Automatic test image generation using procedural noise

It is difficult to test programs that input images, due to the large number of (pixel) values that must be chosen and the complex ways these values interact. Typically, such programs are tested manually, using images that have known results. However, this is a laborious process and limited in the range of tests that can be applied. We introduce a new approach for testing programs that input images automatically, using procedural noise and spatial statistics to create inputs that are both realistic and can easily be tuned to have specific properties. The effectiveness of our approach is illustrated on an epidemiological simulation of a recently introduced tree pest in Great Britain: Oriental Chestnut Gall Wasp. Our approach produces images that match the real landscapes more closely than other techniques and can be used (alongside metamorphic relations) to detect smaller (artificially introduced) errors with greater accuracy.This work was supported by the University of Cambridge/Wellcome Trust Junior Interdisciplinary Fellowship “Making scientific software easier to understand, test and communicate through modern advances in software engineering.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by IEEE

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques. In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human intervention needed to train the detection and classification algorithms. The idea is to procedurally generate large synthetic training datasets randomizing the key features of the target environment (i.e., crop and weed species, type of soil, light conditions). More specifically, by tuning these model parameters, and exploiting a few real-world textures, it is possible to render a large amount of realistic views of an artificial agricultural scenario with no effort. The generated data can be directly used to train the model or to supplement real-world images. We validate the proposed methodology by using as testbed a modern deep learning based image segmentation architecture. We compare the classification results obtained using both real and synthetic images as training data. The reported results confirm the effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201

Unsupervised three-dimensional reconstruction of small rocks from a single two-dimensional image

Surfaces covered with pebbles and small rocks can often be found in nature or in human shaped environments. Generating an accurate three-dimensional model of those kind of surfaces from a reference image can be challenging, especially if one wants to be able to animate each pebble individually. To undertake this kind of job manually is time consuming and impossible to achieve in dynamic terrains animations. The method described in this paper allows unsupervised automatic generation of three-dimensional textured rocks from a two-dimensional image aiming to closely match the original image as much as possible

Towards Rapid Generation and Visualisation of Large 3D Urban Landscapes for Mobile Device Navigation

In this paper a procedural 3D modelling solution for mobile devices is presented based on scripting algorithms allowing for both the automatic and also semi-automatic creation of photorealistic quality virtual urban content. The combination of aerial images, GIS data, 2D ground maps and terrestrial photographs as input data coupled with a user-friendly customized interface permits the automatic and interactive generation of large-scale, accurate, georeferenced and fully-textured 3D virtual city content, content that can be specially optimized for use with mobile devices but also with navigational tasks in mind. Furthermore, a user-centred mobile virtual reality (VR) visualisation and interaction tool operating on PDAs (Personal Digital Assistants) for pedestrian navigation is also discussed. Via this engine, the import and display of various navigational file formats (2D and 3D) is supported, including a comprehensive front-end user-friendly graphical user interface providing immersive virtual 3D navigation

Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control

Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control

Digital technologies for virtual recomposition : the case study of Serpotta stuccoes

The matter that lies beneath the smooth and shining surface of stuccoes of the Serpotta family, who used to work in Sicily from 1670 to 1730, has been thoroughly studied in previous papers, disclosing the deep, even if empirical, knowledge of materials science that guided the artists in creating their master- works. In this work the attention is focused on the solid perspective and on the scenographic sculpture by Giacomo Serpotta, who is acknowledged as the leading exponent of the School. The study deals with some particular works of the artist, the so-called "teatrini" (Toy Theater), made by him for the San Lorenzo Oratory in Palermo. On the basis of archive documents and previous analogical photogrammetric plotting, integrated with digital solutions and methodologies of computer- based technologies, the study investigates and interprets the geometric-formal genesis of the examined works of art, until the prototyping of the whole scenic apparatus.peer-reviewe