On Rendering Synthetic Images for Training an Object Detector

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances. A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available

Texture and color descriptors as a tool for context-aware patch-based image inpainting

State-of-the-art results in image inpainting are obtained with patch-based methods that fill in the missing region patch-by-patch by searching for similar patches in the known region and placing them at corresponding locations. In this paper, we introduce a context-aware patch-based inpainting method, where the context is represented by texture and color features of a block surrounding the patch to be filled in. We use this context to recognize other blocks in the image that have similar features and then we constrain the search for similar patches within them. Such an approach guides the search process towards less ambiguous matching candidates, while also speeding up the algorithm. Experimental results demonstrate benefits of the proposed context-aware approach, both in terms of inpainting quality and computation time

Vision-based detection of aircrafts and UAVs

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects increases. Avoiding such collisions would be a relatively easy task, if all the aircrafts in the neighboring airspace could communicate with each other and share their location information. However, it is often the case that either location information is unavailable (e.g. flying in GPS-denied environments) or communication is not possible (e.g. different communication channels or non-cooperative flight scenario). To ensure flight safety in this kind of situations drones need a way to autonomously detect other objects that are intruding the neighboring airspace. Visual-based collision avoidance is of particular interest as cameras generally consume less power and are more lightweight than active sensor alternatives such as radars and lasers. We have therefore developed a set of increasingly sophisticated algorithms to provide drones with a visual collision avoidance capability. First, we present a novel method for detecting flying objects such as drones and planes that occupy a small part of the camera field of view, possibly move in front of complex backgrounds, and are filmed by a moving camera. In order to be solved this problem requires combining motion and appearance information, as neither of the two alone is capable of providing reliable enough detections. We therefore propose a machine learning technique that operates on spatio- temporal cubes of image intensities where individual patches are aligned using an object-centric regression-based motion stabilization algorithm. Second, in order to reduce the need to collect a large training dataset and to manual annotate it, we introduce a way to generate realistic synthetic images. Given only a small set of real examples and a coarse 3D model of the object, synthetic data can be generated in arbitrary quantities and further used to supplement real examples for training a detector. The key ingredient of our method is that the synthetically generated images need to be as close as possible to the real ones not in terms of image quality, but according to the features, used by a machine learning algorithm. Third, though the aforementioned approach yields a substantial increase in performance when using Adaboost and DPM detectors, it does not generalize well to Convolutional Neural Networks, which have become the state-of-the-art. This happens because, as we add more and more synthetic data, the CNNs begin to overfit to the synthetic images at the expense of the real ones. We therefore propose a novel deep domain adaptation technique that allows efficiently combining real and synthetic images without overfitting to either of the two. While most of the adaptation techniques aim at learning features that are invariant to the possible difference of the images, coming from different sources (real and synthetic). Unlike those methods, we suggest modeling this difference with a special two-stream architecture. We evaluate our approach on three different datasets and show its effectiveness for various classification and regression tasks