Interactive perception based on Gaussian Process classification for house-hold objects recognition &amp; sorting

Khan, Aamir; Sun, Li; Aragon-Camarasa, Gerardo; Siebert, J. Paul

doi:10.1109/robio.2016.7866470

Cited by 5 publications

(2 citation statements)

References 18 publications

(16 reference statements)

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“…GP models can be used for multi-class classification by using a softmax function over the latent variables [54]. A GP model provides both a prediction and a confidence score of the prediction [55,56].…”

Section: Traditional Classification Approachesmentioning

confidence: 99%

Extending Convolution to Semantic Segmentation with Graph Neural Networks

Armstrong,

Grobler

2023

Preprint

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Scene understanding enables multiple applications such as robotic automation. To enable scene understanding, semantic segmentation is an essential component. Highly accurate per-voxel semantic labels can efficiently be obtained through the use of encoder-decoder Convolutional Neural Networks (CNNs). However, scene understanding requires instances of objects to be discovered in a scene. Per-voxel labelling is unable to distinguish between separate instances of a given object. A novel method is proposed that enables segment-level predictions to be formed from the voxel-level predictions provided by the CNN architecture. This method uses an existing 3D CNN architecture to provide rich and computationally inexpensive features. The scene is then segmented with a graph-based segmentation technique to identify objects in the scene. Finally, a graph-based neural network is used to capture hierarchical information improving overall accuracy. The approach is tested on the S3DIS and ScanNet datasets to provide a comparable baseline to more modern architectures.

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Section: Traditional Classification Approachesmentioning

confidence: 99%

Extending Convolution to Semantic Segmentation with Graph Neural Networks

Armstrong,

Grobler

2023

Preprint

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“…The real challenge of AI is that this sophisticated methodology proved to be decisive for computer tasks apparently easy for people, but difficult to describe formally and extremely time-consuming. Among these, there are problems that we solve intuitively and automatically such as recognizing words, faces, or structures within images [35][36][37][38] .…”

mentioning

confidence: 99%

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Donadio

Brescia

Riccardo

et al. 2021

Sci Rep

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Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.

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Advances in Hand-Eye Robot Interactions

Siebert

2017

Advances in Intelligent Systems and Computing

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Interactive perception based on Gaussian Process classification for house-hold objects recognition & sorting

Cited by 5 publications

References 18 publications

Extending Convolution to Semantic Segmentation with Graph Neural Networks

Extending Convolution to Semantic Segmentation with Graph Neural Networks

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Advances in Hand-Eye Robot Interactions

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