Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose a novel approach that models future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. Future frame synthesis is challenging, as it involves low- and high-level image and motion understanding. We propose a novel network structure, namely a Cross Convolutional Network to aid in synthesizing future frames; this network structure encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-wold videos. We also show that our model can be applied to tasks such as visual analogy-making, and present an analysis of the learned network representations.Comment: The first two authors contributed equally to this wor

Visual Importance-Biased Image Synthesis Animation

Present ray tracing algorithms are computationally intensive, requiring hours of computing time for complex scenes. Our previous work has dealt with the development of an overall approach to the application of visual attention to progressive and adaptive ray-tracing techniques. The approach facilitates large computational savings by modulating the supersampling rates in an image by the visual importance of the region being rendered. This paper extends the approach by incorporating temporal changes into the models and techniques developed, as it is expected that further efficiency savings can be reaped for animated scenes. Applications for this approach include entertainment, visualisation and simulation

The effect direction plot: visual display of non-standardised effects across multiple outcome domains

Visual display of reported impacts is a valuable aid to both reviewers and readers of systematic reviews. Forest plots are routinely prepared to report standardised effect sizes, but where standardised effect sizes are not available for all included studies a forest plot may misrepresent the available evidence. Tabulated data summaries to accompany the narrative synthesis can be lengthy and inaccessible. Moreover, the link between the data and the synthesis conclusions may be opaque. This paper details the preparation of visual summaries of effect direction for multiple outcomes across 29 quantitative studies of the health impacts of housing improvement. A one page summary of reported health outcomes was prepared to accompany a 10 000-word narrative synthesis. The one page summary included details of study design, internal validity, sample size, time of follow-up, as well as changes in intermediate outcomes, for example, housing condition. This approach to visually summarising complex data can aid the reviewer in cross-study analysis and improve accessibility and transparency of the narrative synthesis where standardised effect sizes are not available

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis

Functional Imaging of the Outer Retinal Complex using High Fidelity Imaging Retinal Densitometry

We describe a new technique, high fidelity Imaging Retinal Densitometry (IRD), which probes the functional integrity of the outer retinal complex. We demonstrate the ability of the technique to map visual pigment optical density and synthesis rates in eyes with and without macular disease. A multispectral retinal imaging device obtained precise measurements of retinal reflectance over space and time. Data obtained from healthy controls and 5 patients with intermediate AMD, before and after photopigment bleaching, were used to quantify visual pigment metrics. Heat maps were plotted to summarise the topography of rod and cone pigment kinetics and descriptive statistics conducted to highlight differences between those with and without AMD. Rod and cone visual pigment synthesis rates in those with AMD (v = 0.043 SD 0.019 min-1 and v = 0.119 SD 0.046 min-1, respectively) were approximately half those observed in healthy controls (v = 0.079 SD 0.024 min-1 for rods and v = 0.206 SD 0.069 min-1 for cones). By mapping visual pigment kinetics across the central retina, high fidelity IRD provides a unique insight into outer retinal complex function. This new technique will improve the phenotypic characterisation, diagnosis and treatment monitoring of various ocular pathologies, including AMD

The Goldfish as a Model for Studying Neuroestrogen Synthesis, Localization, and Action in the Brain and Visual System

Organizational and activational effects of estrogen (E) in the central nervous system (CNS) are exerted directly by circulating E and indirectly after aromatization of circulating androgen to E in the brain itself. Understanding an environmental chemical's ability to disrupt E-dependent neural processes, therefore, requires attention to both pathways. Because aromatase (Aro) is highly expressed in teleost brain, when compared to mammals and other vertebrates, fish are technically advantageous for localization and regulation studies and may also provide a model in which the functional consequences of brain-derived (neuro-)E synthesis are exaggerated. Recently, Aro was immunolocalized in cell bodies and fiber projections of second- and third-order neurons of the goldfish retina and in central visual processing areas. Authentic Aro enzyme activity was verified biochemically, suggesting a heretofore unrecognized role of sex steroids in the visual system. Initial studies show that in vivo treatment with aromatizable androgen or E increases calmodulin synthesis and calmodulin protein in retina and also affects retinal protein and DNA. Whether there are related changes in the processing of visual information that is essential for seasonal reproduction or in the generative and regenerative capacity of the goldfish visual system requires further investigation. IMAGES.National Science Foundation (DCB8916809