Gotowe bibliografie tematyczne / Image analysis

Gotowa bibliografia na temat „Image analysis”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 30 lipca 2024

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Artykuły w czasopismach na temat "Image analysis"

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Vijayalakshmi, A., i V. Girish. "Affordable image analysis using NIH Image/ImageJ". Indian Journal of Cancer 41, nr 1 (2004): 47. http://dx.doi.org/10.4103/0019-509x.12345.

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Legland, David, i Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab". F1000Research 10 (30.04.2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.
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Schneider, Caroline A., Wayne S. Rasband i Kevin W. Eliceiri. "NIH Image to ImageJ: 25 years of image analysis". Nature Methods 9, nr 7 (28.06.2012): 671–75. http://dx.doi.org/10.1038/nmeth.2089.

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Deserno, T. M., H. P. Meinzer, T. Tolxdorff i H. Handels. "Image Analysis and Modeling in Medical Image Computing". Methods of Information in Medicine 51, nr 05 (2012): 395–97. http://dx.doi.org/10.1055/s-0038-1627047.

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Summary Background: Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. Objectives: In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Methods: Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Results: Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. Conclusions: The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.
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Friel, J. J., i E. B. Prestridge. "Image Analysis—Turning Images Into Data". Microscopy and Microanalysis 4, S2 (lipiec 1998): 58–59. http://dx.doi.org/10.1017/s1431927600020419.

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Image analysis is the process of quantifying some aspect of an image—its particle size distribution, for example. Manual methods were in use long before computers made image analysis much faster and more reproducible. Linear measurements of diameter, point counting to measure volume fraction, and intercept counting to determine grain size have been used for over 100 years. Automatic image analysis (AIA), however, can make more measurements, and even calculate derived measurements, such as aspect ratio or circularity. AIA of a specimen or micrograph, of course, is only as good as the contrast mechanism used, so the imaging signal must be chosen carefully to reveal to the computer what is to be measured. Obtaining sufficient contrast is often the limiting task.Once an imaging signal is chosen and the digital resolution set, the computer can analyze the image. The feature descriptors can be generic:
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Gurevich, Igor, i Vera Yashina. "Descriptive Image Analysis. Foundations and Descriptive Image Algebras". International Journal of Pattern Recognition and Artificial Intelligence 33, nr 11 (październik 2019): 1940018. http://dx.doi.org/10.1142/s0218001419400184.

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The paper is devoted to Descriptive Image Analysis (DA) — a leading line of the modern mathematical theory of image analysis. DA is a logically organized set of descriptive methods, mathematical objects, and models and representations aimed at analyzing and evaluating the information represented in the form of images, as well as for automating the extraction from images of knowledge and data needed for intelligent decision-making. The basic idea of DA consists of embedding all processes of analysis (processing, recognition, understanding) of images into an image formalization space and reducing it to (1) construction of models/representations/formalized descriptions of images; (2) construction of models/representations/formalized descriptions of transformations over models and representations of images. We briefly discuss the basic ideas, methodological principles, mathematical methods, objects, and components of DA and the basic results determining the current state of the art in the field. Image algebras (IA) are considered in the context of a unified language for describing mathematical objects and operations used in image analysis (the standard IA by Ritter and the descriptive IA by Gurevich).
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Lim, P. C., T. Kim, S. I. Na, K. D. Lee, H. Y. Ahn i J. Hong. "ANALYSIS OF UAV IMAGE QUALITY USING EDGE ANALYSIS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (19.09.2018): 359–64. http://dx.doi.org/10.5194/isprs-archives-xlii-4-359-2018.

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<p><strong>Abstract.</strong> UAVs (Unmanned aerial Vehicles) can acquire images easily without large cost. For this reason, use of UAV is spreading to diverse fields such as orthoimages and DEM/DSM production. The spatial resolution of images is usually expressed as a GSD (Ground Sampling Distance). The GSD from UAV has higher performance than other platforms such as satellites and aircraft because it shoot at low altitude. However, blurring and noise may occur on UAV images due to the weather and the stability of UAV. However, since the GSD from UAV cannot sufficiently meet the spatial resolving power of the actual image system, a criterion for determining the spatial resolution of image is needed. Therefore we emphasize that the quality of the image needs to be analysed. Actual performance indicators such as GRD (Ground Resolved Distance) and NIIRS (National Image Interpretability Rating Scales), which can be measured through image analysis, are representative examples of image quality interpretation. It is possible to extract NIIRS form image quality related parameters such as MTF (Modulation Transfer Function), RER (Relative Edge Response) and SNR (Signal to Noise Ratio). In this paper, we aim to apply the Edge analysis method to UAV and to analyse the result. The analysis result showed that while GSD and NIIRS were highly dependent to imaging altitude, GRD and image sharpness showed optimal altitude ranges. The exact optimal range varied between images taken at different weather conditions. While we need a further study, this may indicate that edge analysis may provide an optimal operational altitude range suitable for the sensors.</p>
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Rakhimov, Bakhtiyar Saidovich, Feroza Bakhtiyarovna Rakhimova, Sabokhat Kabulovna Sobirova, Furkat Odilbekovich Kuryazov i Dilnoza Boltabaevna Abdirimova. "Review And Analysis Of Computer Vision Algorithms". American Journal of Applied sciences 03, nr 05 (31.05.2021): 245–50. http://dx.doi.org/10.37547/tajas/volume03issue05-39.

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Computer vision as a scientific discipline refers to the theories and technologies for creating artificial systems that receive information from an image. Despite the fact that this discipline is quite young, its results have penetrated almost all areas of life. Computer vision is closely related to other practical fields like image processing, the input of which is two-dimensional images obtained from a camera or artificially created. This form of image transformation is aimed at noise suppression, filtering, color correction and image analysis, which allows you to directly obtain specific information from the processed image. This information may include searching for objects, keypoints, segments, and annexes;
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Sedlak, René, Andreas Welscher, Patrick Hannawald, Sabine Wüst, Rainer Lienhart i Michael Bittner. "Analysis of 2D airglow imager data with respect to dynamics using machine learning". Atmospheric Measurement Techniques 16, nr 12 (26.06.2023): 3141–53. http://dx.doi.org/10.5194/amt-16-3141-2023.

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Abstract. We demonstrate how machine learning can be easily applied to support the analysis of large quantities of excited hydroxyl (OH*) airglow imager data. We use a TCN (temporal convolutional network) classification algorithm to automatically pre-sort images into the three categories “dynamic” (images where small-scale motions like turbulence are likely to be found), “calm” (clear-sky images with weak airglow variations) and “cloudy” (cloudy images where no airglow analyses can be performed). The proposed approach is demonstrated using image data of FAIM 3 (Fast Airglow IMager), acquired at Oberpfaffenhofen, Germany, between 11 June 2019 and 25 February 2020, achieving a mean average precision of 0.82 in image classification. The attached video sequence demonstrates the classification abilities of the learned TCN. Within the dynamic category, we find a subset of 13 episodes of image series showing turbulence. As FAIM 3 exhibits a high spatial (23 m per pixel) and temporal (2.8 s per image) resolution, turbulence parameters can be derived to estimate the energy diffusion rate. Similarly to the results the authors found for another FAIM station (Sedlak et al., 2021), the values of the energy dissipation rate range from 0.03 to 3.18 W kg−1.
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Khan, Yaser Daanial, M. Khalid Mahmood, Daud Ahmad i Nasser M. Al-Zidi. "Content-Based Image Retrieval Using Gamma Distribution and Mixture Model". Journal of Function Spaces 2022 (5.05.2022): 1–10. http://dx.doi.org/10.1155/2022/8674038.

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Since the last decade, the complexity of multimedia data, specifically images, is emerging exponentially as millions of images are uploaded by users on daily basis. Searching for a relevant image from such a substantial amount of data is very hectic and resource-demanding. To cope with this issue, researchers are working on content-based image retrieval (CBIR) approaches. This article proposes an efficient and novel probabilistic technique as a solution for content-based image retrieval. The patterns formed by the glyph structure of an image are excavated to yield content representations. These representations are accumulatively used to form a distribution, whereas the characteristics of this distribution represent the semantic structure of the image. In the end, the mixture model for gamma distribution is applied and parameters are refined through maximum likelihood. Furthermore, a mechanism is devised to retrieve matching images having comparable distribution patterns. Experiments show not only that the proposed technique yields a comparable precision to other competitive techniques but it also demonstrates that it is sufficiently efficient with high performance compared as compared to the others and requires unsupervised training.
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Rozprawy doktorskie na temat "Image analysis"

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Moëll, Mattias. "Digital image analysis for wood fiber images /". Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 2001. http://epsilon.slu.se/avh/2001/91-576-6309-2.pdf.

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Feng, Sitao. "Image Analysis on Wood Fiber Cross-Section Images". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-156428.

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Lignification of wood fibers has a significant impact on wood properties. To measure the distribution of lignin in compression wood fiber cross-section images, a crisp segmentation method had been developed. It segments the lumen, the normally lignified cell wall and the highly lignified cell wall of each fiber. In order to refine this given segmentation the following two fuzzy segmentation methods were evaluated in this thesis: Iterative Relative Multi Objects Fuzzy Connectedness and Weighted Distance Transform on Curved Space. The crisp segmentation is used for the multi-seed selection. The crisp and the two fuzzy segmentations are then evaluated by comparing with the manual segmentation. It shows that Iterative Relative Multi Objects Fuzzy Connectedness has the best performance on segmenting the lumen, whereas Weighted Distance Transform on Curved Space outperforms the two other methods regarding the normally lignified cell wall and the highly lignified cell wall.
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Gavin, John. "Subpixel image analysis". Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307131.

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Zhao, Xianghong. "Automated image analysis for petrographic image assessments". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ62444.pdf.

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Hoxha, Genc. "IMAGE CAPTIONING FOR REMOTE SENSING IMAGE ANALYSIS". Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/351752.

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Image Captioning (IC) aims to generate a coherent and comprehensive textual description that summarizes the complex content of an image. It is a combination of computer vision and natural language processing techniques to encode the visual features of an image and translate them into a sentence. In the context of remote sensing (RS) analysis, IC has been emerging as a new research area of high interest since it not only recognizes the objects within an image but also describes their attributes and relationships. In this thesis, we propose several IC methods for RS image analysis. We focus on the design of different approaches that take into consideration the peculiarity of RS images (e.g. spectral, temporal and spatial properties) and study the benefits of IC in challenging RS applications. In particular, we focus our attention on developing a new decoder which is based on support vector machines. Compared to the traditional decoders that are based on deep learning, the proposed decoder is particularly interesting for those situations in which only a few training samples are available to alleviate the problem of overfitting. The peculiarity of the proposed decoder is its simplicity and efficiency. It is composed of only one hyperparameter, does not require expensive power units and is very fast in terms of training and testing time making it suitable for real life applications. Despite the efforts made in developing reliable and accurate IC systems, the task is far for being solved. The generated descriptions are affected by several errors related to the attributes and the objects present in an RS scene. Once an error occurs, it is propagated through the recurrent layers of the decoders leading to inaccurate descriptions. To cope with this issue, we propose two post-processing techniques with the aim of improving the generated sentences by detecting and correcting the potential errors. They are based on Hidden Markov Model and Viterbi algorithm. The former aims to generate a set of possible states while the latter aims at finding the optimal sequence of states. The proposed post-processing techniques can be injected to any IC system at test time to improve the quality of the generated sentences. While all the captioning systems developed in the RS community are devoted to single and RGB images, we propose two captioning systems that can be applied to multitemporal and multispectral RS images. The proposed captioning systems are able at describing the changes occurred in a given geographical through time. We refer to this new paradigm of analysing multitemporal and multispectral images as change captioning (CC). To test the proposed CC systems, we construct two novel datasets composed of bitemporal RS images. The first one is composed of very high-resolution RGB images while the second one of medium resolution multispectral satellite images. To advance the task of CC, the constructed datasets are publically available in the following link: https://disi.unitn.it/~melgani/datasets.html. Finally, we analyse the potential of IC for content based image retrieval (CBIR) and show its applicability and advantages compared to the traditional techniques. Specifically, we focus our attention on developing a CBIR systems that represents an image with generated descriptions and uses sentence similarity to search and retrieve relevant RS images. Compare to traditional CBIR systems, the proposed system is able to search and retrieve images using either an image or a sentence as a query making it more comfortable for the end-users. The achieved results show the promising potentialities of our proposed methods compared to the baselines and state-of-the art methods.
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Asplund, Raquel. "Evaluation of a cloud-based image analysis and image display system for medical images". Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-105984.

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Munechika, Curtis K. "Merging panchromatic and multispectral images for enhanced image analysis /". Online version of thesis, 1990. http://hdl.handle.net/1850/11366.

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Jenkinson, Mark. "Saliency in image analysis". Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302069.

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Moore, George G. "Guided aerial image analysis". Thesis, University of Ulster, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326332.

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Hansson, Jonas. "Image analysis, an approach to measure grass roots from images". Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-592.

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In this project a method to analyse images is presented. The images document the development of grassroots in a tilled field in order to study the movement of nitrate in the field. The final aim of the image analysis is to estimate the volume of dead and living roots in the soil. Since the roots and the soil have a broad and overlapping range of colours the fundamental problem is to find the roots in the images. Earlier methods for analysis of root images have used methods based on thresholds to extract the roots. To use a threshold the pixels of the object must have a unique range of colours separating them from the colour of the background, this is not the case for the images in this project. Instead the method uses a neural network to classify the individual pixels. In this paper a complete method to analyse images is presented and although the results are far from perfect, the method gives interesting results

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Książki na temat "Image analysis"

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Kämäräinen, Joni-Kristian, i Markus Koskela, red. Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38886-6.

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Sharma, Puneet, i Filippo Maria Bianchi, red. Image Analysis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1.

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Sharma, Puneet, i Filippo Maria Bianchi, red. Image Analysis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59129-2.

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Salberg, Arnt-Børre, Jon Yngve Hardeberg i Robert Jenssen, red. Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2.

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Ersbøll, Bjarne Kjær, i Kim Steenstrup Pedersen, red. Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73040-8.

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Bigun, Josef, i Tomas Gustavsson, red. Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x.

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Felsberg, Michael, Per-Erik Forssén, Ida-Maria Sintorn i Jonas Unger, red. Image Analysis. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20205-7.

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Heyden, Anders, i Fredrik Kahl, red. Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7.

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Kalviainen, Heikki, Jussi Parkkinen i Arto Kaarna, red. Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b137285.

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Paulsen, Rasmus R., i Kim S. Pedersen, red. Image Analysis. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19665-7.

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Części książek na temat "Image analysis"

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Solli, Martin, i Reiner Lenz. "Content Based Detection of Popular Images in Large Image Databases". W Image Analysis, 218–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_21.

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Mauthner, Thomas, Peter M. Roth i Horst Bischof. "Instant Action Recognition". W Image Analysis, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_1.

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Mansoor, Atif Bin, Maaz Haider, Ajmal S. Mian i Shoab A. Khan. "A Hybrid Image Quality Measure for Automatic Image Quality Assessment". W Image Analysis, 91–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_10.

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Eerola, Tuomas, Joni-Kristian Kämäräinen, Lasse Lensu i Heikki Kälviäinen. "Framework for Applying Full Reference Digital Image Quality Measures to Printed Images". W Image Analysis, 99–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_11.

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Alsam, Ali, i Ivar Farup. "Colour Gamut Mapping as a Constrained Variational Problem". W Image Analysis, 109–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_12.

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Brauers, Johannes, i Til Aach. "Geometric Multispectral Camera Calibration". W Image Analysis, 119–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_13.

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Colantoni, Philippe, i Jean-Baptiste Thomas. "A Color Management Process for Real Time Color Reconstruction of Multispectral Images". W Image Analysis, 128–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_14.

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Moriuchi, Yusuke, Shoji Tominaga i Takahiko Horiuchi. "Precise Analysis of Spectral Reflectance Properties of Cosmetic Foundation". W Image Analysis, 138–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_15.

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Fält, Pauli, Jouni Hiltunen, Markku Hauta-Kasari, Iiris Sorri, Valentina Kalesnykiene i Hannu Uusitalo. "Extending Diabetic Retinopathy Imaging from Color to Spectra". W Image Analysis, 149–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_16.

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Tibell, Kajsa, Hagen Spies i Magnus Borga. "Fast Prototype Based Noise Reduction". W Image Analysis, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_17.

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Streszczenia konferencji na temat "Image analysis"

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Varnasuthan, S., W. T. M. Fernando, D. S. Dahanayaka, A. B. N. Dassanayake, M. A. D. M. G. Wickrama i I. M. T. N. Illankoon. "Image analysis approach to determine the porosity of rocks". W International Symposium on Earth Resources Management & Environment - ISERME 2023. Department of Earth Resources Engineering, 2023. http://dx.doi.org/10.31705/iserme.2023.7.

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Accurate characterisation of rock porosity is essential for assessing its strength and durability. This study explores both conventional and image analysis methods for determining rock porosity of two types of rocks, Bibai sandstone, a hard clastic rock and limestone, a soft rock. Conventional methods for determining rock porosity involve physical measurements and laboratory analysis, while image analysis methods utilize advanced imaging techniques such as CT scans or SEM to assess porosity based on visual information extracted from rock images. While various image analysis approaches exist to determine rock porosity, questions arise as to which approach is applicable and whether the results are comparable to current conventional methods. Hence, this study focuses on comparing the accuracy of alternative image analysis approaches. Representative rock chips from each core sample were examined using SEM, and 2D porosity was evaluated through image processing with ImageJ software. The Avizo visualisation software was employed to assess Bibai sandstone samples' porosity from CT images. The research offers insights into the pros and cons of each approach, contributing to the enhancement of accuracy and efficiency in rock porosity evaluation, particularly in geology, mining, and civil engineering applications.
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Yang, S., Y. Wang i C. Shrivastava. "Sedimentary Analysis Via Automatic Image Segmentation and Clustering with the LWD OBM Resistivity Image: A Case Study from Gulf of Mexico". W SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214908-ms.

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Abstract Microfacies analysis is the first step for depositional environment interpretation and sand body prediction. Textural details from borehole images are building blocks for facies analysis, representing different paleo sediimentation conditions. Associated workflows have been applied on high resolution borehole images by geologists and log analysts manually. Automation via machine learning solutions provides an opportunity to improve the working efficiency and accuracy. Such an approach has given satisfactory results with post-drilling wireline images. In this paper, the improved workflow for sedimentary analysis was applied and validated with a logging-while-drilling (LWD) resistivity imager in oil-based mud environment (OBM). The OBM LWD resistivity image in oil-based mud provides 72 data points at single depth from 4 different frequencies of electromagnetic measurements with a patented processing. The non-gap resistivity image gives more confident texture characterization. The continuous histogram and correlogram derived from image data were used for image segmentation. In each image segmentation, multiple vector properties were extracted from image data representing different texture features including adoptive variogram horizontally. Agglomerative clustering was selected for its stability and repeatability. The internally built dendrogram allows to automatically determine the number of clusters by finding a stable distance between the clusters’ hierarchy branches. In addition to the features extracted from image data, optional petrophysical logs with variable weights may be fed to the algorithm for a better classification. A case study from Gulf of Mexico is being used to demonstrate this workflow with Hi-Res LWD image. More than 10 different sedimentary geometries were classified automatically from image and petrophysical logs. The microfacies were named manually from sedimentary geometries with the related geological concept accordingly. The fluvial channel and delta sedimentary environment were interpretated finally from microfacies association. The interpretation results were compared and validated with published dips-based solution as well. This is the first time for the automatic borehole image segmentation with LWD OBM images. The working efficiency was improved a lot through this workflow and the accuracy of microfacies interpretation was guaranteed by machine learning solution.
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Shi, Zhixin, Srirangaraj Setlur i Venu Govindaraju. "Image Enhancement for Degraded Binary Document Images". W 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.305.

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Mittal, Harshit. "Kidney CT Image Analysis Using CNN". W 4th International Conference on NLP Trends & Technologies. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131403.

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Medical image analysis is a vital component of modern medical practice, and the accuracy of such analysis is critical for accurate diagnosis and treatment. Computed tomography (CT) scans are commonly used to visualize the kidneys and identify abnormalities such as cysts, tumors, and stones. Manual interpretation of CT images can be time-consuming and subject to human error, leading to inaccurate diagnosis and treatment. Deep learning models based on Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy and speed of medical image analysis. In this study, we present a CNN-based model to accurately classify CT images of the kidney into four categories: Normal, Cyst, Tumor, and Stone, using the CT KIDNEY DATASET. The proposed CNN model achieved an accuracy of 99.84% on the test set, with a precision of 0.9964, a recall of 0.9986, and a F1-score of 0.9975 for all categories. The model was able to accurately classify all images in the test set, indicating its high accuracy in identifying abnormalities in CT images of the kidney. The results of this study demonstrate the potential of deep learning models based on CNNs in accurately classifying CT images of the kidney, which could lead to improved diagnosis and treatment outcomes for patients. This study contributes to the growing body of literature on the use of deep learning models in medical image analysis, highlighting the potential of these models in improving the accuracy and efficiency of medical diagnosis.
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Luo, Qingming, Shaoqun Zeng, Hui Gong, Weiguo Chen, Zhi Zhang i Britton Chance. "CW imager and image characteristics analysis". W BiOS Europe '98, redaktorzy David A. Benaron, Britton Chance, Marco Ferrari i Matthias Kohl-Bareis. SPIE, 1998. http://dx.doi.org/10.1117/12.334370.

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Ergin, F. Go¨khan, Bo Beltoft Watz, Kaspars Erglis i Andrejs Cebers. "Poor-Contrast Particle Image Processing in Microscale Mixing". W ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-24900.

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Particle image velocimetry (PIV) often employs the cross-correlation function to identify average particle displacement in an interrogation window. The quality of correlation peak has a strong dependence on the signal-to-noise ratio (SNR), or contrast of the particle images. In fact, variable-contrast particle images are not uncommon in the PIV community: Strong light sheet intensity variations, wall reflections, multiple scattering in densely-seeded regions and two-phase flow applications are likely sources of local contrast variations. In this paper, we choose an image pair obtained in a micro-scale mixing experiment with severe local contrast gradients. In regions where image contrast is sufficiently poor, the noise peaks cast a shadow on the true correlation peak, producing erroneous velocity vectors. This work aims to demonstrate that two image pre-processing techniques — local contrast normalization and Difference of Gaussian (DoG) filter — improve the correlation results significantly in poor-contrast regions.
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Kim, Do-Hyung, No-Cheol Park, Sungbin Jeon i Young-Pil Park. "Novel Method of Crosstalk Analysis in Multiple Image Encryption and Image Quality Equalization Technology". W ASME 2014 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/isps2014-6909.

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Novel crosstalk analysis method is suggested in optical encryption of multiple image. To optimize the total capacity of stored images in optical encryption of multiple images. We analyze the effect of crosstalk noise with each individual image by using suggested method. From the results, individual crosstalk robustness is verified with various target images and unbalance of image qualities among encrypted multiple images could be explained effectively. In addition, simple modulation method is adapted to equalizing image quality and it shows the highly improved results compare to conventional methods.
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Sakai, Kaoru, Osamu Kikuchi, Masafumi Takada, Natsuki Sugaya i Shigeru Ohno. "Image improvement using image processing for scanning acoustic tomograph images". W 2015 IEEE 22nd International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA). IEEE, 2015. http://dx.doi.org/10.1109/ipfa.2015.7224357.

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"Image analysis". W 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2012. http://dx.doi.org/10.1109/ipta.2012.6469491.

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Wu, Ji-Zong, Feng-Lin Zhang i Shi-Ke Huang. "Use of digital image analysis techniques in the image analysis of cataract eyes". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.thm4.

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The images of cataract eyes are analyzed and studied, making the assumption that the digital image is a spatial 2-D curved surface with different grey levels and utilizing the idea of a family of equal grey-level curves and a family of dissected grey-level curves which can be produced separately by cutting in horizontal planes and dissecting in vertical planes.
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Raporty organizacyjne na temat "Image analysis"

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Wendelberger, James G. Image Analysis of Stringer Area: Three Images. Office of Scientific and Technical Information (OSTI), sierpień 2018. http://dx.doi.org/10.2172/1467243.

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Wendelberger, James G., Kimberly Ann Kaufeld, Elizabeth J. Kelly, Juan Duque i John M. Berg. Automated Image Analysis for Screening LCM Images – Examples from 09DE2 Images. Office of Scientific and Technical Information (OSTI), luty 2018. http://dx.doi.org/10.2172/1422981.

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Wood, William Monford. Errors from Image Analysis. Office of Scientific and Technical Information (OSTI), luty 2015. http://dx.doi.org/10.2172/1170637.

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Aida, Toru. Image Analysis: Comparison Metrics. Office of Scientific and Technical Information (OSTI), luty 2021. http://dx.doi.org/10.2172/1765865.

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Nelson, Matthew, Halverson Scot i Srinivasan Gowri. Aquarium Shot Image Analysis. Office of Scientific and Technical Information (OSTI), czerwiec 2024. http://dx.doi.org/10.2172/2375833.

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Wendelberger, James G., Juan Duque, Kimberly Ann Kaufeld i Elizabeth J. Kelly. Automatic Image Analysis Status Report. Office of Scientific and Technical Information (OSTI), grudzień 2017. http://dx.doi.org/10.2172/1412838.

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Petruk, W. Image analysis: an overview of developments. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/307280.

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Amit, Yali. Deformable Topological Templates for Image Analysis. Fort Belvoir, VA: Defense Technical Information Center, sierpień 1996. http://dx.doi.org/10.21236/ada316810.

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Petruk, W. The MP-SEM-IPS image analysis system. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/307078.

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Conner, M. L. PAMS photo image retrieval prototype alternatives analysis. Office of Scientific and Technical Information (OSTI), kwiecień 1996. http://dx.doi.org/10.2172/10154323.

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