Relevant bibliographies by topics / Class-binarization

Academic literature on the topic 'Class-binarization'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Class-binarization"

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Polyakova, Marina V., and Alexandr G. Nesteryuk. "IMPROVEMENT OF THE COLOR TEXT IMAGE BINARIZATION METHOD USING THE MINIMUM-DISTANCE CLASSIFIER." Applied Aspects of Information Technology 4, no. 1 (April 10, 2021): 57–70. http://dx.doi.org/10.15276/aait.01.2021.5.

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Optical character recognition systems for the images are used to convert books and documents into electronic form, to automate accounting systems in business, when recognizing markers using augmented reality technologies and etс. The quality of optical character recognition, provided that binarization is applied, is largely determined by the quality of separation of the foreground pixels from the background. Methods of text image binarization are analyzed and insufficient quality of binarization is noted. As a way of research the minimum-distance classifier for the improvement of the existing method of binarization of color text images is used. To improve the quality of the binarization of color text images, it is advisable to divide image pixels into two classes, “Foreground” and “Background”, to use classification methods instead of heuristic threshold selection, namely, a minimum-distance classifier. To reduce the amount of processed information before applying the classifier, it is advisable to select blocks of pixels for subsequent processing. This was done by analyzing the connected components on the original image. An improved method of the color text image binarization with the use of analysis of connected components and minimum-distance classifier has been elaborated. The research of the elaborated method showed that it is better than existing binarization methods in terms of robustness of binarization, but worse in terms of the error of the determining the boundaries of objects. Among the recognition errors, the pixels of images from the class labeled “Foreground” were more often mistaken for the class labeled “Background”. The proposed method of binarization with the uniqueness of class prototypes is recommended to be used in problems of the processing of color images of the printed text, for which the error in determining the boundaries of characters as a result of binarization is compensated by the thickness of the letters. With a multiplicity of class prototypes, the proposed binarization method is recommended to be used in problems of processing color images of handwritten text, if high performance is not required. The improved binarization method has shown its efficiency in cases of slow changes in the color and illumination of the text and background, however, abrupt changes in color and illumination, as well as a textured background, do not allowing the binarization quality required for practical problems.
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CHI, ZHERU, and QING WANG. "DOCUMENT IMAGE BINARIZATION WITH FEEDBACK FOR IMPROVING CHARACTER SEGMENTATION." International Journal of Image and Graphics 05, no. 02 (April 2005): 281–309. http://dx.doi.org/10.1142/s0219467805001768.

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Binarization of gray scale document images is one of the most important steps in automatic document image processing. In this paper, we present a two-stage document image binarization approach, which includes a top-down region-based binarization at the first stage and a neural network based binarization technique for the problematic blocks at the second stage after a feedback checking. Our two-stage approach is particularly effective for binarizing text images of highlighted or marked text. The region-based binarization method is fast and suitable for processing large document images. However, the block effect and regional edge noise are two unavoidable problems resulting in poor character segmentation and recognition. The neural network based classifier can achieve good performance in two-class classification problem such as the binarization of gray level document images. However, it is computationally costly. In our two-stage binarization approach, the feedback criteria are employed to keep the well binarized blocks from the first stage binarization and to re-binarize the problematic blocks at the second stage using the neural network binarizer to improve the character segmentation quality. Experimental results on a number of document images show that our two-stage binarization approach performs better than the single-stage binarization techniques tested in terms of character segmentation quality and computational cost.
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HRŮZA, JAN, and PETER šTĚPÁNEK. "Speedup of logic programs by binarization and partial deduction." Theory and Practice of Logic Programming 4, no. 3 (April 16, 2004): 355–69. http://dx.doi.org/10.1017/s147106840300190x.

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Binary logic programs can be obtained from ordinary logic programs by a binarizing transformation. In most cases, binary programs obtained this way are less efficient than the original programs. (Demoen, 1992) showed an interesting example of a logic program whose computational behaviour was improved when it was transformed to a binary program and then specialized by partial deduction. The class of B-stratifiable logic programs is defined. It is shown that for every B-stratifiable logic program, binarization and subsequent partial deduction produce a binary program which does not contain variables for continuations introduced by binarization. Such programs usually have a better computational behaviour than the original ones. Both binarization and partial deduction can be easily automated. A comparison with other related approaches to program transformation is given.
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Syahrial, Syahrial, and Rizal Lamusu. "Pembentua Pola Desain Motif Karawo Gorontalo Menggunakan K-Means Color Quantization dan Structured Forest Edge Detecion." Jurnal Teknologi Informasi dan Ilmu Komputer 8, no. 3 (June 15, 2021): 625. http://dx.doi.org/10.25126/jtiik.2021834491.

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<p class="Abstrak">Sulaman Karawo merupakan kerajinan tangan berupa sulaman khas dari daerah Gorontalo. Motif sulaman diterapkan secara detail berdasarkan suatu pola desain tertentu. Pola desain digambarkan pada kertas dengan berbagai panduannya. Gambar yang diterapkan pada pola memiliki resolusi sangat rendah dan harus mempertahankan bentuknya. Penelitian ini mengembangkan metode pembentukan pola desain motif Karawo dari citra digital. Proses dilakukan dengan pengolahan awal menggunakan <em>k-means color quantization (KMCQ)</em> dan deteksi tepi <em>structured forest</em>. Proses selanjutnya melakukan pengurangan resolusi menggunakan metode <em>pixelation</em> dan <em>binarization</em>. Luaran dari algoritma menghasilkan 3 citra berbeda dengan ukuran yang sama, yaitu: citra tepi, citra biner, dan citra berwarna. Ketiga citra tersebut selanjutnya dilakukan proses pembentukan pola desain motif Karawo dengan berbagai petunjuk pola bagi pengrajin. Hasil menunjukkan bahwa pola desain motif dapat digunakan dan dimengerti oleh para pengrajin dalam menerapkannya di sulaman Karawo. Pengujian nilai-nilai parameter dilakukan pada metode <em>k-means</em>, <em>gaussian filter</em>, <em>pixelation</em>, dan <em>binarization.</em> Parameter-parameter tersebut yaitu: k pada <em>k-means</em>, <em>kernel</em> pada <em>gaussian filter</em>, lebar piksel pada <em>pixelation</em>, dan nilai <em>threshold</em> pada <em>binarization</em>. Pengujian menunjukkan nilai terendah tiap parameter adalah k=4, kernel=3x3, lebar piksel=70, dan <em>threshold</em>=20. Hasil memperlihatkan makin tinggi nilai-nilai tersebut maka semakin baik pola desain motif yang dihasilkan. Nilai-nilai tersebut merupakan nilai parameter terendah dalam pembentukan pola desain motif berkualitas baik berdasarkan indikator-indikator dari desainer.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Karawo embroidery is a unique handicraft from Gorontalo. The embroidery motif is applied in detail based on a certain design pattern. These patterns are depicted on paper with various guides. The image applied to the pattern is very low resolution and retains its shape. This study develops a method to generate a Karawo design pattern from a digital image. The process begins by using k-means color quantization (KMCQ) to reduce the number of colors and edge detection of the structured forest. The next process is to change the resolution using pixelation and binarization methods. The output algorithm produces 3 different state images of the same size, which are: edge image, binary image, and color image. These images are used in the formation of the Karawo motif design pattern. The motif contains various pattern instructions for the craftsman. The results show that it can be used and understood by the craftsmen in its application in Karawo embroidery. Testing parameter values on the k-means method, Gaussian filter, pixelation, and binarization. These parameters are k on KMCQ, the kernel on a gaussian filter, pixel width in pixelation, and threshold value in binarization. The results show that the lowest value of each parameter is k=4, kernel=3x3, pixel width=70, and threshold=20. The results show that the higher these values, the better the results of the pattern design motif. Those values are the lower input to generate a good quality pattern design based on the designer’s indicators.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>
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Saddami, Khairun, Fitri Arnia, Yuwaldi Away, and Khairul Munadi. "Kombinasi Metode Nilai Ambang Lokal dan Global untuk Restorasi Dokumen Jawi Kuno." Jurnal Teknologi Informasi dan Ilmu Komputer 7, no. 1 (February 4, 2020): 163. http://dx.doi.org/10.25126/jtiik.2020701741.

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<p class="Abstrak">Dokumen Jawi kuno merupakan warisan budaya yang berisi informasi penting tentang peradaban masa lalu yang dapat dijadikan pedoman untuk masa sekarang ini. Dokumen Jawi kuno telah mengalami penurunan kualitas yang disebabkan oleh beberapa faktor seperti kualitas kertas atau karena proses penyimpanan. Penurunan kualitas ini menyebabkan informasi yang terdapat pada dokumen tersebut menghilang dan sulit untuk diakses. Artikel ini mengusulkan metode binerisasi untuk membangkitkan kembali informasi yang terdapat pada dokumen Jawi kuno. Metode usulan merupakan kombinasi antara metode binerisasi berbasis nilai ambang lokal dan global. Metode usulan diuji terhadap dokumen Jawi kuno dan dokumen uji standar yang dikenal dengan nama <em>Handwritten</em> <em>Document Image Binarization Contest</em> (HDIBCO) 2016. Citra hasil binerisasi dievaluasi menggunakan metode: <em>F-measure</em>, <em>pseudo F-measure</em>, <em>peak signal-to-noise ratio</em>, <em>distance reciprocal distortion</em>, dan <em>misclasification penalty metric</em>. Secara rata-rata, nilai evaluasi <em>F-measure</em> dari metode usulan mencapai 88,18 dan 89,04 masing-masing untuk dataset Jawi dan HDIBCO-2016. Hasil ini lebih baik dari metode pembanding yang menunjukkan bahwa metode usulan berhasil meningkatkan kinerja metode binerisasi untuk dataset Jawi dan HDIBCO-2016.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Ancient Jawi document is a cultural heritage, which contains knowledge of past civilization for developing a better future. Ancient Jawi document suffers from severe degradation due to some factors such as paper quality or poor retention process. The degradation reduces information on the document and thus the information is difficult to access. This paper proposed a binarization method for restoring the information from degraded ancient Jawi document. The proposed method combined a local and global thresholding method for extracting the text from the background. The experiment was conducted on ancient Jawi document and Handwritten Document Image Binarization Contest (HDIBCO) 2016 datasets. The result was evaluated using F-measure, pseudo F-measure, peak signal-to-noise ratio, distance reciprocal distortion, dan misclassification penalty metric. The average result showed that the proposed method achieved 88.18 and 89.04 of F-measure, for Jawi and HDIBCO-2016, respectively. The proposed method resulted in better performance compared with several benchmarking methods. It can be concluded that the proposed method succeeded to enhance binarization performance.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>
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Ng, Selina, Peter Tse, and Kwok Tsui. "A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects." Sensors 14, no. 1 (January 13, 2014): 1295–321. http://dx.doi.org/10.3390/s140101295.

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Ieno, Egidio, Luis Manuel Garcés, Alejandro José Cabrera, and Tales Cleber Pimenta. "Simple generation of threshold for images binarization on FPGA." Ingeniería e Investigación 35, no. 3 (December 14, 2015): 69–75. http://dx.doi.org/10.15446/ing.investig.v35n3.51750.

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<p class="Abstractandkeywordscontent">This paper proposes the FPGA implementation of a threshold algorithm used in the process of image binarization by simple mathematical calculations. The implementation need only one image iteration and its processing time depends on the size of the image. The threshold values of different images obtained through the FPGA implementation are compared with those obtained by Otsu’s method, showing the differences and the visual results of binarization using both methods. The hardware implementation of the algorithm is performed by model-based design supported by the MATLAB<sup>®</sup>/Simulink<sup>®</sup> and Xilinx System Generator<sup>®</sup> tools. The results of the implementation proposal are presented in terms of resource consumption and maximum operating frequency in a Spartan-6 FPGA-based development board. The experimental results are obtained in co-simulation system and show the effectiveness of the proposed method.</p>
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Vizilter, Y. V., A. Y. Rubis, S. Y. Zheltov, and O. V. Vygolov. "CHANGE DETECTION VIA MORPHOLOGICAL COMPARATIVE FILTERS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 279–86. http://dx.doi.org/10.5194/isprsannals-iii-3-279-2016.

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In this paper we propose the new change detection technique based on morphological comparative filtering. This technique generalizes the morphological image analysis scheme proposed by Pytiev. A new class of comparative filters based on guided contrasting is developed. Comparative filtering based on diffusion morphology is implemented too. The change detection pipeline contains: comparative filtering on image pyramid, calculation of morphological difference map, binarization, extraction of change proposals and testing change proposals using local morphological correlation coefficient. Experimental results demonstrate the applicability of proposed approach.
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Vizilter, Y. V., A. Y. Rubis, S. Y. Zheltov, and O. V. Vygolov. "CHANGE DETECTION VIA MORPHOLOGICAL COMPARATIVE FILTERS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 279–86. http://dx.doi.org/10.5194/isprs-annals-iii-3-279-2016.

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In this paper we propose the new change detection technique based on morphological comparative filtering. This technique generalizes the morphological image analysis scheme proposed by Pytiev. A new class of comparative filters based on guided contrasting is developed. Comparative filtering based on diffusion morphology is implemented too. The change detection pipeline contains: comparative filtering on image pyramid, calculation of morphological difference map, binarization, extraction of change proposals and testing change proposals using local morphological correlation coefficient. Experimental results demonstrate the applicability of proposed approach.
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Yanagisawa, Toshifumi, Kohki Kamiya, and Hirohisa Kurosaki. "New NEO Detection Techniques using the FPGA." Publications of the Astronomical Society of Japan 73, no. 3 (March 26, 2021): 519–29. http://dx.doi.org/10.1093/pasj/psab017.

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Abstract We have developed a new method for detecting near-Earth objects (NEOs) based on a Field Programmable Gate-Array (FPGA). Unlike conventional methods, our technique uses 30–40 frames to detect faint NEOs that are almost invisible on a single frame. To reduce analysis time, image binarization and an FPGA-implemented algorithm were used. This method has aided in the discovery of 11 NEOs by analyzing frames captured with 20 cm class telescopes. This new method will contribute to discovering new NEOs that approach the Earth closely.
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Dissertations / Theses on the topic "Class-binarization"

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Ringdahl, Benjamin. "Gaussian Process Multiclass Classification : Evaluation of Binarization Techniques and Likelihood Functions." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87952.

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In binary Gaussian process classification the prior class membership probabilities are obtained by transforming a Gaussian process to the unit interval, typically either with the logistic likelihood function or the cumulative Gaussian likelihood function. Multiclass classification problems can be handled by any binary classifier by means of so-called binarization techniques, which reduces the multiclass problem into a number of binary problems. Other than introducing the mathematics behind the theory and methods behind Gaussian process classification, we compare the binarization techniques one-against-all and one-against-one in the context of Gaussian process classification, and we also compare the performance of the logistic likelihood and the cumulative Gaussian likelihood. This is done by means of two experiments: one general experiment where the methods are tested on several publicly available datasets, and one more specific experiment where the methods are compared with respect to class imbalance and class overlap on several artificially generated datasets. The results indicate that there is no significant difference in the choices of binarization technique and likelihood function for typical datasets, although the one-against-one technique showed slightly more consistent performance. However the second experiment revealed some differences in how the methods react to varying degrees of class imbalance and class overlap. Most notably the logistic likelihood was a dominant factor and the one-against-one technique performed better than one-against-all.
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Book chapters on the topic "Class-binarization"

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Fürnkranz, Johannes. "Class Binarization." In Encyclopedia of Machine Learning and Data Mining, 1–2. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_915-1.

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Fürnkranz, Johannes. "Class Binarization." In Encyclopedia of Machine Learning and Data Mining, 203–4. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_915.

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Krawczyk, Bartosz, Bridget T. McInnes, and Alberto Cano. "Sentiment Classification from Multi-class Imbalanced Twitter Data Using Binarization." In Lecture Notes in Computer Science, 26–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59650-1_3.

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Żak, Michał, and Michał Woźniak. "Performance Analysis of Binarization Strategies for Multi-class Imbalanced Data Classification." In Lecture Notes in Computer Science, 141–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50423-6_11.

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Arab Cohen, Diego, and Elmer Andrés Fernández. "SVMTOCP: A Binary Tree Base SVM Approach through Optimal Multi-class Binarization." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 472–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33275-3_58.

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Méndez, Rebeca, Beatriz Remeseiro, Diego Peteiro-Barral, and Manuel G. Penedo. "Evaluation of Class Binarization and Feature Selection in Tear Film Classification using TOPSIS." In Communications in Computer and Information Science, 179–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44440-5_11.

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Reese, Timothy, and Yu Michael Zhu. "LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi-class Classification." In Advances in Intelligent Systems and Computing, 193–214. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3357-7_8.

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Conference papers on the topic "Class-binarization"

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Gomez, J., and R. Kozma. "Fuzzy class binarization using coupled map lattices." In IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04. IEEE, 2004. http://dx.doi.org/10.1109/nafips.2004.1337438.

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Gao, Zhenyu, and Gongjin Lan. "A NEAT-based multiclass classification method with class binarization." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3459509.

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Reese, Timothy, and Michael Zhu. "LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi-class Classification." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00031.

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"Multi-criteria Evaluation of Class Binarization and Feature Selection in Tear Film Lipid Layer Classification." In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004224300620070.

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Mahandi, Yogi Dwi, Eko Mulyanto Yuniamo, Yoyon Kusnendar Suprapto, and Endang Purwaningsih. "Ink bleed-through binarization of Javanese handwritten ancient document using local adaptive threshold based on local class width." In 2017 International Seminar on Intelligent Technology and its Applications (ISITIA). IEEE, 2017. http://dx.doi.org/10.1109/isitia.2017.8124097.

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Su, Jinhua, and Yuanyuan Zhang. "Triple-O for SHL Recognition Challenge: An Ensemble Framework for Multi-class Imbalance and Training-testing Distribution Inconsistency by OvO Binarization with Confidence Weight of One-class Classification." In UbiComp '21: The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460418.3479375.

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