Dissertationen zum Thema „Image cropping“

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.

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.

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.

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.

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.

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.

Cieľom tejto práce je priniesť detailný pohľad na doterajší prieskum v oblasti sférických videí. Konkrétne sa táto práca zameriava na problém tvorby videa s normálnym zorným poľom zo sférického videa pre potreby zobrazovania. Prináša tiež implementáciu niektorých dostupných metód. Doteraz boli predstavené tri metódy v štyroch článkoch, ktoré riešia tento problém. Všetky priniesli zaujímavé výsledky a táto práca sa dvomi z nich zaoberá hlbšie. Táto práca tiež prináša základnú metódu využívajúcu overené metódy automat- ického orezu obrazu. Táto metóda je využitá na porovnanie so skúmanými metódami, u ktorých zvýrazní ich vylepšenia ale aj nedostatky. Na základe porovnania metód pomocou užívateľského experimentu táto práca usudzuje, že najlepšou zo skúmaných metód pre túto úlohu je upravená varianta metódy od Pavel et al. [14], predstavená v tejto práci.

Today, digital images are commonly used to preserve and present analogue media. To minimize the need for digital storage space, it is important that the object covers as large part of the image as possible. This paper presents a robust methodology, based on common edge and line detection techniques, to automatically identify rectangular objects in digital images. The methodology is tailored to identify posters, photographs and books digitized at the National Library of Sweden (the KB). The methodology has been implemented as a part of DocCrop, a computer program written in Java to automatically identify and crop documents in digital images. With the aid of the developed tool, the KB hopes to decrease the time and manual labour required to crop their digital images. Three multi-paged documents digitized at the KB have been used to evaluate the tool's performance. Each document features different characteristics. The overall identification results, as well as an in-depth analysis of the different methodology stages, are presented in this paper. In average, the developed software identified 98% of the digitized document pages successfully. The software's identification success rate never went below 95% for any of the three documents. The robustness and execution speed of the methodology suggests that the methodology can be a compelling alternative to the manual identification used at the KB today.

碩士
國立中央大學
資訊工程學系
102
A new multi-operator image retargeting approach is proposed in this research. Cropping, seam carving and scaling are applied sequentially on the image to acquire the image with the targeted resolution. The saliency map of the image is first computed to serve as the reference for the subsequent processing. The foreground objects that occupy larger areas will be extracted and the boundaries of objects will be used to determine the edges for cropping. Then, seam carving is applied to remove insignificant content by employing the dynamic programming. The local energy decides when the seam carving process should be stopped. For certain appropriate images, the seams are increased so that the resulting aspect ratio can be approaching the targeted one. Finally, the image is simply scaled to the resolution of the display. The experimental results demonstrate that the essential part the image can be maintained to avoid the serious distortion from the resolution changes. Compared with the images obtained by adopting more complicated methodologies, the image of our scheme is not inferior so the efficiency can be achieved.

碩士
國立成功大學
資訊工程學系碩博士班
98
Cell phone is one of the most general consumer electronic products since modern times. Besides the call or SMS functions of the traditional cell-phone, the user can download and upgrade the applications through the Android Platform. The feature will make the cell-phone more practical. With the rapid development of technology, user can take pictures with the cell-phone whenever and wherever. As memory card capacity of the cell-phone being larger and larger, more photos can be deposited. For this reason, user will waste much time to manage photos, and then we will implement a system that classifies and crops photos automatically in the cell-phone. User will manage photos faster and more convenient. In the thesis, we implement the image classification function by two-stage classification process. In the first stage, we use face detect function in Android to detect face block region, and skin detection to judge possible miscarriage cases. We use this approach to find out photos with characters. In the second stage, we use masks to erode binary image after edge detection, and separate the photos with buildings and landscape photos by erosion rates. Finally, we use the data that analyzed through the image classification function to set the ROI(Region-of-Interest) range, and then crop the picture to ROI. We will develop an appropriate digital image cropping/classification system with low complexity on the Google Android Platform. Then the complete program will be provided based on the spirit of open source.

碩士
大同大學
資訊工程學系(所)
103
Automatic image cropping can be used to segment the main theme of an image on different platforms such as mobile phones, cameras, and web pages. For the same input image the results are different in various systems with the same function. Some of the results may even be completely different. However, we should not say the result is wrong because it involves personal sense of aesthetics. Different methods may focus on different topics like human faces or color contrast. Ma and Guo assessed regions based on entropy, size, and distance from the image center. Zhang et al. used face detection to find regions of interest. Yen and Lin used training set before auto-cropping. Cheng used color contrast to generate a saliency map for cropping. Focused object is usually the saliency regions in an image. Performance of edge detection are often the measurement of saliency map. Therefore, we use L*a*b color space to get color saturation value first and then use star-light mask to compare the pixel color saturation to generate an edge similar saliency map. Finally, calculate the pixel value for image cropping. Unlike ordinary well known Canny edges or Sobel edges, our method can generate better saliency map in which edges are kept while background edges are removed. Usability and reliability are higher than face-based detection or color contrast based approaches. In addition, we propose two algorithms to automatically crop the main theme from saliency map. Our algorithm is both simple and easy to understand. It is an creative development approach compared with other auto-cropping method.

Yes
Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms.
European Regional Development Fund Interreg project Smart Cities and Open Data REuse (SCORE).

Currently, online advertisements are created for specific dimensions and must be laboriously modified by advertisers to support different aspect ratios. In addition, publishers are constrained to design web pages to accommodate this limited set of sizes. As an alternative we present a framework for automatically generating visual banners at arbitrary sizes based on individual prototype ads. This technique can be used to create flexible visual ads that can be resized to accommodate various aspect ratios. In the proposed framework image and text data are stored separately. Resizing involves selecting a sub-region of the original image and updating text parameters (size and position). This problem is posed within an optimization framework that encourages solutions which maintain important structural properties of the original ad. The method can be applied to advertisements containing a wide variety of imagery and provides significantly more flexibility than existing solutions.