Mapping Wide Row Crops with Video Sequences Acquired from a Tractor Moving at Treatment Speed

This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for image sequence stabilization was needed and consequently designed and developed. The proposed stabilization method uses the centers of some crop rows in the image sequence as features to be tracked, which compensates for the lateral movement (sway) of the camera and leaves the pitch unchanged. A region of interest is selected using the tracked features, and an inverse perspective technique transforms the selected region into a bird’s-eye view that is centered on the image and that enables map generation. The algorithm developed has been tested on several video sequences of different fields recorded at different times and under different lighting conditions, with good initial results. Indeed, lateral displacements of up to 66% of the inter-row spacing were suppressed through the stabilization process, and crop rows in the resulting maps appear straight

Vehicle Trajectory from an Uncalibrated Stereo-Rig with Super-Homography

International audienceWe present in this article an original manner to estimate the trajectory of a vehicle running in urban-like areas. The method consists in extracting then tracking features (points, lines) with an uncalibrated stereo-rig from the road assumed as a plane to compute homographies relative to the camera(s) motions. The purposed method copes with the dense traffic conditions: the free space required (first ten meters in front of the vehicle) is slightly equivalent to the security distance between two vehicles. Experimental issues from real data are presented and discussed

Selective Subtraction: An Extension of Background Subtraction

Background subtraction or scene modeling techniques model the background of the scene using the stationarity property and classify the scene into two classes of foreground and background. In doing so, most moving objects become foreground indiscriminately, except for perhaps some waving tree leaves, water ripples, or a water fountain, which are typically learned as part of the background using a large training set of video data. Traditional techniques exhibit a number of limitations including inability to model partial background or subtract partial foreground, inflexibility of the model being used, need for large training data and computational inefficiency. In this thesis, we present our work to address each of these limitations and propose algorithms in two major areas of research within background subtraction namely single-view and multi-view based techniques. We first propose the use of both spatial and temporal properties to model a dynamic scene and show how Mapping Convergence framework within Support Vector Mapping Convergence (SVMC) can be used to minimize training data. We also introduce a novel concept of background as the objects other than the foreground, which may include moving objects in the scene that cannot be learned from a training set because they occur only irregularly and sporadically, e.g. a walking person. We propose a selective subtraction method as an alternative to standard background subtraction, and show that a reference plane in a scene viewed by two cameras can be used as the decision boundary between foreground and background. In our definition, the foreground may actually occur behind a moving object. Our novel use of projective depth as a decision boundary allows us to extend the traditional definition of background subtraction and propose a much more powerful framework. Furthermore, we show that the reference plane can be selected in a very flexible manner, using for example the actual moving objects in the scene, if needed. We present diverse set of examples to show that: (i) the technique performs better than standard background subtraction techniques without the need for training, camera calibration, disparity map estimation, or special camera configurations; (ii) it is potentially more powerful than standard methods because of its flexibility of making it possible to select in real-time what to filter out as background, regardless of whether the object is moving or not, or whether it is a rare event or a frequent one; (iii) the technique can be used for a variety of situations including when images are captured using stationary cameras or hand-held cameras and for both indoor and outdoor scenes. We provide extensive results to show the effectiveness of the proposed framework in a variety of very challenging environments

Geometric uncertainty models for correspondence problems in digital image processing

Many recent advances in technology rely heavily on the correct interpretation of an enormous amount of visual information. All available sources of visual data (e.g. cameras in surveillance networks, smartphones, game consoles) must be adequately processed to retrieve the most interesting user information. Therefore, computer vision and image processing techniques gain significant interest at the moment, and will do so in the near future. Most commonly applied image processing algorithms require a reliable solution for correspondence problems. The solution involves, first, the localization of corresponding points -visualizing the same 3D point in the observed scene- in the different images of distinct sources, and second, the computation of consistent geometric transformations relating correspondences on scene objects. This PhD presents a theoretical framework for solving correspondence problems with geometric features (such as points and straight lines) representing rigid objects in image sequences of complex scenes with static and dynamic cameras. The research focuses on localization uncertainty due to errors in feature detection and measurement, and its effect on each step in the solution of a correspondence problem. Whereas most other recent methods apply statistical-based models for spatial localization uncertainty, this work considers a novel geometric approach. Localization uncertainty is modeled as a convex polygonal region in the image space. This model can be efficiently propagated throughout the correspondence finding procedure. It allows for an easy extension toward transformation uncertainty models, and to infer confidence measures to verify the reliability of the outcome in the correspondence framework. Our procedure aims at finding reliable consistent transformations in sets of few and ill-localized features, possibly containing a large fraction of false candidate correspondences. The evaluation of the proposed procedure in practical correspondence problems shows that correct consistent correspondence sets are returned in over 95% of the experiments for small sets of 10-40 features contaminated with up to 400% of false positives and 40% of false negatives. The presented techniques prove to be beneficial in typical image processing applications, such as image registration and rigid object tracking

High-speed Video from Asynchronous Camera Array

This paper presents a method for capturing high-speed video using an asynchronous camera array. Our method sequentially fires each sensor in a camera array with a small time offset and assembles captured frames into a high-speed video according to the time stamps. The resulting video, however, suffers from parallax jittering caused by the viewpoint difference among sensors in the camera array. To address this problem, we develop a dedicated novel view synthesis algorithm that transforms the video frames as if they were captured by a single reference sensor. Specifically, for any frame from a non-reference sensor, we find the two temporally neighboring frames captured by the reference sensor. Using these three frames, we render a new frame with the same time stamp as the non-reference frame but from the viewpoint of the reference sensor. Specifically, we segment these frames into super-pixels and then apply local content-preserving warping to warp them to form the new frame. We employ a multi-label Markov Random Field method to blend these warped frames. Our experiments show that our method can produce high-quality and high-speed video of a wide variety of scenes with large parallax, scene dynamics, and camera motion and outperforms several baseline and state-of-the-art approaches.Comment: 10 pages, 82 figures, Published at IEEE WACV 201

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

A trifocal transfer based virtual microscope for robotic manipulation of MEMS components.

International audienceThe paper deals with the problem of imaging at the microscale. The trifocal transfer based novel view synthesis approach is developed and applied to the images from two photon microscopes mounted in a stereoscopic configuration and observing vertically the work scene. The final result is a lateral virtual microscope working up to 6 frames per second with a resolution up to that of the real microscopes. Visual feedback, accurate measurements and control have been performed with, showing it ability to be used for robotic manipulation of MEMS parts. Keywords: Novel view synthesis, trifocal tensor, photon microscope, microassembly, micromanipulation, MEMS