Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.Comment: 19 pages, 10 figure

Turbidity influences individual and group level responses to predation in guppies, Poecilia reticulata

© 2015 The Association for the Study of Animal Behaviour. Increasing turbidity (either sedimentary or organic) from anthropogenic sources has significant negative impacts on aquatic fauna, both directly and indirectly by disrupting behaviour. In particular, antipredator responses of individuals are reduced, which has been attributed to a reduced perception of risk. Here, we explored the effect of turbidity on shoaling behaviour, which is known to carry important antipredator benefits, predicting that fish in turbid water should show reduced shoal cohesion (increased interindividual distances) and reduced responses to a simulated predatory threat. We explored both the individual and shoal level responses to a predation threat at four different levels of turbidity. At the shoal level, we found that shoals were less cohesive in more turbid water, but that there was no effect of turbidity on shoal level response to the predation threat. At an individual level, guppies in turbid water were more likely to freeze (rather than dart then freeze), and those that darted moved more slowly and over a shorter distance than those in clear water. Fish in turbid water also took longer to recover from a predation threat than fish in clear water. We suggest that because fish in turbid water behaved in a manner more similar to that expected from lone fish than to those in a shoal, the loss of visual contact between individuals in turbid water explains the change in behaviour, rather than a reduced perception of individual risk as is widely supposed. We suggest that turbidity could lead to a reduced collective response to predators and a loss of the protective benefits of shoaling

Do-It-Yourself Single Camera 3D Pointer Input Device

We present a new algorithm for single camera 3D reconstruction, or 3D input for human-computer interfaces, based on precise tracking of an elongated object, such as a pen, having a pattern of colored bands. To configure the system, the user provides no more than one labelled image of a handmade pointer, measurements of its colored bands, and the camera's pinhole projection matrix. Other systems are of much higher cost and complexity, requiring combinations of multiple cameras, stereocameras, and pointers with sensors and lights. Instead of relying on information from multiple devices, we examine our single view more closely, integrating geometric and appearance constraints to robustly track the pointer in the presence of occlusion and distractor objects. By probing objects of known geometry with the pointer, we demonstrate acceptable accuracy of 3D localization.Comment: 8 pages, 6 figures, 2018 15th Conference on Computer and Robot Visio