Comparative Study on Interaction of Form and Motion Processing Streams by Applying Two Different Classifiers in Mechanism for Recognition of Biological Movement

Abstract

Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility.

Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

“…Kinetic-geometric models also used for modification of the methods involving "what" and "where" pathways and in general models with two separated pathways and improved form pathway (answer to "what"). Spatiotemporal filters often used to replace the MLD ( [15,16,38,39,[100][101][102][103][104][105][106]) and expanded into 3D structural methods ( [1,51]), even new deep learning approaches involving 3D recognition of human action (i.e., [99]). Table 1 summarizes the pros and cons of action recognition approaches.…”

The Visual Neuroscience of Robotic Grasping

“…Strongly biologically inspired models of visual areas have been implemented, focusing mainly on the primary visual cortex (see e.g. Bio-inspired systems for object recognition (Khosla et al 2014) and biological motion detection (Yousefi and Loo 2014a, b) are also available Complex agent-object interactionssuch as in grasping actions-are not a usual target of computational models. Bio-inspired systems for object recognition (Khosla et al 2014) and biological motion detection (Yousefi and Loo 2014a, b) are also available Complex agent-object interactionssuch as in grasping actions-are not a usual target of computational models.…”

Vision-Based Grasping, Where Robotics Meets Neuroscience

“…Sabatini et al 2001;McLaughlin et al 2003;Lourens and Barakova 2007), or on increasingly invariant object representations for recognition as performed along the ventral stream (Ullman 1996; O'Reilly Fagg and Arbib (1998) and Munakata 2000; Riesenhuber and Poggio 2000;Cadieu et al 2007). Bio-inspired systems for object recognition (Khosla et al 2014) and biological motion detection (Yousefi and Loo 2014a, b) are also available Complex agent-object interactionssuch as in grasping actions-are not a usual target of computational models. The integration between the contributions of the two visual pathways is a subject almost unexplored (Rolls and Deco 2002;Rolls and Webb 2014), and only a few studies explicitly deal with dorsal stream processing.…”