The making of a neuromorphic visual system
著者
書誌事項
The making of a neuromorphic visual system
Springer, c2005
大学図書館所蔵 件 / 全1件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references (p. [129]-135) and index
内容説明・目次
内容説明
Arma virumque cano, Trojae qui primus ab oris Italiamfato profugus, Laviniaque venit litora. This is the beginning of Ovid's story about Odysseus leaving Trojae to find his way home. I here tell about my own Odysee-like ex- riences that I have undergone when I attempted to simulate visual recognition. The Odyssee started with a structural description - tempt, then continued with region encoding with wave propagation and may possibly continue with a mixture of several shape descr- tion methods. Although my odyssey is still under its way I have made enough progress to convey the gist of my approach and to compare it to other vision systems. My driving intuition is that visual category representations need to be loose in order to be able to cope with the visual structural va- ability existent within categories and that these loose representations are somehow expressed as neural activity in the nervous system. I - gard such loose representations as the cause for experiencing visual illusions and the cause for many of those effects discovered in att- tional experiments.
During my effort to find such loose represen- tions, I have made sometimes unexpected experiences that forced me to continuously rethink my approach and to abandon or turn over some of my initially strongly believed viewpoints.
目次
1: Seeing: Blazing Processing Characteristics
1.1 An Infinite Reservoir of Information
1.2 Speed
1.3 Illusions
1.4 Recognition Evolvement
1.5 Basic-Level Categorization
1.6 Memory Capacity and Access
1.7 Summary
2: Category Representation and Recognition Evolvement
2.1 Structural Variability Independence
2.2 Viewpoint Independence
2.3 Representation and Evolvement
2.4 Recapitulation
2.5 Refining the Primary Engineering Goal
3: Neuroscientific Inspiration
3.1 Hierarchy and Models
3.2 Criticism and Variants
3.3 Speed
3.4 Alternative 'Codes'
3.5 Alternative Shape Recognition
3.6 Insight from Cases of Visual Agnosia
3 7 Neuronal Level
3.8 Recapitulation and Conclusion
4: Neuromorphic Tools
4.1 The Transistor
4.2 A Synaptic Circuit
4.3 Dendritic Compartments
4.4 An Integrate-and-Fire Neuron
4.5 A Silicon Cortex
4.6 Fabrication Vagrancies require Simplest Models
4.7 Recapitulation
5: Insight From Line Drawings Studies
5.1 A Representation with Polygons
5.2 A Representation with Polygons and their Context
5.3 Recapitulation
6: Retina Circuits Signaling and Propagating Contours
6.1 The Input: a Luminance Landscape
6.2 Spatial Analysis in the Real Retina
6.3 The Propagation Map
6.4 Signaling Contours in Gray-Scale Images
6.5 Recapitulation
7: The Symmetric-Axis Transform
7.1 The Transform
7.2 Architecture
7.3 Performance
7.4 SAT Variants
7.5 Fast Waves
7.6 Recapitulation
8: Motion Detection
8.1 Models
8.2 Speed Detecting Architectures
8.3 Simulation
8.4 Biophysical Plausibility
8.5 Recapitulation
9: Neuromorphic Architectures: Pieces and Proposals
9.1 Integration Perspectives
9.2 Position and Size Invariance
9.3 Architecture for a Template Approach
9.4 Basic-Level Representations
9.5 Recapitulation
10: Shape Recognition with ContourPropagation Fields
10.1 The Idea of the Contour Propagation Field
10.2 Architecture
10.3 Testing
10.4 Discussion
10.5 Learning
10.6 Recapitulation
11: Scene Recognition
11.1 Objects in Scenes, Scene Regularity
11.2 Representation, Evolvement, Gist
11.3 Scene Exploration
11.4 Engineering
11.5 Recapitulation
12: Summary
12.1 The Quest for Efficient Representation and Evolvement
12.2 Contour Extraction and Grouping
12.3 Neuroscientific Inspiration
12.4 Neuromorphic Implementation
12.5 Future Approach
Terminology
References
Index
Keywords
Abbreviations
「Nielsen BookData」 より
