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J. Imaging, Volume 6, Issue 1 (January 2020) – 3 articles

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Cover Story (view full-size image) This paper proposes an effective and fast method to produce automatically holographic 3D images [...] Read more.
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Open AccessEditorial
Acknowledgement to Reviewers of Journal of Imaging in 2019
J. Imaging 2020, 6(1), 3; https://doi.org/10.3390/jimaging6010003 - 17 Jan 2020
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Open AccessArticle
Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning
J. Imaging 2020, 6(1), 2; https://doi.org/10.3390/jimaging6010002 - 17 Jan 2020
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Abstract
We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the [...] Read more.
We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual feature/descriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the user’s high-level semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database. Full article
(This article belongs to the Special Issue Advances in Image Feature Extraction and Selection)
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Open AccessArticle
Morphing a Stereogram into Hologram
J. Imaging 2020, 6(1), 1; https://doi.org/10.3390/jimaging6010001 - 02 Jan 2020
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Abstract
We developed a method to transform stereoscopic two-dimensional (2D) images into holograms via unsupervised morphing deformations between left (L) and right (R) input images. By using robust DeepFlow and light-field rendering algorithms, we established correlations between a 2D scene and its three-dimensional (3D) [...] Read more.
We developed a method to transform stereoscopic two-dimensional (2D) images into holograms via unsupervised morphing deformations between left (L) and right (R) input images. By using robust DeepFlow and light-field rendering algorithms, we established correlations between a 2D scene and its three-dimensional (3D) display on a Looking Glass HoloPlay monitor. The possibility of applying this method, together with a lookup table for multi-view glasses-free 3D streaming with a stereo webcam, was also analyzed. Full article
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