62 research outputs found
Accurate calibration of stereo cameras for machine vision
Camera calibration is an important task for machine vision, whose goal is to obtain the internal and external parameters of each camera. With these parameters, the 3D positions of a scene point, which is identified and matched in two stereo images, can be determined by the triangulation theory. This paper presents a new accurate estimation of CCD camera parameters for machine vision. We present a fast technique to estimate the camera center with special arrangement of calibration target and the camera model is aimed at efficient computation of camera parameters considering lens distortion. Built on strict geometry constraint, our calibration method has compensated the error for distortion cased by circular features on calibration target, which gets over the relativity influence of every unknown parameters of traditional calibration way and make the error distributed among the constraint relation of parameters, in order to guarantee the accuracy and consistency of calibration results. Experimental results are provided to show that the calibration accuracy is high.Facultad de Informátic
Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation
Tractography traces the peak directions extracted from fiber orientation
distribution (FOD) suffering from ambiguous spatial correspondences between
diffusion directions and fiber geometry, which is prone to producing erroneous
tracks while missing true positive connections. The peaks-based tractography
methods 'locally' reconstructed streamlines in 'single to single' manner, thus
lacking of global information about the trend of the whole fiber bundle. In
this work, we propose a novel tractography method based on a bundle-specific
tractogram distribution function by using a higher-order streamline
differential equation, which reconstructs the streamline bundles in 'cluster to
cluster' manner. A unified framework for any higher-order streamline
differential equation is presented to describe the fiber bundles with disjoint
streamlines defined based on the diffusion tensor vector field. At the global
level, the tractography process is simplified as the estimation of
bundle-specific tractogram distribution (BTD) coefficients by minimizing the
energy optimization model, and is used to characterize the relations between
BTD and diffusion tensor vector under the prior guidance by introducing the
tractogram bundle information to provide anatomic priors. Experiments are
performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography
Challenge data, FiberCup data, and in vivo data from the Human Connectome
Project (HCP) data for qualitative and quantitative evaluation. The results
demonstrate that our approach can reconstruct the complex global fiber bundles
directly. BTD reduces the error deviation and accumulation at the local level
and shows better results in reconstructing long-range, twisting, and large
fanning tracts
Deep-Learned Regularization and Proximal Operator for Image Compressive Sensing
Deep learning has recently been intensively studied in the context of image compressive sensing (CS) to discover and represent complicated image structures. These approaches, however, either suffer from nonflexibility for an arbitrary sampling ratio or lack an explicit deep-learned regularization term. This paper aims to solve the CS reconstruction problem by combining the deep-learned regularization term and proximal operator. We first introduce a regularization term using a carefully designed residual-regressive net, which can measure the distance between a corrupted image and a clean image set and accurately identify to which subspace the corrupted image belongs. We then address a proximal operator with a tailored dilated residual channel attention net, which enables the learned proximal operator to map the distorted image into the clean image set. We adopt an adaptive proximal selection strategy to embed the network into the loop of the CS image reconstruction algorithm. Moreover, a self-ensemble strategy is presented to improve CS recovery performance. We further utilize state evolution to analyze the effectiveness of the designed networks. Extensive experiments also demonstrate that our method can yield superior accurate reconstruction (PSNR gain over 1 dB) compared to other competing approaches while achieving the current state-of-the-art image CS reconstruction performance. The test code is available at https://github.com/zjut-gwl/CSDRCANet
Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods
The corticospinal tract (CST) is a critically important white matter fiber
tract in the human brain that enables control of voluntary movements of the
body. Diffusion MRI tractography is the only method that enables the study of
the anatomy and variability of the CST pathway in human health. In this work,
we explored the performance of six widely used tractography methods for
reconstructing the CST and its somatotopic organization. We perform experiments
using diffusion MRI data from the Human Connectome Project. Four quantitative
measurements including reconstruction rate, the WM-GM interface coverage,
anatomical distribution of streamlines, and correlation with cortical volumes
to assess the advantages and limitations of each method. Overall, we conclude
that while current tractography methods have made progress toward the
well-known challenge of improving the reconstruction of the lateral projections
of the CST, the overall problem of performing a comprehensive CST
reconstruction, including clinically important projections in the lateral (hand
and face area) and medial portions (leg area), remains an important challenge
for diffusion MRI tractography.Comment: 41 pages, 19 figure
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