Bibliografías temáticas / Natural images

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Literatura académica sobre el tema "Natural images"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 6 de septiembre de 2023

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Artículos de revistas sobre el tema "Natural images"

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Moorhead, Ian y Tom Trościanko. "Natural Images". Perception 29, n.º 9 (septiembre de 2000): 1013–15. http://dx.doi.org/10.1068/p2909ed.

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Jeurissen, D. J. J. D. M. y P. R. Roelfsema. "Image Parsing, From Curves to Natural Images". Journal of Vision 12, n.º 9 (10 de agosto de 2012): 269. http://dx.doi.org/10.1167/12.9.269.

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Tanaka, Go, Noriaki Suetake y Eiji Uchino. "Simple multiscale image enhancement for natural images". Optical Review 17, n.º 3 (mayo de 2010): 130–38. http://dx.doi.org/10.1007/s10043-010-0023-6.

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George, J., G. Padmanabhan y M. Brady. "Image features predict edge causation in natural images". Journal of Vision 9, n.º 8 (22 de marzo de 2010): 1044. http://dx.doi.org/10.1167/9.8.1044.

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Zhang, S., C. Abbey y M. Eckstein. "Classification Images for Search in Natural Images". Journal of Vision 10, n.º 7 (17 de agosto de 2010): 1355. http://dx.doi.org/10.1167/10.7.1355.

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Banafar, Lokendra Singh y Dr Lalita Gupta. "Text Detection from Natural Images using MSER Algorithm". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 de abril de 2018): 73–81. http://dx.doi.org/10.31142/ijtsrd10806.

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V. Seeri, Shivananda, J. D. Pujari y P. S. Hiremath. "PNN Based Character Recognition in Natural Scene Images". Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (31 de octubre de 2016): 109–13. http://dx.doi.org/10.9756/bijsesc.8254.

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Ahmad, Khairul Adilah, Sharifah Lailee Syed Abdullah y Mahmod Othman. "Natural Images Contour Segmentation". Journal of Computing Research and Innovation 2, n.º 4 (30 de enero de 2018): 39–47. http://dx.doi.org/10.24191/jcrinn.v2i4.62.

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This paper, a combination of edge detection and contour based segmentation approach for object contour delineation is proposed. The proposed approach employs a new methodology for segmenting the fruit contour from the indoor and outdoo r natural images more effectively. The overall process is carried out in five steps. The first step is to pre - process the image in order to convert the colour image to grayscale image. Second step is the adoption of Laplacian of Gaussian edge detection and a new corner template detection algorithm for adjustment of the pixels along the edge map in the interpolation process. Third step is the reconstruction process by implementing two morphology operators with embedded of inversion condition and dynamic thr eshold to preserve and reconstruct object contour. Fifth step is ground mask process in which the outputs of the inference obtained for each pixel is combined to a final segmented output, which provides a segmented foreground against the black background. This proposed algorithm is tested over 150 indoor and 40 outdoor fruit images in order to analyse its efficiency. From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy of 100 % in segmenting indoor and outdoor natural images. This algorithm also present a fully automatic model based system for segmenting fruit images of the natural environment.
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Hall, Ronald L. "Images of natural evil". International Journal for Philosophy of Religion 87, n.º 3 (25 de abril de 2020): 213–16. http://dx.doi.org/10.1007/s11153-020-09757-9.

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SATO, TAKASHI, MAKOTO MATSUOKA y HIDEKI TAKAYASU. "FRACTAL IMAGE ANALYSIS OF NATURAL SCENES AND MEDICAL IMAGES". Fractals 04, n.º 04 (diciembre de 1996): 463–68. http://dx.doi.org/10.1142/s0218348x96000571.

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We construct color map images of fractal dimension distribution from natural scenes and medical images by applying the box-counting method locally. The map images clearly show the difference between clouds and rocks, as well as between cancer parts and normal tissue in the colon. The method is simple and may be expected to be applicable to a real-time video-data processing.
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Tesis sobre el tema "Natural images"

1

Chen, Ting-Li. "On the statistics of natural images /". View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174586.

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Miflah, Hussain Ismail Ahamed. "Higher-level representations of natural images". Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/39759.

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The traditional view of vision is that neurons in early cortical areas process information about simple features (e.g. orientation and spatial frequency) in small, spatially localised regions of visual space (the neuron's receptive field). This piecemeal information is then fed-forward into later stages of the visual system where it gets combined to form coherent and meaningful global (higher-level) representations. The overall aim of this thesis is to examine and quantify this higher level processing; how we encode global features in natural images and to understand the extent to which our perception of these global representations is determined by the local features within images. Using the tilt after-effect as a tool, the first chapter examined the processing of a low level, local feature and found that the orientation of a sinusoidal grating could be encoded in both a retinally and spatially non-specific manner. Chapter 2 then examined these tilt aftereffects to the global orientation of the image (i.e., uprightness). We found that image uprightness was also encoded in a retinally / spatially non-specific manner, but that this global property could be processed largely independently of its local orientation content. Chapter 3 investigated if our increased sensitivity to cardinal (vertical and horizontal) structures compared to inter-cardinal (45° and 135° clockwise of vertical) structures, influenced classification of unambiguous natural images. Participants required relatively less contrast to classify images when they retained near-cardinal as compared to near-inter-cardinal structures. Finally, in chapter 4, we examined category classification when images were ambiguous. Observers were biased to classify ambiguous images, created by combining structures from two distinct image categories, as carpentered (e.g., a house). This could not be explained by differences in sensitivity to local structures and is most likely the result of our long-term exposure to city views. Overall, these results show that higher-level representations are not fully dependent on the lower level features within an image. Furthermore, our knowledge about the environment influences the extent to which we use local features to rapidly identify an image.
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Tavakoli, Fatemeh. "On Visual Attention in Natural Images". Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-48256.

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By visual attention process biological and machine vision systems are able to select the most relevant regions from a scene. The relevancy process is achieved either by top-down factors, driven by task, or bottom-up factors, the visual saliency, which distinguish a scene region that are different from its surrounding. During the past 20 years numerous research efforts have aimed to model bottom-up visual saliency with many successful applications in computer vision and robotics.In this thesis we have performed a comparison between a state-of-the-art saliency model and subjective test (human eye tracking) using different evaluation methods over three generated dataset of synthetic patterns and natural images. Our results showed that the objective model is partially valid and highly center-biased.By using empirical data obtained from subjective experiments we propose a special function, the Probability of Characteristic Radially Dependency Function, to model the lateral distribution of visual attention process.
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Kim, Kyu-Heon. "Segmentation of natural texture images using a robust stochastic image model". Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307927.

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Azzabou, Noura. "Variable Bandwidth Image Models for Texture-Preserving Enhancement of Natural Images". Paris Est, 2008. http://pastel.paristech.org/4041/01/ThesisNouraAzzabou.pdf.

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Cette thèse s’intéresse aux problèmes de restauration d’images et de préservation de textures. Cette tâche nécessite un modèle image qui permet de caractériser le signal qu’on doit obtenir. Un tel model s’appuie sur la définition de l’interaction entre les pixels et qui est caractérisé par deux aspects : (1) la similarité photométrique entre les pixels (2) la distance spatiale entre les pixels qui peut être comparée à une grandeur d’échelle. La première partie de la thèse introduit un nouveau modèle non paramétrique d’image. Ce modèle permet d’obtenir une description adaptative de l’image en utilisant des noyaux de taille variable obtenue à partir d’une étape de classification effectuée au préalable. La deuxième partie introduit une autre approche pour décrire la dépendance entre pixels d’un point de vue géométrique. Ceci est effectué à l’aide d’un modèle statistique de la co-occurrence entre les observations de point de vue géométrique. La dernière partie est une nouvelle technique de sélection automatique (pour chaque pixel) de la taille des noyaux utilisé au cours du filtrage. Cette thèse est conclue avec l’application de cette dernière approche dans différents contextes de filtrage ce qui montre sa flexibilité vis-à-vis des contraintes liées aux divers problèmes traités
This thesis is devoted to image enhancement and texture preservation issues. This task involves an image model that describes the characteristics of the recovered signal. Such a model is based on the definition of the pixels interaction that is often characterized by two aspects (i) the photometric similarity between pixels (ii) the spatial distance between them that can be compared to a given scale. The first part of the thesis, introduces novel non-parametric image models towards more appropriate and adaptive image description using variable bandwidth approximations driven from a soft classification in the image. The second part introduces alternative means to model observations dependencies from geometric point of view. This is done through statistical modeling of co-occurrence between observations and the use of multiple hypotheses testing and particle filters. The last part is devoted to novel adaptive means for spatial bandwidth selection and more efficient tools to capture photometric relationships between observations. The thesis concludes with providing other application fields of the last technique towards proving its flexibility toward various problem requirements
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Viklund, Alexander y Emma Nimstad. "Character Recognition in Natural Images Utilising TensorFlow". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208385.

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Convolutional Neural Networks (CNNs) are commonly used for character recognition. They achieve the lowest error rates for popular datasets such as SVHN and MNIST. Usage of CNN is lacking in research about character classification in natural images regarding the whole English alphabet. This thesis conducts an experiment where TensorFlow is used to construct a CNN that is trained and tested on the Chars74K dataset, with 15 images per class for training and 15 images per class for testing. This is done with the aim of achieving a higher accuracy than the non-CNN approach by de Campos et al. [1], that achieved 55.26%. The thesis explores data augmentation techniques for expanding the small training set and evaluates the result of applying rotation, stretching, translation and noise-adding. The result of this is that all of these methods apart from adding noise gives a positive effect on the accuracy of the network. Furthermore, the experiment shows that with a three layered convolutional neural network it is possible to create a character classifier that is as good as de Campos et al.'s. It is believed that even better results can be achieved if more experiments would be conducted on the parameters of the network and the augmentation.
Det är vanligt att använda konvolutionära artificiella neuronnät (CNN) för bildigenkänning, då de ger de minsta felmarginalerna på kända datamängder som SVHN och MNIST. Dock saknas det forskning om användning av CNN för klassificering av bokstäver i naturliga bilder när det gäller hela det engelska alfabetet. Detta arbete beskriver ett experiment där TensorFlow används för att bygga ett CNN som tränas och testas med bilder från Chars74K. 15 bilder per klass används för träning och 15 per klass för testning. Målet med detta är att uppnå högre noggrannhet än 55.26%, vilket är vad de campos et al. [1] uppnådde med en metod utan artificiella neuronnät. I rapporten utforskas olika tekniker för att artificiellt utvidga den lilla datamängden, och resultatet av att applicera rotation, utdragning, translation och bruspåslag utvärderas. Resultatet av det är att alla dessa metoder utom bruspåslag ger en positiv effekt på nätverkets noggrannhet. Vidare visar experimentet att med ett CNN med tre lager går det att skapa en bokstavsklassificerare som är lika bra som de Campos et al.s klassificering. Om fler experiment skulle genomföras på nätverkets och utvidgningens parametrar är det troligt att ännu bättre resultat kan uppnås.
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Granlund, Oskar y Kai Böhrnsen. "Improving character recognition by thresholding natural images". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208899.

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The current state of the art optical character recognition (OCR) algorithms are capable of extracting text from images in predefined conditions. OCR is extremely reliable for interpreting machine-written text with minimal distortions, but images taken in a natural scene are still challenging. In recent years the topic of improving recognition rates in natural images has gained interest because more powerful handheld devices are used. The main problem faced dealing with recognition in natural images are distortions like illuminations, font textures, and complex backgrounds. Different preprocessing approaches to separate text from its background have been researched lately. In our study, we assess the improvement reached by two of these preprocessing methods called k-means and Otsu by comparing their results from an OCR algorithm. The study showed that the preprocessing made some improvement on special occasions, but overall gained worse accuracy compared to the unaltered images.
Dagens optisk teckeninläsnings (OCR) algoritmer är kapabla av att extrahera text från bilder inom fördefinierade förhållanden. De moderna metoderna har uppnått en hög träffsäkerhet för maskinskriven text med minimala förvrängningar, men bilder tagna i en naturlig scen är fortfarande svåra att hantera. De senaste åren har ett stort intresse för att förbättra tecken igenkännings algoritmerna uppstått, eftersom fler kraftfulla och handhållna enheter används. Det huvudsakliga problemet när det kommer till igenkänning i naturliga bilder är olika förvrängningar som infallande ljus, textens textur och komplicerade bakgrunder. Olika metoder för förbehandling och därmed separation av texten och dess bakgrund har studerats under den senaste tiden. I våran studie bedömer vi förbättringen som uppnås vid förbehandlingen med två metoder som kallas för k-means och Otsu genom att jämföra svaren från en OCR algoritm. Studien visar att Otsu och k-means kan förbättra träffsäkerheten i vissa förhållanden men generellt sett ger det ett sämre resultat än de oförändrade bilderna.
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Johnson, Samuel Alan. "Articulated human pose estimation in natural images". Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598026.

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In this thesis the problem of estimating the 2-D articulated pose, or configuration of a person in unconstrained images such as consumer photographs is addressed. Contributions are split among three major chapters. In previous work the Pictorial Structure Model approach has proven particularly successful. and is appealing because of its moderate computational cost. However, the accuracy of resulting pose estimates has been limited by the use of simple representations of limb appearance. In this thesis strong discriminatively trained limb detectors combining gradient and colour segmentation cues are proposed. The approach improves significantly on the "iterative image parsing" method which was the state-of-the-art at the time, and shows significant promise for combination with other models of pose and appearance. In the second pan of this thesis higher fidelity models of pose and appearance are proposed. The aim is to tackle extremely challenging properties of the human pose estimation task arising from variation in pose, anatomy, clothing. and imaging conditions. Current methods use simple models of body part appearance and plausible configurations due to limitations of available training data and constraints on computational expense. It is shown that such models severely limit accuracy. A new annotated database of challenging consumer images is introduced, an order of magnitude larger than currently available datasets. This larger amount of data allows partitioning of the pose space and the learning of multiple, clustered Pictorial Structure Models. A relative improvement in accuracy of over 50% is achieved compared to the standard, single model approach. In the final part of this thesis the clustered Pictorial Structure Model framework is extended to handle much larger quantities of training data. Furthermore it is shown how to utilise Amazon Mechanical Turk and a latent annotation update scheme to achieve high quality annotations at low cost. A significant increase in pose estimation accuracy is presented, while the computational expense of the framework is improved by a factor of
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Nasrallah, Alexandre James. "Statistics of gradient directions in natural images". Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1444886/.

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Interest in finding statistical regularities in natural images has been growing since the advent of information theory and the advancement of the efficient coding hypothesis that the human visual system is optimised to encode natural visual stimuli. In this thesis, a statistical analysis of gradient directions in an ensemble of natural images is reported. Information-theoretic measures have been used to compute the amount of dependency which exists between triples of gradient directions at separate image locations. Control experiments are performed on other image classes: phase randomized natural images, whitened natural images, and Gaussian noise images. The main results show that for an ensemble of natural images the average amount of de pendency between two and three gradient directions is the same as for an ensemble of phase randomized natural images. This result does not extend to i) the amount dependency between gradient magnitudes, ii) gradient directions at high gradient magnitude locations, or iii) individual natural images. Furthermore, no significant synergetic dependencies are found between triples of gradient directions in an ensemble natural images a synergetic dependency is an increase in dependency between a pair of gradient directions given the interaction of a third gradient direction. Additional experiments are performed to establish both the generality and specificity of the main results by studying the gradient direction dependencies of ensembles of noise (random phases) images with varying power law power spectra. The results of the additional experiments indicate that, for ensembles of images with varying power law power spectra, the amount of dependency between two and three gradient directions is determined by the ensemble's mean power spectrum rather than the phase spectrum. A framework is also presented for future work and preliminary results are provided for the dependency between second order derivative measurements (shape index) for up to 9-point configurations.
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Ma, Yufeng. "Going Deeper with Images and Natural Language". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/99993.

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One aim in the area of artificial intelligence (AI) is to develop a smart agent with high intelligence that is able to perceive and understand the complex visual environment around us. More ambitiously, it should be able to interact with us about its surroundings in natural languages. Thanks to the progress made in deep learning, we've seen huge breakthroughs towards this goal over the last few years. The developments have been extremely rapid in visual recognition, in which machines now can categorize images into multiple classes, and detect various objects within an image, with an ability that is competitive with or even surpasses that of humans. Meanwhile, we also have witnessed similar strides in natural language processing (NLP). It is quite often for us to see that now computers are able to almost perfectly do text classification, machine translation, etc. However, despite much inspiring progress, most of the achievements made are still within one domain, not handling inter-domain situations. The interaction between the visual and textual areas is still quite limited, although there has been progress in image captioning, visual question answering, etc. In this dissertation, we design models and algorithms that enable us to build in-depth connections between images and natural languages, which help us to better understand their inner structures. In particular, first we study how to make machines generate image descriptions that are indistinguishable from ones expressed by humans, which as a result also achieved better quantitative evaluation performance. Second, we devise a novel algorithm for measuring review congruence, which takes an image and review text as input and quantifies the relevance of each sentence to the image. The whole model is trained without any supervised ground truth labels. Finally, we propose a brand new AI task called Image Aspect Mining, to detect visual aspects in images and identify aspect level rating within the review context. On the theoretical side, this research contributes to multiple research areas in Computer Vision (CV), Natural Language Processing (NLP), interactions between CVandNLP, and Deep Learning. Regarding impact, these techniques will benefit related users such as the visually impaired, customers reading reviews, merchants, and AI researchers in general.
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Libros sobre el tema "Natural images"

1

Ian, Moorhead y Trościanko Tom, eds. Natural images. London: Pion, 2000.

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de, Miranda Marco Antonio, ed. Terra América: Imagens = Images. Rio de Janeiro, RJ: Sextante Artes, 2003.

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Éamon, De Buitléar, ed. Images of Irish nature. Bandon, Co. Cork: Mike Brown Photography, 2006.

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1923-, Smith Dean, ed. Timeless images. [Phoenix, Ariz: Arizona Dept. of Transportation, State of Arizona, 1990.

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1910-, Lavender David Sievert, ed. Images from the southwest. Flagstaff, Ariz: Northland Press, 1986.

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Parish, Steve. Australia from the heart: Words and images. Paddington, Qld., Australia: S. Parish Pub., 1990.

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Adar, Pelah, ed. The vision of natural and complex images. Exeter: Elsevier Science, 1997.

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Damon, James, Peter Giblin y Gareth Haslinger. Local Features in Natural Images via Singularity Theory. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41471-3.

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Ahmed, Saad Bin, Muhammad Imran Razzak y Rubiyah Yusof. Cursive Script Text Recognition in Natural Scene Images. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1297-1.

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Colorado, images of the alpine landscape. Englewood, Colo: Westcliffe Publishers, 1985.

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Capítulos de libros sobre el tema "Natural images"

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Wolff, Robert S. y Larry Yaeger. "Images and Image Processing". En Visualization of Natural Phenomena, 1–26. New York, NY: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-0646-7_1.

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Chevrier, Vincent, Christine Bourjot y Vincent Thomas. "Region Detection in Images". En Natural Computing Series, 425–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17348-6_17.

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Fleet, David J. "Application to Natural Images". En Measurement of Image Velocity, 133–47. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3648-2_11.

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Zhu, Song-Chun y Ying Wu. "Statistics of Natural Images". En Computer Vision, 19–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96530-3_2.

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Verbyla, David L. "Satellite Images". En Satellite Remote Sensing of Natural Resources, 1–14. London: CRC Press, 2022. http://dx.doi.org/10.1201/9780138740191-1.

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Zheng, Gang, Jingsong Yang, Antony K. Liu, Xiaofeng Li, William G. Pichel, Shuangyan He y Shui Yu. "Observing Typhoons from Satellite-Derived Images". En Springer Natural Hazards, 183–214. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2893-9_9.

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Erdem, Erkut y Sibel Tari. "Revisiting Skeletons from Natural Images". En Association for Women in Mathematics Series, 101–13. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16348-2_7.

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Hyvärinen, Aapo, Jarmo Hurri y Patrik O. Hoyer. "Temporal Sequences of Natural Images". En Computational Imaging and Vision, 325–61. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-491-1_16.

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Kataoka, Hirokatsu, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura y Yutaka Satoh. "Pre-training Without Natural Images". En Computer Vision – ACCV 2020, 583–600. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69544-6_35.

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Serrano, J. F., J. H. Sossa, C. Avilés, R. Barrón, G. Olague y J. Villegas. "Scene Retrieval of Natural Images". En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 774–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_91.

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Actas de conferencias sobre el tema "Natural images"

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Li, Jizhizi, Jing Zhang y Dacheng Tao. "Deep Automatic Natural Image Matting". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/111.

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Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap, which is useful for image editing. Prior methods try to learn semantic features to aid the matting process while being limited to images with salient opaque foregrounds such as humans and animals. In this paper, we investigate the difficulties when extending them to natural images with salient transparent/meticulous foregrounds or non-salient foregrounds. To address the problem, a novel end-to-end matting network is proposed, which can predict a generalized trimap for any image of the above types as a unified semantic representation. Simultaneously, the learned semantic features guide the matting network to focus on the transition areas via an attention mechanism. We also construct a test set AIM-500 that contains 500 diverse natural images covering all types along with manually labeled alpha mattes, making it feasible to benchmark the generalization ability of AIM models. Results of the experiments demonstrate that our network trained on available composite matting datasets outperforms existing methods both objectively and subjectively. The source code and dataset are available at https://github.com/JizhiziLi/AIM.
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2

Qin, Shaoling y Ning Cao. "Chroma discrimination for natural images". En 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5648227.

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Mansoor, Atif Bin y Shoab A. Khan. "Contoulet denoising of natural images". En 2008 International Conference on Audio, Language and Image Processing. IEEE, 2008. http://dx.doi.org/10.1109/icalip.2008.4590267.

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Tseng, Ting-En, Wei-Yi Chang, Chu-Song Chen y Yu-Chiang Frank Wang. "Style retrieval from natural images". En 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471939.

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Wu, Qi y Peter Hall. "Prime Shapes in Natural Images". En British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.45.

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Ushiku, Yoshitaka, Tatsuya Harada y Yasuo Kuniyoshi. "Understanding images with natural sentences". En the 19th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2072298.2072417.

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Almeida, Mariana S. C. y Luis B. Almeida. "Blind deblurring of natural images". En ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517846.

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Hasler, David y Sabine E. Suesstrunk. "Measuring colorfulness in natural images". En Electronic Imaging 2003, editado por Bernice E. Rogowitz y Thrasyvoulos N. Pappas. SPIE, 2003. http://dx.doi.org/10.1117/12.477378.

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Su, Ya-Fan y Homer H. Chen. "Shadow removal from natural images". En 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5537886.

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Thai, Thanh Hai, Florent Retraint y Remi Cogranne. "Statistical model of natural images". En 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6467412.

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Informes sobre el tema "Natural images"

1

Liu, Yong y Harel Shouval. Principal Components of Natural Images: An Analytical Solution. Fort Belvoir, VA: Defense Technical Information Center, mayo de 1993. http://dx.doi.org/10.21236/ada264800.

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Midak, Lilia Ya, Ivan V. Kravets, Olga V. Kuzyshyn, Jurij D. Pahomov, Victor M. Lutsyshyn y Aleksandr D. Uchitel. Augmented reality technology within studying natural subjects in primary school. [б. в.], febrero de 2020. http://dx.doi.org/10.31812/123456789/3746.

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The purpose of the research is creation of mobile app (supported by Android) for visualization of chemical structure of water and to display video- data of laboratory experiments that can be used by the teacher and pupils for an effective background for learning natural cycle subjects and performance of laboratory experiments in the elementary school using lapbook. As a result of work, aimed at visualizing the education material, a free mobile app LiCo.STEM was developed; it can be downloaded from the overall-available resource Google Play Market. Representation of the developed video materials on the mobile gadgets is conducted by “binding” them to individual images- “markers” for every laboratory experiment. Applying such technologies gives an opportunity to establish educational activity, based on interference of adults with children, oriented on interests and abilities of each kid, development of curiosity, cognitive motivation and educational energy; development of imagination, creative initiative, including the speech, ability to chose the materials, types of work, participants of the common activity, promotion of conditions for parents participate in the common study activity.
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Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir y Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, diciembre de 2014. http://dx.doi.org/10.32747/2014.7594391.bard.

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Extending the previous two years of research results (Mizarch, et al, 2012, Tao, 2011, 2012), the third year’s efforts in both Maryland and Israel were directed towards the engineering of the system. The activities included the robust chick handling and its conveyor system development, optical system improvement, online dynamic motion imaging of chicks, multi-image sequence optimal feather extraction and detection, and pattern recognition. Mechanical System Engineering The third model of the mechanical chick handling system with high-speed imaging system was built as shown in Fig. 1. This system has the improved chick holding cups and motion mechanisms that enable chicks to open wings through the view section. The mechanical system has achieved the speed of 4 chicks per second which exceeds the design specs of 3 chicks per second. In the center of the conveyor, a high-speed camera with UV sensitive optical system, shown in Fig.2, was installed that captures chick images at multiple frames (45 images and system selectable) when the chick passing through the view area. Through intensive discussions and efforts, the PIs of Maryland and ARO have created the protocol of joint hardware and software that uses sequential images of chick in its fall motion to capture opening wings and extract the optimal opening positions. This approached enables the reliable feather feature extraction in dynamic motion and pattern recognition. Improving of Chick Wing Deployment The mechanical system for chick conveying and especially the section that cause chicks to deploy their wings wide open under the fast video camera and the UV light was investigated along the third study year. As a natural behavior, chicks tend to deploy their wings as a mean of balancing their body when a sudden change in the vertical movement was applied. In the latest two years, this was achieved by causing the chicks to move in a free fall, in the earth gravity (g) along short vertical distance. The chicks have always tended to deploy their wing but not always in wide horizontal open situation. Such position is requested in order to get successful image under the video camera. Besides, the cells with checks bumped suddenly at the end of the free falling path. That caused the chicks legs to collapse inside the cells and the image of wing become bluer. For improving the movement and preventing the chick legs from collapsing, a slowing down mechanism was design and tested. This was done by installing of plastic block, that was printed in a predesign variable slope (Fig. 3) at the end of the path of falling cells (Fig.4). The cells are moving down in variable velocity according the block slope and achieve zero velocity at the end of the path. The slop was design in a way that the deacceleration become 0.8g instead the free fall gravity (g) without presence of the block. The tests showed better deployment and wider chick's wing opening as well as better balance along the movement. Design of additional sizes of block slops is under investigation. Slops that create accelerations of 0.7g, 0.9g, and variable accelerations are designed for improving movement path and images.
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Struthers, Kim. Natural resource conditions at Fort Pulaski National Monument: Findings and management considerations for selected resources. National Park Service, diciembre de 2023. http://dx.doi.org/10.36967/2300064.

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The National Park Service (NPS) Water Resources Division’s Natural Resource Condition Assessment (NRCA) Program initiated an NRCA project with Fort Pulaski National Monument (FOPU) in 2022. The purpose of an NRCA is to synthesize information related to the primary drivers and stressors affecting natural resource conditions at a park and to report conditions for natural resource topics selected by park managers. Resource conditions are evaluated as either a condition assessment or a gap analysis, depending on data availability. For FOPU’s NRCA, managers selected salt marsh, shorebirds, Eastern oyster (Crassostrea virginica), and butterflies as the focal resources. FOPU is comprised of two islands in coastal Georgia, McQueens and Cockspur, which are separated by the Savannah River near its confluence with the Atlantic Ocean. Cockspur Island contains the 19th century masonry fort, Fort Pulaski, and the monument’s visitor services and facilities and is primarily constructed with dredge material from the Savannah River. McQueens Island is almost entirely salt marsh habitat and most of its area is eligible federal wilderness, containing one of Georgia’s oyster recreational harvest areas (RHAs), Oyster Creek RHA. Both McQueens and Cockspur islands are designated as a National Oceanic and Atmospheric Administration Marine Protected Area (MPA), underscoring FOPU’s natural resource significance. Riverine, freshwater, and estuarine wetlands cover 83.81% of FOPU, with the latter accounting for almost 99% of all monument wetlands. Persistently emergent vegetation of smooth cordgrasses (Spartina spp.) and unconsolidated shore represent the dominant wetland types. McQueens Island estuarine wetlands were evaluated for 11 functions and were rated primarily as high functioning, except for the wetland north of Highway 80, where the causeway has altered its ability to function properly. The wetland west of the Highway 80 bend is composed of unconsolidated material so was rated as moderately functioning in carbon sequestration, retention of sediments, and shore stabilization. In contrast, the unconsolidated shore wetland in the Oyster Creek RHA, where the highest concentration of FOPU’s oysters occurs, were rated high for all expected wetland functions. In 2013, over 75% of the total oyster area from within four of Georgia’s RHAs was in the Oyster Creek RHA. A spectral analysis of oyster density in Oyster Creek RHA, comparing 2013 and 2018 images, reported an increase in the high-density class, a decrease in the moderate-low class, and an increase in the no oyster class, with the latter likely a function of how oyster areas were drawn between the images. A successful 2013 enhanced reef project in Oyster Creek RHA reported a pre-enhancement oyster area of 2.68 m2 (28.8 ft2) that increased to 894.2 m2 (0.22 ac) of oysters by 2018. FOPU’s extensive salt marsh habitat and beaches provide critical food sources and habitat for shorebirds in the Atlantic Flyway, especially during the pre-breeding season. The American Oystercatcher (Haematopus palliates), Whimbrel (Numenius phaeopus), and the federally threatened rufa subspecies of Red Knot (Calidris canutus rufa) are identified as high priority species in the flyway and have been observed on Cockspur Island during the Manomet International Shorebird Surveys (2019–2022) at FOPU. The USFWS (2023) is seeking additional critical habitat designation, which will include Cockspur Island, for the rufa subspecies of Red Knot, whose estimated population abundance trend is declining throughout its entire range. FOPU’s non-wetland, upland habitat is primarily located on Cockspur Island and supports vegetation that can serve as host, roost and/or nectar plants for pollinators, especially butterflies. Cedar–Live Oak–Cabbage Palmetto (Juniperus virginiana var. silicicola–Q. virginiana–Sabal palmetto) Marsh Hammock and Cabbage Palmetto Woodland contain the most diversity of beneficial butterfly plants. While a comprehensive butterfly inventory is needed, fall migration surveys have recorded three target species of the Butterflies of the Atlantic Flyway (BAFA): monarch (Danaus plexippus), gulf fritillary (Agraulis vanillae), and cloudless sulphur (Phoebis sennae). Collectively, FOPU’s natural resources are affected by the sea level, which has risen by 0.35 m (1.15 ft) from 1935 to 2022. Hardened shorelines, such as causeways or armored structures, are identified as the greatest threat to the salt marsh habitat’s ability to migrate upland with continued sea level rise. Erosion along Cockspur Island’s north shore is an ongoing issue and FOPU managers have been working with the U.S. Army Corps of Engineers to develop solutions to address the erosion, while also creating habitat for shorebirds. Several agencies routinely monitor for water and sediment pollution in and around FOPU, which, if managed collectively, can inform landscape-level management actions to address drivers that are influencing resource conditions at the ecosystem level.
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Choe, B.-H., A. Blais-Stevens, S. Samsonov y J. Dudley. RADARSAT Constellation Mission (RCM) InSAR preliminary observations of slope movements in British Columbia, Alberta, and Nunavut. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331099.

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The Geological Survey of Canada (GSC)mp;gt;'s Public Safety Geoscience Program (PSGP) has collaborated with the Canada Centre for Remote Sensing (CCRS) to assess the performance of new RCM data for monitoring slope movements. The PSGP has the mandate to study natural hazards and provide baseline geoscience information to help stakeholders and decision-makers mitigate against potential risk. This report provides preliminary results observed from new RCM InSAR data acquired over 21 sites in British Columbia (BC), Alberta (AB), and Nunavut (NU) from April 2020 to September 2021. , In some cases, comparisons with RCM imagery were made with RADARSAT-2 and Sentinel-1 observations. A total of 13 sites in BC, two sites in AB, and six sites in NU that are located close to communities and/or infrastructure were investigated. From these, we acquired a total of 1235 RCM single look complex (SLC) images of HH polarization (ascending: 514, descending: 721) from April 2020 to September 2021. Most were acquired with 3 m very-high-resolution and/or 5 m high-resolution modes. Based on the preliminary observations, the advantages and limitations of RCM InSAR for landslide monitoring are highlighted.
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Lasko, Kristofer y 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.), abril de 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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Anderson, Gerald L. y Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, diciembre de 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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Khan, Mahreen. The Environmental Impacts of War and Conflict. Institute of Development Studies, marzo de 2022. http://dx.doi.org/10.19088/k4d.2022.060.

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In modern warfare, the first widely acknowledged scientific study and documented case of environmental damage during conflict was the (direct and deliberate) use of Agent Orange and other toxic chemicals by US forces, from 1961-1971, during the Vietnam War in a policy known as herbicide. The Vietnam War has been relatively well documented for the sheer horror and magnitude of the devastation to natural habitats and because it was the first war where television and global media brought vivid images and accounts into people’s homes, making the war a matter of political and public conscience This helped stir academic and scientific interest and facilitated evidence collection and documentation of environmental damages. This helpdesk report is a rapid literature review on the main environmental impacts of war and conflict, drawing primarily on academic, and peer reviewed literature and only some policy and practitioner sources, as per the request. Where current situations are discussed, such as the ongoing Ukraine war, a few blogs are referred to. Within the literature focused on the environmental impacts of conflict, common case studies include: the North Atlantic Treaty Organisation (NATO) bombing of Kosovo (1999), and the conflict in the Donbas region of Ukraine (2014). Interestingly there is comparatively less literature on the conflicts in Afghanistan (2001-2021), the Iraq-Iran War (1980-1988), the Gulf Wars (1991 and 2003), the Yemeni civil war (2014 – present) and the ongoing war in Syria (since 2011) despite their relatively greater severity, intensity and duration.
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Randell. L51857 Evaluation of Digital Image Acquisition and Processing Technologies for Ground Movement Monitoring. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), enero de 2008. http://dx.doi.org/10.55274/r0011244.

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Ground movement may occur due to landslides, seismic activity, adjacent earth works, thaw settlement of permafrost, frost heave, or a variety of other causes. When soil moves relative to a pipeline, loads are imposed on the pipeline that will tend to stress it. Portions of the pipeline are anchored or loaded by the moving soil mass, while adjacent portions are anchored in the intact soil and tend to restrain the pipeline. These soil movements and restraints set up stresses within the pipeline that, depending upon the magnitude of the stresses and the nature of the pipeline, may cause damage or failure of the line. The objectives of this project were to evaluate and, where appropriate, enhance the ability of satellite-based interferometric synthetic aperture radar (InSAR) and airborne laser range-finding to delineate and monitor slope movements along pipeline right-of-ways. Particular emphasis was placed on operational issues, and especially the problems associated with applying these technologies in areas where natural vegetation precludes a straightforward analysis.
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O'Carroll, David. Motion Adaptation, its Role in Motion Detection Under Natural Image Conditions and Target Detection. Fort Belvoir, VA: Defense Technical Information Center, junio de 2005. http://dx.doi.org/10.21236/ada451630.

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