This paper presents an effective cost aggregation strategy for dense stereo matching. Based on the guided image filtering (GIF), we propose a new aggregation scheme called Pervasive Guided Image Filtering (PGIF) to introduce weightings to the energy function of the filter which allows the whole image pair to be taken into account. The filter parameters of PGIF are calculated as two-dimensional convolution using the bright and spatial differences between the corresponding pixels, which can be incrementally calculated for efficient aggregation. The complexity of the proposed algorithm is O(N), which is linear to the number of image pixels. Furthermore, the algorithm can be further simplified into O(N/4) without significantly sacrificing accuracy if subsampling is applied in the stage of parameter calculation. We also found that a step function to attenuate noise is required in calculating the weights. Experimental evaluation on version 3 of the Middlebury stereo evaluation datasets shows that the proposed method achieves superior disparity accuracy over state-of-the-art aggregation methods with comparable processing speed.
Stereo matching is complicated by the uneven distribution of textures on the image pairs. We address this problem by applying the edge-preserving guided-Image-filtering (GIF) at different resolutions. In contrast to most multi-scale stereo matching algorithms, parameters of the proposed hierarchical GIF model are in an innovative weighted-combination scheme to generate an improved matching cost volume. Our method draws its strength from exploiting texture in various resolution levels and performing an effective mixture of the derived parameters. This novel approach advances our recently proposed algorithm, the pervasive guided-image-filtering scheme, by equipping it with hierarchical filtering modules, leading to disparity images with more details. The approach ensures as many different-scale patterns as possible to be involved in the cost aggregation and hence improves matching accuracy. The experimental results show that the proposed scheme achieves the best matching accuracy when compared with six well-recognized cutting-edge algorithms using version 3 of the Middlebury stereo evaluation data sets.
Developing matching algorithms from stereo image pairs to obtain correct disparity maps for 3D reconstruction has been the focus of intensive research. A constant computational complexity algorithm to calculate dissimilarity aggregation in assessing disparity based on separable successive weighted summation (SWS) among horizontal and vertical directions was proposed but still not satisfactory. This paper presents a novel method which enables decoupled dissimilarity measure in the aggregation, further improving the accuracy and robustness of stereo correspondence. The aggregated cost is also used to refine disparities based on a local curve-fitting procedure. According to our experimental results on Middlebury benchmark evaluation, the proposed approach has comparable performance when compared with the selected state-of-the-art algorithms and has the lowest mismatch rate. Besides, the refinement procedure is shown to be capable of preserving object boundaries and depth discontinuities while smoothing out disparity maps.
Applying edge preservation filters for cost aggregation has been a leading technique in generating dense disparity maps. However, traditional approaches usually require intensive calculations, and their design parameters must be tuned for different scenarios to obtain the best performance. This paper shows that a simple texture-independent aggregation approach can achieve similar high performance. The proposed algorithm is equivalent to a sequence of matrix multiplications involving two weighting matrices and a primary matching cost. Notably, the weighting matrices are constant for image pairs with the same resolution. For higher matching accuracy, we integrate the algorithm with a multi-scale scheme to fully exploit the spatial distribution of textures in the image pairs. The resultant hybrid approach is efficient and accurate enough to surpass most existing approaches in stereo matching. The performance of the proposed approach is verified by extensive simulation results using the Middlebury (3rd Edition) benchmark stereo database.
Tier-to-tier Multi-Shuttle Systems (TT-MSS) become increasingly adopted due to their high flexibility and efficiency. In the Tier-to-tier MSS, shuttles work across different tiers with the help of lifts. How to schedule these devices matters a lot in terms of lowering the expected order cycle time. In this paper, we propose a time sequence task scheduling model considering the movements of shuttles and lifts. The model describes the task scheduling problem between shuttles and lifts, which is generated in a specified time window. An improved genetic algorithm is proposed to solve the objective optimization function in the task scheduling problem. Finally, we illustrate the advantages of the Tier-to-tier MSS by conducting experiments, and the results indicate that with the increase of retrieval tasks, the efficiency of Tier-to-tier MSS is approaching that of TC-MSS with fewer shuttles used.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.