scholarly journals Framework for optimal region of interest–based quality assessment in wireless imaging

2010 ◽  
Vol 19 (1) ◽  
pp. 011005 ◽  
Author(s):  
Ulrich Engelke
Keyword(s):  
Quality Assessment ◽  
Region Of Interest ◽  
Optimal Region

On-Line Task Decomposition for Collaborative Surveillance of Marine Environment by a Team of Unmanned Surface Vehicles

2017 ◽  
Author(s):  
Shaurya Shriyam ◽  
Brual C. Shah ◽  
Satyandra K. Gupta
Keyword(s):  
Maximum Velocity ◽  
Region Of Interest ◽  
Surface Currents ◽  
Time Step ◽  
Physical Constraints ◽  
Unmanned Surface Vehicles ◽  
Optimal Region ◽  
On Line ◽  
The Given ◽  
Effective Distribution

In this paper, we introduce an approach for decomposing exploration tasks among multiple Unmanned Surface Vehicles (USVs) in port regions. In order to ensure effective distribution of the workload, the algorithm has to consider the effects of the environment on the physical constraints of the USVs. The performance of the USV is influenced by the surface currents, risk of collision with the civilian traffic, and varying depths as a result of tides, and weather. In our approach, we want the team of USVs to explore certain region of the harbor. The algorithm has to decompose the region of interest into multiple sub-regions by considering the maximum operating velocity of each USV in the given environmental conditions. The algorithm overlays a 2D grid upon a given map to convert it to an occupancy grid, and then proceeds to partition the region of interest among the multiple USVs assigned to explore the region. During partitioning, each USV covers the maximum area that is possible by operating at maximum velocity at each time-step. The objective is to minimize the time taken for the last USV to finish claiming its area exploration. We use the particle swarm optimization (PSO) method to compute the optimal region partitions. The method is verified by running simulations in different test environments. We also analyze the performance of the developed method in environments with unknown velocity profiles.

scholarly journals Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
María Luisa Sánchez Brea ◽  
Noelia Barreira Rodríguez ◽  
Antonio Mosquera González ◽  
Katharine Evans ◽  
Hugo Pena-Verdeal
Keyword(s):  
Region Of Interest ◽  
Bulbar Conjunctiva ◽  
Ocular Diseases ◽  
Conjunctival Hyperemia ◽  
Limited Region ◽  
Computer Aided ◽  
Optimal Region ◽  
Regression Techniques ◽  
Grading Scale ◽  
Conjunctival Redness

Conjunctival hyperemia or conjunctival redness is a symptom that can be associated with a broad group of ocular diseases. Its levels of severity are represented by standard photographic charts that are visually compared with the patient’s eye. This way, the hyperemia diagnosis becomes a nonrepeatable task that depends on the experience of the grader. To solve this problem, we have proposed a computer-aided methodology that comprises three main stages: the segmentation of the conjunctiva, the extraction of features in this region based on colour and the presence of blood vessels, and, finally, the transformation of these features into grading scale values by means of regression techniques. However, the conjunctival segmentation can be slightly inaccurate mainly due to illumination issues. In this work, we analyse the relevance of different features with respect to their location within the conjunctiva in order to delimit a reliable region of interest for the grading. The results show that the automatic procedure behaves like an expert using only a limited region of interest within the conjunctiva.

scholarly journals Portrait Quality Assessment using Multi-Scale CNN

2021 ◽  
Vol 2021 (1) ◽  
pp. 5-10
Author(s):  
Chahine Nicolas ◽  
Belkarfa Salim
Keyword(s):  
Neural Network ◽  
Quality Assessment ◽  
Deep Neural Network ◽  
Image Quality Assessment ◽  
Region Of Interest ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Texture Measurement ◽  
Multi Scale ◽  
Lighting Conditions

In this paper, we propose a novel and standardized approach to the problem of camera-quality assessment on portrait scenes. Our goal is to evaluate the capacity of smartphone front cameras to preserve texture details on faces. We introduce a new portrait setup and an automated texture measurement. The setup includes two custom-built lifelike mannequin heads, shot in a controlled lab environment. The automated texture measurement includes a Region-of-interest (ROI) detection and a deep neural network. To this aim, we create a realistic mannequins database, which contains images from different cameras, shot in several lighting conditions. The ground-truth is based on a novel pairwise comparison technology where the scores are generated in terms of Just-Noticeable-differences (JND). In terms of methodology, we propose a Multi-Scale CNN architecture with random crop augmentation, to overcome overfitting and to get a low-level feature extraction. We validate our approach by comparing its performance with several baselines inspired by the Image Quality Assessment (IQA) literature.

Image Evaluation Based on Region of Interest

2013 ◽  
Vol 339 ◽  
pp. 253-258
Author(s):  
Jun Qing Liu ◽  
Lei Ma ◽  
Yan Xiang ◽  
San Li Yi ◽  
Hong Lei Chen ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Image Quality Assessment ◽  
Region Of Interest ◽  
Image Evaluation ◽  
Visual Evaluation ◽  
Edge Structure ◽  
Structure Similarity ◽  
Weighted Combination ◽  
Dual Scale

Image quality assessment has broad applications in many fields, how to assess the quality of the image is an attractive research topic. In this paper, a ROIMDE method is proposed based on region of interest (ROI) and dual-scale edge structure similarity (SSIM), the quality assessment of the image is a weighted combination of ROI and non-ROI, the dual-scale edge structure similarity is used in ROI, and the classical structure similarity is applied in non-ROI. Experimental results show that, considering the influence of ROI, our model is more consistent with human subjective visual evaluation.

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