A Fuzzy Connectedness Segmentation of Image Sequences Based on 3D Seed Points Selection

2008 ◽  
Author(s):  
Chen Yanda ◽  
Bao Susu ◽  
Peng Fengping
Keyword(s):  
Image Sequences ◽  
Fuzzy Connectedness ◽  
Seed Points

High-Precision 3D Modeling Method Based on Terrestrial Image Sequences for Alpine-Gorge Area

2012 ◽  
Vol 182-183 ◽  
pp. 1270-1275 ◽  
Author(s):  
Bo Su ◽  
Hao Li ◽  
Ya Qin Wang ◽  
Biao Yang
Keyword(s):  
Stereo Matching ◽  
Three Dimensional ◽  
Digital Camera ◽  
Image Sequences ◽  
Three Dimensional Modeling ◽  
Modeling Process ◽  
Dimensional Modeling ◽  
Left Image ◽  
The Right ◽  
Seed Points

The traditional measurement methods cannot adapt to the arduous topography of alpine-gorge area. Aiming at the topographical features of alpine-gorge area, we will introduce a general terrestrial method of multi-baseline photogrammetry basing on digital camera here, and then the paper mainly studies the metrization method of common digital camera and matching method of the digital image sequences of alpine-gorge area. Through the metrization of common digital camera, the efficiency of terrain data collection will increase in the alpine-gorge area, and the requirements of operations on the image control and algorithm will reduce. The combination of seed points and multiple constraints in multi-baseline stereo matching will help to solve many problems, such as shading, severe distortion between the left image and the right one, and the inconformity of scale. The modeling process stated above is quite fast and highly precise, and the three-dimensional modeling experiments show that the relative accuracy can reach from 1 / 8000 to 1 / 12000.

Region Growing Based Optical Fiber Panel Shadow Detection

2011 ◽  
Vol 403-408 ◽  
pp. 1914-1917
Author(s):  
Ming Ji Wang ◽  
Yun Wu ◽  
Dong Hua Fu
Keyword(s):  
Optical Fiber ◽  
Region Growing ◽  
Shadow Detection ◽  
Redundant Information ◽  
Fuzzy Connectedness ◽  
Segmentation Quality ◽  
Evaluation Operator ◽  
Seed Points ◽  
Detection Evaluation

Automatic defect detection of light image is very important in optical fiber panel (OFP) research now. In order to achieve detection of shadow defect automatically, proposed a new region growing algorithm. Using gray characteristics and fuzzy connectedness of image, realized automatic seeds selection by first selecting seed window then selecting seed points. Realized region growing algorithm with adaptive threshold by using maximum between-class variance method (OTSU). Proposed one OFP shadow detection evaluation operator, evaluation results showed that algorithm proposed in this paper was more accurate positioning of the shadow, achieved significant reduction in redundant information, and improved segmentation quality of the image effectively.

scholarly journals A Driver’s Visual Attention Prediction Using Optical Flow

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3722
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee
Keyword(s):  
Visual Attention ◽  
Optical Flow ◽  
Prediction Models ◽  
Apparent Movement ◽  
Image Sequences ◽  
Motion Information ◽  
Accuracy Improvement ◽  
Flow Maps ◽  
Current State ◽  
Motion Features

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.

scholarly journals Frequency and average gray-level information for thermal ablation status in ultrasound B-Mode sequences

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Jens Ziegle ◽  
Alfredo Illanes ◽  
Axel Boese ◽  
Michael Friebe
Keyword(s):  
Thermal Ablation ◽  
Ex Vivo ◽  
Cell Damage ◽  
Potential Temperature ◽  
Image Sequences ◽  
Target Tissue ◽  
Gray Level ◽  
Porcine Liver ◽  
Time Frequency ◽  
Average Gray Level

AbstractDuring thermal ablation in a target tissue the information about temperature is crucial for decision making of successful therapy. An observable temporal and spatial temperature propagation would give a visual feedback of irreversible cell damage of the target tissue. Potential temperature features in ultrasound (US) B-Mode image sequences during radiofrequency (RF) ablation in ex-vivo porcine liver were found and analysed. These features could help to detect the transition between reversible and irreversible damage of the ablated target tissue. Experimental RF ablations of ex-vivo porcine liver were imaged with US B-Mode imaging and image sequences were recorded. Temperature was simultaneously measured within the liver tissue around a bipolar RF needle electrode. In the B-Mode images, regions of interest (ROIs) around the centre of the measurement spots were analysed in post-processing using average gray-level (AVGL) compared against temperature. The pole of maximum energy level in the time-frequency domain of the AVGL changes was investigated in relation to the measured temperatures. Frequency shifts of the pole were observed which could be related to transitions between the states of tissue damage.

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