Perceptual abstraction and attention

This is a report on the preliminary achievements of WP4 of the IM-CleVeR project on abstraction for cumulative learning, in particular directed to: (1) producing algorithms to develop abstraction features under top-down action influence; (2) algorithms for supporting detection of change in motion pictures; (3) developing attention and vergence control on the basis of locally computed rewards; (4) searching abstract representations suitable for the LCAS framework; (5) developing predictors based on information theory to support novelty detection. The report is organized around these 5 tasks that are part of WP4. We provide a synthetic description of the work done for each task by the partners

A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot

Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors

A Focus on Selection for Fixation

A computational explanation of how visual attention, interpretation of visual stimuli, and eye movements combine to produce visual behavior, seems elusive. Here, we focus on one component: how selection is accomplished for the next fixation. The popularity of saliency map models drives the inference that this is solved, but we argue otherwise. We provide arguments that a cluster of complementary, conspicuity representations drive selection, modulated by task goals and history, leading to a hybrid process that encompasses early and late attentional selection. This design is also constrained by the architectural characteristics of the visual processing pathways. These elements combine into a new strategy for computing fixation targets and a first simulation of its performance is presented. A sample video of this performance can be found by clicking on the "Supplementary Files" link under the "Article Tools" heading

A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot

Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

A Portable Bio-Inspired Architecture for Efficient Robotic Vergence Control

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Hierarchical Object-Based Visual Attention for Machine Vision

Institute of Perception, Action and BehaviourHuman vision uses mechanisms of covert attention to selectively process interesting information and overt eye movements to extend this selectivity ability. Thus, visual tasks can be effectively dealt with by limited processing resources. Modelling visual attention for machine vision systems is not only critical but also challenging. In the machine vision literature there have been many conventional attention models developed but they are all space-based only and cannot perform object-based selection. In consequence, they fail to work in real-world visual environments due to the intrinsic limitations of the space-based attention theory upon which these models are built. The aim of the work presented in this thesis is to provide a novel human-like visual selection framework based on the object-based attention theory recently being developed in psychophysics. The proposed solution – a Hierarchical Object-based Attention Framework (HOAF) based on grouping competition, consists of two closely-coupled visual selection models of (1) hierarchical object-based visual (covert) attention and (2) object-based attention-driven (overt) saccadic eye movements. The Hierarchical Object-based Attention Model (HOAM) is the primary selection mechanism and the Object-based Attention-Driven Saccading model (OADS) has a supporting role, both of which are combined in the integrated visual selection framework HOAF. This thesis first describes the proposed object-based attention model HOAM which is the primary component of the selection framework HOAF. The model is based on recent psychophysical results on object-based visual attention and adopted grouping-based competition to integrate object-based and space-based attention together so as to achieve object-based hierarchical selectivity. The behaviour of the model is demonstrated on a number of synthetic images simulating psychophysical experiments and real-world natural scenes. The experimental results showed that the performance of our object-based attention model HOAM concurs with the main findings in the psychophysical literature on object-based and space-based visual attention. Moreover, HOAM has outstanding hierarchical selectivity from far to near and from coarse to fine by features, objects, spatial regions, and their groupings in complex natural scenes. This successful performance arises from three original mechanisms in the model: grouping-based saliency evaluation, integrated competition between groupings, and hierarchical selectivity. The model is the first implemented machine vision model of integrated object-based and space-based visual attention. The thesis then addresses another proposed model of Object-based Attention-Driven Saccadic eye movements (OADS) built upon the object-based attention model HOAM, ii as an overt saccading component within the object-based selection framework HOAF. This model, like our object-based attention model HOAM, is also the first implemented machine vision saccading model which makes a clear distinction between (covert) visual attention and overt saccading movements in a two-level selection system – an important feature of human vision but not yet explored in conventional machine vision saccading systems. In the saccading model OADS, a log-polar retina-like sensor is employed to simulate the human-like foveation imaging for space variant sensing. Through a novel mechanism for attention-driven orienting, the sensor fixates on new destinations determined by object-based attention. Hence it helps attention to selectively process interesting objects located at the periphery of the whole field of view to accomplish the large-scale visual selection tasks. By another proposed novel mechanism for temporary inhibition of return, OADS can simulate the human saccading/ attention behaviour to refixate/reattend interesting objects for further detailed inspection. This thesis concludes that the proposed human-like visual selection solution – HOAF, which is inspired by psychophysical object-based attention theory and grouping-based competition, is particularly useful for machine vision. HOAF is a general and effective visual selection framework integrating object-based attention and attentiondriven saccadic eye movements with biological plausibility and object-based hierarchical selectivity from coarse to fine in a space-time context

Coherence and recurrency: maintenance, control and integration in working memory

Working memory (WM), including a ‘central executive’, is used to guide behavior by internal goals or intentions. We suggest that WM is best described as a set of three interdependent functions which are implemented in the prefrontal cortex (PFC). These functions are maintenance, control of attention and integration. A model for the maintenance function is presented, and we will argue that this model can be extended to incorporate the other functions as well. Maintenance is the capacity to briefly maintain information in the absence of corresponding input, and even in the face of distracting information. We will argue that maintenance is based on recurrent loops between PFC and posterior parts of the brain, and probably within PFC as well. In these loops information can be held temporarily in an active form. We show that a model based on these structural ideas is capable of maintaining a limited number of neural patterns. Not the size, but the coherence of patterns (i.e., a chunking principle based on synchronous firing of interconnected cell assemblies) determines the maintenance capacity. A mechanism that optimizes coherent pattern segregation, also poses a limit to the number of assemblies (about four) that can concurrently reverberate. Top-down attentional control (in perception, action and memory retrieval) can be modelled by the modulation and re-entry of top-down information to posterior parts of the brain. Hierarchically organized modules in PFC create the possibility for information integration. We argue that large-scale multimodal integration of information creates an ‘episodic buffer’, and may even suffice for implementing a central executive