Relevant bibliographies by topics / Gray-level histogram

Contents

  1. Journal articles
  2. Books
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Gray-level histogram'

Author: Grafiati
Published: 3 June 2025
Last updated: 20 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Gray-level histogram.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Gray-level histogram"

1

Tan, Yanli, and Yongqiang Zhao. "A Fast Otsu Thresholding Method Based on an Improved 2D Histogram." International Journal of Circuits, Systems and Signal Processing 15 (August 12, 2021): 953–59. http://dx.doi.org/10.46300/9106.2021.15.102.

Full text
Abstract:
The regional division of a traditional 2D histogram is difficult to obtain satisfactory image segmentation results. Based on the gray level-gradient 2D histogram, we proposed a fast 2D Otsu method based on integral image. In this method, the average gray level is replaced by the gray level gradient in the neighborhood of pixels, and the edge features of the image are extracted according to the gray level difference between adjacent pixels to improve the segmentation effect. Calculating the integral image from the two-dimensional histogram reduces the computational complexity of searching the o
APA, Harvard, Vancouver, ISO, and other styles
2

Chang, Jeng-Horng, Kuo-Chin Fan, and Yang-Lang Chang. "Multi-modal gray-level histogram modeling and decomposition." Image and Vision Computing 20, no. 3 (2002): 203–16. http://dx.doi.org/10.1016/s0262-8856(01)00095-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Nalbant, MO, and E. Inci. "The Efficiency of Gray-Level Ultrasound Histogram Analysis in Patients with Supraspinatus Tendinopathy." Nigerian Journal of Clinical Practice 26, no. 11 (2023): 1709–15. http://dx.doi.org/10.4103/njcp.njcp_325_23.

Full text
Abstract:
ABSTRACT Background: Musculoskeletal ultrasonography is a viable substitute for magnetic resonance imaging (MRI) that offers advantages in terms of time efficiency and cost-effectiveness. The gray-level histogram is a tool used to depict the distribution of pixel gray levels that provide quantitative data. Aim: The objective of our research was to establish a threshold value for ultrasonography-measured supraspinatus tendon gray-level values by comparing patients with tendinopathy to those without. Materials and Methods: This study comprised a cohort of 271 individuals, consisting of 124 patie
APA, Harvard, Vancouver, ISO, and other styles
4

Yousuf, M. A., and M. R. H. Rakib. "An Effective Image Contrast Enhancement Method Using Global Histogram Equalization." Journal of Scientific Research 3, no. 1 (2010): 43. http://dx.doi.org/10.3329/jsr.v3i1.5299.

Full text
Abstract:
Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0. Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution)
APA, Harvard, Vancouver, ISO, and other styles
5

Zheng, Xiulian, Hong Ye, and Yinggan Tang. "Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram." Entropy 19, no. 5 (2017): 191. http://dx.doi.org/10.3390/e19050191.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Prasad, M. Seetharama, C. Naga Raju, and L. S. S. Reddy. "Fuzzy Entropic Thresholding Using Gray Level Spatial Correlation Histogram." i-manager's Journal on Software Engineering 6, no. 2 (2011): 20–30. http://dx.doi.org/10.26634/jse.6.2.2894.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kanika, Kapoor, and Arora Shaveta. "COLOUR IMAGE ENHANCEMENT BASED ON HISTOGRAM EQUALIZATION." Electrical & Computer Engineering: An International Journal (ECIJ) 4, no. 3 (2015): 73–82. https://doi.org/10.5281/zenodo.3593819.

Full text
Abstract:
Histogram equalization is a nonlinear technique for adjusting the contrast of an image using its histogram. It increases the brightness of a gray scale image which is different from the mean brightness of the original image. There are various types of Histogram equalization techniques like Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Brightness Preserving Bi Histogram Equalization, Dualistic Sub Image Histogram Equalization, Minimum Mean Brightness Error Bi Histogram Equalization, Recursive Mean Separate Histogram Equalization and Recursive Sub Image Histogram Equa
APA, Harvard, Vancouver, ISO, and other styles
8

Lv, Ying. "Typhoon Cloud Tracking by Kalman Filter." Applied Mechanics and Materials 58-60 (June 2011): 2487–92. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2487.

Full text
Abstract:
Typhoon cloud has its changeability, so it is difficult to track and predict compared with the rigid targets. Region of interest (ROI) and reference region were selected by using interactive methods. Bezier curve is used to smooth the gray level histogram of ROI and obtain Bezier histogram. The gray level value which is corresponding to the valley of the Bezier histogram is used to segment the ROI in order to get the tracking target. And target parameters could be predicted by using Kalman filter, then getting the moving track of the target. Experimental results show that the proposed algorith
APA, Harvard, Vancouver, ISO, and other styles
9

Kadhum, Zainab Abdulrazzaq. "Equalize The Histogram Equalization for Image enhancement." Journal of Kufa for Mathematics and Computer 1, no. 5 (2012): 14–21. http://dx.doi.org/10.31642/jokmc/2018/010502.

Full text
Abstract:
Histogram Equalization is one of the technique most commonly used in contrast enhancement. it tends to change the mean brightness of the image to the middle level of the gray level range. However, In this paper, a simple contrast enhancement technique based on conventional histogram equalization algorithm is proposed. This Equalize The histogram equalization technique which takes control over the effect of  histogram equalization technique so that it performs the enhancement of an image
APA, Harvard, Vancouver, ISO, and other styles
10

Baqer, Ismail Sh. "Image Quality Enhancing by Efficient Histogram Equalization." Wasit Journal of Engineering Sciences 2, no. 2 (2014): 47–58. http://dx.doi.org/10.31185/ejuow.vol2.iss2.29.

Full text
Abstract:
A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-N
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Gray-level histogram"

1

Chubb, Charles, Joseph Darcy, Michael S. Landy, John Econopouly, Dan Bindman Jong-Ho Nam, and George Sperling. The Scramble Illusion. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0096.

Full text
Abstract:
A “scramble” is a visual texture in which different gray levels are randomly mixed together. Past research has demonstrated that human vision has three dimensions of sensitivity to the different sorts of scrambles that can be created by varying the proportions of different gray levels included in the scramble. This chapter demonstrates two scrambles with dramatically different gray level histograms that appear identical unless the observer is specifically instructed to scrutinize each of them individually. It is argued that people fail to notice any difference between these two scrambles becau
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Gray-level histogram"

1

Iñesta, José Manuel, and Jorge Calera-Rubio. "Robust Gray-Level Histogram Gaussian Characterisation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-70659-3_88.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Pun, Chi-Man, and Xiaochen Yuan. "Robust Block and Gray-Level Histogram Based Watermarking Scheme." In Advances in Multimedia Information Processing - PCM 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_52.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Martín-Rodríguez, Fernando. "New Tools for Gray Level Histogram Analysis, Applications in Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39094-4_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Patricio, M. A., and D. Maravall. "Segmentation of Text and Graphics/Images Using the Gray-Level Histogram Fourier Transform." In Advances in Pattern Recognition. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44522-6_78.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Chen, Yihao. "Identification of Tea Leaf Based on Histogram Equalization, Gray-Level Co-Occurrence Matrix and Support Vector Machine Algorithm." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51100-5_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Maeda, Kazuo, Masaji Utsu, Nobuhiro Yamamoto, and Mariko Serizawa. "Ultrasonic Tissue Characterization with Gray Level Histogram Width." In Donald School Textbook of Transvaginal Sonography. Jaypee Brothers Medical Publishers (P) Ltd., 2005. http://dx.doi.org/10.5005/jp/books/10230_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Maeda, Kazuo, Masaji Utsu, Nobuhiro Yamamoto, and Mariko Serizawa. "Ultrasonic Tissue Characterization with Gray Level Histogram Width." In Donald School Textbook of Transvaginal Sonography. Jaypee Brothers Medical Publishers (P) Ltd., 2013. http://dx.doi.org/10.5005/jp/books/12096_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Manuel Domínguez Nicolás, Saúl. "Binarization Based on Maximum and Average Gray Values." In Digital Image Processing Applications. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.99932.

Full text
Abstract:
Many image processing techniques use binarization for object detection in images, where the objects and background are well distinct by their brightness values, where, the threshold level is globally assigned, on the other hand, if it’s adaptive, the threshold level is locally calculated. In order to determine the optimal binarization threshold, from an image with the mean gray values and extreme gray values, exchanging the mean gray values relating to automatic analisis for a standard histogram equalization, which can evaluate a wide range of image features, even when the gray values in both the object of interest and background of the image are not uniform.
APA, Harvard, Vancouver, ISO, and other styles
9

Dhal, Krishna Gopal, Swarnajit Ray, Mandira Sen, and Sanjoy Das. "Proper Enhancement and Segmentation of the Overexposed Color Skin Cancer Image." In Advances in Multimedia and Interactive Technologies. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5246-8.ch009.

Full text
Abstract:
Proper enhancement and segmentation of the overexposed color skin cancer images is a great challenging task in medical image processing field. Computer-aided diagnosis (CAD) facilitates quantitative analysis of digital images with a high throughput processing rate. But, analysis of CAD purely depends on the input image quality. Therefore, in this study, overexposed and washed out skin cancer images are enhanced properly with the help of exact hue-saturation-intensity (eHSI) color model and contrast limited adaptive histogram equalization (CLAHE) method which is applied through this model. eHSI color model is hue preserving and gamut problem free. Any gray level image enhancement method can be easily employed for color image through this eHSI model. The segmentation of these enhanced color images has been done by employing one unsupervised clustering approach with the assistance of seven different gray level thresholding methods. Comparison of the segmentation efficiency of gray level thresholding methods has been done in the cases of overexposed as well as for enhanced images.
APA, Harvard, Vancouver, ISO, and other styles
10

Oluwasegun, Abioye Abiodun, Abraham Evwiekpaefe, Philip Oshiokhaimhele Odion, et al. "Optimizing Healthcare Operations With AI Algorithms by Enhancing Skin Cancer Diagnosis Using Advanced Image Processing and Classification Techniques." In Advances in Healthcare Information Systems and Administration. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-7277-7.ch008.

Full text
Abstract:
Optimizing healthcare through AI algorithms offers significant potential in skin cancer diagnosis. Skin cancer, involving abnormal skin cell growth, includes melanoma, the most dangerous form. Early detection is crucial, but traditional methods like visual inspection and biopsy are time-consuming and subjective. AI provides a more efficient, objective approach. This chapter enhances diagnostic accuracy using advanced image processing and classification on a comprehensive skin cancer dataset with seven classes. Initially imbalanced, data augmentation balanced it, generating 2000 images per class. Gray Level Co-occurrence Matrix (GLCM) and Color Histogram were used for feature extraction, combined with a Random Forest classifier. The best model achieved 97% accuracy, emphasizing balanced data and effective feature extraction in AI-based skin cancer diagnosis.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Gray-level histogram"

1

Tanaka, Hideaki, and Akira Taguchi. "Generalized Differential Gray-level Histogram Equalization." In 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2019. http://dx.doi.org/10.1109/ispacs48206.2019.8986331.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Youlian Zhu and Cheng Huang. "Histogram equalization algorithm for variable gray level mapping." In 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcica.2010.5554587.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Sha, Chunshi, Jian Hou, Hongxia Cui, and Jianxin Kang. "Gray Level-Median Histogram Based 2D Otsu's Method." In 2015 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII). IEEE, 2015. http://dx.doi.org/10.1109/iciicii.2015.95.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kim, Bongjoe, Gi Yeong Gim, and Hyung Jun Park. "Dynamic histogram equalization based on gray level labeling." In IS&T/SPIE Electronic Imaging, edited by Reiner Eschbach, Gabriel G. Marcu, and Alessandro Rizzi. SPIE, 2014. http://dx.doi.org/10.1117/12.2042606.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Yang Xiao, Zhiguo Cao, and Tianxu Zhang. "Entropic thresholding based on gray-level spatial correlation histogram." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761626.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ma, Xinjun, and Hongjun Zhang. "Joint geometry and gray-level histogram model for lip-reading." In 2016 12th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2016. http://dx.doi.org/10.1109/wcica.2016.7578278.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

He, Chu, Xin-Ping Deng, Gui-Song Xia, Wen Yang, and Hong Sun. "Topographic gray level multiscale analysis and its application to histogram modification." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5654256.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yuan, Hai-Dong. "Identification of global histogram equalization by modeling gray-level cumulative distribution." In 2013 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2013. http://dx.doi.org/10.1109/chinasip.2013.6625421.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Chen Yu, Chen Dian-ren, Li Yang, and Chen Lei. "Otsu's thresholding method based on gray level-gradient two-dimensional histogram." In 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010). IEEE, 2010. http://dx.doi.org/10.1109/car.2010.5456687.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Milles, Julien, Yue Min Zhu, Nankuei Chen, Lawrence P. Panych, Gerard Gimenez, and Charles R. Guttmann. "MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information." In Medical Imaging 2004, edited by J. Michael Fitzpatrick and Milan Sonka. SPIE, 2004. http://dx.doi.org/10.1117/12.532325.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Gray-level histogram' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography