Fusion of Geometry and Color Information for Scene Segmentation

Mutto, Carlo Dal; Zanuttigh, Pietro; Cortelazzo, G.M.

doi:10.1109/jstsp.2012.2194474

Cited by 42 publications

(72 citation statements)

References 21 publications

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“…In order to obtain robust non-local cost aggregation, those regions should be merged together. Using both color and disparity cues is also proved to be helpful for improving scene segmentation [58,59].…”

Section: Methods Based On Aggregation Over Segment-treementioning

confidence: 99%

Stereo Matching Based on Edge-Aware T-MST

Zhou¹,

Zhao

2016

2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and I

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Dense stereo matching is one of the most extensively investigated topics in computer vision, since it plays an important role in many applications such as 3D scene reconstruction.In this thesis, a novel dense stereo matching method is proposed based on edgeaware truncated minimum spanning tree (T-MST). Instead of employing non-local cost aggregation on traditional MST which is only generated from color differences of neighbouring pixels, a new tree structure, "Edge-Aware T-MST", is proposed to aggregate the cost according to the image texture. Specifically, cost aggregations are strongly enforced in large planar textureless regions due to the truncated edge weights. Meanwhile, the "edge fatten" effect is suppressed by employing a novel hybrid edge-prior which combines edge-prior and superpixel-prior to locate the potential disparity edges. Then a widely used Winner-Takes-All (WTA) strategy is performed to establish initial disparity map. An adaptive non-local refinement is also performed based on the stability of initial disparity estimation.Given the stereo images from Middlebury benchmark, we estimate the disparity maps by using our proposed method and other five state-of-the-art tree-based nonlocal matching methods. The experimental results show that the proposed method successfully produced reliable disparity values within large planar textureless regions and around object disparity boundaries. Performance comparisons demonstrate that our proposed non-local stereo matching method based on edge-aware T-MST outperforms current non-local tree-based state-of-the-art stereo matching methods in most cases, especially in large textureless planar regions and around disparity bounaries.ii Acknowledgements First and foremost, I would like to express my deep and sincere gratitude to my thesis supervisor Professor Jiying Zhao for his patience, motivation, enthusiasm, and immense knowledge, as well as the continuous support that he provided me throughout this entire research endeavour.

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Section: Methods Based On Aggregation Over Segment-treementioning

confidence: 99%

Stereo Matching Based on Edge-Aware T-MST

Zhou¹,

Zhao

2016

2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and I

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“…Our observation is that neighboring regions with different color distributions might still have similar disparities, and such regions should be merged for robust cost aggregation. Besides, it has been shown that improved scene segmentation results can be achieved when both cues are exploited as feature vectors [9,12].…”

Section: Enhancement With Color-depth Segmentationmentioning

confidence: 99%

Segment-Tree Based Cost Aggregation for Stereo Matching

Mei

Sun

Dong

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

162

125

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“…Such ambiguity and uncertainty in representation should be considered, in data modelling for sake of robustness/ efficiency. Probability theory was the first model used to cope with value uncertainty problem, but they are not suitable to solve ambiguity categorizing problem [13]. Theories based on Fuzzy sets [14], proposed later on, by Zadeh, can model ambiguity but they fail in case of uncertain data.…”

Section: Introductionmentioning

confidence: 99%

Indoor/outdoor navigation system based on possibilistic traversable area segmentation for visually impaired people

Elleuch¹,

Sellami²,

Kallel³

2016

ELCVIA

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Autonomous collision avoidance for visually impaired people requires a specific processing for an accurate definition of traversable area. Processing of a real time image sequence for traversable area segmentation is mandatory. Low cost systems suggest use of poor quality cameras. However, low cost camera suffers from great variability of traversable area appearance at indoor as well as outdoor environments. Taking into account ambiguity affecting object and traversable area appearance induced by reflections, illumination variations, occlusions (, etc...), an accurate segmentation of traversable area in such conditions remains a challenge. Moreover, at indoor and outdoor environments, more added variability is induced. In this paper, we present a fast traversable area segmentation approach from image sequence recorded by a low-cost monocular camera for navigation system. Taking into account all kinds of variability in the image, we apply possibility theory for modeling information ambiguity. An efficient way of updating the traversable area model in each environment condition is to consider traversable area samples from the same processed image for building its possibility maps. Then fusing these maps allows making a fair model definition of the traversable area. Performance of the proposed system was evaluated on public databases, at indoor and outdoor environments. Experimental results show that this method is challenging leading to higher segmentation rates.

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Fusion of Geometry and Color Information for Scene Segmentation

Cited by 42 publications

References 21 publications

Stereo Matching Based on Edge-Aware T-MST

Stereo Matching Based on Edge-Aware T-MST

Segment-Tree Based Cost Aggregation for Stereo Matching

Indoor/outdoor navigation system based on possibilistic traversable area segmentation for visually impaired people

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