scholarly journals Vector Isolated Minimum Distance Filtering for Image De-Noising in Digital Color Images

2019 ◽  
Vol 8 (4) ◽  
pp. 2401-2405
Keyword(s):  
Minimum Distance ◽  
State Of The Art ◽  
Color Images ◽  
Median Filtering ◽  
Color Component ◽  
Directional Filtering ◽  
Crucial Component ◽  
Vector Median ◽  
Digital Color Images ◽  
Color Components

Image de-noising forms a crucial component of digital image processing. The state-of-the-art vector median filtering based image de-noising approaches like the median filtering, the vector median filtering and the basic vector directional filtering and their extensions process the vector pixels jointly in the red, green and blue components. Consequently any smoothing applied therein is leveraged on all the color components equally. In this paper we propose that processing the vectors in isolation, that is, each color component taken separately, and then smoothed by minimising the aggregate distance between the pixels in each color component will lead to more efficient de-noising of noisy images. We demonstrate the superiority of the proposed method compared against vector filtering approaches using several images and test measures.

scholarly journals Covid-19 detection via deep neural network and occlusion sensitivity maps

2021 ◽  
Author(s):  
Noor Ahmad ◽  
Muhammad Aminu ◽  
Mohd Halim Mohd Noor
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Color Images ◽  
Fine Tuning ◽  
Training Dataset ◽  
Learning Approaches ◽  
Learning Models ◽  
Sensitivity Maps

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

scholarly journals A MEDIAN-BASED DEPTHMAP FUSION STRATEGY FOR THE GENERATION OF ORIENTED POINTS

2016 ◽  
Vol III-3 ◽  
pp. 115-122
Author(s):  
M. Rothermel ◽  
N. Haala ◽  
D. Fritsch
Keyword(s):  
Surface Reconstruction ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Depth Map ◽  
Median Filtering ◽  
Tree Structures ◽  
Fusion Strategy ◽  
Depth Maps ◽  
Disparity Maps ◽  
Nearest Neighbor Queries

Due to good scalability, systems for image-based dense surface reconstruction often employ stereo or multi-baseline stereo methods. These types of algorithms represent the scene by a set of depth or disparity maps which eventually have to be fused to extract a consistent, non-redundant surface representation. Generally the single depth observations across the maps possess variances in quality. Within the fusion process not only preservation of precision and detail but also density and robustness with respect to outliers are desirable. Being prune to outliers, in this article we propose a local median-based algorithm for the fusion of depth maps eventually representing the scene as a set of oriented points. Paying respect to scalability, points induced by each of the available depth maps are streamed to cubic tiles which then can be filtered in parallel. Arguing that the triangulation uncertainty is larger in the direction of image rays we define these rays as the main filter direction. Within an additional strategy we define the surface normals as the principle direction for median filtering/integration. The presented approach is straight-forward to implement since employing standard oc- and kd-tree structures enhanced by nearest neighbor queries optimized for cylindrical neighborhoods. We show that the presented method in combination with the MVS (Rothermel et al., 2012) produces surfaces comparable to the results of the Middlebury MVS benchmark and favorably compares to an state-of-the-art algorithm employing the Fountain dataset (Strecha et al., 2008). Moreover, we demonstrate its capability of depth map fusion for city scale reconstructions derived from large frame airborne imagery.

scholarly journals Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2480
Author(s):  
Igor Rodriguez ◽  
Unai Zabala ◽  
Pedro Marín-Reyes ◽  
Ekaitz Jauregi ◽  
Javier Lorenzo-Navarro ◽  
...  
Keyword(s):  
Minimum Distance ◽  
State Of The Art ◽  
Complex Problem ◽  
Communication Strategy ◽  
Tour Guides ◽  
Personal Services ◽  
San Sebastian ◽  
Heterogeneous Robots ◽  
Robot Communication

GidaBot is an application designed to setup and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, the robots can only service a single floor, and thus, a guiding task may require collaboration among several robots. The designed system makes use of a robust inter-robot communication strategy to share goals and paths during the guiding tasks. Such tours work as personal services carried out by one or more robots. In this paper, a face re-identification/verification module based on state-of-the-art techniques is developed, evaluated offline, and integrated into GidaBot’s real daily activities, to avoid new visitors interfering with those attended. It is a complex problem because, as users are casual visitors, no long-term information is stored, and consequently, faces are unknown in the training step. Initially, re-identification and verification are evaluated offline considering different face detectors and computing distances in a face embedding representation. To fulfil the goal online, several face detectors are fused in parallel to avoid face alignment bias produced by face detectors under certain circumstances, and the decision is made based on a minimum distance criterion. This fused approach outperforms any individual method and highly improves the real system’s reliability, as the tests carried out using real robots at the Faculty of Informatics in San Sebastian show.

Regularization of optic flow estimates by means of weighted vector median filtering

1999 ◽  
Vol 8 (10) ◽  
pp. 1462-1467 ◽  
Author(s):  
L. Alparone ◽  
M. Barni ◽  
F. Bartolini ◽  
R. Caldelli
Keyword(s):  
Optic Flow ◽  
Median Filtering ◽  
Vector Median
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