Relevant bibliographies by topics / Image Merging

Academic literature on the topic 'Image Merging'

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Published: 25 May 2024

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Journal articles on the topic "Image Merging"

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Ushakov, K. D., and O. N. Kaneva. "COMPARATIVE ANALYSIS OF IMAGE MERGING ALGORITHMS." Applied Mathematics and Fundamental Informatics 9, no. 3 (2022): 53–59. http://dx.doi.org/10.25206/2311-4908-2022-9-3-53-59.

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The paper discusses algorithms for merging images by machine learning. Both statistical algorithms and machine learning algorithms are used in image merging. Fusion algorithms based on machine learning give a clearer and sharper image while preserving details. Pairs of images with different blurred areas were used to train the algorithm. The comparison of the developed algorithms by quality metrics is carried out.
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S, Sadagopan. "Image Segmentation and Hiding Using Statistical Region Merging." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 1010–15. http://dx.doi.org/10.5373/jardcs/v11/20192665.

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GU, JIANPING, LI ZHANG, and CUN CHENG. "DYNAMIC GRAPH MERGING FOR IMAGE SEGMENTATION." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 06 (November 2013): 1350051. http://dx.doi.org/10.1142/s0219691313500513.

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A new algorithm named dynamic graph merging (DGM) for automatic image segmentation is explored. Firstly a novel variational model for multi-section cut is introduced by decomposing the traditional cut into two parts, the harmonic cut and the elastic energy of the boundary. The new energy is called the continuous combined cut. Secondly a new algorithm that removes those edges with higher energy and synchronously merges their starting and ending vertices in an ordered manner is proposed. The continual merging process would iteratively contract the graph, merge those homogeneous vertices into bigger and bigger super-pixels, and fuse the remainder edges into longer and longer boundaries. So we call this algorithm dynamic graph merging. Merging criterions based on the continuous combined cut model are also discussed, which will be used to determine whether a given edge should collapse. Since the merging condition should be highly related to the image content, we present different predicates for structure images and texture images respectively. This algorithm whose efficiency is showed by experiments has a linear time/space complexity, and can efficiently segment gray/color and 2D/3D images.
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Wang, Haoyu, Zhanfeng Shen, Zihan Zhang, Zeyu Xu, Shuo Li, Shuhui Jiao, and Yating Lei. "Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria." Remote Sensing 13, no. 14 (July 15, 2021): 2782. http://dx.doi.org/10.3390/rs13142782.

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Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.
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Andersone, Ilze. "Map Merging in the Context of Image Processing." Scientific Journal of Riga Technical University. Computer Sciences 44, no. 1 (January 1, 2011): 124–30. http://dx.doi.org/10.2478/v10143-011-0030-5.

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Map Merging in the Context of Image ProcessingThe area of map merging is tightly connected to the area of image processing. Usually metric maps created by robots are represented as occupancy grids. It is easy to apply algorithms used in image processing to this kind of map representation. The image processing subfield that is closest to the map merging is the image registration. It can be assumed that metric map merging methods similarly to the image registration methods consist of three components: feature space, search strategy and similarity metric. Algorithms from image processing can also be used in map merging for map preprocessing. The goal of this paper is to explore similarities between the fields of map merging and image processing and to determine how the results of this research can be used for the development of a map merging framework and consequently new map merging approaches.
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Fagel, Sascha. "Merging methods of speech visualization." ZAS Papers in Linguistics 40 (January 1, 2005): 19–32. http://dx.doi.org/10.21248/zaspil.40.2005.255.

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The author presents MASSY, the MODULAR AUDIOVISUAL SPEECH SYNTHESIZER. The system combines two approaches of visual speech synthesis. Two control models are implemented: a (data based) di-viseme model and a (rule based) dominance model where both produce control commands in a parameterized articulation space. Analogously two visualization methods are implemented: an image based (video-realistic) face model and a 3D synthetic head. Both face models can be driven by both the data based and the rule based articulation model. The high-level visual speech synthesis generates a sequence of control commands for the visible articulation. For every virtual articulator (articulation parameter) the 3D synthetic face model defines a set of displacement vectors for the vertices of the 3D objects of the head. The vertices of the 3D synthetic head then are moved by linear combinations of these displacement vectors to visualize articulation movements. For the image based video synthesis a single reference image is deformed to fit the facial properties derived from the control commands. Facial feature points and facial displacements have to be defined for the reference image. The algorithm can also use an image database with appropriately annotated facial properties. An example database was built automatically from video recordings. Both the 3D synthetic face and the image based face generate visual speech that is capable to increase the intelligibility of audible speech. Other well known image based audiovisual speech synthesis systems like MIKETALK and VIDEO REWRITE concatenate pre-recorded single images or video sequences, respectively. Parametric talking heads like BALDI control a parametric face with a parametric articulation model. The presented system demonstrates the compatibility of parametric and data based visual speech synthesis approaches.
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Lor, Kuo-Lung, and Chung-Ming Chen. "FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES." Biomedical Engineering: Applications, Basis and Communications 33, no. 02 (March 9, 2021): 2150012. http://dx.doi.org/10.4015/s1016237221500125.

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The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from the background scene. This iterative region-merging is based on a modified Region Adjacency Graph (RAG) model built from initial segmented results of the mean shift to speed up the merging process. The foreground region of interest (ROI) is segmented by the reduction of the background region and discrimination of uncertain regions. We then compare our method against state-of-the-art interactive image segmentation algorithms in both natural images and histological images. Taking into account the homogeneity of both color and texture, the resulting semi-supervised classification and interactive segmentation capture histological structures more completely than other intensity or color-based methods. Experimental results show that the merging of the RAG model runs in a linear time according to the number of graph edges, which is essentially faster than both traditional graph-based and region-based methods.
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Yocky, David A. "Artifacts in wavelet image merging." Optical Engineering 35, no. 7 (July 1, 1996): 2094. http://dx.doi.org/10.1117/1.600765.

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Panić, Branislav, Marko Nagode, Jernej Klemenc, and Simon Oman. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks." Mathematics 10, no. 22 (November 16, 2022): 4301. http://dx.doi.org/10.3390/math10224301.

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Unsupervised image segmentation is one of the most important and fundamental tasks in many computer vision systems. Mixture model is a compelling framework for unsupervised image segmentation. A segmented image is obtained by clustering the pixel color values of the image with an estimated mixture model. Problems arise when the selected optimal mixture model contains a large number of mixture components. Then, multiple components of the estimated mixture model are better suited to describe individual segments of the image. We investigate methods for merging the components of the mixture model and their usefulness for unsupervised image segmentation. We define a simple heuristic for optimal segmentation with merging of the components of the mixture model. The experiments were performed with gray-scale and color images. The reported results and the performed comparisons with popular clustering approaches show clear benefits of merging components of the mixture model for unsupervised image segmentation.
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Gonet, Michal, Boris Epel, Howard J. Halpern, and Martyna Elas. "Merging Preclinical EPR Tomography with other Imaging Techniques." Cell Biochemistry and Biophysics 77, no. 3 (August 22, 2019): 187–96. http://dx.doi.org/10.1007/s12013-019-00880-7.

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Abstract This paper presents a survey of electron paramagnetic resonance (EPR) image registration. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints and/or different techniques. EPR-imaging (EPRI) techniques belong to the functional-imaging modalities and therefore suffer from a lack of anatomical reference which is mandatory in preclinical imaging. For this reason, it is necessary to merging EPR images with other modalities which allow for obtaining anatomy images. Methodological analysis and review of the literature were done, providing a summary for developing a good foundation for research study in this field which is crucial in understanding the existing levels of knowledge. Out of these considerations, the aim of this paper is to enhance the scientific community’s understanding of the current status of research in EPR preclinical image registration and also communicate to them the contribution of this research in the field of image processing.
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Dissertations / Theses on the topic "Image Merging"

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Ipson, Heather. "T-spline Merging." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd804.pdf.

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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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Tang, Weiran. "Frequency merging for demosaicking /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?ECED%202009%20TANGW.

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Tan, Zhigang, and 譚志剛. "A region merging methodology for color and texture image segmentation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43224143.

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Tan, Zhigang. "A region merging methodology for color and texture image segmentation." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43224143.

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Cui, Ying. "Image merging in a dynamic visual communication system with multiple cameras." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0030/NQ27126.pdf.

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USUI, Shin'ichi, Masayuki TANIMOTO, Toshiaki FUJII, Tadahiko KIMOTO, and Hiroshi OHYAMA. "Fractal Image Coding Based on Classified Range Regions." Institute of Electronics, Information and Communication Engineers, 1998. http://hdl.handle.net/2237/14996.

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Zhao, Guang. "Automatic boundary extraction in medical images based on constrained edge merging." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22030207.

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Zhao, Guang, and 趙光. "Automatic boundary extraction in medical images based on constrained edge merging." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31223904.

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Ocampo, Blandon Cristian Felipe. "Patch-Based image fusion for computational photography." Electronic Thesis or Diss., Paris, ENST, 2018. http://www.theses.fr/2018ENST0020.

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Dans de nombreuses situations, la dynamique des capteurs ou la profondeur de champ des appareils photographiques conventionnels sont insuffisantes pour capturer fidèlement des scènes naturelles. Une méthode classique pour contourner ces limitations est de fusionner des images acquises avec des paramètres de prise de vue variables. Ces méthodes nécessitent que les images soient parfaitement alignées et que les scènes soient statiques, faute de quoi des artefacts (fantômes) ou des structures irrégulières apparaissent lors de la fusion. Le but de cette thèse est de développer des techniques permettant de traiter directement des images dynamiques et non-alignées, en exploitant des mesures de similarité locales par patchs entre images.Dans la première partie de cette thèse, nous présentons une méthode pour la fusion d'images de scènes dynamiques capturées avec des temps d'exposition variables. Notre méthode repose sur l'utilisation jointe d'une normalisation de contraste, de combinaisons non-locales de patchs et de régularisations. Ceci permet de produire de manière efficace des images contrastées et bien exposées, même dans des cas difficiles (objets en mouvement, scènes non planes, déformations optiques, etc.).Dans la deuxième partie de la thèse nous proposons, toujours dans des cas dynamiques, une méthode de fusion d'images acquises avec des mises au point variables. Le cœur de notre méthode repose sur une comparaison de patchs entre images ayant des niveaux de flou variables.Nos méthodes ont été évaluées sur des bases de données classiques et sur d'autres, nouvelles, crées pour les besoins de ce travail. Les expériences montrent la robustesse des méthodes aux distortions géométriques, aux variations d'illumination et au flou. Ces méthodes se comparent favorablement à des méthodes de l'état de l'art, à un coût algorithmique moindre. En marge de ces travaux, nous analysons également la capacité de l'algorithme PatchMatch à reconstruire des images en présence de flou et de changements d'illumination, et nous proposons différentes stratégies pour améliorer ces reconstructions
The most common computational techniques to deal with the limited high dynamic range and reduced depth of field of conventional cameras are based on the fusion of images acquired with different settings. These approaches require aligned images and motionless scenes, otherwise ghost artifacts and irregular structures can arise after the fusion. The goal of this thesis is to develop patch-based techniques in order to deal with motion and misalignment for image fusion, particularly in the case of variable illumination and blur.In the first part of this work, we present a methodology for the fusion of bracketed exposure images for dynamic scenes. Our method combines a carefully crafted contrast normalization, a fast non-local combination of patches and different regularization steps. This yields an efficient way of producing contrasted and well-exposed images from hand-held captures of dynamic scenes, even in difficult cases (moving objects, non planar scenes, optical deformations, etc.).In a second part, we propose a multifocus image fusion method that also deals with hand-held acquisition conditions and moving objects. At the core of our methodology, we propose a patch-based algorithm that corrects local geometric deformations by relying on both color and gradient orientations.Our methods were evaluated on common and new datasets created for the purpose of this work. From the experiments we conclude that our methods are consistently more robust than alternative methods to geometric distortions and illumination variations or blur. As a byproduct of our study, we also analyze the capacity of the PatchMatch algorithm to reconstruct images in the presence of blur and illumination changes, and propose different strategies to improve such reconstructions
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Books on the topic "Image Merging"

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Thomas, Wedell, ed. Type, image, message: Merging pictures and ideas : a graphic design layout workshop. Gloucester, MA: Rockport Publishers, 2006.

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Kuperberg, Marcia Clare. The integrated image: An investigation into the merging of video and computer graphics techniques incorporating the production of a video as a practical element in the investigation. [London]: Middlesex University, 1994.

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Morris, Burnis R. Carter G. Woodson. University Press of Mississippi, 2017. http://dx.doi.org/10.14325/mississippi/9781496814074.001.0001.

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Historian Carter G. Woodson’s employment of the black press and modern public-relations techniques to preserve and popularize black history during the first half of the 20th century is rediscovered and examined in this study. This rarely explored side of Woodson, often called “The Father of Black History,” resurrects the lost image of a leading cultural icon who used his celebrity in multiple roles as an opinion journalist, newsmaker, and CEO/publicist of black history to bring veneration to a subject whose past was clouded by misinformation and contempt. During his era, 1915-1950, Woodson cultivated and won crucial press support for his Black History Movement, while merging his interests and the interests of the black newspapers. His cause became their cause.
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Banks, Kathryn. ‘Look Again’, ‘Listen, Listen’, ‘Keep Looking’. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794776.003.0008.

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This chapter offers a way of understanding the effects of poetic images (metaphorical or literal). It employs and extends the notion of ‘emergent properties’, as well as relevance theory’s account of how communicative acts can ‘show’ as much as they mean. The images examined are from poems by Mary Oliver (‘Wings’, ‘Wild Geese’, and ‘Mindful’). The chapter suggests that such poetry is particularly in need of a new theoretical approach capable of engaging with its focus on embodied experience and ‘merging’ with nature. It shows how ‘emergent properties’—for example, a complex sense of what continuity with nature might feel like—can result from engaging in a range of imaginary sensorimotor experiences. The final section of the chapter turns to an abstract painting by Natalia Wróbel which dialogues with Oliver’s poetry, and fleshes out the relevance theory account of communicative showing to articulate differences between artistic genres and media.
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Book chapters on the topic "Image Merging"

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Plattner, Michael, and Gerald Ostermayer. "Filtering Specular Reflections by Merging Stereo Images." In Image Analysis, 164–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20205-7_14.

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Mitiche, Amar, and Ismail Ben Ayed. "Region Merging Priors." In Variational and Level Set Methods in Image Segmentation, 123–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15352-5_6.

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McInerney, Daniel, and Pieter Kempeneers. "Image (Re-)projections and Merging." In Open Source Geospatial Tools, 99–127. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01824-9_8.

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Flood, Gabrielle, David Gillsjö, Anders Heyden, and Kalle Åström. "Efficient Merging of Maps and Detection of Changes." In Image Analysis, 348–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20205-7_29.

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Karlsson, Anders, Jon Bjärkefur, Joakim Rydell, and Christina Grönwall. "Smoothing-Based Submap Merging in Large Area SLAM." In Image Analysis, 134–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_13.

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Besl, Paul J. "Description Combination and Region Merging." In Surfaces in Range Image Understanding, 176–89. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3906-2_5.

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Kyöstilä, Tomi, Daniel Herrera C., Juho Kannala, and Janne Heikkilä. "Merging Overlapping Depth Maps into a Nonredundant Point Cloud." In Image Analysis, 567–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38886-6_53.

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Shahid, Mohd, and Sumana Gupta. "Image Merging Based on Perceptual Information." In Pattern Recognition and Image Analysis, 683–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552499_75.

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Zhang, David, Hongzhi Zhang, and Bob Zhang. "Tongue Segmentation by Gradient Vector Flow and Region Merging." In Tongue Image Analysis, 103–13. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2167-1_6.

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Gorte, Ben. "Supervised Segmentation by Region Merging." In Machine Vision and Advanced Image Processing in Remote Sensing, 328–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60105-7_30.

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Conference papers on the topic "Image Merging"

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Borisova, I. V., V. N. Gorenok, A. N. Oparin, and P. G. Popov. "Image merging device." In SPIE Proceedings, edited by Anatoly M. Filachev and Alexander I. Dirochka. SPIE, 2003. http://dx.doi.org/10.1117/12.517335.

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Nielsen, Frank, and Richard Nock. "Consensus Region Merging for Image Segmentation." In 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2013. http://dx.doi.org/10.1109/acpr.2013.142.

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Mishiba, Kazu, and Masaaki Ikehara. "Image resizing using improved seam merging." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288118.

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Berretti, S., A. Del Bimbo, and P. Pala. "Merging results of distributed image libraries." In 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698). IEEE, 2003. http://dx.doi.org/10.1109/icme.2003.1221241.

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Moscheni, Fabrice, and Frederic Dufaux. "Regions merging based on robust statistical testing." In Visual Communications and Image Processing '96, edited by Rashid Ansari and Mark J. T. Smith. SPIE, 1996. http://dx.doi.org/10.1117/12.233184.

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Gopalakrishnan Nair, T. R., and R. Sharma. "Accurate merging of images for predictive analysis using combined image." In 2013 International Conference on Signal Processing, Image Processing, and Pattern Recognition (ICSIPR). IEEE, 2013. http://dx.doi.org/10.1109/icsipr.2013.6497980.

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Zheng, Xiqiang. "Image Segmentation for Hexagonally Sampled Images Using Statistical Region Merging." In 2022 6th International Conference on Imaging, Signal Processing and Communications (ICISPC). IEEE, 2022. http://dx.doi.org/10.1109/icispc57208.2022.00015.

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Trenary, M. J., C. A. Micchelli, J. Q. Trelewicz, M. Martens, and J. L. Mitchell. "Fast DCT domain image shifting and merging." In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.987675.

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Khanykov, Igor G., Mikhail V. Kharinov, and Chirag Patel. "Image segmentation improvement by reversible segment merging." In 2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp). IEEE, 2017. http://dx.doi.org/10.1109/icsoftcomp.2017.8280096.

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Boujemaa, Nozha, Georges Stamon, and Jacques Lemoine. "Fuzzy iterative image segmentation with recursive merging." In Applications in Optical Science and Engineering, edited by Petros Maragos. SPIE, 1992. http://dx.doi.org/10.1117/12.131398.

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Reports on the topic "Image Merging"

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Alevizos, E., T. Schoenning, K. Köser, M. Snellen, and J. Greinert. Merging AUV-based multibeam and image data to map the small-scale heterogeneity of Mn-nodule distribution. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305404.

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O'Neill, Francis, Kristofer Lasko, and Elena Sava. Snow-covered region improvements to a support vector machine-based semi-automated land cover mapping decision support tool. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45842.

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This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.
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