Relevant bibliographies by topics / Image cropping

Contents

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

Academic literature on the topic 'Image cropping'

Author: Grafiati
Published: 25 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Image cropping"

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Li, Ya Feng, and Ying Lin. "Adaptive Image Cropping Based Depth of Field." Advanced Engineering Forum 6-7 (September 2012): 895–99. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.895.

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The communication and sharing of visual media show a strong cross-platform feature. Massive images are shown on various devices with different resolution. Automatic image cropping, usually used to change the resolution of image, often discards important content in the image. This paper proposed a novel adaptive photo cropping method. The main idea is exploiting the characteristics of photograph aesthetics in photograph works. The algorithm infers the intention of photographer according to the depth of field in the image. The effects brought out by the focus and unfocus are utilized to exact importance information of the image. So, the method can be more satisfied with the subjective evaluation. And, it has advantage in term of computational speed. Experimentations are presented to demonstrate the validity of proposed method.
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Gao, Shangbing, Youdong Zhang, Wanli Feng, and Dashan Chen. "Image Cropping by Patches Dissimilarities." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 8 (August 31, 2015): 79–88. http://dx.doi.org/10.14257/ijsip.2015.8.8.09.

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Yuhandri. "Perbandingan Metode Cropping pada Sebuah Citra untuk Pengambilan Motif Tertentu pada Kain Songket Sumatera Barat." Jurnal KomtekInfo 6, no. 1 (June 1, 2019): 97–107. http://dx.doi.org/10.35134/komtekinfo.v6i1.45.

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At the time of image processing where we only need a certain part of an image according to the needs called the Region of Interest (ROI), in order to obtain that, the processing is carried out in a cropping process. Cropping is mostly done by researchers, especially those who research in the field of image processing in order to do data processing on an image, the results of cropping process on an image are usually done to make it easier for researchers to focus on something that is needed only. In this study is to compare existing cropping methods to get a motif found in an image of West Sumatra songket fabric. In this study using the method of cropping rectangle, square, circle, ellipse, polygon and tested using the Matlab programming language. The results of comparison of 5 cropping methods for taking certain motifs on the songket image with 5 different songket image samples, shows that the best results are obtained by using the polygon method. Polygon method can reach certain coordinate points in a songket image, so that the results of cropping are better and other motives that are carried along during the cropping process can be reduced.
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Darwis, Dedi, Akmal Junaidi, Dewi Asiah Shofiana, and Wamiliana. "A New Digital Image Steganography Based on Center Embedded Pixel Positioning." Cybernetics and Information Technologies 21, no. 2 (June 1, 2021): 89–104. http://dx.doi.org/10.2478/cait-2021-0021.

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Abstract In this study we propose a new approach to tackle the cropping problem in steganography which is called Center Embedded Pixel Positioning (CEPP) which is based on Least Significant Bit (LSB) Matching by setting the secret image in the center of the cover image. The evaluation of the experiment indicated that the secret image can be retrieved by a maximum of total 40% sequential cropping on the left, right, up, and bottom of the cover image. The secret image also can be retrieved if the total asymmetric cropping area is 25% that covered two sides (either left-right, left-up or right-up). In addition, the secret image can also be retrieved if the total asymmetric cropping area is 70% if the bottom part is included. If asymmetric cropping area included three sides, then the algorithm fails to retrieve the secret image. For cropping in the botom the secret image can be extracted up to 70%.
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Lu, Weirui, Xiaofen Xing, Bolun Cai, and Xiangmin Xu. "Listwise View Ranking for Image Cropping." IEEE Access 7 (2019): 91904–11. http://dx.doi.org/10.1109/access.2019.2925430.

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Khalid, Shamsul Kamal Ahmad, Mustafa Mat Deris, and Kamaruddin Malik Mohamad. "Anti-cropping digital image watermarking using Sudoku." International Journal of Grid and Utility Computing 4, no. 2/3 (2013): 169. http://dx.doi.org/10.1504/ijguc.2013.056253.

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Ciocca, Gianluigi, Claudio Cusano, Francesca Gasparini, and Raimondo Schettini. "Self-Adaptive Image Cropping for Small Displays." IEEE Transactions on Consumer Electronics 53, no. 4 (November 2007): 1622–27. http://dx.doi.org/10.1109/tce.2007.4429261.

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Chang, Chin Chen, I. Ta Lee, Tsung Ta Ke, and Wen Kai Tai. "An Object-Based Image Reducing Approach." Advanced Materials Research 1044-1045 (October 2014): 1049–52. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1049.

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Common methods for reducing image size include scaling and cropping. However, these two approaches have some quality problems for reduced images. In this paper, we propose an image reducing algorithm by separating the main objects and the background. First, we extract two feature maps, namely, an enhanced visual saliency map and an improved gradient map from an input image. After that, we integrate these two feature maps to an importance map. Finally, we generate the target image using the importance map. The proposed approach can obtain desired results for a wide range of images.
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Yang, Rong, Robert Wang, Yunkai Deng, Xiaoxue Jia, and Heng Zhang. "Rethinking the Random Cropping Data Augmentation Method Used in the Training of CNN-Based SAR Image Ship Detector." Remote Sensing 13, no. 1 (December 23, 2020): 34. http://dx.doi.org/10.3390/rs13010034.

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The random cropping data augmentation method is widely used to train convolutional neural network (CNN)-based target detectors to detect targets in optical images (e.g., COCO datasets). It can expand the scale of the dataset dozens of times while consuming only a small amount of calculations when training the neural network detector. In addition, random cropping can also greatly enhance the spatial robustness of the model, because it can make the same target appear in different positions of the sample image. Nowadays, random cropping and random flipping have become the standard configuration for those tasks with limited training data, which makes it natural to introduce them into the training of CNN-based synthetic aperture radar (SAR) image ship detectors. However, in this paper, we show that the introduction of traditional random cropping methods directly in the training of the CNN-based SAR image ship detector may generate a lot of noise in the gradient during back propagation, which hurts the detection performance. In order to eliminate the noise in the training gradient, a simple and effective training method based on feature map mask is proposed. Experiments prove that the proposed method can effectively eliminate the gradient noise introduced by random cropping and significantly improve the detection performance under a variety of evaluation indicators without increasing inference cost.
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DHARWADKAR, NAGARAJ V., and B. B. AMBERKER. "STEGANOGRAPHIC SCHEME FOR GRAY-LEVEL IMAGE USING PIXEL NEIGHBORHOOD AND LSB SUBSTITUTION." International Journal of Image and Graphics 10, no. 04 (October 2010): 589–607. http://dx.doi.org/10.1142/s0219467810003901.

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The exchange of secret message using images has vital importance in secret communication. Steganographic scheme is employed to achieve the task of secret message communication using images. The existing scheme based on pixel value differencing (PVD) with least significant bit (LSB) sequential substitution suffer from low embedding capacity. The embedding capacity is increased by using the edge regions of image obtained by neighborhood connectivity of pixel. We propose an adaptive steganographic scheme for gray-level images. Our scheme relies on the neighborhood connectivity of pixels to estimate the embedding capacity and resolves the problem of sequential substitution by jumbling the bits of secret message. The effect of cropping and filtration attacks on stegoimage is minimized by embedding the copies of secret message into four different regions of the cover image. The performance of the scheme is analyzed for various types of image processing attacks like cropping, blurring, filtering, adding noise, and sharpening. The proposed scheme is found rigid to these attacks.
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Dissertations / Theses on the topic "Image cropping"

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Deigmoeller, Joerg. "Intelligent image cropping and scaling." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/4745.

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Nowadays, there exist a huge number of end devices with different screen properties for watching television content, which is either broadcasted or transmitted over the internet. To allow best viewing conditions on each of these devices, different image formats have to be provided by the broadcaster. Producing content for every single format is, however, not applicable by the broadcaster as it is much too laborious and costly. The most obvious solution for providing multiple image formats is to produce one high resolution format and prepare formats of lower resolution from this. One possibility to do this is to simply scale video images to the resolution of the target image format. Two significant drawbacks are the loss of image details through ownscaling and possibly unused image areas due to letter- or pillarboxes. A preferable solution is to find the contextual most important region in the high-resolution format at first and crop this area with an aspect ratio of the target image format afterwards. On the other hand, defining the contextual most important region manually is very time consuming. Trying to apply that to live productions would be nearly impossible. Therefore, some approaches exist that automatically define cropping areas. To do so, they extract visual features, like moving reas in a video, and define regions of interest (ROIs) based on those. ROIs are finally used to define an enclosing cropping area. The extraction of features is done without any knowledge about the type of content. Hence, these approaches are not able to distinguish between features that might be important in a given context and those that are not. The work presented within this thesis tackles the problem of extracting visual features based on prior knowledge about the content. Such knowledge is fed into the system in form of metadata that is available from TV production environments. Based on the extracted features, ROIs are then defined and filtered dependent on the analysed content. As proof-of-concept, this application finally adapts SDTV (Standard Definition Television) sports productions automatically to image formats with lower resolution through intelligent cropping and scaling. If no content information is available, the system can still be applied on any type of content through a default mode. The presented approach is based on the principle of a plug-in system. Each plug-in represents a method for analysing video content information, either on a low level by extracting image features or on a higher level by processing extracted ROIs. The combination of plug-ins is determined by the incoming descriptive production metadata and hence can be adapted to each type of sport individually. The application has been comprehensively evaluated by comparing the results of the system against alternative cropping methods. This evaluation utilised videos which were manually cropped by a professional video editor, statically cropped videos and simply scaled, non-cropped videos. In addition to and apart from purely subjective evaluations, the gaze positions of subjects watching sports videos have been measured and compared to the regions of interest positions extracted by the system.
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Abdulla, Ghaleb. "An image processing tool for cropping and enhancing images." Master's thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-12232009-020207/.

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Li, Yuxia. "Traffic and tillage effects on dryland cropping systems in north-east Australia /." [St. Lucia, Qld.], 2001. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16335.pdf.

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Ling, Haibin. "Techniques for image retrieval deformation insensitivity and automatic thumbnail cropping /." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3859.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Mennborg, Alexander. "AI-Driven Image Manipulation : Image Outpainting Applied on Fashion Images." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85148.

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The e-commerce industry frequently has to deal with displaying product images in a website where the images are provided by the selling partners. The images in question can have drastically different aspect ratios and resolutions which makes it harder to present them while maintaining a coherent user experience. Manipulating images by cropping can sometimes result in parts of the foreground (i.e. product or person within the image) to be cut off. Image outpainting is a technique that allows images to be extended past its boundaries and can be used to alter the aspect ratio of images. Together with object detection for locating the foreground makes it possible to manipulate images without sacrificing parts of the foreground. For image outpainting a deep learning model was trained on product images that can extend images by at least 25%. The model achieves 8.29 FID score, 44.29 PSNR score and 39.95 BRISQUE score. For testing this solution in practice a simple image manipulation pipeline was created which uses image outpainting when needed and it shows promising results. Images can be manipulated in under a second running on ZOTAC GeForce RTX 3060 (12GB) GPU and a few seconds running on a Intel Core i7-8700K (16GB) CPU. There is also a special case of images where the background has been digitally replaced with a solid color and they can be outpainted even faster without deep learning.
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Fredericks, Erin P. K. "Preferred color correction for mixed taking-illuminant placement and cropping /." Online version of thesis, 2009. http://hdl.handle.net/1850/11350.

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Retsas, Ioannis. "A DCT-based image watermarking algorithm robust to cropping and compression." Thesis, Monterey California. Naval Postgraduate School, 2002. http://hdl.handle.net/10945/6032.

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Approved for public release; distribution is unlimited.
Digital watermarking is a highly evolving field, which involves the embedding of a certain kind of information under a digital object (image, video, audio) for the purpose of copyright protection. Both the image and the watermark are most frequently translated into a transform domain where the embedding takes place. The selection of both the transform domain and the particular algorithm that is used for the embedding of the watermark, depend heavily on the application. One of the most widely used transform domains for watermarking of still digital images is the Discrete Cosine Transform domain. The reason is that the Discrete Cosine Transform is a part of the JPEG standard, which in turn is widely used for storage of digital images. In our research we propose a unique methodfor DCT-based image watermarking. In an effort to achieve robustness to cropping and JPEG compression wehave developed an algorithm for rating the 8.8 blocks of the image DCT coefficients taking into account theirembedding capacity and their spatial location within the image. Our experiments show that the proposed schemeoffers adequate transparency, and works exceptionally well against cropping while at the same time maintainssufficient robustness to JPEG compression.
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Swathanthira, Kumar Murali Murugavel M. "Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-dissertations/280.

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The Pulse Couple Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this dissertation, three novel PCNN based automatic segmentation algorithms were developed to segment Magnetic Resonance Imaging (MRI) data: (a) PCNN image 'signature' based single region cropping; (b) PCNN - Kittler Illingworth minimum error thresholding and (c) PCNN -Gaussian Mixture Model - Expectation Maximization (GMM-EM) based multiple material segmentation. Among other control tests, the proposed algorithms were tested on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes, 20 T1 weighted MR human brain volumes from Harvard's Internet Brain Segmentation Repository and 5 human MR breast volumes. The results were compared against manually segmented gold standards, Brain Extraction Tool (BET) V2.1 results, published results and single threshold methods. The Jaccard similarity index was used for numerical evaluation of the proposed algorithms. Our quantitative results demonstrate conclusively that PCNN based multiple material segmentation strategies can approach a human eye's intensity delineation capability in grayscale image segmentation tasks.
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Chea, Sareth. "Economics of rice double-cropping in rainfed lowland areas of Cambodia : a farm-level analysis /." [St. Lucia, Qld.], 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16913.pdf.

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Janurberg, Norman, and Christian Luksitch. "Exploring Deep Learning Frameworks for Multiclass Segmentation of 4D Cardiac Computed Tomography." Thesis, Linköpings universitet, Institutionen för hälsa, medicin och vård, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178648.

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By combining computed tomography data with computational fluid dynamics, the cardiac hemodynamics of a patient can be assessed for diagnosis and treatment of cardiac disease. The advantage of computed tomography over other medical imaging modalities is its capability of producing detailed high resolution images containing geometric measurements relevant to the simulation of cardiac blood flow. To extract these geometries from computed tomography data, segmentation of 4D cardiac computed tomography (CT) data has been performed using two deep learning frameworks that combine methods which have previously shown success in other research. The aim of this thesis work was to develop and evaluate a deep learning based technique to segment the left ventricle, ascending aorta, left atrium, left atrial appendage and the proximal pulmonary vein inlets. Two frameworks have been studied where both utilise a 2D multi-axis implementation to segment a single CT volume by examining it in three perpendicular planes, while one of them has also employed a 3D binary model to extract and crop the foreground from surrounding background. Both frameworks determine a segmentation prediction by reconstructing three volumes after 2D segmentation in each plane and combining their probabilities in an ensemble for a 3D output.  The results of both frameworks show similarities in their performance and ability to properly segment 3D CT data. While the framework that examines 2D slices of full size volumes produces an overall higher Dice score, it is less successful than the cropping framework at segmenting the smaller left atrial appendage. Since the full size 2D slices also contain background information in each slice, it is believed that this is the main reason for better segmentation performance. While the cropping framework provides a higher proportion of each foreground label, making it easier for the model to identify smaller structures. Both frameworks show success for use in 3D cardiac CT segmentation, and with further research and tuning of each network, even better results can be achieved.
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Books on the topic "Image cropping"

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Paint Shop Pro 9 and Studio in Easy Steps: Edit Photos like a Pro! Southam: Computer Step, 2005.

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Book chapters on the topic "Image cropping"

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Ardizzone, Edoardo, Alessandro Bruno, and Giuseppe Mazzola. "Saliency Based Image Cropping." In Image Analysis and Processing – ICIAP 2013, 773–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41181-6_78.

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Chen, Haixia, Shangpeng Wang, Hongyan Zhang, and Wei Wu. "Image Authentication for Permissible Cropping." In Information Security and Cryptology, 308–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14234-6_17.

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Smith, Jan, and Roman Joost. "Image Straightening, Cropping, Scaling, and Perspective." In GIMP for Absolute Beginners, 61–83. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-3169-1_4.

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Qu, Zhan, Jinqiao Wang, Min Xu, and Hanqing Lu. "Fusing Warping, Cropping, and Scaling for Optimal Image Thumbnail Generation." In Computer Vision – ACCV 2012, 445–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37447-0_34.

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Lim, Bryan, and Richard LaFranchi. "Building an Image-Cropping Tool with Vue and Active Storage." In Vue on Rails, 135–51. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5116-4_6.

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Yang, Yang, Linjun Yang, and Gangshan Wu. "Smart Thumbnail: Automatic Image Cropping by Mining Canonical Query Objects." In Lecture Notes in Computer Science, 337–49. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03731-8_32.

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Rajabi, Mohammad Javad, Shahidan M. Abdullah, Majid Bakhtiari, and Saeid Bakhtiari. "A Robust DCT Based Technique for Image Watermarking Against Cropping Attacks." In Recent Trends in Information and Communication Technology, 747–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59427-9_77.

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Sebé, Francesc, Josep Domingo-Ferrer, and Jordi Herrera. "Spatial-Domain Image Watermarking Robust against Compression, Filtering, Cropping, and Scaling." In Lecture Notes in Computer Science, 44–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44456-4_4.

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Koh, Sung Shik, and Chung Hwa Kim. "Cropping, Rotation and Scaling Invariant LBX Interleaved Voice-in-Image Watermarking." In Lecture Notes in Computer Science, 498–507. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30583-5_53.

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Lu, JunFeng, and MingXue Liao. "Target Cropping: A New Data Augmentation Method of Fine-Grained Image Classification." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 343–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32456-8_37.

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Conference papers on the topic "Image cropping"

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Cheatle, Phil. "Automatic image cropping for republishing." In IS&T/SPIE Electronic Imaging, edited by Qian Lin, Zhigang Z. Fan, Theo Gevers, Raimondo Schettini, and Cees Snoek. SPIE, 2010. http://dx.doi.org/10.1117/12.838452.

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Jieying She, Duo Wang, and Mingli Song. "Automatic image cropping using sparse coding." In 2011 First Asian Conference on Pattern Recognition (ACPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/acpr.2011.6166623.

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Jaiswal, Nehal, and Yogesh K. Meghrajani. "Automatic image cropping using saliency map." In 2015 International Conference on Industrial Instrumentation and Control (ICIC). IEEE, 2015. http://dx.doi.org/10.1109/iic.2015.7150885.

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Steinebach, Martin, Huajian Liu, and York Yannikos. "Efficient Cropping-Resistant Robust Image Hashing." In 2014 Ninth International Conference on Availability, Reliability and Security (ARES). IEEE, 2014. http://dx.doi.org/10.1109/ares.2014.85.

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Lian, Tianpei, Zhiguo Cao, Ke Xian, Zhiyu Pan, and Weicai Zhong. "Context-Aware Candidates for Image Cropping." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506111.

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Yan, Jianzhou, Stephen Lin, Sing Bing Kang, and Xiaoou Tang. "Learning the Change for Automatic Image Cropping." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.130.

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Chen, Jiansheng, Gaocheng Bai, Shaoheng Liang, and Zhengqin Li. "Automatic Image Cropping: A Computational Complexity Study." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.61.

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Li, Zhuopeng, and Xiaoyan Zhang. "Collaborative Deep Reinforcement Learning for Image Cropping." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00052.

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Ciocca, G., C. Cusano, F. Gasparini, and R. Schettini. "Self-Adaptive Image Cropping for Small Displays." In 2007 Digest of Technical Papers International Conference on Consumer Electronics. IEEE, 2007. http://dx.doi.org/10.1109/icce.2007.341331.

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Ziabari, Seyed Sahand Mohamadi. "Intelligent image watermarking robust against cropping attack." In 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI). IEEE, 2015. http://dx.doi.org/10.1109/kbei.2015.7436220.

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You might also be interested in the extended bibliographies on the topic 'Image cropping' for particular source types:
Journal articles Dissertations / Theses
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