Relevant bibliographies by topics / Binary image processing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Binary image processing'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Binary image processing"

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Sathesh, A., and Edriss Eisa Babikir Adam. "Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique." September 2021 3, no. 3 (2021): 243–58. http://dx.doi.org/10.36548/jaicn.2021.3.007.

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Image thinning is the most essential pre-processing technique that plays major role in image processing applications such as image analysis and pattern recognition. It is a process that reduces a thick binary image into thin skeleton. In the present paper we have used hybrid parallel thinning algorithm to obtain the skeleton of the binary image. The result skeleton contains one pixel width which preserves the topological properties and retains the connectivity.
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Robert, L., and G. Malandain. "Fast Binary Image Processing Using Binary Decision Diagrams." Computer Vision and Image Understanding 72, no. 1 (1998): 1–9. http://dx.doi.org/10.1006/cviu.1997.0655.

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Iqbal, Saima, Wilayat Khan, Abdulrahman Alothaim, Aamir Qamar, Adi Alhudhaif, and Shtwai Alsubai. "Proving Reliability of Image Processing Techniques in Digital Forensics Applications." Security and Communication Networks 2022 (March 31, 2022): 1–17. http://dx.doi.org/10.1155/2022/1322264.

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Binary images have found its place in many applications, such as digital forensics involving legal documents, authentication of images, digital books, contracts, and text recognition. Modern digital forensics applications involve binary image processing as part of data hiding techniques for ownership protection, copyright control, and authentication of digital media. Whether in image forensics, health, or other fields, such transformations are often implemented in high-level languages without formal foundations. The lack of formal foundation questions the reliability of the image processing te
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Atmaja, Ratri Dwi, Muhammad Ary Murti, Junartho Halomoan, and Fiky Yosef Suratman. "An Image Processing Method to Convert RGB Image into Binary." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 2 (2016): 377. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp377-382.

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It is important in image processing to extract objects from their background into binary image. Binary image is used as input to feature extraction process and have an important role in generating unique feature to distinguish several classes in pattern recognition. This paper propose an image processing algorithm to obtain a binary image from RGB. The results showed that the binary image of the proposed algorithm contained the desired object.
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Myung-Ho Lee, Oh-Jin Kwon, and Yong-Hwan Lee. "Robust Method for Hiding Binary Image into JPEG HDR Base Layer Image against Common Image Processing." Research Briefs on Information and Communication Technology Evolution 1 (January 15, 2015): 183–97. http://dx.doi.org/10.56801/rebicte.v1i.22.

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Hiding a binary image into color or grey images has been adopted as a useful watermarking methodfor protecting ownership rights. We propose a practical method for this purpose. We generate aparticular binary pseudo-random sequence resembling the sequence used in code division multipleaccess (CDMA) method. We scramble the input binary image normally representing the ownershipby using the modified Hadamard kernel, generate the CDMA binary sequence robust to image processingdistortions, and hide the resulting sequence in the normalized host image. Experimentalresults show that our method guarante
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Zhao, Hang, and Zhao Xue Chen. "A Simple Hole Filling Algorithm for Binary Cell Images." Applied Mechanics and Materials 433-435 (October 2013): 1715–19. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1715.

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Filling holes in binary images is often required during medical image processing and analysis. However, traditional hole filling algorithms for medical images expose some disadvantages such as possible edge degradations and relatively low efficiency. To overcome such limits, a hole filling algorithm for binary cell images based on largest connected region extraction is proposed in this paper. Since there are less pixels for foreground areas in usual binary cell images, the holes in the binary images can be simply filled by extracting & filling the largest connected region in correspondent
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Bin Zhang, Kuizhi Mei, and Nanning Zheng. "Reconfigurable Processor for Binary Image Processing." IEEE Transactions on Circuits and Systems for Video Technology 23, no. 5 (2013): 823–31. http://dx.doi.org/10.1109/tcsvt.2012.2223872.

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Qian, Kai, and Prabir Bhattacharya. "Binary image processing by polynomial approach." Pattern Recognition Letters 11, no. 6 (1990): 395–403. http://dx.doi.org/10.1016/0167-8655(90)90110-n.

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V, Srujana, Chaithanya P, Ramesh B, Manoranjan S, and Mahesh V. "Crop Analysis Using Image Processing." International Journal of Engineering Technology and Management Sciences 4, no. 3 (2020): 9–15. http://dx.doi.org/10.46647/ijetms.2020.v04i03.002.

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To detect the uniqueness and quantities of agriculture product images a new method is proposed using MATLAB software .In this paper we propose a method to increase the contrast level of a image with exponential low pass filter and histogram equalization technique. Next by using region props function we extract the binary features of the image, and then we calculated the number of targets in gray level image. This method can be easily applied in modern agriculture.
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BIAN, ZHAOQI, DAVID ZHANG, and WEI SHU. "KNOWLEDGE-BASED FINGERPRINT POST-PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 01 (2002): 53–67. http://dx.doi.org/10.1142/s021800140200154x.

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True minutiae extraction in fingerprint image is critical to the performance of an automated identification system. Generally, a set of endings and bifurcations (both called feature points) can be obtained by the thinning image from which the true minutiae of the fingerprint are extracted by using the rules based on the structure of ridges. However, considering some false and true minutiae have similar ridge structures in the thinning image, in a lot of cases, we have to explore their difference in the binary image or the original gray image. In this paper, we first define the different types
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Dissertations / Theses on the topic "Binary image processing"

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Li, Ming De 1937. "Maximum likelihood restoration of binary objects." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276574.

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A new approach, based on maximum likelihood, is developed for binary object image restoration. This considers the image formation process as a stochastic process, with noise as a random variable. The likelihood function is constructed for the cases of Gaussian and Poisson noise. An uphill climb method is used to find the object, defined by its "grain" positions, through maximizing the likelihood function for grain positions. In addition, some a priori information regarding object size and contour of shapes is used. This is summarized as a "neighbouring point" rule. Some examples of computer ge
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Valero, Valbuena Silvia. "Hyperspectral image representation and processing with binary partition trees." Doctoral thesis, Universitat Politècnica de Catalunya, 2012. http://hdl.handle.net/10803/130832.

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The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeeds in presenting: (i) the
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Shen, Yijiang. "Binary image restoration by positive semidefinite programming and signomial programming." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39557431.

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沈逸江 and Yijiang Shen. "Binary image restoration by positive semidefinite programming and signomial programming." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39557431.

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Lindahl, Tobias. "Study of Local Binary Patterns." Thesis, Linköping University, Department of Science and Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9415.

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<p>This Masters thesis studies the concept of local binary patterns, which describe the neighbourhood of a pixel in a digital image by binary derivatives. The operator is often used in texture analysis and has been successfully used in facial recognition.</p><p>This thesis suggests two methods based on some basic ideas of Björn Kruse and studies of literature on the subject. The first suggested method presented is an algorithm which reproduces images from their local binary patterns by a kind of integration of the binary derivatives. This method is a way to prove the preservation of informatio
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Merkl, Frank J. "Binary image compression using run length encoding and multiple scanning techniques /." Online version of thesis, 1988. http://hdl.handle.net/1850/8309.

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Joo, Hyonam. "Binary tree classifier and context classifier." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/53076.

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Two methods of designing a point classifier are discussed in this paper, one is a binary decision tree classifier based on the Fisher's linear discriminant function as a decision rule at each nonterminal node, and the other is a contextual classifier which gives each pixel the highest probability label given some substantially sized context including the pixel. Experiments were performed both on a simulated image and real images to illustrate the improvement of the classification accuracy over the conventional single-stage Bayes classifier under Gaussian distribution assumption.<br>Master of
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Ellinwood, Jacquelyn S. "Comparison of four digital halftone screen (dither) patterns using quantitative analyses of the binary image microstructure /." Online version of thesis, 1989. http://hdl.handle.net/1850/11494.

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Paschalakis, Stavros. "Moment methods and hardware architectures for high speed binary, greyscale and colour pattern recognition." Thesis, University of Kent, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246603.

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Davey, B. L. K., W. J. Cocke, R. H. T. Bates, D. W. jr McCarthy, J. C. Christou, and M. L. Cobb. "Infrared Speckle Observations of Binary Ross 614 AB: Combined Shift-and-Add and Zero-and-Add Analysis." Steward Observatory, The University of Arizona (Tucson, Arizona), 1988. http://hdl.handle.net/10150/623921.

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One -dimensional infrared speckle scans of Ross 614 AB were recorded at a wavelength of 2.2μm. For each scan an estimate of the instantaneous quality of the seeing was calculated and the scan was binned accordingly. The three bins corresponding to the three best seeing conditions were further processed by applying the shift -and -add algorithm to the set of images contained within each bin, thereby generating three shift- and -add images with differing shift -and -add point -spread- functions. After windowing the shift -and -add images (using edge -extension) to reduce the effect of cont
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Books on the topic "Binary image processing"

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Marchand-Maillet, Stéphane. Binary digital image processing: A discrete approach. Academic Press, 2000.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Analysis of objects in binary images. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1991.

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Abdenour, Hadid, Zhao Guoying, Ahonen Timo, and SpringerLink (Online service), eds. Computer Vision Using Local Binary Patterns. Springer-Verlag London Limited, 2011.

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Binary Digital Image Processing. Elsevier, 2000. http://dx.doi.org/10.1016/b978-0-12-470505-0.x5000-x.

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Marchand-Maillet, Stéphane, and Yazid M. Sharaiha. Binary Digital Image Processing: A Discrete Approach. Elsevier Science & Technology Books, 1999.

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Marchand-Maillet, Stéphane, and Yazid M. Sharaiha. Binary Digital Image Processing: A Discrete Approach. Academic Press, 1999.

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Marchand-Maillet, Stéphane, and Yazid M. Sharaiha. Binary Digital Image Processing: A Discrete Approach. Academic Press, 1999.

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Talbot, Hugues, and Richard Beare. Mathematical Morphology. CSIRO Publishing, 2002. http://dx.doi.org/10.1071/9780643107342.

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Mathematical morphology is a powerful methodology for processing and analysing the shape and form of objects in images. The advances in this area of science allow for application in the digital recognition and modeling of faces and other objects by computers. &#x0D; Mathematical Morphology is comprehensive work that provides a broad sampling of the most recent theoretical and practical developments in applications to image processing and analysis. Subject areas covered include: binary morphology, regularised region growing, morphological scale-space techniques, levelings, reconstruction, model
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Sbrilli, Antonella. Storia Dell'arte in Codice Binario: La Riproduzione Digitale Delle Opere Artistiche. Guerini e associazioni, 2001.

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Book chapters on the topic "Binary image processing"

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Pinoli, Jean-Charles. "Binary Image Processing and Analysis." In Mathematical Foundations of Image Processing and Analysis 1. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118625705.ch4.

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Toriwaki, Junichiro, and Hiro Yoshida. "Algorithm Of Binary Image Processing." In Fundamentals of Three-Dimensional Digital Image Processing. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-173-2_5.

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Burger, Wilhelm, and Mark James Burge. "Regions in Binary Images." In Principles of Digital Image Processing. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-195-4_2.

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Pinoli, Jean-Charles. "Binary Images." In Mathematical Foundations of Image Processing and Analysis 1. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118625705.ch3.

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Dougherty, Edward R., and Robert M. Haralick. "Unification of Nonlinear Filtering in the Context of Binary Logical Calculus, Part I: Binary Filters." In Mathematical Nonlinear Image Processing. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3148-7_6.

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Lech, Piotr, and Krzysztof Okarma. "Binary Line Oriented Histogram." In Image Processing and Communications Challenges 9. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68720-9_1.

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Iñesta, José M., Pedro J. Sanz, and Ángel P. del Pobil. "An automatic transformation from bimodal to pseudo-binary images." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_206.

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Cuevas, Erik, and Alma Nayeli Rodríguez. "Segmentation and Processing of Binary Images." In Image Processing and Machine Learning, Volume 1. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003287414-4.

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Çarkacioĝlu, Abdurrahman, and Fatoş T. Yarman-Vural. "Similarity measures for binary and gray level Markov Random Field textures." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_193.

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Anelli, Giovanni, Alberto Broggi, and Giulio Destri. "Experiments on the decomposition of arbitrarily shaped binary morphological structuring elements." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_243.

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Conference papers on the topic "Binary image processing"

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Kondo, Ryota, Hiroaki Minoura, Tsubasa Hirakawa, Takayoshi Yamashita, and Hironobu Fujiyoshi. "Binary-Decomposed Vision Transformer: Compressing and Accelerating Vision Transformer by Binary Decomposition." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647259.

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Huang, K. S., B. K. Jenkins, and A. A. Sawchuk. "Image processing applications of binary image algebra." In OSA Annual Meeting. Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.tup3.

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Digital optical computing is well suited to image processing tasks because of its parallelism and massive computational capability. Binary image algebra (BIA) provides a systematic parallel mathematical structure for developing parallel optical numerical array computation and image processing algorithms. A BIA representation and comparison of different kinds of parallel optical binary arithmetic (including symbolic substitution arithmetic) are discussed in Ref. 1. Here we extend this study to image processing applications.
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Duncan, Bradley D., Ting-Chung Poon, and Ronald J. Pieper. "Nonlinear scanning image processing." In OSA Annual Meeting. Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mi6.

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Image processing often employs sophisticated digital algorithms or precisely aligned coherent optical techniques. Although these methods are typically efficient in their image processing capabilities, neither method readily lends itself to simple, real-time implementation. For binary image processing, the importance of optical morphological filtering has recently been recognized.1,2 Through the optical implementation of mathematical morphology, systems capable of performing operations, such as median filtering, skeletonization, edge extraction, and impulsive noise removal have been studied. Th
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Athale, Ravindra A., Joseph N. Mait, and Dennis W. Prather. "Optical Morphological Image Processing with Acoustooptic Devices." In Optical Computing. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/optcomp.1991.tub3.

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Morphological image processing based on binary set representation of an image has been receiving increasing attention as a viable alternative to the linear image processing based on Fourier domain filtering [1], The fundamental morphological filtering operations of erosion and dilation are nonlinear operations from which more complex operations (opening, closing, pattern spectra) suitable for shape extraction and analysis can be synthesized. The morphological operations are defined between a working image and a much smaller image called the structuring element. In most cases the structuring el
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Bhatia, Parmeet S., Amit Kale, and Zhigang Peng. "Volumetric texture modeling using dominant and discriminative binary patterns." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2019. http://dx.doi.org/10.1117/12.2512296.

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van der Putten, Joost, Fons van der Sommen, and Peter H. N. de With. "Influence of decoder size for binary segmentation tasks in medical imaging." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2020. http://dx.doi.org/10.1117/12.2542199.

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Vardi-Gonen, E., and G. Herman. "Sequential Binary Image Estimation Algorithms." In 2006 IEEE 12th Digital Signal Processing Workshop & 4th IEEE Signal Processing Education Workshop. IEEE, 2006. http://dx.doi.org/10.1109/dspws.2006.265473.

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Hong, Canaan S., and Richard I. Hornsey. "On-chip binary image processing with CMOS image sensors." In Electronic Imaging 2002, edited by Morley M. Blouke, John Canosa, and Nitin Sampat. SPIE, 2002. http://dx.doi.org/10.1117/12.463419.

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Salgado-Albiter, A. Camila, S. Ivvan Valdez, and Jorge Paredes-Tavares. "Improving Geomorphological Classification via Binary Image Processing." In 2022 IEEE Mexican International Conference on Computer Science (ENC). IEEE, 2022. http://dx.doi.org/10.1109/enc56672.2022.9882949.

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Dantas, Daniel Oliveira, and Helton Danilo Passos Leal. "Fast multidimensional binary image processing with OpenCL." In 2019 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2019. http://dx.doi.org/10.1109/hpcs48598.2019.9188210.

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Reports on the topic "Binary image processing"

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McGuire, Dennis W. The Morphological Processing of Binary Images. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada274310.

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