Where is cognition? Towards an embodied, situated, and distributed interactionist theory of cognitive activity

In recent years researchers from a variety of cognitive science disciplines have begun to challenge some of the core assumptions of the dominant theoretical framework of cognitivism including the representation-computational view of cognition, the sense-model-plan-act understanding of cognitive architecture, and the use of a formal task description strategy for investigating the organisation of internal mental processes. Challenges to these assumptions are illustrated using empirical findings and theoretical arguments from the fields such as situated robotics, dynamical systems approaches to cognition, situated action and distributed cognition research, and sociohistorical studies of cognitive development. Several shared themes are extracted from the findings in these research programmes including: a focus on agent-environment systems as the primary unit of analysis; an attention to agent-environment interaction dynamics; a vision of the cognizer's internal mechanisms as essentially reactive and decentralised in nature; and a tendency for mutual definitions of agent, environment, and activity. It is argued that, taken together, these themes signal the emergence of a new approach to cognition called embodied, situated, and distributed interactionism. This interactionist alternative has many resonances with the dynamical systems approach to cognition. However, this approach does not provide a theory of the implementing substrate sufficient for an interactionist theoretical framework. It is suggested that such a theory can be found in a view of animals as autonomous systems coupled with a portrayal of the nervous system as a regulatory, coordinative, and integrative bodily subsystem. Although a number of recent simulations show connectionism's promise as a computational technique in simulating the role of the nervous system from an interactionist perspective, this embodied connectionist framework does not lend itself to understanding the advanced 'representation hungry' cognition we witness in much human behaviour. It is argued that this problem can be solved by understanding advanced cognition as the re-use of basic perception-action skills and structures that this feat is enabled by a general education within a social symbol-using environment

Consensus multi-view photometric stereo

We propose a multi-view photometric stereo technique that uses photometric normal consistency to jointly estimate surface position and orientation. The underlying scene representation is based on oriented points, yielding more flexibility compared to smoothly varying surfaces. We demonstrate that the often employed least squares error of the Lambertian image formation model fails for wide-baseline settings without known visibility information. We then introduce a multi-view normal consistency approach and demonstrate its efficiency on synthetic and real data. In particular, our approach is able to handle occlusion, shadows, and other sources of outliers

Example-Based Stereo with General BRDFs

This paper presents an algorithm for voxel-based reconstruction of objects with general reflectance properties from multiple calibrated views. It is assumed that one or more reference objects with known geometry are imaged under the same lighting and camera conditions as the object being reconstructed. The unknown object is reconstructed using a radiance basis inferred from the reference objects. Each view may have arbitrary, unknown distant lighting. If the lighting is calibrated, our model also takes into account shadows that the object casts upon itself. To our knowledge, this is the first stereo method to handle general, unknown, spatially-varying BRDFs under possibly varying, distant lighting, and shadows. We demonstrate our algorithm by recovering geometry and surface normals for objects with both uniform and spatially-varying BRDFs. The normals reveal fine-scale surface detail, allowing much richer renderings than the voxel geometry alone

View Invariant Gait Recognition

Recognition by gait is of particular interest since it is the biometric that is available at the lowest resolution, or when other biometrics are (intentionally) obscured. Gait as a biometric has now shown increasing recognition capability. There are many approaches and these show that recognition can achieve excellent performance on large databases. The majority of these approaches are planar 2D, largely since the early large databases featured subjects walking in a plane normal to the camera view. To extend deployment capability, we need viewpoint invariant gait biometrics. We describe approaches where viewpoint invariance is achieved by 3D approaches or in 2D. In the first group the identification relies on parameters extracted from the 3D body deformation during walking. These methods use several video cameras and the 3D reconstruction is achieved after a camera calibration process. On the other hand, the 2D gait biometric approaches use a single camera, usually positioned perpendicular to the subject’s walking direction. Because in real surveillance scenarios a system that operates in an unconstrained environment is necessary, many of the recent gait analysis approaches are orientated towards viewinvariant gait recognition