Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

doi:10.3390/s18041074

Cited by 6 publications

(4 citation statements)

References 41 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, WTA performs disparity pixels on that image by disregarding the global information of the pixels. 15 As WTA yields the better disparity map optimization.…”

Section: Proposed Systemmentioning

confidence: 99%

A novel enhanced intensity‐based automatic registration: Augmented reality for visualization and localization cancer tumors

Tan

Alsadoon

Prasad

et al. 2020

Robotics Computer Surgery

View full text Add to dashboard Cite

The purpose of this study is to replace the manual process (selecting the landmarks on mesh and anchor points on the video) by Intensity‐based Automatic Registration method to reach registration accuracy and low processing time. The proposed system consists of an Enhanced Intensity‐based Automatic Registration (EIbAR) using Modified Zero Normalized Cross Correlation (MZNCC) algorithm. The proposed system was implemented on videos of breast cancer tumors. Results showed that the proposed algorithm—as compared to a reference—improved registration accuracy by an average of 2 mm. In addition, the proposed algorithm—as compared to a reference—reduced the number of pixel matching, thereby reducing processing time on the video by an average of 22 ms/frame. The proposed system can, thus, provide an acceptable accuracy and processing time during scene augmentation of videos, which provides a seamless use of augmented‐reality for surgeons in visualizing cancer tumors.

show abstract

“…Here, WTA performs disparity pixels on that image by disregarding the global information of the pixels. 15 As WTA yields the better disparity map optimization.…”

Section: Proposed Systemmentioning

confidence: 99%

A novel enhanced intensity‐based automatic registration: Augmented reality for visualization and localization cancer tumors

Tan

Alsadoon

Prasad

et al. 2020

Robotics Computer Surgery

View full text Add to dashboard Cite

show abstract

“…The global algorithm usually uses the idea of normalization to seek the most suitable disparity of the image as a whole to restore three-dimensional information. Common global algorithms such as dynamic programming [4], confidence interval propagation [5] and graph cut algorithm [6], etc. Although the accuracy of the global algorithm is high, the algorithm has a large amount of computation, so it is difficult to meet the requirements in terms of speed; the local algorithm is not mainly concerned with the entire image.…”

Section: Introductionmentioning

confidence: 99%

Research on binocular stereo matching algorithm based on dynamic tilt window

Yu,

2023

International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023)

View full text Add to dashboard Cite

“…Humans can recognize objects from sketches alone, even in cases where a significant portion of the boundary is missing [5,19]. Boundaries have also been shown to be useful for 3D reconstruction [23,21,38], localization [35,31], and image generation [18,32].…”

Section: Introductionmentioning

confidence: 99%

Devil Is in the Edges: Learning Semantic Boundaries From Noisy Annotations

Acuna

Kar

Fidler

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

147

102

View full text Add to dashboard Cite

We tackle the problem of semantic boundary prediction, which aims to identify pixels that belong to object(class) boundaries. We notice that relevant datasets consist of a significant level of label noise, reflecting the fact that precise annotations are laborious to get and thus annotators trade-off quality with efficiency. We aim to learn sharp and precise semantic boundaries by explicitly reasoning about annotation noise during training. We propose a simple new layer and loss that can be used with existing learning-based boundary detectors. Our layer/loss enforces the detector to predict a maximum response along the normal direction at an edge, while also regularizing its direction. We further reason about true object boundaries during training using a level set formulation, which allows the network to learn from misaligned labels in an end-to-end fashion. Experiments show that we improve over the CASENet [36] backbone network by more than 4% in terms of MF(ODS) and 18.61% in terms of AP, outperforming all current state-ofthe-art methods including those that deal with alignment. Furthermore, we show that our learned network can be used to significantly improve coarse segmentation labels, lending itself as an efficient way to label new data.

show abstract

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors

Cited by 6 publications

References 41 publications

A novel enhanced intensity‐based automatic registration: Augmented reality for visualization and localization cancer tumors

A novel enhanced intensity‐based automatic registration: Augmented reality for visualization and localization cancer tumors

Research on binocular stereo matching algorithm based on dynamic tilt window

Devil Is in the Edges: Learning Semantic Boundaries From Noisy Annotations

Contact Info

Product

Resources

About