A Contour Co-tracking Method for Image Pairs

2021 ◽  
pp. 1-1
Author(s):  
Bin Wang ◽  
Dapeng Tao ◽  
Rui Dong ◽  
Yuanyan Tang ◽  
Xinbo Gao
Keyword(s):  
Tracking Method ◽  
Image Pairs

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

Three-dimensional Reconstruction of Microscopic Image Based on Multi-ST Algorithm

2020 ◽  
Vol 64 (2) ◽  
pp. 20506-1-20506-7
Author(s):  
Min Zhu ◽  
Rongfu Zhang ◽  
Pei Ma ◽  
Xuedian Zhang ◽  
Qi Guo
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
Scale Model ◽  
Microscopic Image ◽  
Multi Scale ◽  
Gaussian Pyramid ◽  
Cost Aggregation ◽  
Dimensional Reconstruction ◽  
Image Pairs ◽  
Accurate Matching

Abstract Three-dimensional (3D) reconstruction is extensively used in microscopic applications. Reducing excessive error points and achieving accurate matching of weak texture regions have been the classical challenges for 3D microscopic vision. A Multi-ST algorithm was proposed to improve matching accuracy. The process is performed in two main stages: scaled microscopic images and regularized cost aggregation. First, microscopic image pairs with different scales were extracted according to the Gaussian pyramid criterion. Second, a novel cost aggregation approach based on the regularized multi-scale model was implemented into all scales to obtain the final cost. To evaluate the performances of the proposed Multi-ST algorithm and compare different algorithms, seven groups of images from the Middlebury dataset and four groups of experimental images obtained by a binocular microscopic system were analyzed. Disparity maps and reconstruction maps generated by the proposed approach contained more information and fewer outliers or artifacts. Furthermore, 3D reconstruction of the plug gauges using the Multi-ST algorithm showed that the error was less than 0.025 mm.

Can Mouse-tracking Reveal Attribute Processing Speeds in Dietary Self-control? Commentary on Sullivan et al. (2015) and Lim et al. (2018) with A Simulation Study

2018 ◽  
Author(s):  
Chao Zhang ◽  
Martijn Willemsen ◽  
Daniel Lakens
Keyword(s):  
Simulation Study ◽  
Processing Speed ◽  
Empirical Data ◽  
Self Control ◽  
Alternative Mechanism ◽  
Mouse Tracking ◽  
Tracking Method ◽  
Cognitive Mechanism ◽  
Speed Difference ◽  
Original Hypothesis

In this commentary, we re-examine the use of a mouse-tracking method for revealing attribute processing speed difference in dietary self-control (Sullivan et al. 2015; Lim et al., 2018). Through re-analyses of Sullivan et al. (2015)’s data and a simulation study, it can be shown that the attribute-angle correlations in the empirical data, which were used to estimate processing speeds, are attributed primarily to their common correlations with choice. The simulation study further suggests that when we account for the choice-mediated attribute-angle correlations, the data patterns used for supporting the original hypothesis can be produced by implementing a plausible alternative mechanism unrelated to processing speeds. The mouse-tracking method therefore fails to provide clear evidence for processing speed difference as a cognitive mechanism of self-control. Researchers should be cautious when using the mouse-tracking method to estimate attribute processing speeds.

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