Local Support Region Research Articles

Automated rock mass discontinuity set characterisation using amplitude and phase decomposition of point cloud data

Laser scanning is an efficient approach for collecting rock mass point cloud scans to characterise structural discontinuities. However, developing computationally efficient and robust analytical workflows remains an open research problem. Existing semi-automated and automated approaches rely on point normals which are prone to mapping error when high variability exists in the local-support region. This study proposes a new automated algorithm that uses the spatial distribution of points on discontinuities to capture unique signatures in the form of sinusoidal waves. The discontinuities are then effectively characterised by clustering the amplitude and phase profiles of the sinusoidal waves. The presented amplitude and phase decomposition (APD) approach requires minimal pre-processing. Moreover, it can be applied directly to raw point clouds as filtering is inherently included through the fast Fourier transform (FFT) based decomposition of the signals. The method was evaluated on an underground tunnel dataset with exposed structural discontinuity planes. The efficacy of the developed approach was tested against manual segmentation using virtual compass plugin in open-source software (Cloud Compare), semi-automated open-source (discontinuity set extractor) and proprietary (Maptek PointStudio) software, and other automated algorithmic approaches based on point normal clustering and region growing. The APD approach produced the least error in estimating mean discontinuity dip angle and dip direction which was ±1.15° and ±1.39° with a dispersion error of ±2.24° and ±1.54°, respectively.

Spatial‐tree filter for cost aggregation in stereo matching

The desired solution to many labelling problems in computer vision is a spatially smooth result where label changes are aligned with the edges of the guidance image. It can be obtained traditionally by smoothing the label costs using edge-aware filters. However, local filters incorporate the information in a local support region to obtain locally-optimal and non-local tree-based filters, which often overuse piece-wise constant assumptions. In this paper, we propose a spatial-tree filter for cost aggregation. The tree model incorporates the spatial affinity into the tree structure. The tree distance between two pixels on our spatial tree is an approximated geodesic distance, which acts as a pixel similarity metric. The filtering process was implemented by recursively techniques in four directions: Top-to-bottom, left-to-right, and vice-versa. Thus, the complexity of our approach is linear to the number of image pixels. Extensive experiments demonstrate the effectiveness and efficiency of our spatial-tree filter in image smoothing and stereo matching. Our method performs better than the existing tree-based non-local method in cost aggregation.

Interwoven texture-based description of interest points in images

Local feature description is to assign a unique signature to a key-point such that it becomes distinctive from the others regardless of changes in viewpoint, illumination, rotation, scale as well as distortions and noise. This paper proposes a novel approach to construct such a descriptor. For preserving both homogeneous and heterogeneous features of a given support region, we interweave the texture information so that the key-point is more likely to be assigned a distinctive signature and neighboring key-points will be less likely to share the same texture information. The main idea behind our descriptor is to increase the areas of our observations in the given scene while the length of the local support region is fixed. Gradient magnitude and divergence, as measurement parameters of texture information, are applied to a group of pixels instead of employing a pixel-wise strategy that make the descriptor more resistant to noise, distortions and illumination variation. The required storage of the proposed descriptor is just 72 floats and its computational complexity is much lower than those of existing ones. A comparative study between the proposed method and the selected state-of-the-art ones over multiple publicly accessible datasets with different characteristics shows its superiority, robustness and computational efficiency under various geometric changes, illumination variation, distortions and noise. The code and supplementary materials can be found at https://github.com/mogvision/InterTex-Feature-Descriptor.

Stereo Matching Using Tree Filtering.

Matching cost aggregation is one of the oldest and still popular methods for stereo correspondence. While effective and efficient, cost aggregation methods typically aggregate the matching cost by summing/averaging over a user-specified, local support region. This is obviously only locally-optimal, and the computational complexity of the full-kernel implementation usually depends on the region size. In this paper, the cost aggregation problem is re-examined and a non-local solution is proposed. The matching cost values are aggregated adaptively based on pixel similarity on a tree structure derived from the stereo image pair to preserve depth edges. The nodes of this tree are all the image pixels, and the edges are all the edges between the nearest neighboring pixels. The similarity between any two pixels is decided by their shortest distance on the tree. The proposed method is non-local as every node receives supports from all other nodes on the tree. The proposed method can be naturally extended to the time domain for enforcing temporal coherence. Unlike previous methods, the non-local property guarantees that the depth edges will be preserved when the temporal coherency between all the video frames are considered. A non-local weighted median filter is also proposed based on the non-local cost aggregation algorithm. It has been demonstrated to outperform all local weighted median filters on disparity/depth upsampling and refinement.

Efficient Depth Map Estimation Method Based on Gradient Weight Cost Aggregation Strategy for Distributed Video Sensor Networks

Video sensor networking technologies have developed very rapidly in the last ten years. In this paper, a cross-based framework strategy for cost aggregation is presented for the depth map estimation based on video sensor networks. We formulate the process as a local regression problem consisting of two main steps with a pair of video sensors. The first step is to calculate estimates for a set of points within a shape-adaptive local support region. The second step is to aggregate the matching cost for the gradient-based weight of the support region at the outmost pixel. The proposed algorithm achieves strong results in an efficient manner using the two main steps. We have achieved improvement of up to 6.9%, 8.4%, and 8.3%, when compared with adaptive support weight (ASW) algorithm. Comparing to cross-based algorithm, the proposed algorithm gives 2.0%, 1.3%, and 1.0% in terms of nonocclusion, all, and discontinuities, respectively.

Anchor-diagonal-based shape adaptive local support region for efficient stereo matching

Local stereo algorithms are preferred for real-time applications due to their computational efficiency. Deciding the size of the required local support region is a challenging task. It fails to estimate accurate disparity for small support region and introduces fattening effect for big support region. Hence, a shape adaptive local support region is necessary to achieve accurate disparity. This paper proposes an anchor-diagonal-based shape adaptive support region construction for stereo matching. The proposed algorithm dynamically constructs local support region, and the aggregated matching cost is used for Normalized Cross-Correlation-based similarity measure. The algorithm is evaluated using benchmarked Middlebury stereo evaluation, and the obtained disparities are efficient as compared to state-of-the-art methods.