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Article summary of Breaking position-invariant object recognition by Cox, Meier, et. al - Chapter
Each object can take an infinite number of positions on the retina. After all, objects can change in size, location, light, etc. The ability to identify objects despite all these changes is principal to human visual object recognition. However, the neuronal mechanisms behind this process are a mystery. Several authors have suggested that a solution to the invariance problem is to learn representations through experience in the real world.
Visual features that follow each other quickly are more likely to correspond to different images of the same object. This way someone can slowly build an invariant representation of associative patterns. The patterns are built up through neuronal activity in retinal images of an object. Changes in the retinal position of an object can occur due to rapid eye movements. A possible strategy is to associate representations with neuronal series of activity patterns.
If the correct position invariance has been learned through experience, incorrect invariances can be created. Testing with an experiment where images resembled each other prove this. In line with the results, it turned out that the movement makes it appear as if it is the same image. The images were changed so quickly that the test subjects did not notice this. The subjects were more often confused by object pairs that changed positions than by alternating images (in which they did not see the image being swapped). Invariance with objects takes place quickly. The confusion arises because people have been taught to expect something to stay the same when it moves, and as soon as this changes during the movement, something is wrong in your saccade.
A third study has shown that specific adjustments in the object spatiotemporal experience can lead to position-invariant recognition of test objects. This study shows that visual processes can be changed by visual statistics that do not reach consciousness. The invariance comes from the experience.
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Summaries per article with Artificial intelligences and Neurocognition at Leiden University 20/21
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Study Guide with article summaries for Artificial Intelligence at Leiden University
Article summaries with Artificial Intelligence at Leiden University
Table of content
- Modeling visual recognition from neurobiological constraints
- Achieving machine realization of truly human-like intelligence
- Untangling invariant object recognition
- Computing machinery and intelligence
- Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project
- Robots with instincts
- Male and female robots
- Speed of processing in the human visual system
- Sparse but not ""Grandmother-cell"" coding in the medial temporal lobe
- Perceptrons
- Learning and neural plasticity in visual object recognition
- Breaking position-invariant object recognition
- A feedforward architecture accounts for rapid categorization
- Hierarchical models of object recognition in cortex
- Articlesummaries with prescribed articles for AI - 2020/2021
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