Bridging Between Computer and Robot Vision Through Data Augmentation: A Case Study on Object Recognition

Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem. The most successful convolutional architectures are developed starting from ImageNet, a large scale collection of images of object categories downloaded from the Web. This kind of images is very different from the situated and embodied visual experience of robots deployed in unconstrained settings. To reduce the gap between these two visual experiences, this paper proposes a simple yet effective data augmentation layer that zooms on the object of interest and simulates the object detection outcome of a robot vision system. The layer, that can be used with any convolutional deep architecture, brings to an increase in object recognition performance of up to 7{\%}, in experiments performed over three different benchmark databases. An implementation of our robot data augmentation layer has been made publicly available

Augmenting human memory using personal lifelogs

Memory is a key human facility to support life activities, including social interactions, life management and problem solving. Unfortunately, our memory is not perfect. Normal individuals will have occasional memory problems which can be frustrating, while those with memory impairments can often experience a greatly reduced quality of life. Augmenting memory has the potential to make normal individuals more effective, and those with significant memory problems to have a higher general quality of life. Current technologies are now making it possible to automatically capture and store daily life experiences over an extended period, potentially even over a lifetime. This type of data collection, often referred to as a personal life log (PLL), can include data such as continuously captured pictures or videos from a first person perspective, scanned copies of archival material such as books, electronic documents read or created, and emails and SMS messages sent and received, along with context data of time of capture and access and location via GPS sensors. PLLs offer the potential for memory augmentation. Existing work on PLLs has focused on the technologies of data capture and retrieval, but little work has been done to explore how these captured data and retrieval techniques can be applied to actual use by normal people in supporting their memory. In this paper, we explore the needs for augmenting human memory from normal people based on the psychology literature on mechanisms about memory problems, and discuss the possible functions that PLLs can provide to support these memory augmentation needs. Based on this, we also suggest guidelines for data for capture, retrieval needs and computer-based interface design. Finally we introduce our work-in-process prototype PLL search system in the iCLIPS project to give an example of augmenting human memory with PLLs and computer based interfaces

Accessibility and adaptability of learning objects: responding to metadata, learning patterns and profiles of needs and preferences

The case for learning patterns as a design method for accessible and adaptable learning objects is explored. Patterns and templates for the design of learning objects can be derived from successful existing learning resources. These patterns can then be reused in the design of new learning objects. We argue that by attending to criteria for reuse in the definition of these patterns and in the subsequent design of new learning objects, those new resources can be themselves reusable and also adaptable to different learning contexts. Finally, if the patterns identified can be implemented as templates for standard authoring tools, the design of effective, reusable and adaptable resources can be made available to those with limited skills in multimedia authoring and result in learning resources that are more widely accessible

Sensory augmentation and the tactile sublime

This paper responds to recent developments in the field of sensory augmentation by analysing several technological devices that augment the sensory apparatus using the tactile sense. First, I will define the term sensory augmentation, as the use of technological modification to enhance the sensory apparatus, and elaborate on the preconditions for successful tactile sensory augmentation. These are the adaptability of the brain to unfamiliar sensory input and the specific qualities of the skin lending themselves to be used for the perception of additional sensory information. Two devices, Moon Ribas’ Seismic Sense and David Eagleman’s vest, will then be discussed as potential facilitators of aesthetic experiences in virtue of the tactile sensory augmentation that these devices allow. I will connect the experiences afforded by these devices to the Kantian categories of the mathematical and the dynamical sublime, and to existing accounts of tactile sublimity. Essentially, the objects these devices make sensible, earthquakes for the Seismic Sense and digital information for the vest, produce pleasurable feelings of potential danger, awe, and respect. The subsequent acclimation to this new way of sensing and the aim to comprehend its sensed object are then discussed as possible objections to the interpretation of these experiences as sublime, and as aesthetic in general. To exemplify these issues and concretise my thesis of tactile sensory augmentation as a trigger of the sublime, I will outline an experiment to use the vest as an aid for faster decision making on the stock market