Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: How Far Can We Go with Local Optimization in Real-Time Stereo Matching
Abstract: Applications such as robot navigation and augmented reality require high-accuracy dense disparity maps in real-time and online. Due to time constraint, most realtime stereo applications rely on local winner-take-all optimization in the disparity computation process. These local approaches are generally outperformed by offline global optimization based algorithms. However, recent research shows that, through carefully selecting and aggregating the matching costs of neighboring pixels, the disparity maps produced by a local approach can be more accurate than those generated by many global optimization techniques. We are therefore motivated to investigate whether these cost aggregation approaches can be adopted in real-time stereo applications and, if so, how well they perform under the real-time constraint. The evaluation is conducted on a real-time stereo platform, which utilizes the processing power of programmable graphics hardware. Several recent cost aggregation approaches are also implemented and optimized for graphics hardware so that real-time speed can be achieved. The performances of these aggregation approaches in terms of both processing speed and result quality are reported.
Extracted statement — Method: Local
Research field: Science
Title: Adaptive support-weight approach for correspondence search
Abstract: We present a new window-based method for correspondence search using varying support-weights. We adjust the support-weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity. Our method outperforms other local methods on standard stereo benchmarks.
Extracted statement — Method: Local
Research field: Science
Title: Vision based autonomous vehicle navigation with self-organizing map feature matching technique
Abstract: Vision is becoming more and more common in applications such as localization, autonomous navigation, path finding and many other computer vision applications. This paper presents an improved technique for feature matching in the stereo images captured by the autonomous vehicle. The Scale Invariant Feature Transform (SIFT) algorithm is used to extract distinctive invariant features from images but this algorithm has a high complexity and a long computational time. In order to reduce the computation time, this paper proposes a SIFT improvement technique based on a Self-Organizing Map (SOM) to perform the matching procedure more efficiently for feature matching problems. Experimental results on real stereo images show that the proposed algorithm performs feature group matching with lower computation time than the original SIFT algorithm. The results showing improvement over the original SIFT are validated through matching examples between different pairs of stereo images. The proposed algorithm can be applied to stereo vision based autonomous vehicle navigation for obstacle avoidance, as well as many other feature matching and computer vision applications.
Extracted statement — Method: Local
Research field: Science
Title: A local iterative refinement method for adaptive support-weight stereo matching
Abstract: A new stereo matching algorithm is introduced that performs iterative refinement on the results of adaptive support-weight stereo matching. During each iteration of disparity refinement, adaptive support-weights are used by the algorithm to penalize disparity differences within local windows. Analytical results show that the addition of iterative refinement to adaptive support-weight stereo matching does not significantly increase complexity. In addition, this new algorithm does not rely on image segmentation or plane fitting, which are used by the majority of the most accurate stereo matching algorithms. As a result, this algorithm has lower complexity, is more suitable for parallel implementation, and does not force locally planar surfaces within the scene. When compared to other algorithms that do not rely on image segmentation or plane fitting, results show that the new stereo matching algorithm is one of the most accurate listed on the Middlebury performance benchmark.
Extracted statement — Method: Local
Research field: Science
Title: A non-local cost aggregation method for stereo matching
Abstract: 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. As can be expected, the proposed non-local solution outperforms all local cost aggregation methods on the standard (Middlebury) benchmark. Besides, it has great advantage in extremely low computational complexity: only a total of 2 addition/subtraction operations and 3 multiplication operations are required for each pixel at each disparity level. It is very close to the complexity of unnormalized box filtering using integral image which requires 6 addition/subtraction operations. Unnormalized box filter is the fastest local cost aggregation method but blurs across depth edges. The proposed method was tested on a MacBook Air laptop computer with a 1.8 GHz Intel Core i7 CPU and 4 GB memory. The average runtime on the Middlebury data sets is about 90 milliseconds, and is only about 1.25× slower than unnormalized box filter. A non-local disparity refinement method is also proposed based on the non-local cost aggregation method.
Extracted statement — Method: Local
Research field: Science
Title: Local Disparity Estimation With Three-Moded Cross Census and Advanced Support Weight
Abstract: The classical local disparity methods use simple and efficient structure to reduce the computation complexity. To increase the accuracy of the disparity map, new local methods utilize additional processing steps such as iteration, segmentation, calibration and propagation, similar to global methods. In this paper, we present an efficient one-pass local method with no iteration. The proposed method is also extended to video disparity estimation by using motion information as well as imposing spatial temporal consistency. In local method, the accuracy of stereo matching depends on precise similarity measure and proper support window. For the accuracy of similarity measure, we propose a novel three-moded cross census transform with a noise buffer, which increases the robustness to image noise in flat areas. The proposed similarity measure can be used in the same form in both stereo images and videos. We further improve the reliability of the aggregation by adopting the advanced support weight and incorporating motion flow to achieve better depth map near moving edges in video scene. The experimental results show that the proposed method is the best performing local method on the Middlebury stereo benchmark test and outperforms the other state-of-the-art methods on video disparity evaluation.
Extracted statement — Method: Local
Research field: Science
Title: Matching Cost Filtering for Dense Stereo Correspondence
Abstract: Dense stereo correspondence enabling reconstruction of depth information in a scene is of great importance in the field of computer vision. Recently, some local solutions based on matching cost filtering with an edge-preserving filter have been proved to be capable of achieving more accuracy than global approaches. Unfortunately, the computational complexity of these algorithms is quadratically related to the window size used to aggregate the matching costs. The recent trend has been to pursue higher accuracy with greater efficiency in execution. Therefore, this paper proposes a new cost-aggregation module to compute the matching responses for all the image pixels at a set of sampling points generated by a hierarchical clustering algorithm. The complexity of this implementation is linear both in the number of image pixels and the number of clusters. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art local methods in terms of both accuracy and speed. Moreover, performance tests indicate that parameters such as the height of the hierarchical binary tree and the spatial and range standard deviations have a significant influence on time consumption and the accuracy of disparity maps.
Extracted statement — Method: Local
Research field: Science
Title: Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching
Abstract: Binocular stereo matching is one of the most important algorithms in the field of computer vision. Adaptive support-weight approaches, the current state-of-the-art local methods, produce results comparable to those generated by global methods. However, excessive time consumption is the main problem of these algorithms since the computational complexity is proportionally related to the support window size. In this paper, we present a novel cost aggregation method inspired by domain transformation, a recently proposed dimensionality reduction technique. This transformation enables the aggregation of 2-D cost data to be performed using a sequence of 1-D filters, which lowers computation and memory costs compared to conventional 2-D filters. Experiments show that the proposed method outperforms the state-of-the-art local methods in terms of computational performance, since its computational complexity is independent of the input parameters. Furthermore, according to the experimental results with the Middlebury dataset and real-world images, our algorithm is currently one of the most accurate and efficient local algorithms.
Extracted statement — Method: Local
Research field: Science
Title: A modified census transform based on the neighborhood information for stereo matching algorithm
Abstract: Census transform is a non-parametric local transform. Its weakness is that the results relied on the center pixel too much. This paper proposes a modified Census transform based on the neighborhood information for stereo matching. By improving the classic Census transform, the new technique utilizes more bits to represent the differences between the pixel and its neighborhood information. The result image of the modified Census transform has more detailed information at depth discontinuity. After stereo correspondence, sub-pixel interpolation and the disparity refinement, a better dense disparity map can be obtained. The experiments present that the proposed algorithm has simple mechanism and strong robustness. It can improve the accuracy of matching and is applicable to hardware systems.
Extracted statement — Method: Local
Research field: Science
Title: Fast and Accurate Stereo Vision System on FPGA
Abstract: In this article, we present a fast and high quality stereo matching algorithm on FPGA using cost aggregation (CA) and fast locally consistent (FLC) dense stereo. In many software programs, global matching algorithms are used in order to obtain accurate disparity maps. Although their error rates are considerably low, their processing speeds are far from that required for real-time processing because of their complex processing sequences. In order to realize real-time processing, many hardware systems have been proposed to date. They have achieved considerably high processing speeds; however, their error rates are not as good as those of software programs, because simple local matching algorithms have been widely used in those systems. In our system, sophisticated local matching algorithms (CA and FLC) that are suitable for FPGA implementation are used to achieve low error rate while maintaining the high processing speed. We evaluate the performance of our circuit on Xilinx Vertex-6 FPGAs. Its error rate is comparable to that of top-level software algorithms, and its processing speed is nearly 2 clock cycles per pixel, which reaches 507.9 fps for 640 480 pixel images.
Extracted statement — Method: Local
Research field: Science
Title: Efficient edge-awareness propagation via single-map filtering for edge-preserving stereo matching
Abstract: In this paper, we propose an efficient framework for edge-preserving stereo matching. Local methods for stereo matching are more suitable than global methods for real-time applications. Moreover, we can obtain accurate depth maps by using edge-preserving filter for the cost aggregation process in local stereo matching. The computational cost is high, since we must perform the filter for every number of disparity ranges if the order of the edge-preserving filter is constant time. Therefore, we propose an efficient iterative framework which propagates edge-awareness by using single time edge preserving filtering. In our framework, box filtering is used for the cost aggregation, and then the edge-preserving filtering is once used for refinement of the obtained depth map from the box aggregation. After that, we iteratively estimate a new depth map by local stereo matching which utilizes the previous result of the depth map for feedback of the matching cost. Note that the kernel size of the box filter is varied as coarse-to-fine manner at each iteration. Experimental results show that small and large areas of incorrect regions are gradually corrected. Finally, the accuracy of the depth map estimated by our framework is comparable to the state-of-the-art of stereo matching methods with global optimization methods. Moreover, the computational time of our method is faster than the optimization based method.
Extracted statement — Method: Local
Research field: Science
Title: A fast trilateral filterbased adaptive support weight method for stereo matching
Abstract: Adaptive support weight (ASW) methods represent the state of the art in local stereo matching, while the bilateral filter-based ASW method achieves outstanding performance. However, this method fails to resolve the ambiguity induced by nearby pixels at different disparities but with similar colors. In this paper, we introduce a novel trilateral filter (TF)-based ASW method that remedies such ambiguities by considering the possible disparity discontinuities through color discontinuity boundaries, i.e., the boundary strength between two pixels, which is measured by a local energy model. We also present a recursive TF-based ASW method whose computational complexity is O(N) for the cost aggregation step, and O(NLog2(N)) for boundary detection, where N denotes the input image size. This complexity is thus independent of the support window size. The recursive TF-based method is a nonlocal cost aggregation strategy. The experimental evaluation on the Middlebury benchmark shows that the proposed method, whose average error rate is 4.95%, outperforms other local methods in terms of accuracy. Equally, the average runtime of the proposed TF-based cost aggregation is roughly 260 ms on a 3.4-GHz Inter Core i7 CPU, which is comparable with state-of-the-art efficiency.
Extracted statement — Method: Local
Research field: Science
Title: Reversible data embedding using a difference expansion
Abstract: Reversible data embedding has drawn lots of interest recently. Being reversible, the original digital content can be completely restored. We present a novel reversible data-embedding method for digital images. We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low.
Extracted statement — Invisibility: Low
Research field: Science
Title: Reversible data embedding using a difference expansion
Abstract: Reversible data embedding has drawn lots of interest recently. Being reversible, the original digital content can be completely restored. We present a novel reversible data-embedding method for digital images. We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low.
Extracted statement — Payload
Capacity: Low
Research field: Science
Title: Reversible data embedding using a difference expansion
Abstract: Reversible data embedding has drawn lots of interest recently. Being reversible, the original digital content can be completely restored. We present a novel reversible data-embedding method for digital images. We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low.
Extracted statement — Tolerance to RS
Steganalysis: Low
Research field: Science
Title: Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems
Abstract: This paper proposes a new adaptive least-significant- bit (LSB) steganographic method using pixel-value differencing (PVD) that provides a larger embedding capacity and imperceptible stegoimages. The method exploits the difference value of two consecutive pixels to estimate how many secret bits will be embedded into the two pixels. Pixels located in the edge areas are embedded by a k-bit LSB substitution method with a larger value of k than that of the pixels located in smooth areas. The range of difference values is adaptively divided into lower level, middle level, and higher level. For any pair of consecutive pixels, both pixels are embedded by the k-bit LSB substitution method. However, the value k is adaptive and is decided by the level which the difference value belongs to. In order to remain at the same level where the difference value of two consecutive pixels belongs, before and after embedding, a delicate readjusting phase is used. When compared to the past study of Wu et al.'s PVD and LSB replacement method, our experimental results show that our proposed approach provides both larger embedding capacity and higher image quality.
Extracted statement — Tolerance to RS
Steganalysis: Low
Research field: Science
Title: Reversible data embedding using a difference expansion
Abstract: Reversible data embedding has drawn lots of interest recently. Being reversible, the original digital content can be completely restored. We present a novel reversible data-embedding method for digital images. We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low.
Extracted statement — Utilization of edge
areas: Low
Research field: Science
Title: Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems
Abstract: This paper proposes a new adaptive least-significant- bit (LSB) steganographic method using pixel-value differencing (PVD) that provides a larger embedding capacity and imperceptible stegoimages. The method exploits the difference value of two consecutive pixels to estimate how many secret bits will be embedded into the two pixels. Pixels located in the edge areas are embedded by a k-bit LSB substitution method with a larger value of k than that of the pixels located in smooth areas. The range of difference values is adaptively divided into lower level, middle level, and higher level. For any pair of consecutive pixels, both pixels are embedded by the k-bit LSB substitution method. However, the value k is adaptive and is decided by the level which the difference value belongs to. In order to remain at the same level where the difference value of two consecutive pixels belongs, before and after embedding, a delicate readjusting phase is used. When compared to the past study of Wu et al.'s PVD and LSB replacement method, our experimental results show that our proposed approach provides both larger embedding capacity and higher image quality.
Extracted statement — Utilization of edge
areas: Low
Research field: Science
Title: Integrating Routing and Inventory Decisions in One-Warehouse Multiretailer Multiproduct Distribution Systems
Abstract: We consider distribution systems with a central warehouse and many retailers that stock a number of different products. Deterministic demand occurs at the retailers for each product. The warehouse acts as a break-bulk center and does not keep any inventory. The products are delivered from the warehouse to the retailers by vehicles that combine the deliveries to several retailers into efficient vehicle routes. The objective is to determine replenishment policies that specify the delivery quantities and the vehicle routes used for the delivery, so as to minimize the long-run average inventory and transportation costs. A new heuristic that develops a stationary nested joint replenishment policy for the problem is presented in this paper. Unlike existing methods, the proposed heuristic is capable of solving problems involving distribution systems with multiple products. Results of a computational study on randomly generated single-product problems are also presented.
Extracted statement — Fleet size: multiple
Research field: Science
Title: Dynamic Programming Approximations for a Stochastic Inventory Routing Problem
Abstract: This work is motivated by the need to solve the inventory routing problem when implementing a business practice called vendor managed inventory replenishment (VMI). With VMI, vendors monitor their customers' inventories and decide when and how much inventory should be replenished at each customer. The inventory routing problem attempts to coordinate inventory replenishment and transportation in such a way that the cost is minimized over the long run. We formulate a Markov decision process model of the stochastic inventory routing problem and propose approximation methods to find good solutions with reasonable computational effort. We indicate how the proposed approach can be used for other Markov decision processes involving the control of multiple resources.
Extracted statement — Fleet size: multiple
Research field: Science
Title: Liner shipping fleet deployment with cargo transshipment and demand uncertainty
Abstract: This paper addresses a novel liner shipping fleet deployment problem characterized by cargo transshipment, multiple container routing options and uncertain demand, with the objective of maximizing the expected profit. This problem is formulated as a stochastic program and solved by the sample average approximation method. In this technique the objective function of the stochastic program is approximated by a sample average estimate derived from a random sample, and then the resulting deterministic program is solved. This process is repeated with different samples to obtain a good candidate solution along with the statistical estimate of its optimality gap. We apply the proposed model to a case study inspired from real-world problems faced by a major liner shipping company. Results show that the case is efficiently solved to 1% of relative optimality gap at 95% confidence level.
Extracted statement — oute: multiple