Unsupervised object individuation from RGB-D image sequences

Koo, Seongyong; Lee, Dongheui; Kwon, Dong‐Soo

doi:10.1109/iros.2014.6943192

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…NB model performed worse in this study and might not be suitable for leaf-wood separation, unlike its high efficiency in text classification (Kim et al, 2006). GMM model is typically used in unsupervised classification problems (Koo et al, 2014), although it was previously used in separating leaf, wood, and ground points (Ma et al, 2016). We briefly tested the performance of the GMM classifier without training data, so that the data were clustered into two groups in the feature space.…”

Section: Classifier Performancementioning

confidence: 99%

Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Wang

Hollaus

Pfeifer

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an Erytrophleum fordii and a Betula pendula (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

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Section: Classifier Performancementioning

confidence: 99%

Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Wang

Hollaus

Pfeifer

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Object separation is used as a source to leverage perceptual information from interaction following the paradigm of interactive perception [2]. As demonstrated in our experiments, our singulation method can be used to generate informative sensory signals that lower the scene complexity for interactive object segmentation [26,11,28,16]. The criteria for a positive label are: the object got singulated, the push did not move multiple objects, the object was pushed close to its center of mass and the pushed object was not already singulated.…”

Section: Related Workmentioning

confidence: 98%

Learning to Singulate Objects Using a Push Proposal Network

Eitel

Hauff

Burgard

2019

Springer Proceedings in Advanced Robotics

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Learning to act in unstructured environments, such as cluttered piles of objects, poses a substantial challenge for manipulation robots. We present a novel neural network-based approach that separates unknown objects in clutter by selecting favourable push actions. Our network is trained from data collected through autonomous interaction of a PR2 robot with randomly organized tabletop scenes. The model is designed to propose meaningful push actions based on over-segmented RGB-D images. We evaluate our approach by singulating up to 8 unknown objects in clutter. We demonstrate that our method enables the robot to perform the task with a high success rate and a low number of required push actions. Our results based on real-world experiments show that our network is able to generalize to novel objects of various sizes and shapes, as well as to arbitrary object configurations. Videos of our experiments can be viewed at

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“…It serves various robotic applications, including object pushing [4], [5], grasping [6]- [8], and re-arrangement [22]- [25]. Various segmentation methods, such as graphbased segmentation [26], surface patches with SVM [27], and ambiguity graph [28] have been proposed, and recent advancements in deep learning have led to the introduction of category-agnostic instance segmentation networks trained on large-scale synthetic data [1]- [3], [8]- [10], [13], [29]. These networks learn object-ness, distinguishing foreground object instances from the background from RGB-D images, thereby enabling the segmentation of unseen objects such as tabletop scenarios [27], [30].…”

Section: A Unseen Object Instance Segmentationmentioning

confidence: 99%

Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling

Back¹,

Lee²,

Kim³

et al. 2022

2022 International Conference on Robotics and Automation (ICRA)

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Efficient and accurate segmentation of unseen objects is crucial for robotic manipulation. However, it remains challenging due to over-or under-segmentation. Although existing refinement methods can enhance the segmentation quality, they fix only minor boundary errors or are not sufficiently fast. In this work, we propose INSTAnce Boundary Explicit Error Estimation and Refinement (INSTA-BEEER), a novel refinement model that allows for adding and deleting instances and sharpening boundaries. Leveraging an error-estimationthen-refinement scheme, the model first estimates the pixelwise boundary explicit errors: true positive, true negative, false positive, and false negative pixels of the instance boundary in the initial segmentation. It then refines the initial segmentation using these error estimates as guidance. Experiments show that the proposed model significantly enhances segmentation, achieving state-of-the-art performance. Furthermore, with a fast runtime (less than 0.1 s), the model consistently improves performance across various initial segmentation methods, making it highly suitable for practical robotic applications.

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Unsupervised object individuation from RGB-D image sequences

Cited by 9 publications

References 22 publications

Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Learning to Singulate Objects Using a Push Proposal Network

Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling

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