DeWAFF: A novel image abstraction approach to improve the performance of a cell tracking system
Tallennettuna:
| Tekijät: | , , , , , |
|---|---|
| Aineistotyyppi: | comunicación de congreso |
| Julkaisupäivä: | 2015 |
| Kuvaus: | This paper presents a new image abstraction approach, aiming to improve typical image related pattern recognition tasks such as segmentation, tracking, and classification. The proposed image abstraction framework performs image denoising and homogeneous region simplification, along with border and region enhancement. The proposed framework consists in a novel generalized approach of common weighted averaging denoising algorithms mixed with Unsharp Masking (USM) border enhancement techniques, to avoid typical USM artifacts as ringing. Results of the different configurations within the image abstraction framework for a cell tracking application are presented. |
| Maa: | Kérwá |
| Organisaatio: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| Kieli: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/103905 |
| Linkit: | https://hdl.handle.net/10669/103905 https://doi.org/10.1109/IWOBI.2015.7160148 |
| Sanahaku: | Tracking System Cell Tracking Cell Tracking System Homogeneous Regions Pattern Recognition Tasks Window Size Additive Noise Spatial Domain Weight Function Median Filter Energy Region Image Sharpness Original Proposal Histogram Equalization Boolean Function Impulsive Noise Bilateral Filter Ring Artifact Adaptive Histogram Equalization Edge Enhancement Guided Filter Edge Preservation Handful Of Papers Intensity Domain Impulsive Features Contrast Agent Binary Image Noise Conditions Input Image Defocus |