Defocus Video Matting

Video matting is the process of pulling a high-quality alpha matte and foreground from a video sequence. Current techniques require either a known background (e.g., a blue screen) or extensive user interaction (e.g., to specify known foreground and background elements). The matting problem is generally under-constrained, since not enough information has been collected at capture time. We propose a novel, fully autonomous method for pulling a matte using multiple synchronized video streams that share a point of view but differ in their plane of focus. The solution is obtained by directly minimizing the error in filter-based image formation equations, which are over-constrained by our rich data stream. Our system solves the fully dynamic video matting problem without user assistance: both the foreground and background may be high frequency and have dynamic content, the foreground may resemble the background, and the scene is lit by natural (as opposed to polarized or collimated) illumination.Engineering and Applied Science

Dairy wintering systems in southern New Zealand : quantification and modelling of nutrient transfers and losses from contrasting wintering systems : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand

Traditional dairy wintering practice in the lower South Island of New Zealand has been to graze brassica crops in situ. This practice has been under increasing scrutiny from local Regional Councils due to the relatively high nitrogen (N) leaching losses from this component of the whole farm system. Alternative wintering options to reduce N leaching losses that are currently available to farmers (such as barns and permanent wintering pads) are high cost and involve a large capital investment. In this work a new wintering system (termed a ‘portable pad’) was developed for use on support blocks (which can be located many kilometres from the milking platform) as an interim measure for reducing N leaching losses that is low cost and low input. This system is designed as a mitigation strategy that is available for use immediately while research investigates more permanent solutions. This system is a hybrid of the traditional crop grazing system and an off-paddock system, where effluent is captured. It makes use of the advantages of each of the original systems utilising the low cost feed source of the brassica crops, grazed in situ, while also utilising the benefits of duration controlled grazing with its associated effluent capture and irrigation at low rates. The aim of the research was to generate whole system N leaching loss values for each of the three farm systems investigated (crop wintering, deep-litter wintering barn, and portable pad). Field and laboratory research was conducted to fill identified knowledge gaps such that system N loss values could be estimated. OVERSEER Nutrient Budget software tool was used in conjunction with measured and modelled (APSIM) data to simulate whole farm N leaching loss values for the three farm systems investigated. Nitrogen leaching losses from the portable pad and barn systems were between 5 and 26 % and between 13 and 26 % lower, respectively, than the crop wintering system

Photorealistic Style Transfer with Screened Poisson Equation

Recent work has shown impressive success in transferring painterly style to images. These approaches, however, fall short of photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. In this paper we propose an approach that takes as input a stylized image and makes it more photorealistic. It relies on the Screened Poisson Equation, maintaining the fidelity of the stylized image while constraining the gradients to those of the original input image. Our method is fast, simple, fully automatic and shows positive progress in making a stylized image photorealistic. Our results exhibit finer details and are less prone to artifacts than the state-of-the-art.Comment: presented in BMVC 201

Where and Who? Automatic Semantic-Aware Person Composition

Image compositing is a method used to generate realistic yet fake imagery by inserting contents from one image to another. Previous work in compositing has focused on improving appearance compatibility of a user selected foreground segment and a background image (i.e. color and illumination consistency). In this work, we instead develop a fully automated compositing model that additionally learns to select and transform compatible foreground segments from a large collection given only an input image background. To simplify the task, we restrict our problem by focusing on human instance composition, because human segments exhibit strong correlations with their background and because of the availability of large annotated data. We develop a novel branching Convolutional Neural Network (CNN) that jointly predicts candidate person locations given a background image. We then use pre-trained deep feature representations to retrieve person instances from a large segment database. Experimental results show that our model can generate composite images that look visually convincing. We also develop a user interface to demonstrate the potential application of our method.Comment: 10 pages, 9 figure