scholarly journals Image-Based Modeling from a High -Resolution Route Panorama

2010 ◽  
Vol 9 (1) ◽  
pp. 27-35
Author(s):  
Ryuji Shibata ◽  
Hajime Nagahara
Keyword(s):  
Belief Propagation ◽  
3D Models ◽  
Single Image ◽  
Multiple Viewpoints ◽  
Modeling Methods ◽  
Image Based Modeling ◽  
Spatio Temporal ◽  
Effective Use ◽  
Scene Depth ◽  
Route Panorama

Image-based modeling methods for generating 3D models from an image sequence have been widely studied. Most of these methods, however, require huge redundant spatio-temporal images to estimate scene depth. This is not an effective use of capturing higher resolution texture. On the other hand, a route panorama, which is a continuous panoramic image along a path, is an efficient way of consolidating information from multiple viewpoints into a single image. A route panorama captured by a line camera also has the advantage of capturing higher resolution easily. In this paper, we propose a method for estimating the depth of an image from a route panorama using color drifts. The proposed method detects color drift by deformable window matching of the color channels. It also uses a hierarchical belief propagation to estimate the depth stably and decrease the computation cost thereof.

scholarly journals TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution

2021 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Fayaz Ali Dharejo ◽  
Farah Deeba ◽  
Yuanchun Zhou ◽  
Bhagwan Das ◽  
Munsif Ali Jatoi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Single Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Image Super Resolution ◽  
Spatio Temporal ◽  
Single Image Super Resolution

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR) . However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 × 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers.

scholarly journals Multiscale Single Image Dehazing Based on Adaptive Wavelet Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Wang ◽  
Wenhui Li ◽  
Qingji Guan ◽  
Miao Qi
Keyword(s):  
State Of The Art ◽  
Local Similarity ◽  
Fusion Method ◽  
Single Image ◽  
Image Dehazing ◽  
Adaptive Wavelet ◽  
Atmospheric Scattering ◽  
Dark Channel ◽  
Computer Vision Applications ◽  
Scene Depth

Removing the haze effects on images or videos is a challenging and meaningful task for image processing and computer vision applications. In this paper, we propose a multiscale fusion method to remove the haze from a single image. Based on the existing dark channel prior and optics theory, two atmospheric veils with different scales are first derived from the hazy image. Then, a novel and adaptive local similarity-based wavelet fusion method is proposed for preserving the significant scene depth property and avoiding blocky artifacts. Finally, the clear haze-free image is restored by solving the atmospheric scattering model. Experimental results demonstrate that the proposed method can yield comparative or even better results than several state-of-the-art methods by subjective and objective evaluations.

Placing Three-Dimensional Models in an Uncalibrated Single Image of an Architectural Scene

2004 ◽  
Vol 13 (6) ◽  
pp. 692-707 ◽  
Author(s):  
Sara Keren ◽  
Ilan Shimshoni ◽  
Ayellet Tal
Keyword(s):  
Augmented Reality ◽  
Three Dimensional ◽  
3D Models ◽  
Automatic Extraction ◽  
Single Image ◽  
Vanishing Points ◽  
Correct Position ◽  
3D Objects ◽  
Position And Orientation ◽  
Three Dimensional Models

This paper discusses the problem of inserting 3D models into a single image. The main focus of the paper is on the accurate recovery of the camera's parameters, so that 3D models can be inserted in the “correct” position and orientation. The paper addresses two issues. The first is an automatic extraction of the principal vanishing points from an image. The second is a theoretical and an experimental analysis of the errors. To test the concept, a system that “plants” virtual 3D objects in the image was implemented. It was tested on many indoor augmented-reality scenes. Our analysis and experiments have shown that errors in the placement of the objects are unnoticeable.

scholarly journals Bio-inspired artificial pheromone system for swarm robotics applications

2020 ◽  
pp. 105971232091893
Author(s):  
Seongin Na ◽  
Yiping Qiu ◽  
Ali E Turgut ◽  
Jiří Ulrich ◽  
Tomáš Krajník ◽  
...  
Keyword(s):  
Swarm Robotics ◽  
State Of The Art ◽  
Rfid Tags ◽  
Robot Swarm ◽  
Pheromonal Communication ◽  
Pheromone System ◽  
Spatio Temporal ◽  
Effective Use ◽  
Robotic Applications ◽  
Artificial Pheromone

Pheromones are chemical substances released into the environment by an individual animal, which elicit stereotyped behaviours widely found across the animal kingdom. Inspired by the effective use of pheromones in social insects, pheromonal communication has been adopted to swarm robotics domain using diverse approaches such as alcohol, RFID tags and light. COSΦ is one of the light-based artificial pheromone systems which can emulate realistic pheromones and environment properties through the system. This article provides a significant improvement to the state-of-the-art by proposing a novel artificial pheromone system that simulates pheromones with environmental effects by adopting a model of spatio-temporal development of pheromone derived from a flow of fluid in nature. Using the proposed system, we investigated the collective behaviour of a robot swarm in a bio-inspired aggregation scenario, where robots aggregated on a circular pheromone cue with different environmental factors, that is, diffusion and pheromone shift. The results demonstrated the feasibility of the proposed pheromone system for use in swarm robotic applications.

Graphical Model MAP Inference with Continuous Label Space in Computer Vision

2018 ◽  
Author(s):  
Oliver Müller
Keyword(s):  
Graphical Model ◽  
Belief Propagation ◽  
Fundamental Problem ◽  
Parameter Tuning ◽  
3D Models ◽  
Target Object ◽  
State Variables ◽  
Slice Sampling ◽  
Global Consistency ◽  
Continuous State

This thesis deals with monocular object tracking from video sequences. The goal is to improve tracking of previously unseen non-rigid objects under severe articulations without relying on prior information such as detailed 3D models and without expensive offline training with manual annotations. The proposed framework tracks highly articulated objects by decomposing the target object into small parts and apply online tracking. Drift, which is a fundamental problem of online trackers, is reduced by incorporating image segmentation cues and by using a novel global consistency prior. Joint tracking and segmentation is formulated as a high-order probabilistic graphical model over continuous state variables. A novel inference method is proposed, called S-PBP, combining slice sampling and particle belief propagation. It is shown that slice sampling leads to fast convergence and does not rely on hyper-parameter tuning as opposed to competing approaches based on Metropolis-Hastings or heuristi...

Using a Four-Dimensional Mesh Model to Represent a Tool Motion Trajectory in Five-Axis Machining

2014 ◽  
Vol 8 (3) ◽  
pp. 437-444 ◽  
Author(s):  
Hirotaka Kameyama ◽  
◽  
Ikuru Otomo ◽  
Masahiko Onosato ◽  
Fumiki Tanaka ◽  
...  
Keyword(s):  
Cutting Tools ◽  
Three Dimensional ◽  
3D Models ◽  
Temporal Changes ◽  
Cutting Process ◽  
Machining Processes ◽  
Mesh Model ◽  
Spatio Temporal ◽  
Five Axis ◽  
Mesh Models

In the field of machining processes, three-Dimensional (3D) models are commonly used to simulate the motions of cutting tools and workpieces. However, it is difficult for 3D models to represent spatio-temporal changes in their shapes continuously and to a high degree of accuracy. The objective of this study is to represent the 5-axis cutting process of workpiece transformation explicitly using a spatio-temporal model, the “four-Dimensional (4D) mesh model.” Every 4D mesh model is defined with bounding tetrahedral cells, and can continuously represent spatio-temporal changes of shape, position and orientation. In this study, the five-axis cutting process is described using accumulated volumes of 4D mesh models. Accumulated volumes are total volumes determined by spaces through which the object has passed. The use of accumulated volumes enables us to record tool-swept volumes and material removal shapes. First, this report introduces a 4D mesh model and the development of a 4D mesh modeling system. Next, a method of representing accumulated volumes as 4D mesh models is proposed. Generated 4D models are observed as 3D models by means of cross-section extraction.

scholarly journals Development of augmented reality mobile application on android OS

2021 ◽  
Author(s):  
А.М. Galeeva ◽  
◽  
О.А. Medvedeva
Keyword(s):  
Augmented Reality ◽  
Mobile Application ◽  
3D Model ◽  
3D Models ◽  
Educational Process ◽  
Use Case ◽  
Modeling Methods ◽  
Lecture Material ◽  
Android Os ◽  
Educational Motivation

The article describes the use of augmented reality technologies in the modern educational process to increase educational motivation, multimedia, and interactivity of the lecture material. The main purpose of the application: educational and cognitive. In the process of performing work, computer graphics, algorithms, and modeling methods were used. Use case: there are special images on the stand that the mobile application recognizes and shows the created 3D models of the “Sun” and “Milky Way”. In addition, while the 3D model is being displayed, a short training audio lecture will be held. To create a 3D model of objects, the Unity program was used in conjunction with the augmented reality platform Vuforia.

scholarly journals Making Decisions in Planning and Risk Management System of Enterprise in Project Management

2021 ◽  
Vol 110 ◽  
pp. 04016
Author(s):  
Anatolii Zhigir
Keyword(s):  
Project Management ◽  
Effective Management ◽  
Risk And Uncertainty ◽  
Investment Projects ◽  
Modeling Methods ◽  
Optimal Mechanism ◽  
Time Period ◽  
Effective Use ◽  
Theoretical Foundations ◽  
Made In

The purpose of this article is to scientifically and methodically substantiate and improve the mechanism for making effective management decisions in the implementation of investment projects of an enterprise within the time period set by the investor, taking into account risk and uncertainty. The article deals with the theoretical foundations of the construction of a model for the implementation of investment projects, taking into account risk and uncertainty as well as the methodology for the management of investment projects, taking into account the technology and organization of work. The work focuses on the improvement of the mechanism for the effective use of investment resources at the enterprise through the introduction of project management methods and the development of the best option for determining an effective solution for the implementation of the project within the established deadline, taking into account risk factors and uncertainty. The conducted analysis made it possible to conclude that the use of network modeling methods to the fullest extent possible corresponds to the task of project management, performance of work in interconnection and dynamics in the investment sphere. An improved system for the management of investment projects leads to the creation of an optimal mechanism for using investments at an enterprise, allows solving the problem of completing a project at a set date, as well as increasing the validity of decisions made in the management of investment projects of enterprises.

scholarly journals L-systems from 3D-imaging of Phenotypes of Arborized Structures

2020 ◽  
Author(s):  
Fons J. Verbeek ◽  
Lu Cao
Keyword(s):  
Graphical Models ◽  
Environmental Conditions ◽  
3D Models ◽  
Theoretical Computer Science ◽  
Experimental Conditions ◽  
L System ◽  
Computational Sciences ◽  
L Systems ◽  
Spatio Temporal ◽  
Lactiferous Duct

Biology is 3D. Therefore, it is important to be able to analyze phenomena in a spatio-temporal manner. Different fields in computational sciences are useful for analysis in biology; i.e. image analysis, pattern recognition and machine learning. To fit an empirical model to a higher abstraction, however, theoretical computer science methods are probed. We explore the construction of empirical 3D graphical models and develop abstractions from these models in L-systems. These systems are provided with a profound formalization in a grammar allowing generalization and exploration of mathematical structures in topologies. The connections between these computational approaches are illustrated by a case study of the development of the lactiferous duct in mice and the phenotypical effects from different environmental conditions we can observe on it. We have constructed a workflow to get 3D models from different experimental conditions and use these models to extract features. Our aim is to construct an abstraction of these 3D models to an L-system from features that we have measured. From our measurements we can make the productions for an L-system. In this manner we can formalize the arborization of the lactiferous duct under different environmental conditions and capture different observations. All considered, this paper illustrates the joint of empirical with theoretical computational sciences and the augmentation of the interpretation of the results. At the same time, it shows a method to analyze complex 3D topologies and produces archetypes for developmental configurations.

scholarly journals Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 557 ◽  
Author(s):  
Mei Chee Leong ◽  
Dilip K. Prasad ◽  
Yong Tsui Lee ◽  
Feng Lin
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
3D Model ◽  
3D Models ◽  
Temporal Features ◽  
Temporal Encoding ◽  
Spatio Temporal ◽  
Efficient Learning ◽  
2D And 3D ◽  
Temporal Learning

This paper introduces a fusion convolutional architecture for efficient learning of spatio-temporal features in video action recognition. Unlike 2D convolutional neural networks (CNNs), 3D CNNs can be applied directly on consecutive frames to extract spatio-temporal features. The aim of this work is to fuse the convolution layers from 2D and 3D CNNs to allow temporal encoding with fewer parameters than 3D CNNs. We adopt transfer learning from pre-trained 2D CNNs for spatial extraction, followed by temporal encoding, before connecting to 3D convolution layers at the top of the architecture. We construct our fusion architecture, semi-CNN, based on three popular models: VGG-16, ResNets and DenseNets, and compare the performance with their corresponding 3D models. Our empirical results evaluated on the action recognition dataset UCF-101 demonstrate that our fusion of 1D, 2D and 3D convolutions outperforms its 3D model of the same depth, with fewer parameters and reduces overfitting. Our semi-CNN architecture achieved an average of 16–30% boost in the top-1 accuracy when evaluated on an input video of 16 frames.

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