Sharif Digital Repository

Learning strengths and weaknesses of classifiers for RGB-D semantic segmentation

Author

Fooladgar, F.

Publisher

IEEE Computer Society

Pub. Year

2015

Subjects

3D scene understanding. / RGB-D segmentation. / Semantic scene labeling. / Computer vision. / Image segmentation. / Pixels. / Random processes. / Semantics. / Three dimensional computer graphics. / 3D scenes. / Conditional random field. / Contextual relationships. / Feature vectors. / Generation algorithm. / Potential function. / Semantic labeling. / Semantic segmentation. / Image processing.

Semantic segmentation of RGB-D images using 3D and local neighbouring features

Author

Fooladgar, F.

Publisher

Institute of Electrical and Electronics Engineers Inc,

Pub. Year

2015

Subjects

RGB-D image segmentation. / Semantic scene labeling. / Algorithms. / Classification (of information) / Computer vision. / Decision trees. / Image processing. / Semantics. / Three dimensional computer graphics. / 3D features. / Conditional random field. / Discriminative features. / Feature description. / Probabilistic output. / Random forest classifier. / Scene understanding. / Semantic segmentation. / Image segmentation.

3M2RNet: Multi-modal multi-resolution refinement network for semantic segmentation

Author

Fooladgar, F.

Publisher

Springer Verlag,

Pub. Year

2020

Subjects

Deep learning. / RGB-D images. / Semantic segmentation. / Classification (of information) / Decoding. / Deep neural networks. / Image segmentation. / Neural networks. / Semantic Web. / Semantics. / Signal encoding. / Three dimensional computer graphics. / Abstract representation. / Convolutional neural network. / Feature extractor. / Robotic applications. / Scene understanding. / Training procedures. / Computer vision.

Lightweight residual densely connected convolutional neural network

Author

Fooladgar, F.

Publisher

Springer,

Pub. Year

2020

Subjects

Convolutional neural networks. / Deep learning. / Efficient architecture. / Image classification. / Convolution. / Network architecture. / Computational operations. / Embedded and mobile devices. / Inference process. / Proposed architectures. / Software equipment. / Special hardware. / Specific hardware. / State of the art.

A survey on indoor RGB-D semantic segmentation: from hand-crafted features to deep convolutional neural networks

Author

Fooladgar, F.

Publisher

Springer,

Pub. Year

2020

Subjects

Deep learning. / Hand-crafted features. / RGB-Depth images. / Semantic segmentation. / Deep neural networks. / Image enhancement. / Neural networks. / Semantic Web. / Semantics. / Surveys. / Comprehensive analysis. / Convolutional neural network. / Depth image. / Microsoft kinect. / Scene understanding. / Semantic labeling. / Image segmentation.

Geometrical analysis of localization error in stereo vision systems

Author

Fooladgar, F.

Pub. Year

2013

Subjects

Cone intersection. / Quantization error. / Extensive simulations. / Geometrical analysis. / Geometrical error analysis. / Geometrical modeling. / Localization errors. / Object localization. / Quantization errors. / Stereo vision system. / Charge coupled devices. / Computer vision. / Error analysis. / Pinhole cameras. / Pixels. / Stereo vision. / Uncertainty analysis.