Representation Of Multi-view Images Research Articles

Instance-level 3D shape retrieval from a single image by hybrid-representation-assisted joint embedding

We present a novel and effective joint embedding approach for retrieving the most similar 3D shape for a single image query. Our approach builds upon hybrid 3D representations—the octree-based representation and the multi-view image representation, which characterize shape geometry in different ways. We first pre-train a 3D feature space via jointly embedding 3D shapes with hybrid representations and then introduce a transform layer and an image encoder to map both shape codes and real images into a common space via a second joint embedding. Our pre-training benefits from the hybrid representation of 3D shapes and builds a more discriminative 3D shape space than using any one of 3D representations only. The transform layer helps to mind the gap between the 3D shape space and the real image space. We validate the efficacy of our method on the instance-level single-image 3D retrieval task and achieve significant improvements over existing methods.

SAR target recognition via sparse representation of multi-view SAR images with correlation analysis

This study proposes a synthetic aperture radar (SAR) target recognition method via sparse representation of multi-view images with correlation analysis. The multi-view SAR images are first clustered into several view sets and in each set the included SAR images share high correlations. For the view set with only one SAR image, the sparse representation-based classification (SRC) is used for classification. The joint sparse representation (JSR) is employed to classify the view sets with more than one images in order to exploit their correlations. The decisions from different view sets are then fused based on the Bayesian theory. Therefore, both the independency and inner correlations in the multi-view SAR images can be better exploited to improve the target recognition performance. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) data set. The results show the superiority of the proposed approach over some other methods.

Graph-based representation for multiview image geometry.

In this paper, we propose a new geometry representation method for multiview image sets. Our approach relies on graphs to describe the multiview geometry information in a compact and controllable way. The links of the graph connect pixels in different images and describe the proximity between pixels in 3D space. These connections are dependent on the geometry of the scene and provide the right amount of information that is necessary for coding and reconstructing multiple views. Our multiview image representation is very compact and adapts the transmitted geometry information as a function of the complexity of the prediction performed at the decoder side. To achieve this, our graph-based representation (GBR) carefully selects the amount of geometry information needed before coding. This is in contrast with depth coding, which directly compresses with losses the original geometry signal, thus making it difficult to quantify the impact of coding errors on geometry-based interpolation. We present the principles of this GBR and we build an efficient coding algorithm to represent it. We compare our GBR approach to classical depth compression methods and compare their respective view synthesis qualities as a function of the compactness of the geometry description. We show that GBR can achieve significant gains in geometry coding rate over depth-based schemes operating at similar quality. Experimental results demonstrate the potential of this new representation.

Layer-based sparse representation of multiview images

This article presents a novel method to obtain a sparse representation of multiview images. The method is based on the fact that multiview data is composed of epipolar-plane image lines which are highly redundant. We extend this principle to obtain the layer-based representation, which partitions a multiview image dataset into redundant regions (which we call layers) each related to a constant depth in the observed scene. The layers are extracted using a general segmentation framework which takes into account the camera setup and occlusion constraints. To obtain a sparse representation, the extracted layers are further decomposed using a multidimensional discrete wavelet transform (DWT), first across the view domain followed by a two-dimensional (2D) DWT applied to the image dimensions. We modify the viewpoint DWT to take into account occlusions and scene depth variations. Simulation results based on nonlinear approximation show that the sparsity of our representation is superior to the multi-dimensional DWT without disparity compensation. In addition we demonstrate that the constant depth model of the representation can be used to synthesise novel viewpoints for immersive viewing applications and also de-noise multiview images.

３次元画像情報技術 領域分割に基づく多眼画像の３次元レイヤ表現

３次元画像情報技術 領域分割に基づく多眼画像の３次元レイヤ表現