The reader is presented an approach to the construction of a visual system, which is behaviorally, computationally and neurally motivated. The central goal is to characterize the process of visual categorization and to find a suitable representation format that can successfully deal with the structural variability existent within visual categories. It does not define such representations a priori but attempts to show directions on how to gradually work towards them. The book reviews past and existent theories of visual object and shape recognition in the fields of computer vision, neuroscience and psychology. The entire range of computations is discussed, as for example contour extraction in retinal circuits, orientation determination in cortical networks, position and scale independence of shape, as well as the issue of object and shape representation in a neural substrate. Region-based approaches are discussed and are modeled with wave-propagating networks. It is demonstrated how those networks operate on gray-scale images. A completely novel shape recognition architecture is proposed that can recognize simple shapes under various degraded conditions. It is discussed how such networks can be used for constructing basic-level object representations. It is envisioned how those networks can be implemented using the method of neuromorphic engineering, an analog electronic hardware substrate than can run neural computations in real-time and with little power.

Inhaltsangabe1: Seeing: Blazing Processing Characteristics 1.1 An Infinite Reservoir of Information 1.2 Speed 1.3 Illusions 1.4 Recognition Evolvement 1.5 BasicLevel 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 BasicLevel 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