Mid Sweden University

Convolutional Neural Networks (CNNs) have been instrumental in the recent advances in machine learning, with applications to media applications. Multi-Layered Convolutional Sparse Coding (ML-CSC) based on a cascade of convolutional layers in which each layer can be approximately explained by the following layer can be seen as a biologically inspired framework. However, both CNNs and ML-CSC networks lack top-down information flows that are studied in neuroscience for understanding the mechanisms of the mammal cortex. A successful implementation of such top-down connections could lead to another leap in machine learning and media applications.%This study analyses the effects of a feedback connection on an ML-CSC network, considering trade-off between sparsity and reconstruction error, support recovery rate, and mutual coherence in trained dictionaries. We find that using the feedback connection during training impacts the mutual coherence of the dictionary in a way that the equivalence between the $l_0$- and $l_1$-norm is verified for a smaller range of sparsity values. Experimental results show that the use of feedback during training does not favour inference with feedback, in terms of sparse support recovery rates. However, when the sparsity constraints are given a lower weight, the use of feedback at inference time is beneficial, in terms of support recovery rates. 

Virtual and augmented reality is increasingly prevalent in industrial applications, such as remote control of industrial machinery, due to recent advances in head-mounted display technologies and low-latency communications via 5G. However, the influence of augmentations and camera placement-based viewing positions on operator performance in telepresence systems remains unknown. In this paper, we investigate the joint effects of depth-aiding augmentations and viewing positions on the quality of experience for operators in augmented telepresence systems. A study was conducted with 27 non-expert participants using a real-time augmented telepresence system to perform a remote-controlled navigation and positioning task, with varied depth-aiding augmentations and viewing positions. The resulting quality of experience was analyzed via Likert opinion scales, task performance measurements, and simulator sickness evaluation. Results suggest that reducing the reliance on stereoscopic depth perception via camera placement has a significant benefit to operator performance and quality of experience. Conversely, the depth-aiding augmentations can partly mitigate the negative effects of inferior viewing positions. However the viewing-position based monoscopic and stereoscopic depth cues tend to dominate over cues based on augmentations. There is also a discrepancy between the participants’ subjective opinions on augmentation helpfulness, and its observed effects on positioning task performance.

In this paper, we investigate how different viewing positions affect a user's Quality of Experience (QoE) and performance in an immersive telepresence system. A QoE experiment has been conducted with 27 participants to assess the general subjective experience and the performance of remotely operating a toy excavator. Two view positions have been tested, an overhead and a ground-level view, respectively, which encourage reliance on stereoscopic depth cues to different extents for accurate operation. Results demonstrate a significant difference between ground and overhead views: the ground view increased the perceived difficulty of the task, whereas the overhead view increased the perceived accomplishment as well as the objective performance of the task. The perceived helpfulness of the overhead view was also significant according to the participants.