Relevant bibliographies by topics / GRAY SCALE IMAGE

Academic literature on the topic 'GRAY SCALE IMAGE'

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Published: 11 September 2023

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Journal articles on the topic "GRAY SCALE IMAGE"

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Edan, Salam Jabbar, Maryam Nadhim Rasoul, and Amir A. Aljarrah. "A Proposed Technique for Gray Image Colorization." Webology 19, no. 1 (January 20, 2022): 5099–106. http://dx.doi.org/10.14704/web/v19i1/web19343.

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The colorization of the gray-scale image is the process of using color image (has a similar "mood") to add color to a grayscale image. In this paper, we proposed new approach for colorization gray-scale images depending on Singular Value Decomposition (SVD). We used SVD to add color to a grayscale image by determining the best pixel value in the reference image, based on comparing the first column values from the left singular vector matrix of 3x3 block and the pixel value in the center of block of the gray image with other corresponding values in the reference image. Finally, use the best match to transfer color from color image to gray-scale image. The results of the proposed colorization approach are good and plausible.
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Eberly, David, and Jack Lancaster. "On gray scale image measurements." CVGIP: Graphical Models and Image Processing 53, no. 6 (November 1991): 538–49. http://dx.doi.org/10.1016/1049-9652(91)90004-4.

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Eberly, David, Jack Lancaster, and Abdalmajeid Alyassin. "On gray scale image measurements." CVGIP: Graphical Models and Image Processing 53, no. 6 (November 1991): 550–62. http://dx.doi.org/10.1016/1049-9652(91)90005-5.

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Kalra, G. S., and Shilpi Singh. "Efficient digital image denoising for gray scale images." Multimedia Tools and Applications 75, no. 8 (February 8, 2015): 4467–84. http://dx.doi.org/10.1007/s11042-015-2484-x.

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Reddy, V. Varshith, Y. Shiva Krishna, U. Varun Kumar Reddy, and Shubhangi Mahule. "Gray Scale Image Captioning Using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1566–71. http://dx.doi.org/10.22214/ijraset.2022.41589.

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Abstract: The objective of the project is to generate caption of an image. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. With the advancement in Deep learning techniques and availability of huge datasets and computer power, we can build models that can generate captions for an image. This is what we have implemented in this Python based project where we have used the deep learning techniques of CNN (Convolutional Neural Networks) and LSTM (Long short term memory) which is a type of RNN (Recurrent Neural Network) together so that using computer vision computer can recognize the context of an image and display it in natural language like English. Gray Scale Image captioning can give captions for both monochrome and color images. Keywords: Image, Caption, Convolutional Neural Networks, Long Short Term Memory, Recurrent Neural Network
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Tian, Kai, and Jia Rong Shi. "Color Composite of Digital Radiography Image and Region Growing Defect Detection." Applied Mechanics and Materials 380-384 (August 2013): 938–42. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.938.

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DR image in industrial castings is usually a gray scale image and it has difficulty in simultaneously showing the defects within different thickness parts. To address this issue, the floating point type data obtained by the scanning of DR system is firstly converted into three gray scale images by the more gray stretch technique, where different gray scale images contain different thickness defects of the castings. Secondly, those three gray images are synthesized into one color image. The synthesized image contains lots of details of measured castings and its aim is to reduce the loss of image detail caused by the converting procedure. Then region growing model is exploited to segment the defects of images. Finally, experimental results show that the color synthesis method can better segment the casting defects of the different wall thickness.
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Wu, Shuyue, and Jingfang Wang. "A Quantum Pointer Signal Processing Research." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 3 (June 1, 2016): 675. http://dx.doi.org/10.11591/ijeecs.v2.i3.pp675-683.

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<span lang="EN-US">In quantum gray-scale image processing, the storage in quantum states is the color information and the position information According to the advantage of small range of the gray scale in a gray-scale image, a novel storage expression of quantum gray-scale image is proposed and demonstrated in this study. Besides, a new concept of "quantum pointer" is put forward based on the expression. Quantum pointer is the vinculum between the information of gray-scale and position of each pixel in quantum gray-scale images. The feasibility is verified for the proposed quantum pointer, and the properties of bi-direction and sub-block are used, the storing and other operations of quantum gray-scale image are simpler and more convenient. </span>
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Yang, Yu-Guang, Qian-Qian Zhao, and Si-Jia Sun. "Novel quantum gray-scale image matching." Optik 126, no. 22 (November 2015): 3340–43. http://dx.doi.org/10.1016/j.ijleo.2015.08.010.

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Li, Ming Jing, Yu Bing Dong, and Xiao Li Wang. "Research and Development of Non Multi-Scale to Pixel-Level Image Fusion." Applied Mechanics and Materials 448-453 (October 2013): 3621–24. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3621.

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Image fusion method based on the non multi-scale take the original image as object of study, using various fusion rule of image fusion to fuse images, but not decomposition or transform to original images. So, it can also be called simple multi sensor image fusion methods. Its advantages are low computational complexity and simple principle. Image fusion method based on the non multi-scale is currently the most widely used image fusion methods. The basic principle of fuse method is directly to select large gray, small gray and weighted average among pixel on the source image, to fuse into a new image. Simple pixel level image fusion method mainly includes the pixel gray value being average or weighted average, pixel gray value being selected large and pixel gray value being selected small, etc. Basic principle of fusion process was introduced in detail in this paper, and pixel level fusion algorithm at present was summed up. Simulation results on fusion are presented to illustrate the proposed fusion scheme. In practice, fusion algorithm was selected according to imaging characteristics being retained.
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G, Srinivasa Rao, Sri Krishna A, Mahaboob Basha S, and Prakash Ch. Jeevan. "Object-Based Image Enhancement Technique for Gray Scale Images." International Journal of Advanced Information Technology 4, no. 3 (June 30, 2014): 9–23. http://dx.doi.org/10.5121/ijait.2014.4302.

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Dissertations / Theses on the topic "GRAY SCALE IMAGE"

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Fetter, Paul. "Heuristics for selecting gray scale morphological structuring elements /." Online version of thesis, 1994. http://hdl.handle.net/1850/11728.

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Ström, Bartunek Josef. "FINGERPRINT IMAGE ENHANCEMENT, SEGMENTATION AND MINUTIAE DETECTION." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-11149.

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Prior to 1960's, the fingerprint analysis was carried out manually by human experts and for forensic purposes only. Automated fingerprint identification systems (AFIS) have been developed during the last 50 years. The success of AFIS resulted in that its use expanded beyond forensic applications and became common also in civilian applications. Mobile phones and computers equipped with fingerprint sensing devices for fingerprint-based user identification are common today. Despite the intense development efforts, a major problem in automatic fingerprint identification is to acquire reliable matching features from fingerprint images with poor quality. Images where the fingerprint pattern is heavily degraded usually inhibit the performance of an AFIS system. The performance of AFIS systems is also reduced when matching fingerprints of individuals with large age variations. This doctoral thesis presents contributions within the field of fingerprint image enhancement, segmentation and minutiae detection. The reliability of the extracted fingerprint features is highly dependent on the quality of the obtained fingerprints. Unfortunately, it is not always possible to have access to high quality fingerprints. Therefore, prior to the feature extraction, an enhancement of the quality of fingerprints and a segmentation are performed. The segmentation separates the fingerprint pattern from the background and thus limits possible sources of error due to, for instance, feature outliers. Most enhancement and segmentation techniques are data-driven and therefore based on certain features extracted from the low quality fingerprints at hand. Hence, different types of processing, such as directional filtering, are employed for the enhancement. This thesis contributes by proposing new research both for improving fingerprint matching and for the required pre-processing that improves the extraction of features to be used in fingerprint matching systems. In particular, the majority of enhancement and segmentation methods proposed herein are adaptive to the characteristics of each fingerprint image. Thus, the methods are insensitive towards sensor and fingerprint variability. Furthermore, introduction of the higher order statistics (kurtosis) for fingerprint segmentation is presented. Segmentation of the fingerprint image reduces the computational load by excluding background regions of the fingerprint image from being further processed. Also using a neural network to obtain a more robust minutiae detector with a patch rejection mechanism for speeding up the minutiae detection is presented in this thesis.
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Becer, Huseyin Caner. "A Robust Traffic Sign Recognition System." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612912/index.pdf.

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The traffic sign detection and recognition system is an essential part of the driver warning and assistance systems. In this thesis, traffic sign recognition system is studied. We considered circular, triangular and square Turkish traffic signs. For detection stage, we have two different approaches. In first approach, we assume that the detected signs are available. In the second approach, the region of interest of the traffic sign image is given. Traffic sign is extracted from ROI by using a detection algorithm. In recognition stage, the ring-partitioned method is implemented. In this method, the traffic sign is divided into rings and the normalized fuzzy histogram is used as an image descriptor. The histograms of these rings are compared with the reference histograms. Ring-partitions provide robustness to rotation because the rotation does not change the histogram of the ring. This is very critical for circle signs because rotation is hard to detect in circle signs. To overcome illumination problem, specified gray scale image is used. To apply this method to triangle and square signs, the circumscribed circle of these shapes is extracted. Ring partitioned method is tested for the case where the detected signs are available and the region of interests of the traffic sign is given. The data sets contain about 500 static and video captured images and the images in the data set are taken in daytime.
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Muhammad, Imran. "Colorizing Grey Scale Images." Thesis, Högskolan Dalarna, Datateknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6181.

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The purpose of this thesis is to develop a working methodology to color a grey scale image. This thesis is based on approach of using a colored reference image. Coloring grey scale images has no exact solution till date and all available methods are based on approximation. This technique of using a color reference image for approximating color information in grey scale image is among most modern techniques.Method developed here in this paper is better than existing methods of approximation of color information addition in grey scale images in brightness, sharpness, color shade gradients and distribution of colors over objects.Color and grey scale images are analyzed for statistical and textural features. This analysis is done only on basis of luminance value in images. These features are then segmented and segments of color and grey scale images are mapped on basis of distances of segments from origin. Then chromatic values are transferred between these matched segments from color image to grey scale image.Technique proposed in this paper uses better mechanism of mapping clusters and mapping colors between segments, resulting in notable improvement in existing techniques in this category.
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Kowalewski, Damian. "Edge-based encoding of gray-scale images." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80305.

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This thesis explores image compression based on visual information contained within the edges. This work is based on an observation that not all the information contained within an image is used by the human brain. Previous works demonstrate methods for reconstructing images adequately using edge information. We exploit the fact that some components of the image can be removed while having little effect on human perception: this is the key basis for the compression developed in this thesis. We first construct compression preserving all the edge information extracted from the image. We then consider the impact of removing some of the edge information to further improve the compression ratio. We direct our attention to two types of information associated with edges: (1) its location and shape, (2) intensity and blur values on each side of the edge. First we propose a method to encode edge location and shape in a most efficient manner. We propose a measure of redundancy to remove edges carrying information already encoded in neighboring edges. Then we consider the compression of intensities and blur associated with each edge. Since the intensity information is quite noisy we show that we can remove the noise and reduce intensity functions to linear functions without significant impact on the quality of a reconstructed image. Finally we introduce a method to encode pattern information not included in edge information to improve the overall quality of the reconstructed image.
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Dokladal, Petr. "Grey-scale image segmentation : a topological approach." Marne-la-Vallée, 2000. http://www.theses.fr/2000MARN0065.

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Le domaine aborde par cette these concerne la segmentation des images en niveaux de gris. Deux approches principales et independantes ont ete exploitees : l'application des graphes a l'operateur h-minima pour une segmentation des images multi-classes et la segmentation basee sur la topologie appliquee a l'angiographie du foie par tomographie. Une presentation des modeles mathematiques existants, utilises pour modeliser la topologie est faite dans un premier temps. Les modeles presentes sont discutes du point de vue de leurs applications a la segmentation d'images. La methode des h-minima est une methode de morphologie mathematique. Etant concue pour segmenter des images bi-classes, les images multi-classes peuvent etre segmentees par application des h-minima a l'image de gradient. L'extraction du gradient entraine toutefois une perte inevitable d'information dans l'image. Une modification des h-minima fondee sur des graphes ponderes d'adjacence de regions est etudiee. L'extraction du gradient devenant superflue, cette approche permet l'application des h-minima aux images multi-classes. La segmentation d'angiographie 3-d de foie, proposee dans la derniere partie de ce manuscrit, est fondee sur la topologie du systeme vasculaire du foie. La veine porte ayant une structure sans cycles, il est possible de concevoir des techniques de segmentation orientees topologie garantissant un resultat topologiquement correct. Deux methodes duales de segmentation et une methode de squelettisation ont ete proposees. Une methode de filtrage de squelette, fondee sur la meme approche, a ete etudiee afin d'ameliorer le resultat
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Dale-Jones, Ralph. "Contrast enhancement using grey scale transformation techniques." Thesis, University of Warwick, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387342.

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Ganapathiraman, Subburengan. "QUANTIFICATION OF SURFACE DEFECTS USING PRIMARY HIGHLIGHT IN DIFFUSE ANGLE GRAY SCALE IMAGES." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_theses/348.

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The thesis presented is an effort to gather all possible information of one particular type of common paint defect the seed defect, from gray scale images of highly specular painted surface. The proposed approach in the thesis utilizes a white light source to illuminate the surface and utilizes a camera to capture its gray scale image at different diffused angles. While attempting to explain the physics of highlight formation in terms of location on the surface of a seed defect, the thesis also extends to utilize this information from gray scale images to accurately predict the parameters of seed defects including the height, size and position in real time. Since the primary highlight in a gray scale image is more defined, contrary to the past researches on diffuse angle images that use both primary / seed highlight and mirror highlight to estimate height of the seed, this thesis formulates a theory of highlight translation and estimates the height of seed based on primary / seed highlight. The other common type of surface defect - crater defect, is also addressed in the thesis.
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Al-Gindy, Ahmed M. N. "Design and analysis of Discrete Cosine Transform-based watermarking algorithms for digital images. Development and evaluation of blind Discrete Cosine Transform-based watermarking algorithms for copyright protection of digital images using handwritten signatures and mobile phone numbers." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5450.

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This thesis deals with the development and evaluation of blind discrete cosine transform-based watermarking algorithms for copyright protection of digital still images using handwritten signatures and mobile phone numbers. The new algorithms take into account the perceptual capacity of each low frequency coefficients inside the Discrete Cosine Transform (DCT) blocks before embedding the watermark information. They are suitable for grey-scale and colour images. Handwritten signatures are used instead of pseudo random numbers. The watermark is inserted in the green channel of the RGB colour images and the luminance channel of the YCrCb images. Mobile phone numbers are used as watermarks for images captured by mobile phone cameras. The information is embedded multiple-times and a shuffling scheme is applied to ensure that no spatial correlation exists between the original host image and the multiple watermark copies. Multiple embedding will increase the robustness of the watermark against attacks since each watermark will be individually reconstructed and verified before applying an averaging process. The averaging process has managed to reduce the amount of errors of the extracted information. The developed watermarking methods are shown to be robust against JPEG compression, removal attack, additive noise, cropping, scaling, small degrees of rotation, affine, contrast enhancements, low-pass, median filtering and Stirmark attacks. The algorithms have been examined using a library of approximately 40 colour images of size 512 512 with 24 bits per pixel and their grey-scale versions. Several evaluation techniques were used in the experiment with different watermarking strengths and different signature sizes. These include the peak signal to noise ratio, normalized correlation and structural similarity index measurements. The performance of the proposed algorithms has been compared to other algorithms and better invisibility qualities with stronger robustness have been achieved.
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Ravikumar, Rahul. "Multi-scale texture analysis of remote sensing images using gabor filter banks and wavelet transforms." Thesis, [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3175.

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Books on the topic "GRAY SCALE IMAGE"

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Geological Survey (U.S.), ed. GRIDVECTOR (version 1): An ARC/INFO AML program to extract linear features from a gray-scale image of a paper geologic map. Denver, CO: Dept. of Interior, U.S. Geological Survey, 1998.

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Geological Survey (U.S.), ed. GRIDVECTOR (version 1): An ARC/INFO AML program to extract linear features from a gray-scale image of a paper geologic map. Denver, CO: Dept. of Interior, U.S. Geological Survey, 1998.

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Geological Survey (U.S.), ed. GRIDVECTOR (version 1): An ARC/INFO AML program to extract linear features from a gray-scale image of a paper geologic map. Denver, CO: Dept. of Interior, U.S. Geological Survey, 1998.

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Geological Survey (U.S.), ed. GRIDVECTOR (version 1): An ARC/INFO AML program to extract linear features from a gray-scale image of a paper geologic map. Denver, CO: Dept. of Interior, U.S. Geological Survey, 1998.

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B, Dove Derek, Abe Takao 1933-, Heinzl Joachim, Society of Photo-optical Instrumentation Engineers., IS & T--the Society for Imaging Science and Technology., and Rochester Institute of Technology. Center for Imaging Science., eds. Printing technologies for images, gray scale, and color: 26-28 February 1991, San Jose, California. Bellingham, Wash., USA: SPIE, 1991.

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Publishing, King. Christmas Coloring Book for Adults Relaxation : Adult Coloring Books Christmas Fun 40 Gray Scale Coloring Pages: Beautiful Gray Scale Images of Winter Christmas Holiday Scenes, Santa, Reindeer, Elves, Snow, Holiday Decorations, Christmas Tree Lights. Independently Published, 2019.

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Bischof, Horst, Kristian Bredies, Arjan Kuijper, and Thomas Pock. Scale Space and Variational Methods in Computer Vision: 4th International Conference, SSVM 2013, Schloss Seggau, Graz, Austria, June 2-6, 2013, Proceedings. Springer, 2013.

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Things, S. Stranger Things Coloring Book: A Great Colouring Activity Book for All Stranger Things Fans. Season 1,2,3,4 Exclusive Coloring Pages Kids and Adults Hand Drawn and Gray Scale Images. Independently Published, 2020.

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van Gijn, Daniel R., and Jonathan Dunne. Oxford Handbook of Head and Neck Anatomy. Edited by Susan Standring and Simon Eccles. Oxford University Press, 2022. http://dx.doi.org/10.1093/med/9780198767831.001.0001.

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A sound knowledge of anatomy forms the scientific backbone of medicine and surgery – equipping the owner with the tools necessary to understand, remember and ultimately manage the clinical scenarios encountered throughout ones career. The Oxford Handbook of clinical head and neck anatomy is a novel venture for the Oxford Handbook series into the field of anatomy. It tackles the notoriously difficult three-dimensional anatomy of the head and neck and provides clinically relevant correlates, etymology and a brief insight into the people behind the commonly encountered eponymous structures and syndromes. Traditional anatomy texts and atlases can be overwhelming in terms of volume, descriptions and accompanying images. The Oxford Handbook of clinical Head and neck anatomy offers a succinct yet comprehensive, portable and quick reference guide with over 400 colour and grey-scale illustrations. The schematic nature of the illustrations makes them readily reproducible on the operating theatre whiteboard, outpatient clinic or ward, making it a true bedside companion. It is written and illustrated by a trainee in oral and maxillofacial surgery, with an awareness of the needs of those in a similar position, combined with firsthand insight into the difficulties faced by those attempting to learn and link the anatomy taught in pre-clinical years with cases seen in day to day clinical practice thereafter. This book is aimed at medical and dental students, trainees at all levels in Oral and Maxillofacial surgery, ENT surgery and plastic and reconstructive surgery, and radiologists and anaesthetists with an interest in head and neck. Furthermore, it provides an authoritative source for those preparing for the Member of the Royal College of Surgeons and Member of the Faculty of Dental Surgeons exams.
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Book chapters on the topic "GRAY SCALE IMAGE"

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Weik, Martin H. "gray-scale image data." In Computer Science and Communications Dictionary, 691. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8055.

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Vanhamel, Iris U., Ioannis Pratikakis, and Hichem Sahli. "Scale-Space Representations for Gray-Scale and Color Images." In Optical and Digital Image Processing, 155–78. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527635245.ch8.

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Xu, Mantao, and Pasi Fränti. "Context Clustering in Lossless Compression of Gray-Scale Image." In Image Analysis, 328–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_45.

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Tang, Wen-Sheng, Shao-Hua Jiang, and Shu-Lin Wang. "Gray Scale Potential Theory of Sparse Image." In Advanced Intelligent Computing, 357–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24728-6_48.

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Bose, Ankita, and Kalyani Mali. "Gray Scale Image Segmentation with Vague Set." In Communications in Computer and Information Science, 95–105. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8578-0_8.

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Shioyama, Tadayoshi, Akira Okumura, and Yoshikazu Aoki. "Genetic algorithm for thinning gray-scale images of characters." In Image Analysis and Processing, 563–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_315.

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Bhattacharyya, Siddhartha, and Ujjwal Maulik. "Gray Scale Image Edge Detection Using Rough Sets." In Soft Computing for Image and Multimedia Data Processing, 229–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40255-5_8.

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Ito, Ryutaro, Kazuo Nakazawa, and Masato Nakajima. "Human verification using 3D-gray-scale face image." In Advances in Pattern Recognition, 1005–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033331.

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Zhuang, X. D., and N. E. Mastorakis. "Diffusing Vector Field of Gray-Scale Images for Image Segmentation." In Lecture Notes in Electrical Engineering, 81–95. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-84814-3_9.

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Bodnár, Péter, Péter Balázs, and László G. Nyúl. "A Convexity Measure for Gray-Scale Images Based on hv-Convexity." In Image Analysis and Processing - ICIAP 2017, 586–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68560-1_52.

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Conference papers on the topic "GRAY SCALE IMAGE"

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Kumar, Aditya, and Pardeep Singh. "An image compression algorithm for gray scale image." In 2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC 2011). IEEE, 2011. http://dx.doi.org/10.1109/etncc.2011.6255917.

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Han, Hu, Shiguang Shan, Xilin Chen, and Wen Gao. "Gray-scale super-resolution for face recognition from low Gray-scale resolution face images." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5651505.

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Kore, S. N., and T. B. Bhate. "LSB steganography in gray scale images." In National Conference on Signal and Image Processing Applications. IET, 2009. http://dx.doi.org/10.1049/ic.2009.0133.

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Nilsson, M., M. Dahl, and I. Claesson. "Gray-scale image enhancement using the SMQT." In 2005 International Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1529905.

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Mary Helta Daisy, M., S. TamilSelvi, and L. Prinza. "Gray scale morphological operations for image retrieval." In 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET). IEEE, 2012. http://dx.doi.org/10.1109/icceet.2012.6203886.

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Zaware, Sarika, Divya Pathak, Vaidehi Patil, Gauri Sangale, and Vranda Gupta. "Gray Scale Image Colorization for Human Faces." In 2021 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C). IEEE, 2021. http://dx.doi.org/10.1109/icdi3c53598.2021.00030.

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Couprie, Michel, Francisco-Nivando Bezerra, and Gilles Bertrand. "Gray-scale image processing using topological operators." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Longin J. Latecki, Robert A. Melter, David M. Mount, and Angela Y. Wu. SPIE, 1999. http://dx.doi.org/10.1117/12.364101.

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Wilkinson, Timothy S., and Martin M. Malarkey. "Gray-scale image compression with wavelet transforms." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by Friedrich O. Huck and Richard D. Juday. SPIE, 1994. http://dx.doi.org/10.1117/12.179285.

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Patil, M. K., S. A. Patekar, P. Sankhe, and B. Nemade. "Gabor feature extraction for gray scale image." In the International Conference & Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980028.

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Newmiller, Chris. "Thermal dye transfer color hard-copy image stability." In Printing Technologies for Images, Gray Scale, and Color, edited by Derek B. Dove, Takao Abe, and Joachim L. Heinzl. SPIE, 1991. http://dx.doi.org/10.1117/12.46339.

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Reports on the topic "GRAY SCALE IMAGE"

1

AT AND T TECHNOLOGIES INC GREENSBORO NC. Standard Gray Scale Images Users Manual. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada177361.

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Ravela, S., R. Manmatha, and W. B. Croft. Retrieval of Trademark and Gray-Scale Images Using Global Similarity. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada440282.

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NATIONAL COMMUNICATIONS SYSTEM WASHINGTON DC. Investigation of Vector Quantization for the Coding Gray Scale Images for Group 4 Facsimile. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada206548.

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