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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...
Clonal Selection Algorithm (CLONALG) and Particle Swarm Optimization (PSO) have been applied for wide spectrum of computer vision problems. However, their applications to 3D object recognition receive only little attention. In this paper, CLONALG and PSO algorithms for recognition of 3D object are discussed. Instead of using any predefined model to extract the geometrical information, the 3D object...
Object recognition is a critical next step for autonomous robots, but a solution to the problem has remained elusive. Prior 3D-sensor-based work largely classifies individual point cloud segments or uses class-specific trackers. In this paper, we take the approach of classifying the tracks of all visible objects. Our new track classification method, based on a mathematically principled method of combining...
This paper presents a novel solution to the autonomy of a Portable Robotic Device (PRD) for the visually impaired. The proposed method meets the PRD's requirement of providing 3D navigational information with a small-sized device. The proposed approach is to employ a 3D imaging sensor-the SwissRanger SR4000-for both pose estimation and perception. The SR4000 produces both intensity and range images...
In this paper, we propose a dynamic gesture spotting and recognition algorithm using our stereovision system. The 3D trajectories of hand gestures are first reconstructed by a stereovision-based motion capture platform. Hand gestures can then be segmented from the trajectory in real time by using proposed gesture spotting algorithm. Discrete cosine transforms coefficients, complex index and gesture...
In this paper we present an approach to object segmentation and recognition that combines depth and color cues. We fuse information from color images with depth from a Time-of-Flight (ToF) camera to improve recognition performance under scale and viewpoint changes. Firstly, we use depth and local surface orientation extracted from the ToF image to normalize color and depth image features with regard...
This article presents a novel object recognition module which is adapted to the needs in mobile service robotics. It uses information provided from a stereo camera system as pre-processing part of SIFT or SURF. The principle idea is to filter irrelevant information by selecting regions of interest in the disparity map from stereo images and to use the geometrical constraints of the stereo camera system...
In this paper we present an algorithm that allows a human to naturally and easily teach a mobile robot how to recognize objects in its environment. The human selects the object by pointing at it using a laser pointer. The robot recognizes the laser reflections with its cameras and uses this data to generate an initial 2D segmentation of the object. The 3D position of SURF feature points are extracted...
In this paper, we present a novel method based on clustering for identifying 3D line from point clouds, called “self-organizing fuzzy k-means algorithm”. The algorithm automatically finds the optimal number of cluster and self organizes the clusters based on inter/intra-cluster distances and cluster's performance evaluation. The self-organizing fuzzy k-means is applied in 3D line identification from...
This paper describes a 3D shape reconstruction method using vision sensors targeted at domestic robotics applications. We propose a new method to fuse stereo disparity map and Shape from “Silhouette” (SFS). What we mean by silhouette in this paper is different from the existing silhouette definition. The silhouette here is not obtained from back projecting the object contour to the image plane but...
Objects grasping and manipulation is a major task for housold and industrial robots. Sensing the objects with 3D laser scanners generating point clouds is a common approach for gathering object information. The abstraction from the raw pointclouds to high level representations is a necessity in the real world domain. This paper describes a method for the approximation of objects from scenes and an...
Feature-based methods have found increasing use in many applications such as object recognition, 3D reconstruction and mosaicing. In this paper, we focus on the problem of matching such features. While a histogram-of-gradients type methods such as SIFT, GLOH and Shape Context are currently popular, several papers have suggested using orders of pixels rather than raw intensities and shown improved...
Object segmentation has an important role in the field of computer vision for semantic information inference. Many applications such as 3DTV archive systems, 3D/2D model fitting, object recognition and shape retrieval are strongly dependent to the performance of the segmentation process. In this paper we present a new algorithm for object localization and segmentation based on the spatial information...
We present an algorithm to model 3D workspace and to understand test scene for navigation or human computer interaction in network-based mobile robot. This was done by line-based modelling and recognition algorithm. Line-based recognition using 3D lines has been tried by many researchers however its reliability still needs improvement due to ambiguity of 3D line feature information from original images...
Three-dimensional (3D) urban models, come with huge data size, mainly consisting of the details of geometry and texture of the objects, and simplification of both is often needed for efficient streaming, rendering and visualization. A large number of objects in 3D urban models are buildings and the image files representing their texture contain information of the walls, streets, doors, and windows...
To simplify the matching and recognition of 3D objects, we propose to decompose a complex 3D shape into simpler primitive parts. Our partitioning of objects relies on their topological Reeb graphs. Taking advantage of the properties of Morse theory, we detect the critical points of the global geodesic function. These points define the levels at which the segmentation happens. To preserve the geometry...
In this paper we present a new approach for labeling 3D points with different geometric surface primitives using a novel feature descriptor - the Fast Point Feature Histograms, and discriminative graphical models. To build informative and robust 3D feature point representations, our descriptors encode the underlying surface geometry around a point p using multi-value histograms. This highly dimensional...
In this paper the vision architecture, named ROVIS, of the robotic system FRIEND is presented. The main concept of the ROVIS is the inclusion of feedback structures between different components of the vision system as well as between the vision and other modules of the robotic system to achieve high robustness against external influences of the individual system units as well as of the system as whole...
3D object reconstruction from images involves two important parts: object identification and object modeling. Human beings are very adept at automatically identifying different objects in a scene due to the extensive training they receive over their lifetimes. Similarly, machines need to be trained to perform this task. At present, automated 3D object identification process from aerial video imagery...
Potential Well Space Embedding (PWSE) has been shown to be an effective global method to recognize segmented objects in range data. Here Local PWSE is proposed as an extension of PWSE. LPWSE features are generated by iterating ICP to the local minima of a multiscale registration model at each point. The locations of the local minima are then used to generate feature vectors, which can be matched against...
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