A Recursive and Model-Constrained Region Splitting Algorithm for Cell Clump Decomposition

2010 ◽  
Author(s):  
Wei Xiong ◽  
Sim-Heng Ong ◽  
Joo-Hwee Lim
Keyword(s):  
Splitting Algorithm ◽  
Cell Clump ◽  
Region Splitting

scholarly journals Multi-Focus Image Fusion and Depth Map Estimation Based on Iterative Region Splitting Techniques

2019 ◽  
Vol 5 (9) ◽  
pp. 73 ◽  
Author(s):  
Wen-Nung Lie ◽  
Chia-Che Ho
Keyword(s):  
Depth Map ◽  
Depth Image ◽  
Splitting Algorithm ◽  
Image Stack ◽  
Spatial Propagation ◽  
Focus Image ◽  
Block Based ◽  
Winner Take All ◽  
Region Splitting ◽  
Imaging Plane

In this paper, a multi-focus image stack captured by varying positions of the imaging plane is processed to synthesize an all-in-focus (AIF) image and estimate its corresponding depth map. Compared with traditional methods (e.g., pixel- and block-based techniques), our focus-based measures are calculated based on irregularly shaped regions that have been refined or split in an iterative manner, to adapt to different image contents. An initial all-focus image is first computed, which is then segmented to get a region map. Spatial-focal property for each region is then analyzed to determine whether a region should be iteratively split into sub-regions. After iterative splitting, the final region map is used to perform regionally best focusing, based on the Winner-take-all (WTA) strategy, i.e., choosing the best focused pixels from image stack. The depth image can be easily converted from the resulting label image, where the label for each pixel represents the image index from which the pixel with the best focus is chosen. Regions whose focus profiles are not confident in getting a winner of the best focus will resort to spatial propagation from neighboring confident regions. Our experiments show that the adaptive region-splitting algorithm outperforms other state-of-the-art methods or commercial software in synthesis quality (in terms of a well-known Q metric), depth maps (in terms of subjective quality), and processing speed (with a gain of 17.81~40.43%).

On the Study of Cognitive Bidirectional Relaying with Asymmetric Traffic Demands

Frequenz ◽  
2015 ◽  
Vol 69 (5-6) ◽  
Author(s):  
Xiaodong Ji
Keyword(s):  
Ergodic Capacity ◽  
Base Station ◽  
Transmission Protocol ◽  
Splitting Algorithm ◽  
Performance Simulation ◽  
Information Theoretic ◽  
Primary Users ◽  
Operating Region ◽  
Bidirectional Relaying ◽  
Outage Probabilities

AbstractIn this paper, we consider a cognitive radio network scenario, where two primary users want to exchange information with each other and meanwhile, one secondary node wishes to send messages to a cognitive base station. To meet the target quality of service (QoS) of the primary users and raise the communication opportunity of the secondary nodes, a cognitive bidirectional relaying (BDR) scheme is examined. First, system outage probabilities of conventional direct transmission and BDR schemes are presented. Next, a new system parameter called operating region is defined and calculated, which indicates in which position a secondary node can be a cognitive relay to assist the primary users. Then, a cognitive BDR scheme is proposed, giving a transmission protocol along with a time-slot splitting algorithm between the primary and secondary transmissions. Information-theoretic metric of ergodic capacity is studied for the cognitive BDR scheme to evaluate its performance. Simulation results show that with the proposed scheme, the target QoS of the primary users can be guaranteed, while increasing the communication opportunity for the secondary nodes.

A multi-level splitting algorithm based on differential evolution

2018 ◽  
Vol 09 (02) ◽  
pp. 1850021
Author(s):  
Liping Wang ◽  
Wenhui Fan
Keyword(s):  
Differential Evolution ◽  
Rare Event ◽  
Sampling Strategy ◽  
Splitting Algorithm ◽  
Algorithm Efficiency ◽  
Event Simulation ◽  
Sample Paths ◽  
Level Splitting ◽  
Key Factor ◽  
Multi Level

Multi-level splitting algorithm is a famous rare event simulation (RES) method which reaches rare set through splitting samples during simulation. Since choosing sample paths is a key factor of the method, this paper embeds differential evolution in multi-level splitting mechanism to improve the sampling strategy and precision, so as to improve the algorithm efficiency. Examples of rare event probability estimation demonstrate that the new proposed algorithm performs well in convergence rate and precision for a set of benchmark cases.

Picture segmentation using a recursive region splitting method

1978 ◽  
Vol 8 (3) ◽  
pp. 313-333 ◽  
Author(s):  
Ron Ohlander ◽  
Keith Price ◽  
D. Raj Reddy
Keyword(s):  
Splitting Method ◽  
Region Splitting
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