“…The effectiveness of different shape descriptors (area/perimeter, fractal area, circularity, maximum fractal, rectangularity, multiscale fractal curve—MFC), and the concatenation of all shape descriptors (except MFC), denominated Alldescriptors (AllD)) was evaluated and applied to polygons corresponding to the shapes represented by the convex hull obtained from players’ 2D coordinates. A content-based image retrieval system (CBIR) was applied for 25 users (mean age of 31.9 ± 8.4 years) to evaluate the relevant images. …”
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“…As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. …”
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“…We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. …”
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“…Combined with a straightforward, physically interpretable image retrieval algorithm, the camera can simultaneously perform high-accuracy depth sensing and high-fidelity polarization imaging over an extended depth of field for both static and dynamic scenes in both indoor and outdoor environments. …”
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“…</p> <p align="justify"> Extensions to content-based global and local image retrieval, to mining compressed streams, to multi-modal search and to mining activity data collected by distributed mobile networks are introduced.…”
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“…In particular, the Content-Based Image Retrieval (CBIR) methodology can be involved in the PACSs using a query-by-example task. …”
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“…Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks. CNNs also help radiologists providing more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions. …”
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“…The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. …”
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“…The KL divergence loss function is adopted for content-based image retrieval and fine-grained classification to improve the quality of image representation. …”
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“…This feature can be used for image retrieval, quality assurance or for educational purpose. …”
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“…BACKGROUND: Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method. …”
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“…EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiveness of EMDS-5, for each function, we select the most representative algorithms and price indicators for testing and evaluation. …”
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“…Our method enables automated search of large clinical databases and make targeted anatomical image retrieval possible.…”
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“…Three sizes thereof (up to 1024x768 pixels) are stored on a CD-ROM together with the semantic network for navigation and a set of Java applications for image retrieval and manipulation. The highest resolution formats (3072x2048 pixels) are available from a server in the intranet. …”
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“…As a means of content-based image retrieval, DR-MIL can identify images used as key instances, references, and citers for visual interpretation. …”
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“…In an effort to provide a translatable solution for this poorly constrained problem, Content-based Image Retrieval (CBIR) based on vessel information has been suggested as a method for obtaining a global coarse registration without using tracking information. …”
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“…The bulk extraction of imaging metadata required a median (quartiles) time of 0.09 (0.03–2.25) to 12.52 (4.11–37.30) seconds for a median (quartiles) number of 10 (3–29) to 103 (8–693) images per patient, depending on the extraction approach. The ad hoc image retrieval from the PACS required a median (quartiles) of 2.57 (2.57–2.79) seconds per image for the download, but 5.55 (4.91–6.06) seconds to display the first and 40.77 (38.60–41.63) seconds to display all images using the pure web-based viewer. …”
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“…The synthetic aperture focusing technique was utilized to reconstruct the images; as a result, the elevational resolution particularly within the out-of-focus region was considerably improved compared to the resolution of an image retrieved by multiplexing the PA time-resolved signals with sound velocity. …”
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“…Color, texture, and shape are the most common features used to characterize an image, however extracting the proper features for image retrievals in a similar manner with the human cognition remains a constant challenge. …”
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“…We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. …”
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