3D Object Class Detection in the Wild

Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations. Only recently, there has been a growing interest in revisiting the promise of computer vision from the early days: to precisely delineate the contents of a visual scene, object by object, in 3D. In this paper, we draw from recent advances in object detection and 2D-3D object lifting in order to design an object class detector that is particularly tailored towards 3D object class detection. Our 3D object class detection method consists of several stages gradually enriching the object detection output with object viewpoint, keypoints and 3D shape estimates. Following careful design, in each stage it constantly improves the performance and achieves state-ofthe-art performance in simultaneous 2D bounding box and viewpoint estimation on the challenging Pascal3D+ dataset

Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction

Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo reconstruction, most existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with few textures. To address these limitations, this paper presents a Detail-preserving and Content-aware Variational (DCV) multi-view stereo method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware $\ell_{p}$-minimization algorithm by adaptively estimating the $p$ value and regularization parameters based on the current input. It is much more promising in suppressing noise while preserving sharp features than conventional isotropic mesh smoothing. Experimental results on benchmark datasets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse ring datasets in terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image processin

Representing images of a rotating object with cyclic permutation for view-based pose estimation

In this paper, we propose a novel approach using a cyclic group to model the appearance change in an image sequence of an object rotated about an arbitrary axis (1DOF out-of-plane rotation). In the sequence, an image xj is followed by an image xj+1. We represent the relationship between images by a cyclic group as xj+1 = Gxj , and obtain the matrix G by real block diagonalization. Then, G to the power of a real number is used to represent the image sequence and also for pose estimation. Two estimation methods are proposed and evaluated with real image sequences from the COIL-20, COIL-100, and ALOI datasets, and also compared to the Parametric Eigenspace method. Additionally, we discuss the relationship of the proposed approach to the pixel-wise Discrete Fourier Transform (DFT) and to linear regression, and also outline several extensions

Agro-ecological evaluation of sustainable area for citrus crop production in Ramsar District, Iran

Citrus growing is regarded as an important cash crop in Ramsar, Iran. Ramsar District has a temperate climate zone, while citrus is a sub-tropical fruit. Few studies on citrus crop in terms of negative environmental factors have been carried out by researchers around the world. This study aims to integrate Geographical Information System (GIS) and Analytical Network Process (ANP) model for determination of citrus suitability zones. This study evaluates the agro-ecological suitability, determine potentials and constraints of the region based on effective criteria using ANP model. ANP model was used to determine suitable, moderate and unsuitable areas based on (i) socio-economic, morphometry and hydro-climate factors using 15 layers based on experts’ opinion; (ii) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite image of the year 2003 with 98.45% overall accuracy, and (iii) developed Multiple Linear Regression (MLR) model for citrus prediction. Thereby, weighted overlay of 15 factors was obtained using GIS. In this study, the citrus orchards map of 2003 and the new map of the citrus areas of 2014 namely Citrus State Development Program (CSDP) of the study area were compared. The results of this study demonstrated: (i) suitable areas (free risk areas) based on negative environmental factors and areas which are susceptible to citrus plantation; (ii) high-risk areas which are unsuitable for citrus plantation, and (iii) the high weights derived by ANP model were assigned to altitude, frost and minimum temperature. The MLR model was successfully developed to predict citrus yield in Ramsar District by 10% error. The MLR model would propose optimum citrus crop production areas. As conclusion, the main outcome of this study could help growers and decision makers to enhance the current citrus management activities for current and future citrus planning

Joint Target Tracking and Recognition Using Shape-based Generative Model

Recently a generative model that combines both of identity and view manifolds was proposed for multi-view shape modeling that was originally used for pose estimation and recognition of civilian vehicles from image sequences. In this thesis, we extend this model to both civilian and military vehicles, and examine its effectiveness for real-world automated target tracking and recognition (ATR) applications in both infrared and visible image sequences. A particle filter-based ATR algorithm is introduced where the generative model is used for shape interpolation along both the view and identity manifolds. The ATR algorithm is tested on the newly released SENSIAC (Military Sensing Information Analysis Center) infrared database along with some visible-band image sequences. Overall tracking and recognition performance is evaluated in terms of the accuracy of 3D position/pose estimation and target classification.</School of Electrical & Computer Engineerin

Інтерактивна система розпізнавання 3D об’єктів

Пояснювальна записка дипломного проекту складається з чотирьох розділів, містить 108 сторінок, 22 рисунки, 6 таблиць, 159 джерел, 9 ілюстративних матеріалів – загалом 108 сторінок. Об`єкт дослідження: 3D-об’єкти. Мета магістерської дисертації: підвищення ефективності системи розпізнавання тривимірних об’єктів, на основі аналізу двовимірних зображень. У вступі викладена мета, об’єкт і предмет дослідження, актуальність та новизна дисертації. Перший розділ містить аналіз сучасного стану систем розпізнавання просторових об’єктів. У другому розділі розглянуто та проаналізовано модифікований метод розпізнавання просторових об’єктів. У третьому розділі описано розробку структури та елементів інтерактивної системи розпізнавання тривимірних об’єктів. Четвертий розділ містить дослідження інтерактивної системи розпізнавання 3d-об’єктів.The explanatory note of the diploma project consists of four sections, contains 108 pages, 22 figures, 6 tables, 159 sources, 9 illustrative materials - a total of 108 pages. Object of research: 3D objects. The purpose of the master's dissertation: to increase the efficiency of the system of recognition of three-dimensional objects, based on the analysis of two-dimensional images. The introduction outlines the purpose, object and subject of research, relevance and novelty of the dissertation. The first section provides an analysis of the current state of spatial object recognition systems. The second section discusses and analyzes a modified method of spatial object recognition. The third section describes the development of the structure and elements of an interactive system for recognizing three-dimensional objects. The fourth section contains research on an interactive 3D object recognition system

Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods