinguish stages in the process of ageing and degeneration by stepwise discriminant analysis; their conformity with other measures by comparisons within and between classifications and, comparisons with histologic and chemical data. The degree of agreement for all six intra-observer pairs was 'almost perfect' (weighted kappa > 0.80); for 5 interobserver pairs 'substantial' (weighted kappa > 0.60) and for one MRI interobserver pair 'moderate' (weighted kappa > 0.50). This represented a satisfactory level of agreement and indicated the classifications could be applied consistently (Feinstein, 1981). The linear regression model developed by stepwise discriminant analysis clearly demonstrated the ability of the classifications to distinguish distinct stages in disc aging and degeneration. Wilk's lambda, a likelihood ratio statistic reflecting discriminatory function, approached zero in both the morphologic (0.0408) and MRI (0.0H80) classifications. In both models, parameters pertaining to the nucleus pulposus of the disc accounted for the majority of the variance (morphologic partial R² 0.8598 and MRI partial R² 0.8811) suggesting nuclear parameters are the most important in distinguishing overall category. The correlation table generated by principal component analysis demonstrated that the categories assigned to regional parameters correlated significantly (p > 0.0001) with each other and with the overall category. From the linear combinations of parameters (principal components) generated the weighting of the nucleus pulposus behaved independently attesting to its importance. Comparisons of the morphologic and MRI classifications yielded high indices of trend (Pearson correlation coefficient of 0.81) and concordance (kappa of 0.62). Trends in the histologic and chemical data were consistent with the classifications but could not be evaluated statistically because only 15 specimens were studied. This research suggests that the classifications are valid and will form a basis for the interpretation of MRI. Preliminary evidence suggested MRI is sensitive to early changes in extracellular matrix composition not apparent in gross morphology.
Medicine, Faculty of
Graduate
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Arshad, Irshad Ahmad. "Using statistical methods for automatic classifications of clouds in ground-based photographs of the sky." Thesis, University of Essex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250129.

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Ngo, Ho Anh Khoi. "Méthodes de classifications dynamiques et incrémentales : application à la numérisation cognitive d'images de documents." Thesis, Tours, 2015. http://www.theses.fr/2015TOUR4006/document.

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Анотація:
Cette thèse s’intéresse à la problématique de la classification dynamique en environnements stationnaires et non stationnaires, tolérante aux variations de quantités des données d’apprentissage et capable d’ajuster ses modèles selon la variabilité des données entrantes. Pour cela, nous proposons une solution faisant cohabiter des classificateurs one-class SVM indépendants ayant chacun leur propre procédure d’apprentissage incrémentale et par conséquent, ne subissant pas d’influences croisées pouvant émaner de la configuration des modèles des autres classificateurs. L’originalité de notre proposition repose sur l’exploitation des anciennes connaissances conservées dans les modèles de SVM (historique propre à chaque SVM représenté par l’ensemble des vecteurs supports trouvés) et leur combinaison avec les connaissances apportées par les nouvelles données au moment de leur arrivée. Le modèle de classification proposé (mOC-iSVM) sera exploité à travers trois variations exploitant chacune différemment l’historique des modèles. Notre contribution s’inscrit dans un état de l’art ne proposant pas à ce jour de solutions permettant de traiter à la fois la dérive de concepts, l’ajout ou la suppression de concepts, la fusion ou division de concepts, tout en offrant un cadre privilégié d’interactions avec l’utilisateur. Dans le cadre du projet ANR DIGIDOC, notre approche a été appliquée sur plusieurs scénarios de classification de flux d’images pouvant survenir dans des cas réels lors de campagnes de numérisation. Ces scénarios ont permis de valider une exploitation interactive de notre solution de classification incrémentale pour classifier des images arrivant en flux afin d’améliorer la qualité des images numérisées
This research contributes to the field of dynamic learning and classification in case of stationary and non-stationary environments. The goal of this PhD is to define a new classification framework able to deal with very small learning dataset at the beginning of the process and with abilities to adjust itself according to the variability of the incoming data inside a stream. For that purpose, we propose a solution based on a combination of independent one-class SVM classifiers having each one their own incremental learning procedure. Consequently, each classifier is not sensitive to crossed influences which can emanate from the configuration of the models of the other classifiers. The originality of our proposal comes from the use of the former knowledge kept in the SVM models (represented by all the found support vectors) and its combination with the new data coming incrementally from the stream. The proposed classification model (mOC-iSVM) is exploited through three variations in the way of using the existing models at each step of time. Our contribution states in a state of the art where no solution is proposed today to handle at the same time, the concept drift, the addition or the deletion of concepts, the fusion or division of concepts while offering a privileged solution for interaction with the user. Inside the DIGIDOC project, our approach was applied to several scenarios of classification of images streams which can correspond to real cases in digitalization projects. These different scenarios allow validating an interactive exploitation of our solution of incremental classification to classify images coming in a stream in order to improve the quality of the digitized images
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Alchicha, Élie. "Confidentialité Différentielle et Blowfish appliquées sur des bases de données graphiques, transactionnelles et images." Thesis, Pau, 2021. http://www.theses.fr/2021PAUU3067.

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Анотація:
Les données numériques jouent un rôle crucial dans notre vie quotidienne en communiquant, en enregistrant des informations, en exprimant nos pensées et nos opinions et en capturant nos moments précieux sous forme d'images et de vidéos numériques. Les données numériques présentent d'énormes avantages dans tous les aspects de la vie moderne, mais constituent également une menace pour notre vie privée. Dans cette thèse, nous considérons trois types de données numériques en ligne générées par les utilisateurs des médias sociaux et les clients du commerce électronique : les graphiques, les transactions et les images. Les graphiques sont des enregistrements des interactions entre les utilisateurs qui aident les entreprises à comprendre qui sont les utilisateurs influents dans leur environnement. Les photos postées sur les réseaux sociaux sont une source importante de données qui nécessitent des efforts d'extraction. Les ensembles de données transactionnelles représentent les opérations qui ont eu lieu sur les services de commerce électronique.Nous nous appuyons sur une technique de préservation de la vie privée appelée Differential Privacy (DP) et sa généralisation Blowfish Privacy (BP) pour proposer plusieurs solutions permettant aux propriétaires de données de bénéficier de leurs ensembles de données sans risque de violation de la vie privée pouvant entraîner des problèmes juridiques. Ces techniques sont basées sur l'idée de récupérer l'existence ou la non-existence de tout élément dans l'ensemble de données (tuple, ligne, bord, nœud, image, vecteur, ...) en ajoutant respectivement un petit bruit sur la sortie pour fournir un bon équilibre entre intimité et utilité.Dans le premier cas d'utilisation, nous nous concentrons sur les graphes en proposant trois mécanismes différents pour protéger les données personnelles des utilisateurs avant d'analyser les jeux de données. Pour le premier mécanisme, nous présentons un scénario pour protéger les connexions entre les utilisateurs avec une nouvelle approche où les utilisateurs ont des privilèges différents : les utilisateurs VIP ont besoin d'un niveau de confidentialité plus élevé que les utilisateurs standard. Le scénario du deuxième mécanisme est centré sur la protection d'un groupe de personnes (sous-graphes) au lieu de nœuds ou d'arêtes dans un type de graphes plus avancé appelé graphes dynamiques où les nœuds et les arêtes peuvent changer à chaque intervalle de temps. Dans le troisième scénario, nous continuons à nous concentrer sur les graphiques dynamiques, mais cette fois, les adversaires sont plus agressifs que les deux derniers scénarios car ils plantent de faux comptes dans les graphiques dynamiques pour se connecter à des utilisateurs honnêtes et essayer de révéler leurs nœuds représentatifs dans le graphique.Dans le deuxième cas d'utilisation, nous contribuons dans le domaine des données transactionnelles en présentant un mécanisme existant appelé Safe Grouping. Il repose sur le regroupement des tuples de manière à masquer les corrélations entre eux que l'adversaire pourrait utiliser pour violer la vie privée des utilisateurs. D'un autre côté, ces corrélations sont importantes pour les propriétaires de données dans l'analyse des données pour comprendre qui pourrait être intéressé par des produits, biens ou services similaires. Pour cette raison, nous proposons un nouveau mécanisme qui expose ces corrélations dans de tels ensembles de données, et nous prouvons que le niveau de confidentialité est similaire au niveau fourni par Safe Grouping.Le troisième cas d'usage concerne les images postées par les utilisateurs sur les réseaux sociaux. Nous proposons un mécanisme de préservation de la confidentialité qui permet aux propriétaires des données de classer les éléments des photos sans révéler d'informations sensibles. Nous présentons un scénario d'extraction des sentiments sur les visages en interdisant aux adversaires de reconnaître l'identité des personnes
Digital data is playing crucial role in our daily life in communicating, saving information, expressing our thoughts and opinions and capturing our precious moments as digital pictures and videos. Digital data has enormous benefits in all the aspects of modern life but forms also a threat to our privacy. In this thesis, we consider three types of online digital data generated by users of social media and e-commerce customers: graphs, transactional, and images. The graphs are records of the interactions between users that help the companies understand who are the influential users in their surroundings. The photos posted on social networks are an important source of data that need efforts to extract. The transactional datasets represent the operations that occurred on e-commerce services.We rely on a privacy-preserving technique called Differential Privacy (DP) and its generalization Blowfish Privacy (BP) to propose several solutions for the data owners to benefit from their datasets without the risk of privacy breach that could lead to legal issues. These techniques are based on the idea of recovering the existence or non-existence of any element in the dataset (tuple, row, edge, node, image, vector, ...) by adding respectively small noise on the output to provide a good balance between privacy and utility.In the first use case, we focus on the graphs by proposing three different mechanisms to protect the users' personal data before analyzing the datasets. For the first mechanism, we present a scenario to protect the connections between users (the edges in the graph) with a new approach where the users have different privileges: the VIP users need a higher level of privacy than standard users. The scenario for the second mechanism is centered on protecting a group of people (subgraphs) instead of nodes or edges in a more advanced type of graphs called dynamic graphs where the nodes and the edges might change in each time interval. In the third scenario, we keep focusing on dynamic graphs, but this time the adversaries are more aggressive than the past two scenarios as they are planting fake accounts in the dynamic graphs to connect to honest users and try to reveal their representative nodes in the graph. In the second use case, we contribute in the domain of transactional data by presenting an existed mechanism called Safe Grouping. It relies on grouping the tuples in such a way that hides the correlations between them that the adversary could use to breach the privacy of the users. On the other side, these correlations are important for the data owners in analyzing the data to understand who might be interested in similar products, goods or services. For this reason, we propose a new mechanism that exposes these correlations in such datasets, and we prove that the level of privacy is similar to the level provided by Safe Grouping.The third use-case concerns the images posted by users on social networks. We propose a privacy-preserving mechanism that allows the data owners to classify the elements in the photos without revealing sensitive information. We present a scenario of extracting the sentiments on the faces with forbidding the adversaries from recognizing the identity of the persons. For each use-case, we present the results of the experiments that prove that our algorithms can provide a good balance between privacy and utility and that they outperform existing solutions at least in one of these two concepts
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Thornström, Johan. "Domain Adaptation of Unreal Images for Image Classification." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165758.

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Анотація:
Deep learning has been intensively researched in computer vision tasks like im-age classification. Collecting and labeling images that these neural networks aretrained on is labor-intensive, which is why alternative methods of collecting im-ages are of interest. Virtual environments allow rendering images and automaticlabeling,  which could speed up the process of generating training data and re-duce costs.This  thesis  studies  the  problem  of  transfer  learning  in  image  classificationwhen the classifier has been trained on rendered images using a game engine andtested on real images. The goal is to render images using a game engine to createa classifier that can separate images depicting people wearing civilian clothingor camouflage.  The thesis also studies how domain adaptation techniques usinggenerative  adversarial  networks  could  be  used  to  improve  the  performance  ofthe classifier.  Experiments show that it is possible to generate images that canbe used for training a classifier capable of separating the two classes.  However,the experiments with domain adaptation were unsuccessful.  It is instead recom-mended to improve the quality of the rendered images in terms of features usedin the target domain to achieve better results.
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Pavez, Ojeda Jorge. "Africanismes à Cuba (1812-1917) : textes, images et classes." Paris, EHESS, 2007. http://www.theses.fr/2007EHES0097.

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Анотація:
Cette thèse aborde la constitution du champ des études afro-cubaines au début du XXè siècle par l'œuvre de Fernando Ortiz, avocat, ethnologue, historien et folkloriste. On y verra la tension entre les logiques disciplinaires européennes et les formes de co-production des savoirs avec les sujets afro-cubains qui participent du processus ethnographique. Il est ainsi proposé une déconstruction des principaux sujets et notions sur lesquels s'instituent un regard « scientifique » sur l'Afrique à Cuba : sorcellerie, dégénérescence, « pègre », classements ethniques, écritures afro-cubaines (tatouages, symbolisme, musique, cultes et rites). L'accent mis sur les classes et les systèmes de classification des disciplines médicales et sociales mènera à une généalogie des conceptions de classe et de race noire adoptées par les Afro-cubains. Pour ce faire, on proposera l'analyse d'un corpus d'archives sur l'intellectuel et artiste afro-cubain José Antonio Aponte, exécuté en 1812 comme conspirateur
This dissertation analyzes the constitution of the field of Afro-Cuban Studies at the beginnings of the XXth century in the work of Fernando Ortiz, criminal lawyer, ethnologist, historian and folklorist. We will find in it the tension between the European logics of disciplines and the forms of Afro Cuban agency in the co-production of ethnographical knowledge. In that way, we propose a deconstruction of the principals subjects and concepts on which is instituted a vision of Africa in Cuba: witchcraft, degeneration, "mob", ethnic classifications, Afro-Cubans' writings (tattoos, symbolisms, music, cults and rites). The accent on the classes and the classifications systems of social and medical disciplines will lead to a genealogy of the conceptions of black class and race adopted by the Afro-Cubans. For this, we will propose the analysis of a corpus of archives about the Afro Cuban artist and intellectual Jose Antonio Aponte, accused and executed in 1812 as conspirator and rebel
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BATISTA, LEONARDO VIDAL. "COMPARING AUTOMATIC IMAGE CLASSIFICATION TECHNIQUES OF REMOTE SENSING IMAGES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1993. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8870@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Neste trabalho, diversas técnicas de classificação automática de imagens de sensoriamento remoto são investigadas. Na análise, incluem-se um método não- paramétrico, denominado K-Médias. Adaptativos Hierárquico (KMAH), e seis paramétricos: o Classificador de Máxima Verossimilhança (MV), o de Máxima Probabilidade a Posteriori (MAP), o MAP Adaptativo (MAPA), por Subimagens (MAPSI), o Contextual Tilton-Swain (CXTS) e o Contextual por Subimagens (CXSI). O treinamento necessário à implementação das técnicas paramétricas foi realizado de forma não-supervisionada, usando-se para tanto a classificação efetuada pelo KMAH. Considerações a respeito das vantagens e desvantagens dos classificadores, de acordo com a observação das taxas de erros e dos tempos de processamento, apontaram as técnicas MAPA e MAPSI com as mais convenientes
In this thesis, several techniques of automatic classfication of remote sensing impeages are investigated. Included in the analysis are ane non-parametric method, known as Adaptative hierarchical K-means (KMAH), and six parametric ones: the Maximum Likelihood (MV), the Maximum a Posteriori Probability (MAP), the Adaptative MAP (MAPA), the Subimages MAP (MAPSI), the tilton-Swain Contextual, (CXTS) and the Subimages Contextual (CXSI) classifiers. The necessary training for the parametric case was done in a non-supervised form, by using the KMAH classification. Considerations about the advantages and disadvantages of the classifiers were made and, based on the observation of the error rates and processing time, the MAPA and MAPSI have shown the best performances.
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Råhlén, Oskar, and Sacharias Sjöqvist. "Image Classification of Real Estate Images with Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259759.

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Анотація:
Each minute, over 2 000 searches are made on Sweden’s largest real estate website. The site has over 20 000 apartments for sale in the Stockholm region alone. This makes the search-function a vital tool for the users to find their dream apartment, and thus the quality of the search-function is of significance. As of today, it’s only possible to filter and sort by meta-data such as number of rooms, living area, price, and location, but not on more complex attributes, such as balcony or fireplace. To prevent the need for manual categorization of objects on the market, one option could be to use images of the apartments as data-points in deep neural networks to automatically add rich attributes. This thesis aims to investigate if a high rate of success when classifying apartment images can be achieved using deep neural networks, specifically looking at the categories and attributes balcony, fireplace, as well as type of room. Different types of architectures was compared amongst each other and feature extraction was compared against fine-tuning, in order to exhaustively investigate the thesis. The investigation showed that the balcony model could determine if a balcony exists in an image, with a certainty of 98.1%. For fireplaces, the maximum certainty reached was 85.5%, which is significantly lower. The type-of-room classification reached a certainty of 97,9%. This all proves the possibility of using deep neural networks in order to classify and attribute real estate images.
Varje minut görs 2000 sökningar på Sveriges största webbplats för bostadsannonser som har 20 000 bostadsrätter till salu bara i Stockholm. Detta ställer höga krav på sökfunktionen för att ge användarna en chans att hitta sin drömbostad. Idag finns det möjlighet att filtrera på attribut såsom antal rum, boarea, pris och område men inte på attribut som balkong och eldstad. För att inte behöva kategorisera objekt manuellt för attribut såsom balkong och eldstad finns det möjlighet att använda sig av mäklarbilder samt djupa neurala nätverk för att klassificera objekten automatiskt. Denna uppsats syftar till att utreda om det med hög sannolikhet går att klassificera mäklarbilder efter attributen balkong, eldstad samt typ av rum, med hjälp av djupa neurala nätverk. För att undersöka detta på ett utförligt sätt jämfördes olika arkitekturer med varandra samt feature extraction mot fine-tuning. Testerna visade att balkongmodellen med 98,1% sannolikhet kan avgöra om det finns en balkong på någon av bilderna eller inte. För eldstäder nåddes ett maximum på 85,5% vilket är väsentligt sämre än för balkonger. Under sista klassificeringen, den för rum, nåddes ett resultat på 97,9%.Sammanfattningsvis påvisar detta att det är fullt möjligt att använda djupa neurala nätverk för att klassificera mäklarbilder.
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Vargas, Muñoz John Edgar 1991. "Contextual superpixel-based active learning for remote sensing image classification = Aprendizado ativo baseado em atributos contextuais de superpixel para classificação de imagem de sensoriamento remoto." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275555.

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Анотація:
Orientadores: Alexandre Xavier Falcão, Jefersson Alex dos Santos
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-27T14:43:51Z (GMT). No. of bitstreams: 1 VargasMunoz_JohnEdgar_M.pdf: 9138091 bytes, checksum: bdb40e3a5655df0e10a137f2d08f0d8d (MD5) Previous issue date: 2015
Resumo: Recentemente, técnicas de aprendizado de máquina têm sido propostas para criar mapas temáticos a partir de imagens de sensoriamento remoto. Estas técnicas podem ser divididas em métodos de classificação baseados em pixels ou regiões. Este trabalho concentra-se na segunda abordagem, uma vez que estamos interessados em imagens com milhões de pixels e a segmentação da imagem em regiões (superpixels) pode reduzir consideravelmente o número de amostras a serem classificadas. Porém, mesmo utilizando superpixels, o número de amostras ainda é grande para anotá-las manualmente e treinar o classificador. As técnicas de aprendizado ativo propostas resolvem este problema começando pela seleção de um conjunto pequeno de amostras selecionadas aleatoriamente. Tais amostras são anotadas manualmente e utilizadas para treinar a primeira instância do classificador. Em cada iteração do ciclo de aprendizagem, o classificador atribui rótulos e seleciona as amostras mais informativas para a correção/confirmação pelo usuário, aumentando o tamanho do conjunto de treinamento. A instância do classificador é melhorada no final de cada iteração pelo seu treinamento e utilizada na iteração seguinte até que o usuário esteja satisfeito com o classificador. Observamos que a maior parte dos métodos reclassificam o conjunto inteiro de dados em cada iteração do ciclo de aprendizagem, tornando este processo inviável para interação com o usuário. Portanto, enderaçamos dois problemas importantes em classificação baseada em regiões de imagens de sensoriamento remoto: (a) a descrição efetiva de superpixels e (b) a redução do tempo requerido para seleção de amostras em aprendizado ativo. Primeiro, propusemos um descritor contextual de superpixels baseado na técnica de sacola de palavras, que melhora o resultado de descritores de cor e textura amplamente utilizados. Posteriormente, propusemos um método supervisionado de redução do conjunto de dados que é baseado em um método do estado da arte em aprendizado ativo chamado Multi-Class Level Uncertainty (MCLU). Nosso método mostrou-se tão eficaz quanto o MCLU e ao mesmo tempo consideravelmente mais eficiente. Adicionalmente, melhoramos seu desempenho por meio da aplicação de um processo de relaxação no mapa de classificação, utilizando Campos Aleatórios de Markov
Abstract: In recent years, machine learning techniques have been proposed to create classification maps from remote sensing images. These techniques can be divided into pixel- and region-based image classification methods. This work concentrates on the second approach, since we are interested in images with millions of pixels and the segmentation of the image into regions (superpixels) can considerably reduce the number of samples for classification. However, even using superpixels the number of samples is still large for manual annotation of samples to train the classifier. Active learning techniques have been proposed to address the problem by starting from a small set of randomly selected samples, which are manually labeled and used to train a first instance of the classifier. At each learning iteration, the classifier assigns labels and selects the most informative samples for user correction/confirmation, increasing the size of the training set. An improved instance of the classifier is created by training, after each iteration, and used in the next iteration until the user is satisfied with the classifier. We observed that most methods reclassify the entire pool of unlabeled samples at every learning iteration, making the process unfeasible for user interaction. Therefore, we address two important problems in region-based classification of remote sensing images: (a) the effective superpixel description and (b) the reduction of the time required for sample selection in active learning. First, we propose a contextual superpixel descriptor, based on bag of visual words, that outperforms widely used color and texture descriptors. Second, we propose a supervised method for dataset reduction that is based on a state-of-art active learning technique, called Multi-Class Level Uncertainty (MCLU). Our method has shown to be as effective as MCLU, while being considerably more efficient. Additionally, we further improve its performance by applying a relaxation process on the classification map by using Markov Random Fields
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
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Книги з теми "Classifications des images"

1

Xu, Xiang, Xingkun Wu, and Feng Lin. Cellular Image Classification. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47629-2.

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M, Benning Vivien, and Ching Neville P, eds. Classification of remotely sensed images. Bristol: A. Hilger, 1987.

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3

Ahmed, Nazeer. Image shape classification techniques. Manchester: University of Manchester, 1997.

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4

Geimer, Robert L. Flake classification by image analysis. Madison, WI: U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 1988.

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5

Bi, Ying, Bing Xue, and Mengjie Zhang. Genetic Programming for Image Classification. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65927-1.

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Jenicka, S. Land Cover Classification of Remotely Sensed Images. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66595-1.

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Yin, Xiao-Xia, Sillas Hadjiloucas, and Yanchun Zhang. Pattern Classification of Medical Images: Computer Aided Diagnosis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57027-3.

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Kamusoko, Courage. Remote Sensing Image Classification in R. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8012-9.

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Borra, Surekha, Rohit Thanki, and Nilanjan Dey. Satellite Image Analysis: Clustering and Classification. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6424-2.

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Costa, Luciano da Fontoura. Shape classification and analysis: Theory and practice. 2nd ed. Boca Raton: Taylor & Francis, 2009.

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Частини книг з теми "Classifications des images"

1

Leamons, Rebekah, Hong Cheng, and Ahmad Al Shami. "Vision Transformers for Medical Images Classifications." In Lecture Notes in Networks and Systems, 319–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16075-2_22.

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Wang, Kegang, Liying Qi, and Guohua Geng. "Images Classifications Based on Color-Texture Feature." In Artificial Intelligence and Computational Intelligence, 105–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33478-8_14.

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Balnarsaiah, Battula, G. Rajitha, and Balakrishna Penta. "Classifications of SAR Images Using Sparse Coding." In Springer Series in Geomechanics and Geoengineering, 761–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77276-9_69.

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Parveen, Runa, Cliff Ruff, and Andrew Todd-Pokropek. "Three Dimensional Tissue Classifications in MR Brain Images." In Computer Vision Approaches to Medical Image Analysis, 236–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11889762_21.

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Hatt, Charles, Craig Galban, Wassim Labaki, Ella Kazerooni, David Lynch, and Meilan Han. "Convolutional Neural Network Based COPD and Emphysema Classifications Are Predictive of Lung Cancer Diagnosis." In Image Analysis for Moving Organ, Breast, and Thoracic Images, 302–9. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00946-5_30.

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Chan, Jonathan C. W., Ruth S. DeFries, and John R. G. Townshend. "Improved Recognition of Spectrally Mixed Land Cover Classes Using Spatial Textures and Voting Classifications." In Computer Analysis of Images and Patterns, 217–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44692-3_27.

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Balnarsaiah, Battula, T. S. Prasad, Laxminarayana Parayitam, Balakrishna Penta, and Chandrasekhar Patibandla. "Classifications of High-Resolution SAR and Optical Images Using Supervised Algorithms." In Computational Intelligence in Pattern Recognition, 981–90. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9042-5_84.

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Khanh, Ho Thi Kieu, Tran Cong Hung, Viet-Hung Dang, and Nguyen Duc Thang. "Human Organ Classifications from Computed Tomography Images Using Deep-Convolutional Neural Network." In 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6), 917–23. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4361-1_155.

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Zhong, Ziyuan, Yuchi Tian, and Baishakhi Ray. "Understanding Local Robustness of Deep Neural Networks under Natural Variations." In Fundamental Approaches to Software Engineering, 313–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71500-7_16.

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AbstractDeep Neural Networks (DNNs) are being deployed in a wide range of settings today, from safety-critical applications like autonomous driving to commercial applications involving image classifications. However, recent research has shown that DNNs can be brittle to even slight variations of the input data. Therefore, rigorous testing of DNNs has gained widespread attention.While DNN robustness under norm-bound perturbation got significant attention over the past few years, our knowledge is still limited when natural variants of the input images come. These natural variants, e.g., a rotated or a rainy version of the original input, are especially concerning as they can occur naturally in the field without any active adversary and may lead to undesirable consequences. Thus, it is important to identify the inputs whose small variations may lead to erroneous DNN behaviors. The very few studies that looked at DNN’s robustness under natural variants, however, focus on estimating the overall robustness of DNNs across all the test data rather than localizing such error-producing points. This work aims to bridge this gap.To this end, we study the local per-input robustness properties of the DNNs and leverage those properties to build a white-box (DeepRobust-W) and a black-box (DeepRobust-B) tool to automatically identify the non-robust points. Our evaluation of these methods on three DNN models spanning three widely used image classification datasets shows that they are effective in flagging points of poor robustness. In particular, DeepRobust-W and DeepRobust-B are able to achieve an F1 score of up to 91.4% and 99.1%, respectively. We further show that DeepRobust-W can be applied to a regression problem in a domain beyond image classification. Our evaluation on three self-driving car models demonstrates that DeepRobust-W is effective in identifying points of poor robustness with F1 score up to 78.9%.
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Yanai, Keiji. "Image Classification by Web Images." In Lecture Notes in Computer Science, 613–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45683-x_83.

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Тези доповідей конференцій з теми "Classifications des images"

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Borges, Felipe Silveira Brito, Juliana Velasques Balta, Milad Roghanian, Ariadne Barbosa Gonçalves, Marco Alvarez, and Hemerson Pistori. "The interference of optical zoom in human and machine classification of pollen grain images." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18897.

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Palynology can be applied to different areas, such as archeology and allergy, where it is constantly growing. However, no publication comparing human classifications with machine learning classifications at different optical scales has been found in the literature. An image dataset with 17 pollen species that occur in Brazil was created, and machine learning algorithms were used for their automatic classification and subsequent comparison with humans. The experiments presented here show how machine and human classification behave according to different optical image scales. Satisfactory results were achieved, with 98.88% average accuracy for the machine and 45.72% for human classification. The results impact a single scale pattern for capturing pollen grain images for both future computer vision experiments and for a faster advance in palynology science.
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Pereira, Rafael S., and Fabio Porto. "Deep Learning Application for Plant Classification on Unbalanced Training Set." In XIII Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/bresci.2019.10023.

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Deep learning models expect a reasonable amount of training instances to improve prediction quality. Moreover, in classification problems, the occurrence of an unbalanced distribution may lead to a biased model. In this paper, we investigate the problem of species classification from plant images, where some species have very few image samples. We explore reduced versions of imagenet Neural Network winners architecture to filter the space of candidate matches, under a target accuracy level. We show through experimental results using real unbalanced plant image datasets that our approach can lead to classifications within the 5 best positions with high probability.
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Pereira, Rafael S., and Fabio Porto. "Deep Learning Application for Plant Classification on Unbalanced Training Set." In XIII Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/bresci.2019.6304.

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Deep learning models expect a reasonable amount of training in- stances to improve prediction quality. Moreover, in classification problems, the occurrence of an unbalanced distribution may lead to a biased model. In this paper, we investigate the problem of species classification from plant images, where some species have very few image samples. We explore reduced versions of imagenet Neural Network winners architecture to filter the space of candi- date matches, under a target accuracy level. We show through experimental results using real unbalanced plant image datasets that our approach can lead to classifications within the 5 best positions with high probability.
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4

Callanan, James, Carles Garcia-Cabrera, Niamh Belton, Gennady Roshchupkin, and Kathleen Curran. "Integrating feature attribution methods into the loss function of deep learning classifiers." In 24th Irish Machine Vision and Image Processing Conference. Irish Pattern Recognition and Classification Society, 2022. http://dx.doi.org/10.56541/omxa8857.

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Feature attribution methods are typically used post-training to judge if a deep learning classifier is using meaningful concepts in an input image when making classifications. In this study, we propose using feature attribution methods to give a classifier automated feedback throughout the training process via a novel loss function. We call such a loss function, a heatmap loss function. Heatmap loss functions enable us to incentivize a model to rely on relevant sections of the input image when making classifications. Two groups of models were trained, one group with a heatmap loss function and the other using categorical cross entropy (CCE). Models trained with the heatmap loss function were capable of achieving equivalent classification accuracies on a test dataset of synthesised cardiac MRI slices. Moreover, HiResCAM heatmaps suggest that these models relied to a greater extent on regions of the MRI slices within the heart. A further experiment demonstrated how heatmap loss functions can be used to prevent deep learning classifiers from using noncausal concepts that disproportionately co-occur with images of a certain class when making classifications. This suggests that heatmap loss functions could be used to prevent models from learning dataset biases by directing where the model should be looking when making classifications.
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5

Leavers, V. F., and M. D. Hanlon. "Establishment of the Accuracy and Consistency of Using Automatic Image Analysis to Classify Wear Debris Particles." In World Tribology Congress III. ASMEDC, 2005. http://dx.doi.org/10.1115/wtc2005-64385.

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Wear debris particle analysis is an equipment health monitoring technique used to identify possible failure modes in various engine components. One of the first stages in the analysis involves the examination under a microscope of particles collected from the component’s lubrication system on magnetic drain plugs and filters. However, the subjectivity of technicians’ judgements means that diagnosis may not be consistent between technicians. A software tool capable of automatically classifying the images of wear debris particles has been developed and tested using an 800-image database. It is shown that using automatic image analysis for the classification of wear debris particle images is more consistent, accurate and informative when compared to the classifications assigned by wear debris experts.
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Mohammadyari, Fatemeh, Mir Mehrdad Mirsanjari, Jūratė Sužiedelytė Visockienė, and Ardavan Zarandian. "Evaluation of Change in Land Usage and Land Cover in Karaj, Iran." In 11th International Conference “Environmental Engineering”. VGTU Technika, 2020. http://dx.doi.org/10.3846/enviro.2020.649.

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In this study, classification results were derived from remote sensing data and the Support Vector Machine (SVM) algorithm used in this process, which classifies Landsat land-cover images. The accuracy of image classifications was evaluated by calculation of the Kappa coefficient. The area of study is Karaj, the capital of Alborz province, in north-central Iran. It is situated in the foothills of the Alborz Mountains and occupies a fertile agricultural plain. Landsat data used in the classification of land cover were collected from USGS websites, and multi-temporal images from the data were geometrically corrected. After this process, we calculated 11 metrics at the landscape and class-level scales: five metrics of class level and six metrics of landscape. The results showed that the landscape patterns in Karaj were changed due to the process of urbanization over an 11-year period. At the class level, for all classifications, the AI metric increased and the PD and NP metrics decreased. At the landscape level, the PD, ED, NP, and SHDI metrics decreased, and LPI and AI increased. These results provide insights about urban development policies and about whether the expansion of urban areas is beneficial for environmental sustainability in Iran and elsewhere in the world.
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Johnson, Kristyn B., Donald H. Ferguson, Robert S. Tempke, and Andrew C. Nix. "Application of a Convolutional Neural Network for Wave Mode Identification in a Rotating Detonation Combustor Using High-Speed Imaging." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15676.

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Abstract Utilizing a neural network, individual down-axis images of combustion waves in a Rotating Detonation Engine (RDE) can be classified according to the number of detonation waves present and their directional behavior. While the ability to identify the number of waves present within individual images might be intuitive, the further classification of wave rotational direction is a result of the detonation wave’s profile, which suggests its angular direction of movement. The application of deep learning is highly adaptive and therefore can be trained for a variety of image collection methods across RDE study platforms. In this study, a supervised approach is employed where a series of manually classified images is provided to a neural network for the purpose of optimizing the classification performance of the network. These images, referred to as the training set, are individually labeled as one of ten modes present in an experimental RDE. Possible classifications include deflagration, clockwise and counterclockwise variants of co-rotational detonation waves with quantities ranging from one to three waves, as well as single, double and triple counter-rotating detonation waves. After training the network, a second set of manually classified images, referred to as the validation set, is used to evaluate the performance of the model. The ability to predict the detonation wave mode in a single image using a trained neural network substantially reduces computational complexity by circumnavigating the need to evaluate the temporal behavior of individual pixels throughout time. Results suggest that while image quality is critical, it is possible to accurately identify the modal behavior of the detonation wave based on only a single image rather than a sequence of images or signal processing. Successful identification of wave behavior using image classification serves as a stepping stone for further machine learning integration in RDE research and comprehensive real-time diagnostics.
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Cheng, Qianwei, AKM Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, and Moinul Zaber. "Deep-learning Coupled with Novel Classification Method to Classify the Urban Environment of the Developing World." In 8th International Conference on Artificial Intelligence and Applications (AIAP 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110103.

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Rapid globalization and the interdependence of the countries have engendered tremendous in-flow of human migration towards the urban spaces. With the advent of high definition satellite images, high-resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding the urban area lies in the useful classification of the urban environment that is usable for data collection, analysis, and visualization. In this paper, we propose a novel classification method that is readily usable for machine analysis and it shows the applicability of the methodology in a developing world setting. However, the state-of-the-art is mostly dominated by the classification of building structures, building types, etc., and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh where the surrounding environment is crucial for the classification. Moreover, the traditional classifications propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real-time. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently, a map was drawn. The classification is based broadly on two dimensions the state of urbanization and the architectural form of the urban environment. Consequently, the urban space is divided into four classifications: 1) highly informal area 2) moderately informal area 3) moderately formal area and 4) highly formal area. For semantic segmentation and automatic classification, Google’s DeeplabV3+ model was used. The model uses the Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate a larger context. Image encompassing 70% of the urban space was used to train the model and the remaining 30% was used for testing and validation. The model can segment with 75% accuracy and 60% Mean Intersection over Union (mIoU).
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Teruel, Gilberto F., Tatiany M. Heiderich, Ruth Guinsburg, and Carlos E. Thomaz. "Analysis And Recognition Of Pain In 2d Face Images Of Full Term And Healthy Newborns." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4419.

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This paper proposes a sequence of computational procedures for detecting, interpreting and classifying patterns in frontal two-dimensional images of faces for automatic recognition of pain in newborns. Using data transformation and extraction of statistical characteristics from a real-life, healthy-term newborn image database, it was possible to interpret and model the subjectivity of trained health professionals, quantifying human knowledge in the task of recognizing pain enabling automatic identification. These results were compared with NFCS based classifications by the same professionals of the same images.
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Battula, Balnarsaiah, Laxminarayana Parayitam, T. S. Prasad, Penta Balakrishna, and Chandrasekhar Patibandla. "Classifications of High Resolution Optical Images using Supervised Algorithms." In 2018 IEEE 8th International Advance Computing Conference (IACC). IEEE, 2018. http://dx.doi.org/10.1109/iadcc.2018.8692132.

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Звіти організацій з теми "Classifications des images"

1

Olivier, Jason, and Sally Shoop. Imagery classification for autonomous ground vehicle mobility in cold weather environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42425.

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Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.
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Tabinskyy, Yaroslav. VISUAL CONCEPTS OF PHOTO IN THE MEDIA (ON THE EXAMPLE OF «UKRAINER» AND «REPORTERS»). Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11099.

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The article is devoted to the analysis of the main forms of visualization in the media related to photo. The thematic visual concepts are described in accordance with the content of electronic media, which consider the impact of modern technologies on the development of media space. The researches of the Ukrainian and foreign educational institutions concerning the main features of modern photo is classificate. Modifications and new visual forms in the media are singled out. The main objective of the article is to study the visual concepts of modern photo and identify ideological and thematic priorities in photo projects. To achieve the main objective in the article a certain methodology were used. Due to the historical-theoretical description it was possible to substantiate the study of visual concepts. The conceptual-system method was used to study the subject of media photo projects. The main results of the research are the definition of visual concepts of photo on the example of electronic media and the identification of the main thematic features in the process of visual filling of the media space. Based on the study, we can conclude that today the information field needs quality visual content. For successful creation of visual concepts it is necessary to single out thematic features of modern photo and to carry out classifications on ideological and semantic signs. Given the rapid development of digital technologies, the topic of the scientific article we offer is relevant for scientists, journalists, media researchers, visual journalism experts and photojournalists. Modern space is filled with a large number of pictorial materials, which in most cases form specific images, patterns or stereotypes in the mind of the reader (viewer). Also important is the classification of photo used in journalistic publications. That is why there is a need to explore the content and principles of distribution of ideological priorities of photo in the media. The substantiation of scientists about the important place of photography in the modern media space and the future development of visual technologies, which already use artificial intelligence, is relevant.
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3

Fiebiger, Frank. Map Classification In Image Data. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ad1008925.

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4

Wu, Alex, and Myriam Abramson. Image Classification for Web Genre Identification. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada599790.

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5

Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculation of recognition error and training accuracy are presented.
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Tang, Xiaoou. Dominant Run-Length Method for Image Classification. Fort Belvoir, VA: Defense Technical Information Center, June 1997. http://dx.doi.org/10.21236/ada329351.

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7

Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Sopher, Ariana M., Sally A. Shoop, Jesse Jr M. Stanley, and Brian T. Tracy. Image Analysis and Classification Based on Soil Strength. Fort Belvoir, VA: Defense Technical Information Center, August 2016. http://dx.doi.org/10.21236/ad1014532.

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Fox, Neil D., and Pi-Fuay Chen. Improving Classification Accuracy of Radar Images Using a Multiple-Stage Classifier. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada200291.

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