scholarly journals Mathematical Principles of Object 3D Reconstruction by Shape-from-Focus Methods

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2253
Author(s):  
Dalibor Martišek ◽  
Karel Mikulášek
Keyword(s):  
3D Reconstruction ◽  
Contour Line ◽  
3D Object ◽  
3D Objects ◽  
Output Device ◽  
Finite Resolution ◽  
3D Profile ◽  
Shape From Focus ◽  
Wave Properties

Shape-from-Focus (SFF) methods have been developed for about twenty years. They able to obtain the shape of 3D objects from a series of partially focused images. The plane to which the microscope or camera is focused intersects the 3D object in a contour line. Due to wave properties of light and due to finite resolution of the output device, the image can be considered as sharp not only on this contour line, but also in a certain interval of height—the zone of sharpness. SSFs are able to identify these focused parts to compose a fully focused 2D image and to reconstruct a 3D profile of the surface to be observed.

Semantic Enabled 3D Object Retrieval

2010 ◽  
Vol 159 ◽  
pp. 128-131
Author(s):  
Jiang Zhou ◽  
Xin Yu Ma
Keyword(s):  
Domain Knowledge ◽  
Regions Of Interest ◽  
3D Object Retrieval ◽  
3D Shape Retrieval ◽  
Object Retrieval ◽  
3D Object ◽  
3D Objects ◽  
Retrieval Systems ◽  
3D Shapes ◽  
Spatial Semantics

In the case of traditional 3D shape retrieval systems, the objects retrieved are based mainly on the computation of low-level features that are used to detect so-called regions-of-interest. This paper focuses on obtaining the retrieved objects in a machine understandable and intelligent manner. We explore the different kinds of semantic descriptions for retrieval of 3D shapes. Based on ontology technology, we decompose a 3D objects into meaningful parts semi-automatically. Each part can be regarded as a 3D object, and further be semantically annotated according to ontology vocabulary from the Chinese cultural relics. Three kinds of semantic models such as description semantics of domain knowledge, spatial semantics and scenario semantics, are presented for describing semantic annotations from different viewpoints. These annotated semantics can accurately grasp complete semantic descriptions of 3D shapes.

Efficient 3D Object Segmentation from Densely Sampled Light Fields with Applications to 3D Reconstruction

2016 ◽  
Vol 35 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Kaan Yücer ◽  
Alexander Sorkine-Hornung ◽  
Oliver Wang ◽  
Olga Sorkine-Hornung
Keyword(s):  
3D Reconstruction ◽  
Object Segmentation ◽  
Light Fields ◽  
3D Object ◽  
3D Object Segmentation

scholarly journals Penerapan Teknologi Augmented Reality untuk Pengenalan Komponen Jaringan dan Cara Kerja TCP/IP berbasis Android

2016 ◽  
Vol 1 (2) ◽  
pp. 8-18
Author(s):  
Lena Magdalena ◽  
Kusnadi Kusnadi ◽  
Muhammad Kahfi
Keyword(s):  
Augmented Reality ◽  
Data Collection ◽  
Virtual Worlds ◽  
Real World ◽  
Unified Modeling Language ◽  
Reference Object ◽  
3D Object ◽  
The Real ◽  
Analysis And Design ◽  
3D Objects

Augmented reality is a term for combining real-world environment with virtual worlds. Augmented reality is becoming very popular today because besides interesting, also can be displayed in realtime. In general, augmented reality adds a digital object in reality with reference to specific objects in the real world, for example by using a marker. Therefore, in this thesis aims to create an application that displays a 3D object that contains information about the network components and the workings of TCP / IP by making use of the concept of augmented reality marker technique. A marker is a reference object in the real world as a marker so that the information can be displayed. The marker is made to function to call the 3D objects appear on the screen smartphone that uses the technique of introduction of basic colors of red, green and blue. The method used consists of several stages of research, namely data collection, analysis and system design and system implementation. Data collection method used is literature. Methods of analysis and design of systems using the Unified Modeling Language (UML). The programming language used for software development are C # (C-Sharp) with Unity applications and software SketcUp to create 3D objects as well as Android SDK to convert the file into the form of android. This application is successfully created by displaying a 3D object that can make users excited to get to know the physical network components and requires only an internet connection to download the initial marker.

FINITE AUTOMATA AND DIGITAL IMAGES

2000 ◽  
Vol 14 (04) ◽  
pp. 501-524 ◽  
Author(s):  
S. V. RAMASUBRAMANIAN ◽  
KAMALA KRITHIVASAN
Keyword(s):  
Normal Forms ◽  
Digital Images ◽  
Finite Automata ◽  
Finite State Automata ◽  
3D Object ◽  
3D Objects ◽  
Finite State

In this paper, we initially consider representation of 2D black–white images and 3D objects using finite state automata. We describe transformation of scaling on the 2D image by an operation on the FSA. We also give constructions for getting the projections of a 3D object on to coordinate planes and for reconstructing the 3D object from its projections. We define minimization of nondeterministic FSAs and give an O(e2) (e is the number of edges in the FSA) algorithm for minimization of NFAs. Later, we define a WFA and describe various properties of WFA. We define four normal forms of WFA and show how a WFA can be normalized into any of these forms. We show the equivalence of WFAs with ∊ edges and ∊-free WFAs. Then, we define minimization of WFAs and present an algorithm to minimize a WFA.

Dynamic categorization of 3D objects for mobile service robots

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Reyes Rios-Cabrera ◽  
Ismael Lopez-Juarez ◽  
Alejandro Maldonado-Ramirez ◽  
Arturo Alvarez-Hernandez ◽  
Alan de Jesus Maldonado-Ramirez
Keyword(s):  
Object Detection ◽  
Service Robots ◽  
Mobile Manipulator ◽  
Mobile Service ◽  
Content Type ◽  
3D Object ◽  
3D Objects ◽  
Mobile Service Robots ◽  
Context Free ◽  
Context Free Grammars

Purpose This paper aims to present an object detection methodology to categorize 3D object models in an efficient manner. The authors propose a dynamically generated hierarchical architecture to compute very fast objects’ 3D pose for mobile service robots to grasp them. Design/methodology/approach The methodology used in this study is based on a dynamic pyramid search and fast template representation, metadata and context-free grammars. In the experiments, the authors use an omnidirectional KUKA mobile manipulator equipped with an RGBD camera, to localize objects requested by humans. The proposed architecture is based on efficient object detection and visual servoing. In the experiments, the robot successfully finds 3D poses. The present proposal is not restricted to specific robots or objects and can grow as much as needed. Findings The authors present the dynamic categorization using context-free grammars and 3D object detection, and through several experiments, the authors perform a proof of concept. The authors obtained promising results, showing that their methods can scale to more complex scenes and they can be used in future applications in real-world scenarios where mobile robot are needed in areas such as service robots or industry in general. Research limitations/implications The experiments were carried out using a mobile KUKA youBot. Scalability and more robust algorithms will improve the present proposal. In the first stage, the authors carried out an experimental validation. Practical implications The current proposal describes a scalable architecture, where more agents can be added or reprogrammed to handle more complicated tasks. Originality/value The main contribution of this study resides in the dynamic categorization scheme for fast detection of 3D objects, and the issues and experiments carried out to test the viability of the methods. Usually, state-of-the-art treats categories as rigid and make static queries to datasets. In the present approach, there are no fixed categories and they are created and combined on the fly to speed up detection.

A Study of 3D Objects Pose Estimation for CAT System

2010 ◽  
Vol 36 ◽  
pp. 485-493
Author(s):  
Abu Bakar Elmi ◽  
Tetsuo Miyake ◽  
Shinya Naito ◽  
Takashi Imamura ◽  
Zhong Zhang
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Feature Points ◽  
Block Diagonalization ◽  
Production Lines ◽  
Method Performance ◽  
3D Object ◽  
3D Objects ◽  
Model Based ◽  
Object Feature

In production line, pose estimation of 3D object of products is needed beforehand. In order to perform shape measurement of the objects corresponding to speed of the mass production lines before the contact measurement is done, the information of object pose and matching is become required. In this paper, we conducted a study on the performance of model based and view based pose estimation method using image sequence of a rotating 3D object. In model based, we used object feature points from center of gravity and in view based method, the subspace calculation by block diagonalization of matrix represents a transformation an image to another image. We have confirmed the both method performance and it’s considered useful for pose estimation.

scholarly journals MAKING OBJECT LEARNING AND RECOGNITION AN ACTIVE PROCESS

2008 ◽  
Vol 05 (02) ◽  
pp. 267-286 ◽  
Author(s):  
ALEŠ UDE ◽  
DAMIR OMRČEN ◽  
GORDON CHENG
Keyword(s):  
Object Recognition ◽  
Prior Knowledge ◽  
Experimental Results ◽  
3D Object Recognition ◽  
Object Representations ◽  
Active Process ◽  
Bottom Up ◽  
3D Object ◽  
3D Objects ◽  
Object Learning

The exploration and learning of new objects is an essential capability of a cognitive robot. In this paper we focus on making use of the robot's manipulation abilities to learn complete object representations suitable for 3D object recognition. Taking control of the object allows the robot to focus on relevant parts of the images, thus bypassing potential pitfalls of purely bottom-up attention and segmentation. The main contribution of the paper consists in integrated visuomotor processes that allow the robot to learn object representations by manipulation without having any prior knowledge about the objects. Our experimental results show that the acquired data is of sufficient quality to train a classifier that can recognize 3D objects independently of the viewpoint.

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