Foreground Object Extraction Based on Interactive Color Saliency Map

2014 ◽  
Vol 14 (01n02) ◽  
pp. 1450007
Author(s):  
Hironori Takimoto ◽  
Hitoshi Yamauchi ◽  
Mitsuyoshi Kishihara ◽  
Kensuke Okubo
Keyword(s):  
Computer Vision ◽  
Extraction Method ◽  
Practical Importance ◽  
Saliency Map ◽  
Color Images ◽  
Object Extraction ◽  
Foreground Object ◽  
Foreground Extraction ◽  
Practical Applications ◽  
The Cost

Efficient interactive foreground extraction from color images is of great practical importance in computer vision. In recent years, an approach based on optimization using graph-cuts has been widely used. However, an interactive foreground extraction approach, which is intuitive and efficient for users, is required for practical applications. In this paper, we propose a novel foreground object extraction method based on interactive color saliency map. In our method, the user provides only some foreground pixels as the initial reference. To achieve extraction of multi target objects, likelihoods of the foreground and background are defined by a color saliency map based on the provided reference pixels. These likelihoods are added to the cost of the proposed graph. Finally, image segmentation is performed by optimizing the proposed graph using the graph-cut algorithm.

MSRM Based Object Extraction Method for Image Sequences

2014 ◽  
Vol 1049-1050 ◽  
pp. 1675-1680
Author(s):  
Wen Ting Yu ◽  
Jing Ling Wang ◽  
Long Ye
Keyword(s):  
Computer Vision ◽  
Extraction Method ◽  
Image Sequence ◽  
Graph Cut ◽  
Image Sequences ◽  
Object Extraction ◽  
Foreground Object ◽  
Region Merging ◽  
Still Images ◽  
Computer Vision Applications

Object extraction, which aims to accurately separate a foreground object from its background in still images, plays an important role in many computer vision applications. An interactive object extraction method based on MSRM (maximal similarity based region merging) is presented in this paper. We can manually mark the target and background only one time in any one image of the image sequence to obtain the object extraction result of the image sequence. Compared to currently used method based on graph cut algorithm that manually marks the target and background on all the images one by one to get the object extraction result, our method is more efficient and the result is as precious as with other methods.

Enhanced Object Extraction Method Based on Multi-channel Saliency Map

2016 ◽  
Vol 53 (2) ◽  
pp. 53-61
Author(s):  
Young-jin Choi ◽  
Run Cui ◽  
Kwang-Rag Kim ◽  
Hyoung Joong Kim
Keyword(s):  
Extraction Method ◽  
Saliency Map ◽  
Object Extraction

scholarly journals An effective graph and depth layer based RGB-D image foreground object extraction method

2017 ◽  
Vol 3 (4) ◽  
pp. 387-393 ◽  
Author(s):  
Zhiguang Xiao ◽  
Hui Chen ◽  
Changhe Tu ◽  
Reinhard Klette
Keyword(s):  
Extraction Method ◽  
Object Extraction ◽  
Foreground Object ◽  
Depth Layer

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

scholarly journals Ring-Forming Polymerization toward Perfluorocyclobutyl and Ortho-Diynylarene-Derived Materials: From Synthesis to Practical Applications

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1486
Author(s):  
Eugene B. Caldona ◽  
Ernesto I. Borrego ◽  
Ketki E. Shelar ◽  
Karl M. Mukeba ◽  
Dennis W. Smith
Keyword(s):  
Chemical Resistance ◽  
Structural Features ◽  
Review Article ◽  
Aryl Ether ◽  
Aromatic Rings ◽  
Practical Applications ◽  
Repeat Units ◽  
Wide Range ◽  
Synthetic Routes ◽  
The Cost

Many desirable characteristics of polymers arise from the method of polymerization and structural features of their repeat units, which typically are responsible for the polymer’s performance at the cost of processability. While linear alternatives are popular, polymers composed of cyclic repeat units across their backbones have generally been shown to exhibit higher optical transparency, lower water absorption, and higher glass transition temperatures. These specifically include polymers built with either substituted alicyclic structures or aromatic rings, or both. In this review article, we highlight two useful ring-forming polymer groups, perfluorocyclobutyl (PFCB) aryl ether polymers and ortho-diynylarene- (ODA) based thermosets, both demonstrating outstanding thermal stability, chemical resistance, mechanical integrity, and improved processability. Different synthetic routes (with emphasis on ring-forming polymerization) and properties for these polymers are discussed, followed by their relevant applications in a wide range of aspects.

scholarly journals A Survey on Contrastive Self-Supervised Learning

Technologies ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 2
Author(s):  
Ashish Jaiswal ◽  
Ashwin Ramesh Babu ◽  
Mohammad Zaki Zadeh ◽  
Debapriya Banerjee ◽  
Fillia Makedon
Keyword(s):  
Computer Vision ◽  
Supervised Learning ◽  
Language Processing ◽  
Large Scale ◽  
Performance Comparison ◽  
Extensive Review ◽  
Future Directions ◽  
Dominant Component ◽  
Supervised Methods ◽  
The Cost

Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.

scholarly journals Enhanced Thermoelectric Performance of n-Type Bi2Se3 Nanosheets through Sn Doping

Nanomaterials ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1827
Author(s):  
Mengyao Li ◽  
Yu Zhang ◽  
Ting Zhang ◽  
Yong Zuo ◽  
Ke Xiao ◽  
...  
Keyword(s):  
Figure Of Merit ◽  
Cost Effective ◽  
Low Grade ◽  
Hot Press ◽  
Processing Technologies ◽  
Sn Doping ◽  
Practical Applications ◽  
Alternative Materials ◽  
The Cost ◽  
Low Grade Heat

The cost-effective conversion of low-grade heat into electricity using thermoelectric devices requires developing alternative materials and material processing technologies able to reduce the currently high device manufacturing costs. In this direction, thermoelectric materials that do not rely on rare or toxic elements such as tellurium or lead need to be produced using high-throughput technologies not involving high temperatures and long processes. Bi2Se3 is an obvious possible Te-free alternative to Bi2Te3 for ambient temperature thermoelectric applications, but its performance is still low for practical applications, and additional efforts toward finding proper dopants are required. Here, we report a scalable method to produce Bi2Se3 nanosheets at low synthesis temperatures. We studied the influence of different dopants on the thermoelectric properties of this material. Among the elements tested, we demonstrated that Sn doping resulted in the best performance. Sn incorporation resulted in a significant improvement to the Bi2Se3 Seebeck coefficient and a reduction in the thermal conductivity in the direction of the hot-press axis, resulting in an overall 60% improvement in the thermoelectric figure of merit of Bi2Se3.

Optimal stationary strategies in leavable Markov decision processes

1990 ◽  
Vol 27 (01) ◽  
pp. 134-145
Author(s):  
Matthias Fassbender
Keyword(s):  
Transition Probabilities ◽  
Practical Applications ◽  
Stationary Strategies ◽  
Countable State ◽  
Markov Decision ◽  
Bias Optimality ◽  
The Mean ◽  
Optimal Stationary Strategies ◽  
The Cost ◽  
Reward Criterion

This paper establishes the existence of an optimal stationary strategy in a leavable Markov decision process with countable state space and undiscounted total reward criterion. Besides assumptions of boundedness and continuity, an assumption is imposed on the model which demands the continuity of the mean recurrence times on a subset of the stationary strategies, the so-called ‘good strategies'. For practical applications it is important that this assumption is implied by an assumption about the cost structure and the transition probabilities. In the last part we point out that our results in general cannot be deduced from related works on bias-optimality by Dekker and Hordijk, Wijngaard or Mann.

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