scholarly journals A Robust Approach for Blur and Sharp Regions’ Detection Using Multisequential Deviated Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Awais Khan ◽  
Ali Javed ◽  
Aun Irtaza ◽  
Muhammad Tariq Mahmood
Keyword(s):  
Multiple Scales ◽  
Computationally Efficient ◽  
Direct Integral ◽  
Low Contrast ◽  
Blur Detection ◽  
Adaptive Thresholds ◽  
Background Clutter ◽  
Novel Method ◽  
Computer Vision Applications ◽  
Sharpness Metric

Blur detection (BD) is an important and challenging task in digital imaging and computer vision applications. Accurate segmentation of homogenous smooth and blur regions, low-contrast focal regions, missing patches, and background clutter, without having any prior information about the blur, are the fundamental challenges of BD. Previous work on BD has emphasized much effort on designing local sharpness metric maps from the images. However, the smooth/blurred regions having the same patterns as sharp regions make them problematic. This paper presents a robust novel method to extract the local metric map for blurred and nonblurred regions based on multisequential deviated patterns (MSDPs). Unlike the preceding, MSDP extracts the local sharpness metric map on the images at multiple scales using different adaptive thresholds to overcome the problems of smooth/blur regions and missing patches. By using the integral values of the image along with image masking and Otsu thresholding, highly accurate segmented regions of the images are acquired. We argue/hypothesize that the local sharpness map extraction by using direct integral information of the image is highly affected by the threshold selected for distinction between the regions, whereas MSDP feature extraction overcomes the limitations substantially by using automatic threshold computation over multiple scales of the images. Moreover, the proposed method extracts the relatively accurate sharp regions from the high-dense blur and noisy images. Experiments are conducted on two commonly used SHI and DUT datasets for blur and sharp region classifications. The results indicate the effectiveness of the proposed method in terms of sharp segmented regions. Experimental results of qualitative and quantitative comparisons of the proposed method with ten comparative methods demonstrate the superiority of our method. Moreover, the proposed method is also computationally efficient over state-of-the-art methods.

A Novel Method for Low-Contrast and High-Noise Vessel Segmentation and Location in Venipuncture

2017 ◽  
Vol 36 (11) ◽  
pp. 2216-2227 ◽  
Author(s):  
Yuhe Li ◽  
Zhendong Qiao ◽  
Shaoqin Zhang ◽  
Zhenhuan Wu ◽  
Xueqin Mao ◽  
...  
Keyword(s):  
Vessel Segmentation ◽  
High Noise ◽  
Low Contrast ◽  
Novel Method

3-D CARDIAC MODELING: A COMPUTER VISION APPROACH IN SPATIO-TEMPORAL IMAGE SEQUENCES

1996 ◽  
Vol 04 (02) ◽  
pp. 181-197 ◽  
Author(s):  
J.L. COATRIEUX ◽  
C. TOUMOULIN ◽  
R. COLLOREC
Keyword(s):  
Computer Vision ◽  
Three Dimensional ◽  
Object Identification ◽  
Image Sequences ◽  
Low Contrast ◽  
Dimensional Reconstruction ◽  
Contrast Structures ◽  
Computer Vision Applications ◽  
Spatio Temporal ◽  
Active Research

A very active research was conducted on motion analysis. Most of the concepts, methods and assumptions are well established and lead to additional improvements in computer vision applications. Even in medicine where we have to deal with noisy data, low contrast structures and deformable objects, they bring new cues at all the processing stages. This paper emphasizes the specificities of this area and also the potential difficulties. A compilation of results is given aimed at the quantification of heart kinetics in Digital Subtraction Angiography (DSA). They illustrate the benefits of cooperative schemes such as motion based segmentation, moving object identification, three-dimensional reconstruction and interpretation.

scholarly journals Spectral decomposition method of dialog state tracking via collective matrix factorization

2016 ◽  
Vol 7 (3) ◽  
pp. 34-46
Author(s):  
Julien Perez
Keyword(s):  
Matrix Factorization ◽  
The State ◽  
Computationally Efficient ◽  
Reward Function ◽  
Dialog Management ◽  
Dialog System ◽  
Dependent Variables ◽  
Novel Method ◽  
State Tracking ◽  
Dialog State Tracking

The task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural language understanding modules. The state tracking task is primarily meant to support a dialog policy. From a probabilistic perspective, this is achieved by maintaining a posterior distribution over hidden dialog states composed of a set of context dependent variables. Once a dialog policy is learned, it strives to select an optimal dialog act given the estimated dialog state and a defined reward function. This paper introduces a novel method of dialog state tracking based on a bilinear algebric decomposition model that provides an efficient inference schema through collective matrix factorization. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker gives encouraging results compared to the state-of-the-art trackers that participated in this standard benchmark. Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches.

scholarly journals Programmable Kirigami: Cutting and Folding in Science, Technology and Architecture

2019 ◽  
Author(s):  
Jingyang Liu ◽  
Grace Chuang ◽  
Hun Chun Sang ◽  
Jenny E. Sabin
Keyword(s):  
Multiple Scales ◽  
Three Dimensional ◽  
Computational Design ◽  
Element Analysis ◽  
Human Scale ◽  
Wide Range ◽  
2D Pattern ◽  
Novel Method ◽  
Doubly Curved ◽  
Forward Process

Abstract This paper investigates the potential of kirigami-folding with the addition of strategically placed cuts at multiple scales through both computational design and physical prototyping. The study develops a novel method and workflow for generating two-dimensional (2D) kirigami patterns developed from doubly curved three-dimensional (3D) surfaces (Inverse process). Corresponding simulations of the kirigami folding motion from 2D pattern to 3D goal shape are presented (Forward process). The workflow is based on a reciprocal feedback loop including computational design, finite element analysis, dynamic simulation and physical prototyping. Extended from previous research on kirigami geometry, this paper incorporates material properties into the folding process and successfully develops active kirigami models from the DNA scale to human scale. The results presented in this paper provide an attractive method for kirigami design and fabrication with a wide range of scales and applications.

scholarly journals A 3D trabecular bone homogenization technique

2020 ◽  
Vol 22 (3) ◽  
Author(s):  
Marco C. Marques ◽  
Jorge Belinha ◽  
António F. Oliveira ◽  
Maria Cristinha M. Cespedes ◽  
Renato M. Natal Jorge
Keyword(s):  
Mechanical Properties ◽  
Mechanical Behavior ◽  
Trabecular Bone ◽  
Multiple Scales ◽  
Bone Adaptation ◽  
Computationally Efficient ◽  
Micro Scale ◽  
Special Cases ◽  
Homogeneous Models ◽  
Time Required

Purpose: Bone is a hierarchical material that can be characterized from the microscale to macroscale. Multiscale models make it possible to study bone remodeling, inducing bone adaptation by using information of bone multiple scales. This work proposes a computationally efficient homogenization methodology useful for multiscale analysis. This technique is capable to define the homogenized microscale mechanical properties of the trabecular bone highly heterogeneous medium. Methods: In this work, a morphology - based fabric tensor and a set of anisotropic phenomenological laws for bone tissue was used, in order to define the bone micro-scale mechanical properties. To validate the developed methodology, several examples were performed in order to analyze its numerical behavior. Thus, trabecular bone and fabricated benchmarks patches (representing special cases of trabecular bone morphologies) were analyzed under compression. Results: The results show that the developed technique is robust and capable to provide a consistent material homogenization, indicating that the homogeneous models were capable to accurately reproduce the micro-scale patch mechanical behavior. Conclusions: The developed method has shown to be robust, computationally less demanding and enabling the authors to obtain close results when comparing the heterogeneous models with equivalent homogenized models. Therefore, it is capable to accurately predict the micro-scale patch mechanical behavior in a fraction of the time required by classic homogenization techniques.

Reliable Part Guided Multiple Level Attention Learning for Person Re-Identification

2021 ◽  
pp. 2150246
Author(s):  
Yanbing Geng ◽  
Yongjian Lian ◽  
Shunmin Yang ◽  
Mingliang Zhou ◽  
Jingchao Cao
Keyword(s):  
Multiple Scales ◽  
State Of The Art ◽  
Feature Representation ◽  
Late Fusion ◽  
Fine Grained ◽  
Background Clutter ◽  
Multiple Levels ◽  
Body Components ◽  
Point Detection ◽  
Multiple Task

Person Re-ID is challenged by background clutter, body misalignment and part missing. In this paper, we propose a reliable part-based multiple levels attention deep network to learn multiple scales salience representation. In particular, person alignment and key point detection are sequentially carried out to locate three relative stable body components, then fused attention (FA) mode is designed to capture the fine-grained salient features from effective spatial of valuable channels of each part, regional attention mode is succeeded to weight the importance of different parts for highlighting the representative parts while suppressing the valueless ones. A late fusion-based multiple-task loss is finally adopted to further optimize the valuable feature representation. Experimental results demonstrate that the proposed method achieves state-of-the-art performances on three challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03.

A novel method of fluid identification on low contrast reservoirs

2021 ◽  
Author(s):  
S. Wang ◽  
R. Xie ◽  
C. Xu ◽  
J. Liu ◽  
H. Wei
Keyword(s):  
Low Contrast ◽  
Fluid Identification ◽  
Novel Method

scholarly journals Compressive Background Modeling for Foreground Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Wang ◽  
Qian Lu ◽  
Dianhong Wang ◽  
Wei Liu
Keyword(s):  
Background Subtraction ◽  
Background Modeling ◽  
Video Sequences ◽  
Computationally Efficient ◽  
Subtraction Method ◽  
Foreground Extraction ◽  
Image Frame ◽  
Computer Vision Applications ◽  
Background Image ◽  
Classical Background

Robust and efficient foreground extraction is a crucial topic in many computer vision applications. In this paper, we propose an accurate and computationally efficient background subtraction method. The key idea is to reduce the data dimensionality of image frame based on compressive sensing and in the meanwhile apply sparse representation to build the current background by a set of preceding background images. According to greedy iterative optimization, the background image and background subtracted image can be recovered by using a few compressive measurements. The proposed method is validated through multiple challenging video sequences. Experimental results demonstrate the fact that the performance of our approach is comparable to those of existing classical background subtraction techniques.

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