scholarly journals Robust Region Detection via Consensus Segmentation of Deformable Shapes

2014 ◽  
Vol 33 (5) ◽  
pp. 97-106 ◽  
Author(s):  
E. Rodolà ◽  
S. Rota Bulò ◽  
D. Cremers
Keyword(s):  
Region Detection ◽  
Deformable Shapes

scholarly journals Scale space clustering evolution for salient region detection on 3D deformable shapes

2017 ◽  
Vol 71 ◽  
pp. 414-427 ◽  
Author(s):  
Xupeng Wang ◽  
Ferdous Sohel ◽  
Mohammed Bennamoun ◽  
Yulan Guo ◽  
Hang Lei
Keyword(s):  
Scale Space ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Deformable Shapes

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

Efficient 3D mesh salient region detection using local homogeneity measure

2018 ◽  
Vol 12 (9) ◽  
pp. 1663-1672 ◽  
Author(s):  
Abdul Rahman El Sayed ◽  
Abdallah El Chakik ◽  
Hassan Alabboud ◽  
Adnan Yassine
Keyword(s):  
Salient Region Detection ◽  
3D Mesh ◽  
Local Homogeneity ◽  
Salient Region ◽  
Region Detection

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

VLDNet: Vision-based lane region detection network for intelligent vehicle system using semantic segmentation

Computing ◽  
2021 ◽  
Author(s):  
Deepak Kumar Dewangan ◽  
Satya Prakash Sahu ◽  
Bandi Sairam ◽  
Aditi Agrawal
Keyword(s):  
Semantic Segmentation ◽  
Intelligent Vehicle ◽  
Region Detection ◽  
Vehicle System

Robust vowel region detection method for multimode speech

2021 ◽  
Author(s):  
Kumud Tripathi ◽  
K. Sreenivasa Rao
Keyword(s):  
Detection Method ◽  
Region Detection

Exploiting Surroundedness and Superpixel cues for salient region detection

2020 ◽  
Vol 79 (15-16) ◽  
pp. 10935-10951
Author(s):  
Yifeng Jiang ◽  
Shan Chang ◽  
Enxing Zheng ◽  
Linna Hu ◽  
Ranran Liu
Keyword(s):  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

scholarly journals Planar region detection and motion recovery

1993 ◽  
Vol 11 (4) ◽  
pp. 229-234 ◽  
Author(s):  
D Sinclair ◽  
A Blake ◽  
S Smith ◽  
C Rothwell
Keyword(s):  
Region Detection ◽  
Motion Recovery ◽  
Planar Region
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