Multi-Camera very wide baseline feature matching based on view-adaptive junction detection

This paper presents a strategy for solving the feature matching problem in calibrated very wide-baseline camera settings. In this kind of settings, perspective distortion, depth discontinuities and occlusion represent enormous challenges. The proposed strategy addresses them by using geometrical information, specifically by exploiting epipolar-constraints. As a result it provides a sparse number of reliable feature points for which 3D position is accurately recovered. Special features known as junctions are used for robust matching. In particular, a strategy for refinement of junction end-point matching is proposed which enhances usual junction-based approaches. This allows to compute cross-correlation between perfectly aligned plane patches in both images, thus yielding better matching results. Evaluation of experimental results proves the effectiveness of the proposed algorithm in very wide-baseline environments

Comparative Study between Rectangular Windows and Circular Windows Based Disparity-Map by Stereo Matching

Stereo matching is the basic problem to achieve human like vision capability to machines and robots. Stereo vision researches produced many local and global algorithms for stereo correspondence matching. There are two popular methods one is rectangular window-based cost aggregation another is circular window-based cost aggregation used for solving correspondence problem have attracted researches as it can be implemented in real time using parallel processors. In this paper we have done comparative study between rectangular windows and circular windows based disparity map by stereo matching. Motivated by human stereo vision, the technique uses to enhance the strategy of finding the best match to compute dense disparity map. Performance of the both method is efficient

Disparity Map Algorithm Based on Edge Preserving Filter for Stereo Video Processing

This paper proposes a new local-based stereo matching algorithm for stereo video processing. Fundamentally, the Sum of Absolute Differences (SAD) algorithm produces an accurate results on the stereo video processing for the textured regions. However, this algorithm sensitives to low texture and radiometric distortions (i.e., contrast or brightness). To overcome these problems, the proposed algorithm utilizes edgepreserving filter which is known as Bilateral Filter (BF). The BF algorithm reduces noise and sharpen the images. Additionally, BF works fine on the low or plain texture areas. The proposed algorithm produces an accurate results and performs much better compared to some established algorithms on the standard benchmarking results of the Middlebury and KITTI dataset

Development of stereo matching algorithm based on sum of absolute RGB color differences and gradient Matching

This paper proposes a new stereo matching algorithm which uses local-based method. The Sum of Absolute Differences (SAD) algorithm produces accurate result on the disparity map for the textured regions. However, this algorithm is sensitive to low texture areas and high noise on images with high different brightness and contrast. To get over these problems, the proposed algorithm utilizes SAD algorithm with RGB color channels differences and combination of gradient matching to improve the accuracy on the images with high brightness and contrast. Additionally, an edge-preserving filter is used at the second stage which is known as Bilateral Filter (BF). The BF filter is capable to work with the low texture areas and to reduce the noise and sharpen the images. Additionally, BF is strong  against the  distortions due to high brightness and contrast. The proposed work in this paper produces accurate results and performs much better compared with some established algorithms. This comparison is based on the standard quantitative measurements using the stereo benchmarking evaluation from the Middlebury

A robust cost function for stereo matching of road scenes

International audienceIn this paper different matching cost functions used for stereo matching are evaluated in the context of intelligent vehicles applications. Classical costs are considered, like: sum of squared differences, normalized cross correlation or census transform that were already evaluated in previous studies, together with some recent functions that try to enhance the discriminative power of Census Transform (CT). These are evaluated with two different stereo matching algorithms: a global method based on graph cuts and a fast local one based on cross aggregation regions. Furthermore we propose a new cost function that combines the CT and alternatively a variant of CT called Cross-Comparison Census (CCC), with the mean sum of relative pixel intensity differences (DIFFCensus). Among all the tested cost functions, under the same constraints, the proposed DIFFCensus produces the lower error rate on the KITTI road scenes dataset 1 with both global and local stereo matching algorithms

a critical analysis of internal reliability for uncertainty quantification of dense image matching in multi-view stereo

Nowadays, photogrammetrically derived point clouds are widely used in many civilian applications due to their low cost and flexibility in acquisition. Typically, photogrammetric point clouds are assessed through reference data such as LiDAR point clouds. However, when reference data are not available, the assessment of photogrammetric point clouds may be challenging. Since these point clouds are algorithmically derived, their accuracies and precisions are highly varying with the camera networks, scene complexity, and dense image matching (DIM) algorithms, and there is no standard error metric to determine per-point errors. The theory of internal reliability of camera networks has been well studied through first-order error estimation of Bundle Adjustment (BA), which is used to understand the errors of 3D points assuming known measurement errors. However, the measurement errors of the DIM algorithms are intricate to an extent that every single point may have its error function determined by factors such as pixel intensity, texture entropy, and surface smoothness. Despite the complexity, there exist a few common metrics that may aid the process of estimating the posterior reliability of the derived points, especially in a multi-view stereo (MVS) setup when redundancies are present. In this paper, by using an aerial oblique photogrammetric block with LiDAR reference data, we analyze several internal matching metrics within a common MVS framework, including statistics in ray convergence, intersection angles, DIM energy, etc.Comment: Figure

Automated stereo retrieval of smoke plume injection heights and retrieval of smoke plume masks from AATSR and assessment with CALIPSO and MISR.

The longevity and dispersion of smoke and asso- ciated chemical constituents released from wildfire events are dependent on several factors, crucially including the height at which the smoke is injected into the atmosphere. The aim here is to provide improved emission data for the initialization of chemical transport models in order to better predict aerosol and trace gas dispersion following injection into the free atmosphere. A new stereo-matching algorithm, named M6, which can effec- tively resolve smoke plume injection heights (SPIH), is presented here. M6 is extensively validated against two alternative space- borne earth observation SPIH data sources and demonstrates good agreement. Further, due to the spectral and dual-view configuration of the Advanced Along-Track Scanning Radiometer imaging system, it is possible to automatically differentiate smoke from other atmospheric features effectively—a feat, which currently no other algorithm can achieve. Additionally, as the M6 algorithm shares a heritage with the other M-series matchers, it is here compared against one of its predecessors, M4, which, for the determination of SPIH, M6 is shown to substantially outperform