Invariant visual object recognition: biologically plausible approaches
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Summary
Some requirements for the neurobiological mechanisms of high-level vision, and how some different approaches perform are highlighted, in order to help understand the fundamental underlying principles of invariant visual object recognition in the ventral visual stream.
- Type
- article
- Published
- 2015-09-03
- Cited by
- 32
- References
- 140
- Access
- Open access
- OpenAlex
- https://openalex.org/W1795456626
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17812415
Keywords
Cognitive neuroscience of visual object recognition, Artificial intelligence, Invariant (physics), Visual cortex, Pattern recognition (psychology)
References
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Cited by
- Neural representation for object recognition in inferotemporal cortex.
- A primer on encoding models in sensory neuroscience
- Invariant object recognition based on combination of sparse DBN and SOM with temporal trace rule
- An empirical model of activity in macaque inferior temporal cortex
- Bionic RSTN invariant feature extraction method for image recognition and its application
- Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks
- Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system
- Biological modeling of human visual system for object recognition using GLoP filters and sparse coding on multi-manifolds
- Non‐accidental properties, metric invariance, and encoding by neurons in a model of ventral stream visual object recognition, VisNet
- Soft orthogonal non-negative matrix factorization with sparse representation: Static and dynamic
- Towards a model of visual recognition based on neurosciences
- Affine invariant fusion feature extraction based on geometry descriptor and BIT for object recognition
- Double biologically inspired transform network for robust palmprint recognition
- Computational Foundations of Natural Intelligence
- Multi-channel biomimetic visual transformation for object feature extraction and recognition of complex scenes
- Unsupervised Feature Learning for Visual Place Recognition in Changing Environments
- Deep learning and embodiment
- A perceptual bias for man-made objects in humans
- Spatial coordinate transforms linking the allocentric hippocampal and egocentric parietal primate brain systems for memory, action in space, and navigation
- The Unbearable Shallow Understanding of Deep Learning
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