Relevant bibliographies by topics / Histogram equalization

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Histogram equalization'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Histogram equalization"

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Dhal, Krishna Gopal, Sankhadip Sen, Kaustav Sarkar, and Sanjoy Das. "Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement." International Journal of Computer Vision and Image Processing 6, no. 1 (January 2016): 59–72. http://dx.doi.org/10.4018/ijcvip.2016010105.

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In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).
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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 (December 19, 2010): 43. http://dx.doi.org/10.3329/jsr.v3i1.5299.

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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) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified. Experimental results on medical images demonstrate that the proposed method can enhance the images effectively. The result is also compared with the result of image enhancement technique using local statistics.Keywords: Histogram equalization; Global histogram equalization; Image enhancement; Local statistics.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5299 J. Sci. Res. 3 (1), 43-50 (2011)
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Stoel, B. C., A. M. Vossepoel, F. P. Ottes, P. L. Hofland, H. M. Kroon, and L. J. Schultze Kool. "Interactive histogram equalization." Pattern Recognition Letters 11, no. 4 (April 1990): 247–54. http://dx.doi.org/10.1016/0167-8655(90)90063-8.

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Husain, Nursuci Putri, and Nurseno Bayu Aji. "Pemanfaatan Histogram Equalization pada Local Tri Directional Pattern untuk Sistem Temu Kembali Citra." SPECTA Journal of Technology 4, no. 1 (April 1, 2020): 49–58. http://dx.doi.org/10.35718/specta.v4i1.164.

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Abstract Local tri-directional pattern (LtriDP) is a method of extracting local intensity features from each pixel based on direction. However, this method has not been able to provide good performance in extracting features for image retrieval. One reason that makes image retrieval performance worse is the effect of lighting. Lighting can cause large variations between images. This study proposed utilization of Histogram Equalization (HE). Histogram equalization is a functional method of stretching gray degrees and expanding image contrast. This will make variations in the gray level of the original image can be controlled. There are several main stages in this study, firstly query image and image dataset will be preprocessed with histogram equalization. After that, the image is extracted by a tri-directional pattern and magnitude pattern are searched. A tri-directional pattern will produce two histograms, while a magnitude pattern produces one histogram. The three histograms are combined or joint histogram is performed. Histogram that has been joint is a feature vector. The feature vector will be calculated using a similarity measurement Canberra. After that, an image similar to the query image will be obtained. The experiment was conducted using 3 face datasets namely ORL, BERN, and YALE. The average recall value was 0.422 for the ORL dataset, 0.50 for the BERN dataset, and 0.63 for the YALE dataset. The evaluation show, the proposed method can be used as a process of improving the quality of image datasets in the image retrieval system. Keywords: Image retrieval system, Local tri-directional pattern, Streching Image, Histogram Equalization, Similarity Measurement Canberra. Abstrak Local tri-directional pattern (LtriDP) merupakan salah satu metode ekstraksi fitur intensitas lokal dari setiap piksel berdasarkan arah. Namun, metode ini belum mampu memberikan performa yang baik dalam mengekstrak fitur untuk temu kembali citra. Salah satu alasan yang membuat performa temu kembali citra tidak baik adalah pengaruh pencahayaan. Pencahayaan dapat menyebabkan variasi besar antar citra. Penelitian ini mengusulkan pemanfaatan Histogram Equalization (HE). HE merupakan metode fungsional dalam peregangan derajat keabuan dan memperluas kontras citra. Hal ini akan membuat variasi level keabuan dari citra asli dapat terkendali. Ada beberapa tahapan utama dalam penelitian ini, yang pertama citra query dan citra dataset akan terlebih dahulu di preprocessing dengan histogram equalization. Setelah itu, citra tersebut diekstrak fiturnya, dicari pola tri-directional dan pola magnitude. Pola tri-directional akan menghasilkan dua histogram, sedangkan pola magnitude menghasilkan satu histogram. Ketiga histogram tersebut kemudian disatukan atau dilakukan joint histogram. Histogram yang telah dijoint merupakan vektor fitur. Vektor fitur tersebut akan dihitung rankingnya menggunakan pengukuran jarak canberra. Setelah itu, akan didapatkan citra yang mirip dengan citra query. Uji coba dilakukan dengan menggunakan 3 dataset wajah yaitu ORL, BERN, dan YALE. Nilai rata-rata recall yang di dapatkan 0,422 untuk dataset ORL, 0,50 untuk dataset BERN, dan 0,63 untuk dataset YALE. Dari hasil evaluasi tersebut, dapat disimpulkan metode yang diusulkan dapat digunakan sebagai proses peningkatan kualitas dataset citra pada system temu kembali citra. Keywords: Sistem Temu Kembali Citra, Local tri-directional pattern, Peregangan Kontras, Histogram Equalization, Perhitungan Jarak Canberra.
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Kadhum, Zainab Abdulrazzaq. "Equalize The Histogram Equalization for Image enhancement." Journal of Kufa for Mathematics and Computer 1, no. 5 (May 30, 2012): 14–21. http://dx.doi.org/10.31642/jokmc/2018/010502.

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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
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Rudikov, S. I., V. Yu Tsviatkou, and A. P. Shkadarevich. "Dynamic range reduction of infrared images based on adaptive equalization, stretch and compression of histogram." Proceedings of the National Academy of Sciences of Belarus, Physical-Technical Series 66, no. 4 (December 26, 2021): 470–82. http://dx.doi.org/10.29235/1561-8358-2021-66-4-470-482.

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The problem of reducing the dynamic range and improving the quality of infrared (IR) images with a wide dynamic range for their display on a liquid crystal matrix with 8-bit pixels is considered. To solve this problem in optoelectronic devices in real time, block algorithms based on local equalization of the histogram are widely used, taking into account their relatively low computational complexity and the possibility of taking into account local features of the brightness distribution. The basic adaptive histogram equalization algorithm provides reasonably high image quality after conversion, but may result in excessive contrast for some types of images. In a modified algorithm of adaptive histogram equalization, the contrast is limited by a threshold by truncating local maxima at the edges of the histogram. This leads, however, to a deterioration in other indicators of image quality. This disadvantage is inherent in many algorithms of local histogram equalization, along with limited control over the characteristics of image reproduction quality. To improve the quality and expand the control interval for the characteristics of the reproduction of infrared images, the article proposes an algorithm for double reduction of the dynamic range of the image with intermediate control of the shape of its histogram. This algorithm performs: preliminary reduction of the dynamic range of the image based on adaptive equalization of the histogram, control of the shape of the histogram based on its linear or nonlinear compression, linear stretching of its central part and linear stretching (compression) of its lateral parts, final reduction of the dynamic range based on linear compression of the entire histograms. The characteristics of the proposed algorithm are compared with the characteristics of known algorithms for reducing the dynamic range and improving the image quality. The dependences of the characteristics of the quality of image reproduction after a decrease in their dynamic range on the control parameters of the proposed algorithm and recommendations for their choice taking into account the computational complexity are given.
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Tang, Jing Rui, and Nor Ashidi Mat Isa. "Bi-histogram equalization using modified histogram bins." Applied Soft Computing 55 (June 2017): 31–43. http://dx.doi.org/10.1016/j.asoc.2017.01.053.

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Wijaya Kusuma, I. Wayan Angga, and Afriliana Kusumadewi. "PENERAPAN METODE CONTRAST STRETCHING, HISTOGRAM EQUALIZATION DAN ADAPTIVE HISTOGRAM EQUALIZATION UNTUK MENINGKATKAN KUALITAS CITRA MEDIS MRI." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 11, no. 1 (April 30, 2020): 1–10. http://dx.doi.org/10.24176/simet.v11i1.3153.

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Citra medis adalah suatu pola atau gambar dua dimensi bagian dalam tubuh manusia yang digunakan oleh ahli kesehatan untuk mendeteksi dan menganalisa penyakit pasien. Pada bidang radiologi citra yang sering digunakan saat ini adalah citra Magnetic resonance Imaging (MRI). Keunggulan citra MRI adalah kemampuan menampilkan detail anatomi secara jelas dalam berbagai potongan (multiplanar) tanpa mengubah posisi pasien. Citra MRI ini akan digunakan oleh dokter ataupun peneliti untuk melakukan analisis ada tidaknya suatu tumor, kanker, atau kelainan pada pasien. Penelitian ini mengusulkan metode Contrast Stretching, Histogram Equalization dan Adaptive Histogram Equalization untuk meningkatkan kualitas citra medis. Batasan masalah penelitian ini adalah citra medis MRI yang digunakan sebagai obyek penelitian adalah citra medis MRI Otak baik yang normal atau yang mengalami lesi (gangguan). Dari hasil kualitas citra dan analisa kuantitatif menunjukkan bahwa metode contrast stretching menghasilkan hasil kualitas citra MRI jauh lebih baik dibandingkan dengan metosde histogram equalization, dan adaptive histogram equalization. Nilai MSE yang paling rendah adalah pada metode contrast stretching yaitu 0,00346. Sedangkan nilai MSE yang paling besar dihasilkan oleh metode histogram equalization. Kualitas citra dengan metode contrast stretching menghasilkan nilai PSNR yang paling besar yaitu 22,0677. Ini menandakan bahwa kualitas citra dari metode contrast stretching jauh lebih baik dibandingkan metode histogram equalization, dan adaptive histogram equalization.
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Winarno, Guntur, Muhammad Irsal, Claricia Alamanda Karenina, Gando Sari, and Rinda Nur Hidayati. "Metode Histogram Equalization untuk Peningkatan Kualitas Citra dengan Menggunakan Studi Phantom Lumbosacral." Jurnal Kesehatan Vokasional 7, no. 2 (May 31, 2022): 104. http://dx.doi.org/10.22146/jkesvo.71469.

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Latar Belakang: Pemeriksaan lumbosacral sering kali menghasilkan kualitas citra yang kurang optimal. histogram equalization merupakan tahapan memanipulasi data citra digital untuk meningkatkan kualitas citra yang dapat diimplementasikan pada citra digital radiografi lumbosacral.Tujuan: Mengevaluasi peningkatan kualitas citra digital radiografi lumbosacral dengan menggunakan histogram equalization.Metode: Jenis penelitian ini adalah kuantitatif dengan pendekatan eksperimental. Jumlah sampel terdiri dari 1 Kyouku’s anthropomorphic phantom yang dibagi menjadi citra lumbosacral proyeksi antero posterior (AP) dan lateral sebelum dan setelah direkonstruksi menggunakan histogram equalization. Kualitas citra dinilai dengan analisis grafik histogram, pengukuran nilai signal to noise ratio (SNR) merupakan parameter untuk menentukan kualitas citra radiografi, dan visual grading analysis (VGA) oleh 10 orang radiografer dianalisis dengan menggunakan Uji Wilcoxon Signed-Rank.Hasil: Hasil kualitas citra menunjukkan bahwa analisis grafik histogram memiliki visual kecerahan yang meningkat, grafik histogram terdistribusi merata, dan nilai SNR meningkat setelah direkonstruksi dengan metode histogram equalization. Hasil VGA dengan menggunakan uji wilcoxon Signed-Rank setelah direkonstruksi kembali dengan metode histogram equalization pada proyeksi AP menunjukkan nilai 0,005 dan proyeksi lateral 0,074 dengan p-value > 0,05.Kesimpulan: Terjadi peningkatan kualitas citra radiografi proyeksi AP dan lateral dengan menggunakan metode histogram equalization.
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KWON, S. H., H. C. JEONG, S. T. SEO, I. K. LEE, and C. S. SON. "Histogram Equalization-Based Thresholding." IEICE Transactions on Information and Systems E91-D, no. 11 (November 1, 2008): 2751–53. http://dx.doi.org/10.1093/ietisy/e91-d.11.2751.

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Dissertations / Theses on the topic "Histogram equalization"

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Kurak, Charles W. Jr. "Adaptive Histogram Equalization, a Parallel Implementation." UNF Digital Commons, 1990. http://digitalcommons.unf.edu/etd/260.

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Adaptive Histogram Equalization (AHE) has been recognized as a valid method of contrast enhancement. The main advantage of AHE is that it can provide better contrast in local areas than that achievable utilizing traditional histogram equalization methods. Whereas traditional methods consider the entire image, AHE utilizes a local contextual region. However, AHE is computationally expensive, and therefore time-consuming. In this work two areas of computer science, image processing and parallel processing, are combined to produce an efficient algorithm. In particular, the AHE algorithm is implemented with a Multiple-Instruction-Multiple-Data (MIMD) parallel architecture. It is proposed that, as MIMD machines become more powerful and prevalent, this methodology can be applied to not only this particular algorithm, but also to many others in its class.
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Yakoubian, Jeffrey Scott. "Adaptive histogram equalization for mammographic image processing." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/16387.

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Kvapil, Jiří. "Adaptivní ekvalizace histogramu digitálních obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228687.

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The diploma thesis is focused on histogram equalization method and his extension by the adaptive boundary. This thesis contains explanations of basic notions on that histogram equalization method was created. Next part is described the human vision and priciples of his imitation. In practical part of this thesis was created software that makes it possible to use methods of adaptive histogram equalization on real images. At the end is showed some results that was reached.
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Gomes, David Menotti. "Contrast enhancement in digital imaging using histogram equalization." Phd thesis, Université Paris-Est, 2008. http://tel.archives-ouvertes.fr/tel-00470545.

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Nowadays devices are able to capture and process images from complex surveillance monitoring systems or from simple mobile phones. In certain applications, the time necessary to process the image is not as important as the quality of the processed images (e.g., medical imaging), but in other cases the quality can be sacrificed in favour of time. This thesis focuses on the latter case, and proposes two methodologies for fast image contrast enhancement methods. The proposed methods are based on histogram equalization (HE), and some for handling gray-level images and others for handling color images As far as HE methods for gray-level images are concerned, current methods tend to change the mean brightness of the image to the middle level of the gray-level range. This is not desirable in the case of image contrast enhancement for consumer electronics products, where preserving the input brightness of the image is required to avoid the generation of non-existing artifacts in the output image. To overcome this drawback, Bi-histogram equalization methods for both preserving the brightness and contrast enhancement have been proposed. Although these methods preserve the input brightness on the output image with a significant contrast enhancement, they may produce images which do not look as natural as the ones which have been input. In order to overcome this drawback, we propose a technique called Multi-HE, which consists of decomposing the input image into several sub-images, and then applying the classical HE process to each one of them. This methodology performs a less intensive image contrast enhancement, in a way that the output image presented looks more natural. We propose two discrepancy functions for image decomposition which lead to two new Multi-HE methods. A cost function is also used for automatically deciding in how many sub-images the input image will be decomposed on. Experimental results show that our methods are better in preserving the brightness and producing more natural looking images than the other HE methods. In order to deal with contrast enhancement in color images, we introduce a generic fast hue-preserving histogram equalization method based on the RGB color space, and two instances of the proposed generic method. The first instance uses R-red, G-green, and Bblue 1D histograms to estimate a RGB 3D histogram to be equalized, whereas the second instance uses RG, RB, and GB 2D histograms. Histogram equalization is performed using 7 Abstract 8 shift hue-preserving transformations, avoiding the appearance of unrealistic colors. Our methods have linear time and space complexities with respect to the image dimension, and do not require conversions between color spaces in order to perform image contrast enhancement. Objective assessments comparing our methods and others are performed using a contrast measure and color image quality measures, where the quality is established as a weighed function of the naturalness and colorfulness indexes. This is the first work to evaluate histogram equalization methods with a well-known database of 300 images (one dataset from the University of Berkeley) by using measures such as naturalness and colorfulness. Experimental results show that the value of the image contrast produced by our methods is in average 50% greater than the original image value, and still keeping the quality of the output images close to the original
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Gaddam, Purna Chandra Srinivas Kumar, and Prathik Sunkara. "Advanced Image Processing Using Histogram Equalization and Android Application Implementation." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13735.

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Now a days the conditions at which the image taken may lead to near zero visibility for the human eye. They may usually due to lack of clarity, just like effects enclosed on earth’s atmosphere which have effects upon the images due to haze, fog and other day light effects. The effects on such images may exists, so useful information taken under those scenarios should be enhanced and made clear to recognize the objects and other useful information. To deal with such issues caused by low light or through the imaging devices experience haze effect many image processing algorithms were implemented. These algorithms also provide nonlinear contrast enhancement to some extent. We took pre-existed algorithms like SMQT (Successive mean Quantization Transform), V Transform, histogram equalization algorithms to improve the visual quality of digital picture with large range scenes and with irregular lighting conditions. These algorithms were performed in two different method and tested using different image facing low light and color change and succeeded in obtaining the enhanced image. These algorithms helps in various enhancements like color, contrast and very accurate results of images with low light. Histogram equalization technique is implemented by interpreting histogram of image as probability density function. To an image cumulative distribution function is applied so that accumulated histogram values are obtained. Then the values of the pixels are changed based on their probability and spread over the histogram. From these algorithms we choose histogram equalization, MATLAB code is taken as reference and made changes to implement in API (Application Program Interface) using JAVA and confirms that the application works properly with reduction of execution time.
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Skosan, Marshalleno. "Histogram equalization for robust text-independent speaker verification in telephone environments." Master's thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/5103.

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Gatti, Pruthvi Venkatesh, and Krishna Teja Velugubantla. "Contrast Enhancement of Colour Images using Transform Based Gamma Correction and Histogram Equalization." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14424.

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Contrast is an important factor in any subjective evaluation of image quality. It is the difference in visual properties that makes an object distinguishable from other objects and background. Contrast Enhancement method is mainly used to enhance the contrast in the image by using its Histogram. Histogram is a distribution of numerical data in an image using graphical representation. Histogram Equalization is widely used in image processing to adjust the contrast in the image using histograms. Whereas Gamma Correction is often used to adjust luminance in an image. By combining Histogram Equalization and Gamma Correction we proposed a hybrid method, that is used to modify the histograms and enhance contrast of an image in a digital method. Our proposed method deals with the variants of histogram equalization and transformed based gamma correction. Our method is an automatically transformation technique that improves the contrast of dimmed images via the gamma correction and probability distribution of luminance pixels. The proposed method is converted into an android application. We succeeded in enhancing the contrast of an image by using our method and we have tested for different alpha values. Graphs of the gamma for different alpha values are plotted.
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Mallampati, Vivek. "Image Enhancement & Automatic Detection of Exudates in Diabetic Retinopathy." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18109.

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Diabetic retinopathy (DR) is becoming a global health concern, which causes the loss of vision of most patients with the disease. Due to the vast prevalence of the disease, the automated detection of the DR is needed for quick diagnoses where the progress of the disease is monitored by detection of exudates changes and their classifications in the fundus retina images. Today in the automated system of the disease diagnoses, several image enhancement methods are used on original Fundus images. The primary goal of this thesis is to make a comparison of three of popular enhancement methods of the Mahalanobis Distance (MD), the Histogram Equalization (HE) and the Contrast Limited Adaptive Histogram Equalization (CLAHE). By quantifying the comparison in the aspect of the ability to detect and classify exudates, the best of the three enhancement methods is implemented to detect and classify soft and hard exudates. A graphical user interface is also adopted, with the help of MATLAB. The results showed that the MD enhancement method yielded better results in enhancement of the digital images compared to the HE and the CLAHE. The technique also enabled this study to successfully classify exudates into hard and soft exudates classification. Generally, the research concluded that the method that was suggested yielded the best results regarding the detection of the exudates; its classification and management can be suggested to the doctors and the ophthalmologists.
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Naram, Hari Prasad. "Classification of Dense Masses in Mammograms." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1528.

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This dissertation material provided in this work details the techniques that are developed to aid in the Classification of tumors, non-tumors, and dense masses in a Mammogram, certain characteristics such as texture in a mammographic image are used to identify the regions of interest as a part of classification. Pattern recognizing techniques such as nearest mean classifier and Support vector machine classifier are also used to classify the features. The initial stages include the processing of mammographic image to extract the relevant features that would be necessary for classification and during the final stage the features are classified using the pattern recognizing techniques mentioned above. The goal of this research work is to provide the Medical Experts and Researchers an effective method which would aid them in identifying the tumors, non-tumors, and dense masses in a mammogram. At first the breast region extraction is carried using the entire mammogram. The extraction is carried out by creating the masks and using those masks to extract the region of interest pertaining to the tumor. A chain code is employed to extract the various regions, the extracted regions could potentially be classified as tumors, non-tumors, and dense regions. Adaptive histogram equalization technique is employed to enhance the contrast of an image. After applying the adaptive histogram equalization for several times which will provide a saturated image which would contain only bright spots of the mammographic image which appear like dense regions of the mammogram. These dense masses could be potential tumors which would need treatment. Relevant Characteristics such as texture in the mammographic image are used for feature extraction by using the nearest mean and support vector machine classifier. A total of thirteen Haralick features are used to classify the three classes. Support vector machine classifier is used to classify two class problems and radial basis function (RBF) kernel is used to find the best possible (c and gamma) values. Results obtained in this research suggest the best classification accuracy was achieved by using the support vector machines for both Tumor vs Non-Tumor and Tumor vs Dense masses. The maximum accuracies achieved for the tumor and non-tumor is above 90 % and for the dense masses is 70.8% using 11 features for support vector machines. Support vector machines performed better than the nearest mean majority classifier in the classification of the classes. Various case studies were performed using two distinct datasets in which each dataset consisting of 24 patients’ data in two individual views. Each patient data will consist of both the cranio caudal view and medio lateral oblique views. From these views the region of interest which could possibly be a tumor, non-tumor, or a dense regions(mass).
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Jomaa, Diala. "Fingerprint Segmentation." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4264.

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In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.
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Book chapters on the topic "Histogram equalization"

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Borda, Monica, Romulus Terebes, Raul Malutan, Ioana Ilea, Mihaela Cislariu, Andreia Miclea, and Stefania Barburiceanu. "Histogram Equalization." In Randomness and Elements of Decision Theory Applied to Signals, 165–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90314-5_11.

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Montazeri, Mitra. "Modified Histogram Segmentation Bi-Histogram Equalization." In Advances in Intelligent Systems and Computing, 443–53. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1081-6_38.

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Marques, Oge, and Gustavo Benvenutti Borba. "Recipe 8: Histogram equalization and histogram matching." In Image Processing Recipes in MATLAB®, 52–57. New York: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003170198-11.

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Paul, Raj Kumar, and Saravanan Chandran. "Image Compression Using Histogram Equalization." In Advances in Intelligent Systems and Computing, 47–61. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0475-2_5.

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Austin, John D., and Stephen M. Pizer. "A Multiprocessor Adaptive Histogram Equalization Machine." In Information Processing in Medical Imaging, 375–92. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-7263-3_25.

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Sahoo, Subhasmita, Jagyanseni Panda, and Mihir Narayan Mohanty. "Adaptive Bi-Histogram Equalization Using Threshold (ABHET)." In Communications in Computer and Information Science, 151–58. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3433-6_19.

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Naushad Ali, M. M., and M. Abdullah-Al-Wadud. "Image Enhancement Using a Modified Histogram Equalization." In Communications in Computer and Information Science, 17–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35270-6_3.

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Kunnath, Neeth Xavier, and Suk-Ho Lee. "Meanshift Segmentation Guided Spatially Adaptive Histogram Equalization." In Computer Science and its Applications, 713–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45402-2_100.

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Kim, Hyoung-Joon, Jong-Myung Lee, Jin-Aeon Lee, Sang-Geun Oh, and Whoi-Yul Kim. "Contrast Enhancement Using Adaptively Modified Histogram Equalization." In Advances in Image and Video Technology, 1150–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949534_116.

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Cui, Zhen, Junfeng Wu, Hong Yu, Yizhi Zhou, and Liang Liang. "Underwater Image Saliency Detection Based on Improved Histogram Equalization." In Communications in Computer and Information Science, 157–65. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0121-0_12.

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Conference papers on the topic "Histogram equalization"

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Vlachos, Ioannis K., and George D. Sergiadis. "Hesitancy Histogram Equalization." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295620.

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Roopaei, Mehdi, Sos Agaian, Mehdi Shadaram, and Frank Hurtado. "Cross-entropy Histogram Equalization." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6973900.

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Zhihong, Wu, and Xiao Xiaohong. "Study on Histogram Equalization." In 2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing (IPTC). IEEE, 2011. http://dx.doi.org/10.1109/iptc.2011.52.

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Tanaka, Hideaki, and Akira Taguchi. "Brightness Preserving Generalized Histogram Equalization." In TENCON 2020 - 2020 IEEE REGION 10 CONFERENCE (TENCON). IEEE, 2020. http://dx.doi.org/10.1109/tencon50793.2020.9293837.

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Moniruzzaman, Md, Md Shafuzzaman, and Md Foisal Hossain. "Multiple regions based histogram equalization." In 2014 International Conference on Informatics, Electronics & Vision (ICIEV). IEEE, 2014. http://dx.doi.org/10.1109/iciev.2014.6850750.

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Pizer, Stephen M., John D. Austin, Robert Cromartie, Ari Geselowitz, Bart t. H. Romeny, John B. Zimmerman, and Karel Zuiderveld. "Algorithms For Adaptive Histogram Equalization." In Physics and Engineering of Computerized Multidimensional Imaging and Processing, edited by Thomas F. Budinger, Zang-Hee Cho, and Orhan Nalcioglu. SPIE, 1986. http://dx.doi.org/10.1117/12.966688.

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McCollum, A. J., and W. F. Clocksin. "Multidimensional Histogram Equalization and Modification." In 14th International Conference on Image Analysis and Processing (ICIAP 2007). IEEE, 2007. http://dx.doi.org/10.1109/iciap.2007.4362852.

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Sanny, Andrea, Yi-Hua E. Yang, and Viktor K. Prasanna. "Energy-efficient histogram equalization on FPGA." In 2014 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2014. http://dx.doi.org/10.1109/hpec.2014.7040996.

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Wu, Xiaomeng, Takahito Kawanishi, and Kunio Kashino. "Reflectance-Guided, Contrast-Accumulated Histogram Equalization." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054004.

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Lin, Ping-Hsien, Hsu-Chun Yen, and Chia-Chen Yu. "Gaussian Distributive Filtering in Histogram Equalization." In 2010 International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA 2010). IEEE, 2010. http://dx.doi.org/10.1109/bwcca.2010.129.

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