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OPEB: Open physical environment benchmark for artificial intelligence

[av_textblock size='' font_color='' color=''] OPEB: Open physical environment benchmark for artificial intelligence OPEB: Open physical environment benchmark for artificial intelligence Abstract: Artificial Intelligence methods to solve continuous-control tasks have made significant progress in recent years. However, these algorithms have important limitations and still need significant improvement to be used in industry and real-world applications. This means that this area is still in an active research phase. To involve a large number of research groups, standard benchmarks are needed to evaluate and compare proposed…

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DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features

[av_textblock size='' font_color='' color=''] DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features Abstract: Accurate ground-based cloud image categorization is a critical but challenging task that has not been well addressed. One of the essential issues that affect the performance is to extract the representative visual features. Nearly all of the existing methods rely on the hand-crafted descriptors (e.g., local binary patterns, CENsus TRsansform hISTogram, and scale-invariant feature transform). Their limited discriminative power indeed…

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Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification

[av_textblock size='' font_color='' color=''] Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification Abstract: Most of the existing spatial-spectral-based hyperspectral image classification (HSIC) methods mainly extract the spatial-spectral information by combining the pixels in a small neighborhood or aggregating the statistical and morphological characteristics. However, those strategies can only generate shallow appearance features with limited representative ability for classes with high interclass similarity and spatial diversity and therefore reduce the…

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Domain Adaptation Using Representation Learning for the Classification of Remote Sensing Images

[av_textblock size='' font_color='' color=''] Domain Adaptation Using Representation Learning for the Classification of Remote Sensing Images Domain Adaptation Using Representation Learning for the Classification of Remote Sensing Images Abstract: Traditional machine learning (ML) techniques are often employed to perform complex pattern recognition tasks for remote sensing images, such as land-use classification. In order to obtain acceptable classification results, these techniques require there to be sufficient training data available for every particular image. Obtaining training samples is challenging, particularly for near real-time applications. Therefore,…

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An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

[av_textblock size='' font_color='' color=''] An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition Abstract: Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence…

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Cross-convolutional-layer Pooling for Image Recognition

[av_textblock size='' font_color='' color=''] Cross-convolutional-layer Pooling for Image Recognition Cross-convolutional-layer Pooling for Image Recognition Abstract: Recent studies have shown that a Deep Convolutional Neural Network (DCNN) trained on a large image dataset can be used as a universal image descriptor and that doing so leads to impressive performance for a variety of image recognition tasks. Most of these studies adopt activations from a single DCNN layer, usually the fully-connected layer, as the image representation. In this paper, we proposed a novel way to…

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Phase Vector Incompressible Registration Algorithm for Motion Estimation From Tagged Magnetic Resonance Images

[av_textblock size='' font_color='' color=''] Phase Vector Incompressible Registration Algorithm for Motion Estimation From Tagged Magnetic Resonance Images Phase Vector Incompressible Registration Algorithm for Motion Estimation From Tagged Magnetic Resonance Images Abstract: Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissue. It is challenging to obtain 3-D motion estimates due to a tradeoff between image slice density and acquisition time. Typically, interpolation methods are used either to combine 2-D motion extracted from sparse slice…

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Active Learning-Based Optimized Training Library Generation for Object-Oriented Image Classification

[av_textblock size='' font_color='' color=''] Active Learning-Based Optimized Training Library Generation for Object-Oriented Image Classification Active Learning-Based Optimized Training Library Generation for Object-Oriented Image Classification Abstract: In this paper, we introduce an active learning (AL)-based object training library generation for a multiclassifier object-oriented image analysis (OOIA) system. While several AL approaches do exist for pixel-based training library generation and for hyperspectral image classification, there is no standard training library generation strategy for OOIA of very high spatial resolution images. Given a sufficient number of…

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Objective Quality Assessment of Image Retargeting by Incorporating Fidelity Measures and Inconsistency Detection

[av_textblock size='' font_color='' color=''] Objective Quality Assessment of Image Retargeting by Incorporating Fidelity Measures and Inconsistency Detection Objective Quality Assessment of Image Retargeting by Incorporating Fidelity Measures and Inconsistency Detection Abstract: The tremendous growth in mobile devices has resulted in huge generation and usage of digital images. Image quality assessment is thus an important issue for mobile media applications. In this paper, we focus on the quality evaluation of images generated by content-aware image retargeting, in which the reference and the distorted images…

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Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval

[av_textblock size='' font_color='' color=''] Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval Abstract: We study the problem of fine-grained sketch-based image retrieval. By performing instance-level (rather than category-level) retrieval, it embodies a timely and practical application, particularly with the ubiquitous availability of touchscreens. Three factors contribute to the challenging nature of the problem: 1) free-hand sketches are inherently abstract and iconic, making visual comparisons with photos difficult; 2) sketches and photos are in…

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