Manipulating Attributes of Natural Scenes via Hallucination

In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network which can hallucinate images of a scene as if they were taken at a different season (e.g. during winter), weather condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrate the effectiveness of our approach against the competing methods.Comment: Accepted for publication in ACM Transactions on Graphic

Improved Image Partitioning for Compression and Representation using the Lab Color Space in the LAR Image Codec

International audienceThe LAR codec is an advanced image compression method relying on a quadtree partitioning of the image. The partitioning strongly impacts the LAR codec efficiency and enables both compression and representation efficiency. In order to increase the perceptual representation abilities without penalizing the compression efficiency we introduce and evaluate two partitioning criteria working in the Lab color space. These criteria are confronted to the original criterion and their compression and robustness performances are analyzed

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Advances in Stereo Vision

Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints

Probabilistic framework for image understanding applications using Bayesian Networks

Machine learning algorithms have been successfully utilized in various systems/devices. They have the ability to improve the usability/quality of such systems in terms of intelligent user interface, fast performance, and more importantly, high accuracy. In this research, machine learning techniques are used in the field of image understanding, which is a common research area between image analysis and computer vision, to involve higher processing level of a target image to make sense of the scene captured in it. A general probabilistic framework for image understanding where topics associated with (i) collection of images to generate a comprehensive and valid database, (ii) generation of an unbiased ground-truth for the aforesaid database, (iii) selection of classification features and elimination of the redundant ones, and (iv) usage of such information to test a new sample set, are discussed. Two research projects have been developed as examples of the general image understanding framework; identification of region(s) of interest, and image segmentation evaluation. These techniques, in addition to others, are combined in an object-oriented rendering system for printing applications. The discussion included in this doctoral dissertation explores the means for developing such a system from an image understanding/ processing aspect. It is worth noticing that this work does not aim to develop a printing system. It is only proposed to add some essential features for current printing pipelines to achieve better visual quality while printing images/photos. Hence, we assume that image regions have been successfully extracted from the printed document. These images are used as input to the proposed object-oriented rendering algorithm where methodologies for color image segmentation, region-of-interest identification and semantic features extraction are employed. Probabilistic approaches based on Bayesian statistics have been utilized to develop the proposed image understanding techniques