4 research outputs found
Towards real-time interest point detection and description for mobile and robotic devices
Convolutional Neural Networks (CNNs) have been successfully adopted by state-of-the-art feature point detection and description networks for the past number of years. The focus of these systems has been predominately on the accuracy of the system, rather than on its efficiency or ability to be implemented in real-time on embedded robotic devices. This paper demonstrates how techniques, developed for other CNN use cases, can be integrated into interest point detection and description systems to compress their network size and reduce the computational complexity; this reduces the barrier to their uptake in computationally challenged environments. This paper documents the integration of these techniques into the popular Reliable Detector and Descriptor (R2D2) network. Along with the integration details, a comprehensive Key Performance Indicator (KPI) framework is developed to test all aspects of the networks. As a result, this paper presents a lightweight variant of the R2D2 network that significantly reduces parameters and computational complexity while crucially maintaining an acceptable level of accuracy. Consequently, this new compressed network is more appropriate for use in real world systems and advances the efforts to implement such CNN based system for mobile devices
The development of pre-service design educator’s capacity to make professional judgments on design capability using adaptive comparative judgment
When design educators are faced with assessment tasks it is important they have a good personal
construct of what it means to be capable in design education. The importance of allowing design
students the facility to develop creative and innovative capacities is a priority. With standardised
testing it is harder to allow for open ended and divergent projects to be facilitated and assessed.
Adaptive Comparative Judgment is a dynamic assessment tool to facilitate and capture the
complex iterative design process. The validity and reliability of adaptive comparative judgments
as an assessment tool has been established by many in Design Education. This paper looks at the
impact of A.C.J. on perspective design educators construct of design capability. An ACJ session
was completed by 13 volunteers on 24 design portfolios without giving specific criteria. They
had their own personal construct of capability based on a process of enculturation. During the
study concurrent and retrospective commentaries by the participants were recorded to get an
insight into their thinking during the decision making session. The study found there was
consensus on what was evidence of capability in open ended design projects. Also it showed that
engagement in the ACJ processes led to a further appraisal of what the perspective design
educators construct of capability in design education. This prompts further investigation into the
impact of the engagement in the ACJ on appraisal skills and the affect it has on a student’s
metacognitive awareness of their construct of capability in design education
Towards real-time interest point detection and description for mobile and robotic devices
Convolutional Neural Networks (CNNs) have been successfully adopted by state-of-the-art feature point detection and description networks for the past number of years. The focus of these systems has been predominately on the accuracy of the system, rather than on its efficiency or ability to be implemented in real-time on embedded robotic devices. This paper demonstrates how techniques, developed for other CNN use cases, can be integrated into interest point detection and description systems to compress their network size and reduce the computational complexity; this reduces the barrier to their uptake in computationally challenged environments. This paper documents the integration of these techniques into the popular Reliable Detector and Descriptor (R2D2) network. Along with the integration details, a comprehensive Key Performance Indicator (KPI) framework is developed to test all aspects of the networks. As a result, this paper presents a lightweight variant of the R2D2 network that significantly reduces parameters and computational complexity while crucially maintaining an acceptable level of accuracy. Consequently, this new compressed network is more appropriate for use in real world systems and advances the efforts to implement such CNN based system for mobile devices.</p