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Inferring the complete 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the training set to solve the problem. These methods suffer from poor generalization and may lead to low-quality reconstructions for unseen objects. Nowadays, stereo cameras are pervasive in emerging devices such as dual-lens smartphones and robots, which enables the use of the two-view nature of stereo images to explore the 3D structure and thus improve the reconstruction performance. In this paper, we propose a new deep learning framework for reconstructing the 3D shape of an object from a pair of stereo images, which reasons about the 3D structure of the object by taking bidirectional disparities and feature correspondences between the two views into account. Besides, we present a large-scale synthetic benchmarking dataset, namely StereoShapeNet , containing 1,052,976 pairs of stereo images rendered from ShapeNet along with the corresponding bidirectional depth and disparity maps. Experimental results on the StereoShapeNet benchmark demonstrate that the proposed framework outperforms the state-of-the-art methods. The project page is available at https://haozhexie.com/project/stereo-3d-reconstruction. [ABSTRACT FROM AUTHOR]

In recent years, deep hashing methods have been proved to be effective since it employs convolutional neural network to learn features and hashing codes simultaneously. However, these methods are mostly supervised. In real-world applications, it is a time-consuming and overloaded task for annotating a large number of images. In this paper, we propose a novel unsupervised deep hashing method for large-scale image retrieval. Our method, namely unsupervised semantic deep hashing (USDH), uses semantic information preserved in the CNN feature layer to guide the training of network. We enforce four criteria on hashing codes learning based on VGG-19 model: 1) preserving relevant information of feature space in hashing space; 2) minimizing quantization loss between binary-like codes and hashing codes; 3) improving the usage of each bit in hashing codes by using maximum information entropy, and 4) invariant to image rotation. Extensive experiments on CIFAR-10, NUSWIDE have demonstrated that USDH outperforms several state-of-the-art unsupervised hashing methods for image retrieval. We also conduct experiments on Oxford 17 datasets for fine-grained classification to verify its efficiency for other computer vision tasks. [ABSTRACT FROM AUTHOR]

Image classification is a prominent topic and a challenging task in the field of remote sensing. Recently many various classification methods have been proposed for satellite images specifically the frameworks based on spectral-spatial feature extraction techniques. In this paper, a feature extraction strategy of multispectral data is taken into account in order to develop a new classification framework by combining Extended Multi-Attribute Profiles (EMAP) and Sparse Autoencoder (SAE). Extended Multi-Attribute Profiles is employed to extract the spatial information, then it is joined to the original spectral information to describe the spectral-spatial property of the multispectral images. The obtained features are fed into a Sparse Autoencoder as input. Finally, the learned spectral-spatial features are embedded into the Support Vector Machine (SVM) for classification. Experiments are conducted on two multispectral (MS) images such as we construct the ground truth maps of the corresponding images. Our approach based on EMAP and deep learning (DL), proves its huge potential to achieve a high classification accuracy in reasonable running time and outperforms traditional classifiers and others classification approaches. [ABSTRACT FROM AUTHOR]