Relevant bibliographies by topics / Image feature extraction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image feature extraction'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Image feature extraction"

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Sun, Da Chun. "Investigation of Local Feature Extraction." Applied Mechanics and Materials 644-650 (September 2014): 4653–56. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4653.

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Feature extraction is an important subject of image analysis, pattern recognition, computer vision, etc. It is the fundamental to solve many different image problems. As the local feature has the characteristic of invariability even after image translation and rotation, changing of zoom, illumination or viewpoint, it has been widely applied to image registration, image mosaic, object identification, target tracking, digital watermark and image retrieval. Extracting stable feature of images has attracted lots of interest. In this paper, we provide the definition of local feature and steps of extracting local feature. The difficulties and trend of this technology are also briefly discussed.
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Pan, Tao. "Tracking and Extracting Action Trajectory of Athlete Based on Hierarchical Features." Ingénierie des systèmes d information 25, no. 5 (November 10, 2020): 677–82. http://dx.doi.org/10.18280/isi.250515.

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The feature extraction from athlete action images is a research hotspot. To accurately evaluate athlete actions, it is necessary to partition the original image into refined blocks, and extract different levels of image features. However, the traditional feature extraction algorithms can only roughly divide action images into several classes, failing to acquire the accurate feature sets of the actions. This leads to relatively poor performance of feature extraction from action images. To overcome the defect of the traditional methods, this paper puts forward a feature extraction method for the action images of badminton players based on hierarchical features. The underlying image features were analyzed based on the techniques of badminton players, and mapped to the feature space of the corresponding dimension. Simulation results show that the proposed method can accurately extract the features from athlete action images.
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Hema, Dr A., and R. Saravanakumar. "A Survey on Feature Extraction Technique in Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 448–51. http://dx.doi.org/10.31142/ijtsrd12937.

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Wei, Zhenfeng, and Xiaohua Zhang. "Feature Extraction and Retrieval of Ecommerce Product Images Based on Image Processing." Traitement du Signal 38, no. 1 (February 28, 2021): 181–90. http://dx.doi.org/10.18280/ts.380119.

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The new retail is an industry featured by online ecommerce. One of the key techniques of the industry is the product identification based on image processing. This technique has an important business application value, because it is capable of improving the retrieval efficiency of products and the level of information supervision. To acquire high-level semantics of images and enhance the retrieval effect of products, this paper explores the feature extraction and retrieval of ecommerce product images based on image processing. The improved Fourier descriptor was innovatively into a metric learning-based product image feature extraction network, and the attention mechanism was introduced to realize accurate retrieval of product images. Firstly, the authors detailed how to acquire the product contour and the axis with minimum moment of inertia, and then extracted the shape feature of products. Next, a feature extraction network was established based on the metric learning supervision, which is capable of obtaining distinctive feature, and thus realized the extraction of distinctive and classification features of products. Finally, the authors expounded on the product image retrieval method based on cluster attention neural network. The effectiveness of our method was confirmed through experiments. The research results provide a reference for feature extraction and retrieval in other fields of image processing.
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Liu, Hong Hai, and Xiang Hua Hou. "The Face Detection Research Based on Multi-Scale and Rectangle Feature." Applied Mechanics and Materials 198-199 (September 2012): 1383–88. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1383.

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When extracting the face image features based on pixel distribution in face image, there always exist large amount of calculation and high dimensions of feature sector generated after feature extraction. This paper puts forward a feature extraction method based on prior knowledge of face and Haar feature. Firstly, the Haar feature expressions of face images are classified and the face features are decomposed into edge feature, line feature and center-surround feature, which are further concluded into the expressions of two rectangles, three rectangles and four rectangles. In addition, each rectangle varies in size. However, for face image combination, there exist too much redundancy and large calculation amount in this kind of expression. In order to solve the problem of large amount of calculation, the integral image is adopted to speed up the rectangle feature calculation. In addition, the thought based on classified trainer is adopted to reduce the redundancy expression. The results show that using face image of Haar feature expression can improve the speed and efficiency of recognition.
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Tian, Yang Meng, Yu Duo Zheng, Wei Jin, and Gai Hong Du. "Face Image Feature Extraction and Feature Selection." Applied Mechanics and Materials 432 (September 2013): 587–91. http://dx.doi.org/10.4028/www.scientific.net/amm.432.587.

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In order to solve the problem of face recognition, the method of feature extraction and feature selection is presented in this paper. First using Gabor filters and face image as the convolution Operator to extract the Gabor feature vector of the image and also to uniform sampling; then using the PCA + LDA method to reduce the dimension for high-dimensional Gabor feature vector; Finally, using the nearest neighbor classifier to discriminate and determine the identity of a face image. The result I get is that the sampled Gabor feature in high-dimensional space can be projected onto low-dimensional space though the method of feature selection and compression. The new and original in this paper is that the method of PCA + LDA overcomes the problem of the spread matrix singular in the class and matrix too large which is brought by directly use the LDA.
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Wang, Shenlong, Kaixin Han, and Jiafeng Jin. "Review of image low-level feature extraction methods for content-based image retrieval." Sensor Review 39, no. 6 (November 18, 2019): 783–809. http://dx.doi.org/10.1108/sr-04-2019-0092.

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Purpose In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years. Design/methodology/approach First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared. Findings The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR. Originality/value A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.
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Abdulhamid, Mohanad, and Gitonga Muthomi. "Study of Feature Extraction of Retinal Scans." Scientific Bulletin 24, no. 1 (June 1, 2019): 5–13. http://dx.doi.org/10.2478/bsaft-2019-0001.

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Abstract In this paper, the retina is discussed as part of the feature of extraction of retinal scans for use in security systems as a means of identification. The design system contains a method of image acquisition and processing of the image. A computer system is also incorporated for matching and verifying the image captured to an already present representation of unique features of the retina that are stored as templates for matching and identification. It should then either allow or deny the user depending on the results of the matching process. This paper shows the development of the step undertaken to process the image to the extraction of the features. The high resolution images are taken through a series of image enhancement process before feature extraction technics are applied and before templates are created for future referencing. The main limitation of this process is attributed to capturing the image from the retina. The image obtained may be of poor quality thus making the unique features of the retina unclear.
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Gu, Donghua, Zhenyu Han, and Qinge Wu. "Feature Extraction to Polar Image." Journal of Computer and Communications 05, no. 11 (2017): 16–26. http://dx.doi.org/10.4236/jcc.2017.511002.

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Marnur, Akshata M. "Feature Extraction and Image classification." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 637–49. http://dx.doi.org/10.22214/ijraset.2018.6099.

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Dissertations / Theses on the topic "Image feature extraction"

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Ljumić, Elvis. "Image feature extraction using fuzzy morphology." Diss., Online access via UMI:, 2007.

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Thesis (Ph. D.)--State University of New York at Binghamton, Department of Systems Science and Industrial Engineering, Thomas J. Watson School of Engineering and Applied Science, 2007.
Includes bibliographical references.
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Palma, Alberto de Jesus Pastrana. "Feature Extraction, Correspondence Regions and Image Retrieval using Structured Images." Thesis, University of East Anglia, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502556.

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This thesis is about image descriptors, image retrieval and correspondence regions. The advantages of using scale-space on image descriptors are first discussed and a novel implementation of the sieve algorithm is introduced. We call this implementation 'The Structured Image'. It is shown here how such implementation decomposes the image in to a tree hierarchy collecting colour and texture descriptors throughout scale-space whilst remaining on a nearly linear order complexity. The algorithm is evaluated for correspondence repeatability rates and content based image retrieval. Results confirm the effectiveness of the implementation for both applications. We have also developed a graphic user interface to enable relevance feedback in to our image retrieval model. Our model is prepared to deal with segmentations of images rather than global att~ibutes of the image and it has been tested using two types of segmentations. Results in terms of precision rates are presented here for different iterations of relevance feedback.
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Westin, Carl-Fredrik. "Feature extraction based on a tensor image description." Licentiate thesis, Linköping University, Linköping University, Computer Vision, 1991. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54888.

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Feature extraction from a tensor based local image representation introduced by Knutsson in [37] is discussed. The tensor representation keeps statements of structure, certainty of statement and energy separate. Further processing for obtaining new features also having these three entities separate is achieved by the use of a new concept, tensor field filtering. Tensor filters for smoothing and for extraction of circular symmetries are presented and discussed in particular. These methods are used for corner detection and extraction of more global features such as lines in images. A novel method for grouping local orientation estimates into global line parameters is introduced. The method is based on a new parameter space, the Möbius Strip parameter space, which has similarities to the Hough transform. A local centroid clustering algorithm is used for classification in this space. The procedure automatically divides curves into line segments with appropriate lengths depending on the curvature. A linked list structure is built up for storing data in an efficient way.


Ogiltigt nummer / annan version: I publ. nr 290:s ISBN: 91-7870-815-X.
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Hong, Qi He. "3D feature extraction from a single 2D image." Thesis, University of Reading, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293175.

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Gardiner, Brian Calvin. "Compressive image feature extraction by means of folding." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76812.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 61-62).
We explore the utility of a dimensionality reducing process we term folding for the purposes of image feature extraction. We seek to discover whether image features are preserved under this process and how to efficiently extract them. The application is in size weight and power constrained imaging scenarios where an efficient implementation of this dimensionality reduction can save power and computation costs. The specific features we explore are image corners, rotation, and translation. We present algorithms for recovering these features from folded representations of images followed by simulation results showing the performance of the algorithms when operating on real images.
by Brian Calvin Gardiner.
M.Eng.
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Gunn, Steve R. "Dual active contour models for image feature extraction." Thesis, University of Southampton, 1996. https://eprints.soton.ac.uk/250089/.

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Active contours are now a very popular technique for shape extraction, achieved by minimising a suitably formulated energy functional. Conventional active contour formulations suffer difficulty in appropriate choice of an initial contour and values of parameters. Recent approaches have aimed to resolve these problems, but can compromise other performance aspects. To relieve the problem in initialisation, an evolutionary dual active contour has been developed, which is combined with a local shape model to improve the parameterisation. One contour expands from inside the target feature, the other contracts from the outside. The two contours are inter-linked to provide a balanced technique with an ability to reject weak’local energy minima. Additionally a dual active contour configuration using dynamic programming has been developed to locate a global energy minimum and complements recent approaches via simulated annealing and genetic algorithms. These differ from conventional evolutionary approaches, where energy minimisation may not converge to extract the target shape, in contrast with the guaranteed convergence of a global approach. The new techniques are demonstrated to extract successfully target shapes in synthetic and real images, with superior performance to previous approaches. The new technique employing dynamic programming is deployed to extract the inner face boundary, along with a conventional normal-driven contour to extract the outer face boundary. Application to a database of 75 subjects showed that the outer contour was extracted successfully for 96% of the subjects and the inner contour was successful for 82%. This application highlights the advantages new dual active contour approaches for automatic shape extraction can confer.
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Liu, Xiuwen. "Computational investigation of feature extraction and image organization /." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu148819296016944.

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Lorentzon, Matilda. "Feature Extraction for Image Selection Using Machine Learning." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142095.

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During flights with manned or unmanned aircraft, continuous recording can result in avery high number of images to analyze and evaluate. To simplify image analysis and tominimize data link usage, appropriate images should be suggested for transfer and furtheranalysis. This thesis investigates features used for selection of images worthy of furtheranalysis using machine learning. The selection is done based on the criteria of havinggood quality, salient content and being unique compared to the other selected images.The investigation is approached by implementing two binary classifications, one regardingcontent and one regarding quality. The classifications are made using support vectormachines. For each of the classifications three feature extraction methods are performedand the results are compared against each other. The feature extraction methods used arehistograms of oriented gradients, features from the discrete cosine transform domain andfeatures extracted from a pre-trained convolutional neural network. The images classifiedas both good and salient are then clustered based on similarity measures retrieved usingcolor coherence vectors. One image from each cluster is retrieved and those are the resultingimages from the image selection. The performance of the selection is evaluated usingthe measures precision, recall and accuracy. The investigation showed that using featuresextracted from the discrete cosine transform provided the best results for the quality classification.For the content classification, features extracted from a convolutional neuralnetwork provided the best results. The similarity retrieval showed to be the weakest partand the entire system together provides an average accuracy of 83.99%.
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Lim, Suryani. "Feature extraction, browsing and retrieval of images." Monash University, School of Computing and Information Technology, 2005. http://arrow.monash.edu.au/hdl/1959.1/9677.

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Alfraih, Areej S. "Feature extraction and clustering techniques for digital image forensics." Thesis, University of Surrey, 2015. http://epubs.surrey.ac.uk/808306/.

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This thesis proposes an adaptive algorithm which applies feature extraction and clustering techniques for cloning detection and localization in digital images. Multiple contributions have been made to test the performance of different feature detectors for forensic use. The �first contribution is to improve a previously published algorithm by Wang et al. by localizing tampered regions using the grey-level co-occurrence matrix (GLCM) for extracting texture features from the chromatic component of an image (Cb or Cr component). The main trade-off� is a diminishing detection accuracy as the region size decreases. The second contribution is based on extracting Maximally Stable Extremal Regions (MSER) features for cloning detection, followed by k-means clustering for cloning localization. Then, for comparison purposes, we implement the same approach using Speeded Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). Experimental results show that we can detect and localize cloning in tampered images with an accuracy reaching 97% using MSER features. The usability and effi�cacy of our approach is verified by comparing with recent state-of-the-art approaches. For the third contribution we propose a flexible methodology for detecting cloning in images, based on the use of feature detectors. We determine whether a particular match is the result of a cloning event by clustering the matches using k-means clustering and using a Support Vector Machine (SVM) to classify the clusters. This descriptor-agnostic approach allows us to combine the results of multiple feature descriptors, increasing the potential number of keypoints in the cloned region. Results using MSER, SURF and SIFT outperform state of the art where the highest true positive rate is achieved at approximately 99.60% and the false positive rate is achieved at 1.6%, when different descriptors are combined. A statistical �filtering step, based on computing the median value of the dissimilarity matrix, is also proposed. Moreover, our algorithm uses an adaptive technique for selecting the optimal k value for each image independently, allowing our method to detect multiple cloned regions. Finally, we propose an adaptive technique that chooses feature detectors based on the type of image being tested. Some detectors are robust in detecting features in textured images while other detectors are robust in detecting features in smooth images. Combining the detectors makes them complementary to each other and can generate optimal results. The highest value for the area under ROC curve is achieved at approximately 98.87%. We also test the performance of agglomerative hierarchical clustering for cloning localization. Hierarchical and k-means clustering techniques have a similar performance for cloning localization. The True Positive Rate (TPR) for match level localization is achieved at approximately 97.59% and 96.43% for k-means and hierarchical clustering techniques, respectively. The robustness of our technique is analyzed against additive white Gaussian noise and JPEG compression. Our technique is still reliable even when using a low signal-to-noise (SNR = 20 dB) or a low JPEG compression quality factor (QF = 50).
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Books on the topic "Image feature extraction"

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Chaki, Jyotismita, and Nilanjan Dey. Image Color Feature Extraction Techniques. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5761-3.

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S, Aguado Alberto, ed. Feature extraction and image processing. Oxford: Newnes, 2002.

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Nixon, Mark. Feature Extraction & Image Processing for Computer Vision. 3rd ed. Burlington: Elsevier Science, 2012.

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Chaki, Jyotismita, and Nilanjan Dey. Texture Feature Extraction Techniques for Image Recognition. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0853-0.

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Rand, Robert S. Texture analysis and cartographic feature extraction. Fort Belvoir, Va: U.S. Army Corps of Engineers, Engineer Topographic Laboratories, 1985.

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Li, Jian. Synthetic aperture radar target detection, feature extraction, and image formation techniques. [Washington, DC: National Aeronautics and Space Administration, 1994.

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Puig, Luis. Omnidirectional Vision Systems: Calibration, Feature Extraction and 3D Information. London: Springer London, 2013.

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Feature Extraction and Image Processing. Elsevier, 2002. http://dx.doi.org/10.1016/c2009-0-25049-5.

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Feature Extraction & Image Processing, Second Edition. Academic Press, 2007.

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Nixon, Mark, and Alberto S. Aguado. Feature Extraction & Image Processing, Second Edition. 2nd ed. Academic Press, 2007.

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Book chapters on the topic "Image feature extraction"

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Goshtasby, A. Ardeshir. "Feature Extraction." In Image Registration, 123–217. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2458-0_4.

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Awcock, G. J., and R. Thomas. "Feature Extraction." In Applied Image Processing, 148–75. London: Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13049-8_6.

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Corke, Peter. "Image Feature Extraction." In Springer Tracts in Advanced Robotics, 413–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54413-7_13.

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Corke, Peter. "Image Feature Extraction." In Springer Tracts in Advanced Robotics, 335–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20144-8_13.

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Shirai, Yoshiaki. "Image Feature Extraction." In Three-Dimensional Computer Vision, 32–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-82429-6_3.

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Fernández-Berni, Jorge, Manuel Suárez, Ricardo Carmona-Galán, Víctor M. Brea, Rocío del Río, Diego Cabello, and Ángel Rodríguez-Vázquez. "Image Feature Extraction Acceleration." In Image Feature Detectors and Descriptors, 109–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28854-3_5.

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Suganya, R., S. Rajaram, and A. Sheik Abdullah. "Texture Feature Extraction." In Big Data in Medical Image Processing, 95–124. Boca Raton, FL : CRC Press, [2018] | “A science publishers book.”: CRC Press, 2018. http://dx.doi.org/10.1201/b22456-4.

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Chaki, Jyotismita, and Nilanjan Dey. "Other Image Color Features." In Image Color Feature Extraction Techniques, 57–71. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5761-3_4.

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Chaki, Jyotismita, and Nilanjan Dey. "Introduction to Image Color Feature." In Image Color Feature Extraction Techniques, 1–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5761-3_1.

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Chaki, Jyotismita, and Nilanjan Dey. "Histogram-Based Image Color Features." In Image Color Feature Extraction Techniques, 29–41. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5761-3_2.

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Conference papers on the topic "Image feature extraction"

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Jaiswal, Rachana, and Srikant Satarkar. "Biometric Foetal Contour Extraction using Hybrid Level Set." In 6th International Conference on Signal and Image Processing (SIGI 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.102002.

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In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Due to lack of consistency and accuracy of manual measurements, an automatic technique is highly desirable to obtain foetal biometric measurements. This proposed approach is based on global & local region information for foetal contour extraction from ultrasonic images. The primary goal of this research is to provide a new methodology to aid the analysis and feature extraction from foetal images.
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Kunaver, M., and J. F. Tasic. "Image feature extraction - an overview." In EUROCON 2005 - The International Conference on "Computer as a Tool". IEEE, 2005. http://dx.doi.org/10.1109/eurcon.2005.1629889.

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Ren, Kui. "Secure Outsourcing Image Feature Extraction." In ASIA CCS '15: 10th ACM Symposium on Information, Computer and Communications Security. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2732516.2732517.

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Zhu, Shaojun, and Jieyu Zhao. "Facial Feature Points Extraction." In 2009 International Conference on Image and Graphics (ICIG). IEEE, 2009. http://dx.doi.org/10.1109/icig.2009.9.

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Tang, Dejun, Weishi Zhang, Xiaolu Qu, and Dujuan Wang. "A feature fusion method for feature extraction." In Fourth International Conference on Digital Image Processing (ICDIP 2012), edited by Mohamed Othman, Sukumar Senthilkumar, and Xie Yi. SPIE, 2012. http://dx.doi.org/10.1117/12.946076.

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Wang, Zuyuan, Ruedi Boesch, and Christian Ginzler. "Feature Extraction for Forest Inventory." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.736.

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Li, Tiejun, Yanli Wang, Zhe Chen, and Renxiang Wang. "Linear feature extraction for infrared image." In Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Bir Bhanu, and Ning Shu. SPIE, 2001. http://dx.doi.org/10.1117/12.441474.

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Conilione, Paul C., and Dianhui Wang. "Feature Extraction for Face Image Retrieval." In 2008 3rd International Conference on Innovative Computing Information and Control. IEEE, 2008. http://dx.doi.org/10.1109/icicic.2008.278.

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Tsai, Min-Hsuan, Shen-Fu Tsai, and Thomas S. Huang. "Hierarchical image feature extraction and classification." In the international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1873951.1874136.

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Gui, Feng, and QiWei Lin. "Morphological theory in image feature extraction." In Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, edited by Stephen G. Ungar, Shiyi Mao, and Yoshifumi Yasuoka. SPIE, 2003. http://dx.doi.org/10.1117/12.468079.

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Reports on the topic "Image feature extraction"

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Ling, Hao. Radar Image Enhancement, Feature Extraction and Motion Compensation Using Joint Time-Frequency Techniques. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada390630.

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Hao, Ling. Annual Report on Radar Image Enhancement, Feature Extraction and Motion Compensation Using Joint Time-Frequency Techniques. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada377783.

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Richardson, J. Automatic feature extraction and classification from digital x-ray images. Final report, period ending 1 May 1995. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/224901.

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