87 research outputs found
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view
images is a fundamental yet active research area in computer vision. Despite
the steady progress in multi-view stereo reconstruction, most existing methods
are still limited in recovering fine-scale details and sharp features while
suppressing noises, and may fail in reconstructing regions with few textures.
To address these limitations, this paper presents a Detail-preserving and
Content-aware Variational (DCV) multi-view stereo method, which reconstructs
the 3D surface by alternating between reprojection error minimization and mesh
denoising. In reprojection error minimization, we propose a novel inter-image
similarity measure, which is effective to preserve fine-scale details of the
reconstructed surface and builds a connection between guided image filtering
and image registration. In mesh denoising, we propose a content-aware
-minimization algorithm by adaptively estimating the value and
regularization parameters based on the current input. It is much more promising
in suppressing noise while preserving sharp features than conventional
isotropic mesh smoothing. Experimental results on benchmark datasets
demonstrate that our DCV method is capable of recovering more surface details,
and obtains cleaner and more accurate reconstructions than state-of-the-art
methods. In particular, our method achieves the best results among all
published methods on the Middlebury dino ring and dino sparse ring datasets in
terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image
processin
Subspace Methods for Face Recognition: Singularity, Regularization, and Robustness
Face recognition has been an important issue in computer vision and pattern recognition over the last several decades (Zhao et al., 2003). While human can recognize faces easily, automated face recognition remains a great challenge in computer-based automated recognition research. One difficulty in face recognition is how to handle the variations in expression, pose an
Automated Personal Authentication Using Both Palmprints
Abstract. To satisfy personal interests, different entertainment computing should be performed for different people (called personal entertainment computing). For personal entertainment computing, the personal identity should be first automatically authenticated. This paper proposes a novel approach for automated personal authentication by using both palmprints. The experimental results show that the fusion of the information of both palmprints can dramatically improve the authentication accuracy
Palmprint Texture Analysis Using Derivative of Gaussian Filters
This paper presents a novel approach of palmprint tex-ture analysis based on the derivative of gaussian filter. In this approach, the palmprint image is respectively prepro-cessed along horizontal and vertical direction using deriva-tive of gaussian (DoG) Filters. And then the palmprint is encoded according to the sign of the value of each pixel of the filtered images. This code is called DoGCode of the palmprint. The size of DoGCode is 256 bytes. The simi-larity of two DoGCode is measured using their Hamming distance. This approach is tested on the PolyU Palmprint Database, which containing 7605 samples from 392 palms, and the EER is 0.19%, which is comparable with the exist-ing palmprint recognition methods. 1
Efficient non-uniform deblurring based on generalized additive convolution model
Image with non-uniform blurring caused by camera shake can be modeled as a linear combination of the homographically transformed versions of the latent sharp image during exposure. Although such a geometrically motivated model can well approximate camera motion poses, deblurring methods in this line usually suffer from the problems of heavy computational demanding or extensive memory cost. In this paper, we develop generalized additive convolution (GAC) model to address these issues. In GAC model, a camera motion trajectory can be decomposed into a set of camera poses, i.e., in-plane translations (slice) or roll rotations (fiber), which can both be formulated as convolution operation. Moreover, we suggest a greedy algorithm to decompose a camera motion trajectory into a more compact set of slices and fibers, and together with the efficient convolution computation via fast Fourier transform (FFT), the proposed GAC models concurrently overcome the difficulties of computational cost and memory burden, leading to efficient GAC-based deblurring methods. Besides, by incorporating group sparsity of the pose weight matrix into slice GAC, the non-uniform deblurring method naturally approaches toward the uniform blind deconvolution.Department of Computin
Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images
Left ventricular (LV) volumes estimation is a critical procedure for cardiac
disease diagnosis. The objective of this paper is to address direct LV volumes
prediction task. Methods: In this paper, we propose a direct volumes prediction
method based on the end-to-end deep convolutional neural networks (CNN). We
study the end-to-end LV volumes prediction method in items of the data
preprocessing, networks structure, and multi-views fusion strategy. The main
contributions of this paper are the following aspects. First, we propose a new
data preprocessing method on cardiac magnetic resonance (CMR). Second, we
propose a new networks structure for end-to-end LV volumes estimation. Third,
we explore the representational capacity of different slices, and propose a
fusion strategy to improve the prediction accuracy. Results: The evaluation
results show that the proposed method outperforms other state-of-the-art LV
volumes estimation methods on the open accessible benchmark datasets. The
clinical indexes derived from the predicted volumes agree well with the ground
truth (EDV: R2=0.974, RMSE=9.6ml; ESV: R2=0.976, RMSE=7.1ml; EF: R2=0.828, RMSE
=4.71%). Conclusion: Experimental results prove that the proposed method may be
useful for LV volumes prediction task. Significance: The proposed method not
only has application potential for cardiac diseases screening for large-scale
CMR data, but also can be extended to other medical image research fieldsComment: to appear on Transactions on Biomedical Engineerin
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