3,113 research outputs found
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Comprehensive Use of Curvature for Robust and Accurate Online Surface Reconstruction
Interactive real-time scene acquisition from hand-held depth cameras has recently developed much momentum, enabling
applications in ad-hoc object acquisition, augmented reality and other fields. A key challenge to online reconstruction remains
error accumulation in the reconstructed camera trajectory, due to drift-inducing instabilities in the range scan alignments of the
underlying iterative-closest-point (ICP) algorithm. Various strategies have been proposed to mitigate that drift, including SIFT-based
pre-alignment, color-based weighting of ICP pairs, stronger weighting of edge features, and so on. In our work, we focus on surface
curvature as a feature that is detectable on range scans alone and hence does not depend on accurate multi-sensor alignment. In
contrast to previous work that took curvature into consideration, however, we treat curvature as an independent quantity that we
consistently incorporate into every stage of the real-time reconstruction pipeline, including densely curvature-weighted ICP, range
image fusion, local surface reconstruction, and rendering. Using multiple benchmark sequences, and in direct comparison to other
state-of-the-art online acquisition systems, we show that our approach significantly reduces drift, both when analyzing individual
pipeline stages in isolation, as well as seen across the online reconstruction pipeline as a whole
Fast and Accurate Depth Estimation from Sparse Light Fields
We present a fast and accurate method for dense depth reconstruction from
sparsely sampled light fields obtained using a synchronized camera array. In
our method, the source images are over-segmented into non-overlapping compact
superpixels that are used as basic data units for depth estimation and
refinement. Superpixel representation provides a desirable reduction in the
computational cost while preserving the image geometry with respect to the
object contours. Each superpixel is modeled as a plane in the image space,
allowing depth values to vary smoothly within the superpixel area. Initial
depth maps, which are obtained by plane sweeping, are iteratively refined by
propagating good correspondences within an image. To ensure the fast
convergence of the iterative optimization process, we employ a highly parallel
propagation scheme that operates on all the superpixels of all the images at
once, making full use of the parallel graphics hardware. A few optimization
iterations of the energy function incorporating superpixel-wise smoothness and
geometric consistency constraints allows to recover depth with high accuracy in
textured and textureless regions as well as areas with occlusions, producing
dense globally consistent depth maps. We demonstrate that while the depth
reconstruction takes about a second per full high-definition view, the accuracy
of the obtained depth maps is comparable with the state-of-the-art results.Comment: 15 pages, 15 figure
Real-time object detection using monocular vision for low-cost automotive sensing systems
This work addresses the problem of real-time object detection in automotive environments
using monocular vision. The focus is on real-time feature detection,
tracking, depth estimation using monocular vision and finally, object detection by
fusing visual saliency and depth information.
Firstly, a novel feature detection approach is proposed for extracting stable and
dense features even in images with very low signal-to-noise ratio. This methodology
is based on image gradients, which are redefined to take account of noise as
part of their mathematical model. Each gradient is based on a vector connecting a
negative to a positive intensity centroid, where both centroids are symmetric about
the centre of the area for which the gradient is calculated. Multiple gradient vectors
define a feature with its strength being proportional to the underlying gradient
vector magnitude. The evaluation of the Dense Gradient Features (DeGraF) shows
superior performance over other contemporary detectors in terms of keypoint density,
tracking accuracy, illumination invariance, rotation invariance, noise resistance
and detection time.
The DeGraF features form the basis for two new approaches that perform dense
3D reconstruction from a single vehicle-mounted camera. The first approach tracks
DeGraF features in real-time while performing image stabilisation with minimal
computational cost. This means that despite camera vibration the algorithm can
accurately predict the real-world coordinates of each image pixel in real-time by comparing
each motion-vector to the ego-motion vector of the vehicle. The performance
of this approach has been compared to different 3D reconstruction methods in order
to determine their accuracy, depth-map density, noise-resistance and computational
complexity. The second approach proposes the use of local frequency analysis of
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gradient features for estimating relative depth. This novel method is based on the
fact that DeGraF gradients can accurately measure local image variance with subpixel
accuracy. It is shown that the local frequency by which the centroid oscillates
around the gradient window centre is proportional to the depth of each gradient
centroid in the real world. The lower computational complexity of this methodology
comes at the expense of depth map accuracy as the camera velocity increases, but
it is at least five times faster than the other evaluated approaches.
This work also proposes a novel technique for deriving visual saliency maps by
using Division of Gaussians (DIVoG). In this context, saliency maps express the
difference of each image pixel is to its surrounding pixels across multiple pyramid
levels. This approach is shown to be both fast and accurate when evaluated against
other state-of-the-art approaches. Subsequently, the saliency information is combined
with depth information to identify salient regions close to the host vehicle.
The fused map allows faster detection of high-risk areas where obstacles are likely
to exist. As a result, existing object detection algorithms, such as the Histogram of
Oriented Gradients (HOG) can execute at least five times faster.
In conclusion, through a step-wise approach computationally-expensive algorithms
have been optimised or replaced by novel methodologies to produce a fast object
detection system that is aligned to the requirements of the automotive domain
Computational Imaging for Shape Understanding
Geometry is the essential property of real-world scenes. Understanding the shape of the object is critical to many computer vision applications. In this dissertation, we explore using computational imaging approaches to recover the geometry of real-world scenes. Computational imaging is an emerging technique that uses the co-designs of image hardware and computational software to expand the capacity of traditional cameras. To tackle face recognition in the uncontrolled environment, we study 2D color image and 3D shape to deal with body movement and self-occlusion. Especially, we use multiple RGB-D cameras to fuse the varying pose and register the front face in a unified coordinate system. The deep color feature and geodesic distance feature have been used to complete face recognition. To handle the underwater image application, we study the angular-spatial encoding and polarization state encoding of light rays using computational imaging devices. Specifically, we use the light field camera to tackle the challenging problem of underwater 3D reconstruction. We leverage the angular sampling of the light field for robust depth estimation. We also develop a fast ray marching algorithm to improve the efficiency of the algorithm. To deal with arbitrary reflectance, we investigate polarimetric imaging and develop polarimetric Helmholtz stereopsis that uses reciprocal polarimetric image pairs for high-fidelity 3D surface reconstruction. We formulate new reciprocity and diffuse/specular polarimetric constraints to recover surface depths and normals using an optimization framework. To recover the 3D shape in the unknown and uncontrolled natural illumination, we use two circularly polarized spotlights to boost the polarization cues corrupted by the environment lighting, as well as to provide photometric cues. To mitigate the effect of uncontrolled environment light in photometric constraints, we estimate a lighting proxy map and iteratively refine the normal and lighting estimation. Through expensive experiments on the simulated and real images, we demonstrate that our proposed computational imaging methods outperform traditional imaging approaches
Locally Adaptive Stereo Vision Based 3D Visual Reconstruction
abstract: Using stereo vision for 3D reconstruction and depth estimation has become a popular and promising research area as it has a simple setup with passive cameras and relatively efficient processing procedure. The work in this dissertation focuses on locally adaptive stereo vision methods and applications to different imaging setups and image scenes.
Solder ball height and substrate coplanarity inspection is essential to the detection of potential connectivity issues in semi-conductor units. Current ball height and substrate coplanarity inspection tools are expensive and slow, which makes them difficult to use in a real-time manufacturing setting. In this dissertation, an automatic, stereo vision based, in-line ball height and coplanarity inspection method is presented. The proposed method includes an imaging setup together with a computer vision algorithm for reliable, in-line ball height measurement. The imaging setup and calibration, ball height estimation and substrate coplanarity calculation are presented with novel stereo vision methods. The results of the proposed method are evaluated in a measurement capability analysis (MCA) procedure and compared with the ground-truth obtained by an existing laser scanning tool and an existing confocal inspection tool. The proposed system outperforms existing inspection tools in terms of accuracy and stability.
In a rectified stereo vision system, stereo matching methods can be categorized into global methods and local methods. Local stereo methods are more suitable for real-time processing purposes with competitive accuracy as compared with global methods. This work proposes a stereo matching method based on sparse locally adaptive cost aggregation. In order to reduce outlier disparity values that correspond to mis-matches, a novel sparse disparity subset selection method is proposed by assigning a significance status to candidate disparity values, and selecting the significant disparity values adaptively. An adaptive guided filtering method using the disparity subset for refined cost aggregation and disparity calculation is demonstrated. The proposed stereo matching algorithm is tested on the Middlebury and the KITTI stereo evaluation benchmark images. A performance analysis of the proposed method in terms of the I0 norm of the disparity subset is presented to demonstrate the achieved efficiency and accuracy.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
A Quantification of the 3D Modeling Capabilities of the Kinect Fustion Algorithm
In the eld of three-dimensional modeling, we continually struggle to quantify how closely the resulting model matches the physical object being represented. When precision measurements are required, they are often left to high-end, industrial systems. The aim of this thesis is to quantify the level of precision that can be obtained from commodity systems such as the Microsoft Kinect paired with the KinectFusion algorithm. Although the Kinect alone is considered a noisy sensor, the KinectFusion algorithm has shown the ability to build detailed surface models through the aggregation of depth information taken from multiple perspectives. This work represents the first rigorous validation of the three- dimensional modeling capabilities of the KinectFusion algorithm. One experiment is performed to measure the effects of key algorithm parameters such as resolution and range, while another is performed to measure the lower bounds at which objects can be detected and accurately modeled. The first experiment found that the KinectFusion algorithm reduced the uncertainty of the Kinect sensor alone from 10 mm to just 1.8 mm. Furthermore, the results of the second experiment demonstrate that the KinectFusion algorithm can detect surface deviations as little as 1.3 mm, but cannot accurately measure the deviation. Such results form an initial quantification of the KinectFusion algorithm, thus providing confidence about when and when not to utilize the KinectFusion algorithm for precision modeling. The hope is that this work will open the door for the algorithm to be used in real-world applications, such as alleviating the tedious visual surface inspections required for USAF aircraft
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