A conceptual design tool: Sketch and fuzzy logic based system

A real time sketch and fuzzy logic based prototype system for conceptual design has been developed. This system comprises four phases. In the first one, the system accepts the input of on-line free-hand sketches, and segments them into meaningful parts by using fuzzy knowledge to detect corners and inflection points on the sketched curves. The fuzzy knowledge is applied to capture user’s drawing intention in terms of sketching position, direction, speed and acceleration. During the second phase, each segmented sub-part (curve) can be classified and identified as one of the following 2D primitives: straight lines, circles, circular arcs, ellipses, elliptical arcs or B-spline curves. Then, 2D topology information (connectivity, unitary constraints and pairwise constraints) is extracted dynamically from the identified 2D primitives. From the extracted information, a more accurate 2D geometry can be built up by a 2D geometric constraint solver. The 2D topology and geometry information is then employed to further interpretation of a 3D geometry. The system can not only accept sketched input, but also users’ interactive input of 2D and 3D primitives. This makes it friendly and easier to use, in comparison with ‘sketched input only’, or ‘interactive input only’ systems. Finally, examples are given to illustrate the system

A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds

This paper proposes a segmentation-free, automatic and efficient procedure to detect general geometric quadric forms in point clouds, where clutter and occlusions are inevitable. Our everyday world is dominated by man-made objects which are designed using 3D primitives (such as planes, cones, spheres, cylinders, etc.). These objects are also omnipresent in industrial environments. This gives rise to the possibility of abstracting 3D scenes through primitives, thereby positions these geometric forms as an integral part of perception and high level 3D scene understanding. As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure. At the center of our approach lies a closed form 3D quadric fit, operating in both primal & dual spaces and requiring as low as 4 oriented-points. Around this fit, we design a novel, local null-space voting strategy to reduce the 4-point case to 3. Voting is coupled with the famous RANSAC and makes our algorithm orders of magnitude faster than its conventional counterparts. This is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes. Results on synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201

Localization from semantic observations via the matrix permanent

Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization

Joint A Contrario Ellipse and Line Detection.

This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TPAMI.2016.2558150We propose a line segment and elliptical arc detector that produces a reduced number of false detections on various types of images without any parameter tuning. For a given region of pixels in a grey-scale image, the detector decides whether a line segment or an elliptical arc is present (model validation). If both interpretations are possible for the same region, the detector chooses the one that best explains the data (model selection ). We describe a statistical criterion based on the a contrario theory, which serves for both validation and model selection. The experimental results highlight the performance of the proposed approach compared to state-of-the-art detectors, when applied on synthetic and real images.This work was partially funded by the Qualcomm postdoctoral program at École Polytechnique Palaiseau, a Google Faculty Research Award, the Marie Curie grant CIG-334283-HRGP, a CNRS chaire d’excellence and chaire Jean Marjoulet, and EPSRC grant EP/L010917/1

Point cloud segmentation using hierarchical tree for architectural models

Recent developments in the 3D scanning technologies have made the generation of highly accurate 3D point clouds relatively easy but the segmentation of these point clouds remains a challenging area. A number of techniques have set precedent of either planar or primitive based segmentation in literature. In this work, we present a novel and an effective primitive based point cloud segmentation algorithm. The primary focus, i.e. the main technical contribution of our method is a hierarchical tree which iteratively divides the point cloud into segments. This tree uses an exclusive energy function and a 3D convolutional neural network, HollowNets to classify the segments. We test the efficacy of our proposed approach using both real and synthetic data obtaining an accuracy greater than 90% for domes and minarets.Comment: 9 pages. 10 figures. Submitted in EuroGraphics 201

Analysis of Three-Dimensional Protein Images

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.Comment: See http://www.jair.org/ for any accompanying file

Analyzing helicopter evasive maneuver effectiveness against rocket-propelled grenades

It has long been acknowledged that military helicopters are vulnerable to ground-launched threats, in particular, the RPG-7 rocket-propelled grenade. Current helicopter threat mitigation strategies rely on a combination of operational tactics and selectively placed armor plating, which can help to mitigate but not entirely remove the threat. However, in recent years, a number of active protection systems designed to protect land-based vehicles from rocket and missile fire have been developed. These systems all use a sensor suite to detect, track, and predict the threat trajectory, which is then employed in the computation of an intercept trajectory for a defensive kill mechanism. Although a complete active protection system in its current form is unsuitable for helicopters, in this paper, it is assumed that the active protection system’s track and threat trajectory prediction subsystem could be used offline as a tool to develop tactics and techniques to counter the threat from rocket-propelled grenade attacks. It is further proposed that such a maneuver can be found by solving a pursuit–evasion differential game. Because the first stage in solving this problem is developing the capability to evaluate the game, nonlinear dynamic and spatial models for a helicopter, RPG-7 round, and gunner, and evasion strategies were developed and integrated into a new simulation engine. Analysis of the results from representative vignettes demonstrates that the simulation yields the value of the engagement pursuit–evasion game. It is also shown that, in the majority of cases, survivability can be significantly improved by performing an appropriate evasive maneuver. Consequently, this simulation may be used as an important tool for both designing and evaluating evasive tactics and is the first step in designing a maneuver-based active protection system, leading to improved rotorcraft survivability