Detection and recognition of contour parts based on shape similarity

Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, it is usually impossible to extract complete object contours or to segment the whole objects. However, in many cases parts of contours can be correctly reconstructed either by performing edge grouping or as parts of boundaries of segmented regions. Therefore, recognition of objects based on their contour parts seems to be a promising as well as a necessary research direction.The main contribution of this paper is a system for detection and recognition of contour parts in digital images. Both detection and recognition are based on shape similarity of contour parts. For each contour part produced by contour grouping, we use shape similarity to retrieve the most similar contour parts in a database of known contour segments. A shape-based classification of the retrieved contour parts performs then a simultaneous detection and recognition.An important step in our approach is the construction of the database of known contour segments. First complete contours of known objects are decomposed into parts using discrete curve evolution. Then, their representation is constructed that is invariant to scaling, rotation, and translation.     

Convexity Rule for Shape Decomposition Based on Discrete Contour Evolution

We concentrate here on decomposition of 2D objects into meaningfulparts of visual form, orvisual parts. It is a simple observation that convex parts of objects determine visual parts. However, the problem is that many significant visual parts are not convex, since a visual part may have concavities. We solve this problem by identifying convex parts at different stages of a proposed contour evolution method in which significant visual parts will become convex object parts at higher stages of the evolution. We obtain a novel rule for decomposition of 2D objects into visual parts, called the hierarchical convexity rule, which states that visual parts are enclosed by maximal convex (with respect to the object) boundary arcs at different stages of the contour evolution. This rule determines not only parts of boundary curves but directly the visual parts of objects. Moreover, the stages of the evolution hierarchy induce a hierarchical structure of the visual parts. The more advanced the stage of contour evolution, the more significant is the shape contribution of the obtained visual parts.     

Perception and Grasping of Object Parts from Active Robot Exploration

Like humans, robots that need semantic perception and accurate estimation of the environment can increase their knowledge through active interaction with objects. This paper proposes a novel method for 3D object modeling for a robot manipulator with an eye-in-hand laser range sensor. Since the robot can only perceive the environment from a limited viewpoint, it actively manipulates a target object and generates a complete model by accumulation and registration of partial views. Three registration algorithms are investigated and compared in experiments performed in cluttered environments with complex rigid objects made of multiple parts. A data structure based on proximity graph, that encodes neighborhood relations in range scans, is also introduced to perform efficient range queries. The proposed method for 3D object modeling is applied to perform task-level manipulation. Indeed, once a complete model is available the object is segmented into its constituent parts and categorized. Object sub-parts that are relevant for the task and that afford a grasping action are identified and selected as candidate regions for grasp planning.     

Extending Superquadrics with Exponent Functions: Modeling and Reconstruction

Superquadrics are a family of parametric shapes which can model a diverse set of objects. They have received significant attention because of their compact representation and robust methods for recovery of 3D models. However, their assumption of intrinsical symmetry fails in modeling numerous real-world examples such as the human body, animals, and other naturally occurring objects. In this paper, we present a novel approach, which is called extended superquadric, to extend superquadric's representation power with exponent functions. An extended superquadric model can be deformed in any direction because it extends the exponents of superquadrics from constants to functions of the latitude and longitude angles in the spherical coordinate system. Thus, extended superquadrics can model more complex shapes than superquadrics. It also maintains many desired properties of superquadrics such as compactness, controllability, and intuitive meaning, which are all advantageous for shape modeling, recognition, and reconstruction. In this paper, besides the use of extended superquadrics for modeling, we also discuss the recovery of extended superquadrics from 3D information (reconstruction). Experiments on both realistic modeling and extended superquadric fitting are presented. Our results are very encouraging and indicate that the use of extended superquadric has potential benefits for the generation of synthetic images for computer graphics and that extended superquadric also is a promising paradigm for shape representation and recovery in computer vision.     

Optimizing Object Decomposition to Reduce Visual Artifacts in 3D Printing

We propose a method for the automatic segmentation of 3D objects into parts which can be individually 3D printed and then reassembled by preserving the visual quality of the final object. Our technique focuses on minimizing the surface affected by supports, decomposing the object into multiple parts whose printing orientation is automatically chosen. The segmentation reduces the visual impact on the fabricated model producing non-planar cuts that adapt to the object shape. This is performed by solving an optimization problem that balances the effects of supports and cuts, while trying to place both in occluded regions of the object surface. To assess the practical impact of the solution, we show a number of segmented, 3D printed and reassembled objects.     

An Occlusion-aware Edge-Based Method for Monocular 3D Object Tracking using Edge Confidence

We propose an edge-based method for 6DOF pose tracking of rigid objects using a monocular RGB camera. One of the critical problem for edge-based methods is to search the object contour points in the image corresponding to the known 3D model points. However, previous methods often produce false object contour points in case of cluttered backgrounds and partial occlusions. In this paper, we propose a novel edge-based 3D objects tracking method to tackle this problem. To search the object contour points, foreground and background clutter points are first filtered out using edge color cue, then object contour points are searched by maximizing their edge confidence which combines edge color and distance cues. Furthermore, the edge confidence is integrated into the edge-based energy function to reduce the influence of false contour points caused by cluttered backgrounds and partial occlusions. We also extend our method to multi-object tracking which can handle mutual occlusions. We compare our method with the recent state-of-art methods on challenging public datasets. Experiments demonstrate that our method improves robustness and accuracy against cluttered backgrounds and partial occlusions.     

Compact object recognition using energy-function-based optimization

Describes a method of recognizing objects whose contours can be represented in smoothly varying polar coordinate form. Both low- and high-level information about the object (contour smoothness and edge sharpness at the low level and contour shape at the high level) are incorporated into a single energy function that defines a 1D, cyclic, Markov random field (1DCMRF). This 1DCMRF is based on a polar coordinate object representation whose center can be initialized at any location within the object. The recognition process is based on energy function minimization, which is implemented by simulated annealing     

Multi-view Occlusion Reasoning for Probabilistic Silhouette-Based Dynamic Scene Reconstruction

In this paper, we present an algorithm to probabilistically estimate object shapes in a 3D dynamic scene using their silhouette information derived from multiple geometrically calibrated video camcorders. The scene is represented by a 3D volume. Every object in the scene is associated with a distinctive label to represent its existence at every voxel location. The label links together automatically-learned view-specific appearance models of the respective object, so as to avoid the photometric calibration of the cameras. Generative probabilistic sensor models can be derived by analyzing the dependencies between the sensor observations and object labels. Bayesian reasoning is then applied to achieve robust reconstruction against real-world environment challenges, such as lighting variations, changing background etc. Our main contribution is to explicitly model the visual occlusion process and show: (1) static objects (such as trees or lamp posts), as parts of the pre-learned background model, can be automatically recovered as a byproduct of the inference; (2) ambiguities due to inter-occlusion between multiple dynamic objects can be alleviated, and the final reconstruction quality is drastically improved. Several indoor and outdoor real-world datasets are evaluated to verify our framework.