Robust Matching Cost Function for Stereo Correspondence Using Matching by Tone Mapping and Adaptive Orthogonal Integral Image

2015 ◽  
Vol 24 (12) ◽  
pp. 5416-5431 ◽  
Author(s):  
Vinh Quang Dinh ◽  
Vinh Dinh Nguyen ◽  
Jae Wook Jeon
Keyword(s):  
Cost Function ◽  
Tone Mapping ◽  
Stereo Correspondence ◽  
Integral Image ◽  
Matching Cost

Matching cost function using robust soft rank transformations

2016 ◽  
Vol 10 (7) ◽  
pp. 561-569 ◽  
Author(s):  
Vinh Quang Dinh ◽  
Jae Wook Jeon ◽  
Cuong Cao Pham
Keyword(s):  
Cost Function ◽  
Matching Cost

scholarly journals Improved Cost Computation and Adaptive Shape Guided Filter for Local Stereo Matching of Low Texture Stereo Images

2020 ◽  
Vol 10 (5) ◽  
pp. 1869
Author(s):  
Hua Liu ◽  
Rui Wang ◽  
Yuanping Xia ◽  
Xiaoming Zhang
Keyword(s):  
Cost Function ◽  
Stereo Matching ◽  
Guided Filter ◽  
Disparity Map ◽  
Cost Aggregation ◽  
Dense Stereo Matching ◽  
Gradient Based ◽  
Matching Cost ◽  
Cost Measurement ◽  
Image Pairs

Dense stereo matching has been widely used in photogrammetry and computer vision applications. Even though it has a long research history, dense stereo matching is still challenging for occluded, textureless and discontinuous regions. This paper proposed an efficient and effective matching cost measurement and an adaptive shape guided filter-based matching cost aggregation method to improve the stereo matching performance for large textureless regions. At first, an efficient matching cost function combining enhanced image gradient-based matching cost and improved census transform-based matching cost is introduced. This proposed matching cost function is robust against radiometric variations and textureless regions. Following this, an adaptive shape cross-based window is constructed for each pixel and a modified guided filter based on this adaptive shape window is implemented for cost aggregation. The final disparity map is obtained after disparity selection and multiple steps disparity refinement. Experiments were conducted on the Middlebury benchmark dataset to evaluate the effectiveness of the proposed cost measurement and cost aggregation strategy. The experimental results demonstrated that the average matching error rate on Middlebury standard image pairs is 9.40%. Compared with the traditional guided filter-based stereo matching method, the proposed method achieved a better matching result in textureless regions.

Robust matching cost function based on evolutionary approach

2020 ◽  
Vol 161 ◽  
pp. 113712
Author(s):  
Phuc Nguyen Hong ◽  
Chang Wook Ahn
Keyword(s):  
Cost Function ◽  
Evolutionary Approach ◽  
Matching Cost

Terrain Elevation Map Synthesis Method based on Single Sample and User Sketch

2020 ◽  
pp. 2154016
Author(s):  
Xingquan Cai ◽  
Zhe Yang ◽  
Haiyan Sun ◽  
Amanda Gozho ◽  
Yakun Ge ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Graphics ◽  
Cost Function ◽  
Synthesis Method ◽  
Experimental Results ◽  
Single Sample ◽  
Image Patches ◽  
Map Generation ◽  
Matching Cost ◽  
Terrain Elevation

Terrain synthesis has been a hot topic in the field of computer graphics and image processing. However, there are still issues in terrain synthesis where synthesis results are difficult to control and not realistic enough. To address these problems, this paper proposes an interactive terrain elevation map generation method based on the synthesis of a single sample terrain elevation map. First, we propose a method to extract the skeleton from a terrain elevation map and a user sketch. Second, we construct a skeleton sample feature map based on the terrain elevation map and the user sketch. Finally, we propose a matching cost function to match image patches of the terrain sample and the user sketch. The proposed method can obtain a synthesis result containing the features of both the terrain sample and the user sketch, and then generates a synthetic terrain elevation map. The experimental results demonstrate the effectiveness of the proposed method, where the synthesized results can meet the needs of users.

scholarly journals Matching Cost Filtering for Dense Stereo Correspondence

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Yimin Lin ◽  
Naiguang Lu ◽  
Xiaoping Lou ◽  
Fang Zou ◽  
Yanbin Yao ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Window Size ◽  
Depth Information ◽  
Stereo Correspondence ◽  
Edge Preserving ◽  
Local Methods ◽  
Matching Cost ◽  
Disparity Maps ◽  
Hierarchical Clustering Algorithm ◽  
Image Pixels

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.

IMAGE COMPOSITION WITH COLOR HARMONIZATION

2012 ◽  
Vol 26 (03) ◽  
pp. 1254001
Author(s):  
YANLI WAN ◽  
ZHEN TANG ◽  
ZHENJIANG MIAO ◽  
BO LI
Keyword(s):  
Cost Function ◽  
Optimization Method ◽  
Image Composition ◽  
Local Smoothness ◽  
Color Harmony ◽  
Matching Cost ◽  
Computer Generated Imagery ◽  
The Cost ◽  
Previous Image

Image composition is a very important technique in computer generated imagery. Besides some factors such as contrast, texture and noise that affect the quality of the composition, color harmony between fore- and background is also an important factor that would affect the quality of the composition. However, in the previous image composition techniques, color harmony between fore- and background is seldom considered. In this paper, an optimization method is proposed to deal with the color harmonization problem that used in image composition. A cost function is derived from the local smoothness of the hue values, and the image is harmonized by minimizing the cost function. A new matching cost function is proposed to select the best matching harmonic schemes. Our approach overcomes several shortcomings of the existing color harmonization methods. We validate the performance of our method and demonstrate its effectiveness with a variety of experiments.

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