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The problem of mobile robot navigation is a broad topic and it covers a large spectrum of different methods and technologies. In this paper we will present the new concept for local navigation. The concept is based on object identification technique by means of origin fragment identification algorithm (ICPF). The main idea, theory and practical examples of object identification using ICPF are presented...
The robust perception of robots is strongly needed to handle various objects skillfully. In this paper, we propose a novel approach to recognize objects and estimate their 6-DOF pose using 3D feature descriptors, called Geometric and Photometric Local Feature (GPLF). The proposed descriptors use both the geometric and photometric information of 3D point clouds from RGB-D camera and integrate those...
In this work we use hand configuration and contact points during in-hand object exploration to identify the manipulated objects. Different contact points associated to an object shape can be represented in a latent space and lie on a lower dimensional non-linear manifold in the contact points space which is suitable for modelling and recognition. Associating and learning hand configurations to specific...
This paper represents 3D object recognition, which is an extension of the common feature point-based object recognition, based on novel descriptors utilizing local angles (for shape), gradient orientations (for texture of corners), and color information. First, the proposed algorithm extracts complementary feature points by randomly sampling the positions of the object edges. Then, it generates the...
A method to identify the flexural rigidity and the initial curvature of a deformable belt object from its static images is proposed. Automation of assembly of belt objects such as flexible flat cables hasn't progressed because of the difficulty in estimating their deformation. Furthermore, they have the variation which impedes successful manipulation with a uniform trajectory. In this paper, we consider...
Shape distribution is a common 3D shape descriptor that has been widely used for 3D object retrieval. In this study, we evaluate the feasibility of shape distribution for object recognition based on Kinect-like depth image obtained from RGB-D object dataset; consisting of several household instances from 51 classes; and each instance consists of depth images from different rotational angle view. The...
A novel feature extraction method is proposed in this paper. Dislike contour-based or region-based approaches, an object is first converted to a closed curve by extended central projection (ECP). The derived curve not only keeps the affine transform information, but also is very robust to noise. Then whitening transform is performed to the curve such that the affine transformation is simplified to...
A novel approach to object recognition based on shape matching of repeatable segments is presented. The motivation is to increase the recognition system robustness in handling problems such as noise corruption at a local level, featureless surfaces, and variations in 3D data sources. Inspired by the detection of repeatable interest points, interest segments were extracted through region growing and...
A main challenge associated with 3-dimentional fringe pattern profilometry (3D-FPP) systems is the unwrapping of phase maps resulted from complex object surface shapes with both robustness and speed guaranteed. In this paper we propose a new quality-guided phase unwrapping algorithm. In contrast to the conventional quality-guided methods, we classify pixels on wrapped phase map into two types by detecting...
This paper proposes a novel approach to recognize object categories in point clouds. By quantizing 3D SURF local descriptors, computed on partial 3D shapes extracted from the point clouds, a vocabulary of 3D visual words is generated. Using this codebook, we build a Bag-of-Words representation in 3D, which is used in conjunction with a SVM classification machinery. We also introduce the 3D Spatial...
In this work we seek to move away from the traditional paradigm for 2D object recognition whereby objects are identified in the image as 2D bounding boxes. We focus instead on: i) detecting objects; ii) identifying their 3D poses; iii) characterizing the geometrical and topological properties of the objects in terms of their aspect configurations in 3D. We call such characterization an object's aspect...
Image patches can be factorized into ‘shapelets’ that describe segmentation patterns called structural elements (stels), and palettes that describe how to paint the shapelets. We introduce local palettes for patches, global palettes for entire images and universal palettes for image collections. Using a learned shapelet library, patches from a test image can be analyzed using a variational technique...
Object recognition is one of the most classic problem in computer vision where several techniques in classification have been proposed. In this work, we investigate a new promising improvement on a classification problem using multiple feature types. We assume that different features are suitable for different objects and the occurrence of particular objects usually relate to each other via particular...
In this paper, we discuss an object recognition and tracking system that utilizes the depth information from a low-cost depth sensor. Conventional object recognition methods that utilize RGB cameras are unable to accurately identify objects in the real world since they do not take into consideration the shape and three-dimensional characteristics of the object. Another major factor determining the...
Reliable object recognition is a mandatory prerequisite for service robots that operate in everyday environments. Typical approaches run a single classifier for the purpose of object recognition. However, no single algorithm proved to classify across all types of objects. We propose an approach that combines the recognition result of several methods working on different features. This reduces the...
Ancylis sativa Liu is a kind of insects to cause severe damage to jujube trees. This paper studies the recognition of Ancylis sativa Liu from catchers via using computer vision technique. The image of a Ancylis sativa catcher generally shows not only Ancylis sativa but also other objects. Consequently it leads to that the image has a complicated background and an uncertain interesting region. Ancylis...
In recent years, robotic gripper is widely used for different tasks in various fields. Grippers operate with industrial robots for handling and manipulation of objects. Grippers also operate with hard automation for assembling; micro assembling, machining and packaging. This paper aims to develop vision-based sensor of smart gripper which integrates together with its robotic arm for industrial applications...
We present a nonparametric and efficient method for shape localization that improves on the traditional sub-window search in capturing the fine geometry of an object from a small number of feature points. Our method implies that the discrete set of features capture more appearance and shape information than is commonly exploited. We use the a-complex by Edelsbrunner et al. to build a filtration of...
This paper addresses the problem of Car Make and Model recognition as an example of within-category object class recognition. In this problem, it is assumed that the general category of the object is given and the goal is to recognize the object class within the same category. As compared to general object recognition, this problem is more challenging because the variations among classes within the...
In this study, we address the issue on multilevel object recognition. The multilevel object recognition is object recognition in various levels, that is, simultaneous recognition of its instance, category, material, etc. At each level, many recognition methods have been proposed in the literature. Therefore it is straightforward to design a multilevel object recognition system using conventional methods...
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