Dreaming about Dogs: An Online Survey

Simple Summary

The findings of the survey indicate that waking-life experiences with dogs (owning a dog or negative experiences with dogs in the past) affect dreaming in a significant way. On the one hand, dog owners dream about dogs more often and had overall positively toned dreams, whereas persons with negative experiences with dogs in their waking life reported a higher percentage of dreams with threatening dogs.

Abstract

Dogs have been close human companions for millennia and one would expect—according to the continuity hypothesis of dreaming—that dogs are also quite common in dreams. Previous studies showed that the percentages of dreams that include dogs range from about 1.5% to 5%, but studies relating waking-life experiences with dogs with dreams about dogs have not been carried out. In total, 1695 persons (960 women, 735 men) completed an online survey that included questions about dreams and waking-life experiences that included dogs. The findings indicate that dogs show up, on average, in about 5% of remembered dreams, but this percentage is much higher in the dreams of dog owners and persons with close contacts with dogs. Moreover, the active time spent with a dog and the proximity during sleep is also related to a higher percentage of dreams that include dogs. Although dreams including dogs are on average more positively toned than dreams in general, about 11% of the dog dreams included threatening dogs. Persons who had negative experiences with dogs in their waking lives reported more threatening dog dreams. The results support the continuity hypothesis and it would be very interesting to conduct content analytic studies with dream samples obtained from dog owners to learn more about the variety of interactions between dreamers and dogs.

Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation

Modern large displacement optical flow algorithms usually use an initialization by either sparse descriptor matching techniques or dense approximate nearest neighbor fields. While the latter have the advantage of being dense, they have the major disadvantage of being very outlier-prone as they are not designed to find the optical flow, but the visually most similar correspondence. In this article we present a dense correspondence field approach that is much less outlier-prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields. Our approach does not require explicit regularization, smoothing (like median filtering) or a new data term. Instead we solely rely on patch matching techniques and a novel multi-scale matching strategy. We also present enhancements for outlier filtering. We show that our approach is better suited for large displacement optical flow estimation than modern descriptor matching techniques. We do so by initializing EpicFlow with our approach instead of their originally used state-of-the-art descriptor matching technique. We significantly outperform the original EpicFlow on MPI-Sintel, KITTI 2012, KITTI 2015 and Middlebury. In this extended article of our former conference publication we further improve our approach in matching accuracy as well as runtime and present more experiments and insights.