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Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale. We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions...
We present a learning framework for abstracting complex shapes by learning to assemble objects using 3D volumetric primitives. In addition to generating simple and geometrically interpretable explanations of 3D objects, our framework also allows us to automatically discover and exploit consistent structure in the data. We demonstrate that using our method allows predicting shape representations which...
Robust covariant local feature detectors are important for detecting local features that are (1) discriminative of the image content and (2) can be repeatably detected at consistent locations when the image undergoes diverse transformations. Such detectors are critical for applications such as image search and scene reconstruction. Many learning-based local feature detectors address one of these two...
Statistical decomposition methods are of paramount importance in discovering the modes of variations of visual data. Probably the most prominent linear decomposition method is the Principal Component Analysis (PCA), which discovers a single mode of variation in the data. However, in practice, visual data exhibit several modes of variations. For instance, the appearance of faces varies in identity,...
In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model and new deep learning architecture. We add a binary map that provides rain streak locations to an existing model, which comprises a rain streak layer and a background layer. We create a model consisting of a component...
We propose a combinatorial solution for the problem of non-rigidly matching a 3D shape to 3D image data. To this end, we model the shape as a triangular mesh and allow each triangle of this mesh to be rigidly transformed to achieve a suitable matching to the image. By penalising the distance and the relative rotation between neighbouring triangles our matching compromises between the image and the...
In this work the notion of automated risk assessment for 3D scenes is addressed. Using deep learning techniques smart enabled homes and domestic robots can be equipped with the functionality to detect, draw attention to, or mitigate hazards in a given scene. We extend an existing risk estimation framework that incorporates physics and shape descriptors by introducing a novel CNN architecture allowing...
Human bodies and movements exhibit inherent symmetry. However, an important class of everyday movements, such as walking, does not maintain symmetry at every time instance. The symmetry in these movements is a spatiotemporal glide-reflection symmetry. The ability to measure this type of symmetry will provide us opportunities for various computer-aided applications including health monitoring, rehabilitation,...
Learning the dynamics of shape is at the heart of many computer vision problems: object tracking, change detection, longitudinal shape analysis, trajectory classification, etc. In this work we address the problem of statistical inference of diffusion processes of shapes. We formulate a general Itô diffusion on the manifold of deformable landmarks and propose several drift models for the evolution...
We propose a method to correct the aspect ratio distortion of images using convolutional neural network (CNN). The “aspect ratio”, which is focused on this research, is defined as the relative “image aspect ratio” (i.e. ratio of width to height of image) from non-stretched image. And the aspect ratio can be distorted by vertical or horizontal stretching, which does not maintain the image aspect ratio...
Biologists and pharmacologists commonly use zebrafish embryos during the testing of drugs. The testing of these substances is a tedious and painstaking process, carried out manually by trained experts who determine whether the embryos have been deformed or killed as a result of administering the chemical. This paper proposes a novel automatic system for the detection and classification of abnormal...
Object recognition or object's category recognition under varying conditions is one of the most astonishing capabilities of human visual system. The scientists in computer vision have been trying for decades to reproduce this ability by implementing algorithms and providing computers with appropriate tools. Hence, several intelligent systems have been proposed. To act in this field, numerous approaches...
As an attractive area of application in the sphere of cultural heritage, in recent years automatic analysis of ancient coins has been attracting an increasing amount of research attention from the computer vision community. Recent work has demonstrated that the existing state of the art performs extremely poorly when applied on images acquired in realistic conditions. One of the reasons behind this...
Production of high quality wheat has a great importance especially in the solution of nutrition problems. It is necessary to make decomposition for specifying the quality. Here, high quality and unclassified wheat recognition are realized. The most distinctive feature between high quality and poor quality wheat is the shape difference. In this study, Bag of Contour Fragments (BCF) was used as a shape...
Background Subtraction is the major important step in many image processing applications which can be applied in much of video surveillances. The major result of this method is accuracy as well as processing time. So we mainly focused on these two challenges. We parallelized the Two Layered CodeBook Model on Graphical Processing Unit (GPU) for increasing the processing speed and the accuracy of the...
Small unmanned aerial vehicles (UAVs) have become increasingly popular in the last several years. This paper explores numerous methods to detect and track small UAVs using computer vision.
Human's everyday environment is an open environment in which objects with new shapes, colors or textures frequently appear. Enabling robots to deal with such environments and to manipulate those objects raises a difficult challenge: how to recognize an object? How to distinguish it from the background? An approach is proposed here to allow the robot to find this segmentation on its own. It relies...
We propose a new variational method for the completion of moving shapes through binary video inpainting that works by smoothly recovering the objects into an inpainting hole. We solve it by a simple dynamic shape analysis algorithm based on threshold dynamics. The model takes into account the optical flow and motion occlusions. The resulting inpainting algorithm diffuses the available information...
Vision is vital to decision making, as humans naturally trust their eyes to enhance situation awareness. Yet the modern age has overwhelmed humans with massive amounts of visual information, which is problematic in time sensitive and mission critical situations, such as emergency management and disaster response. More efficient search and retrieval systems address some of these issues, which is why...
Most objects are designed for certain functionalities. For example, a knife is designed for cutting, and a hammer for pounding. Indeed, functionalities are not related to the objects themselves but to certain object parts, e.g., the blade of a knife affords cutting. A part can have different shapes and can exist in different objects such as a scraper or a peeler, but it carries the same functional...
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