Extended Shadow Suppression Method for Outdoor Scenes Using CLAHE with OTSU Binarization and Assorted Color Spaces

2021 ◽  
pp. 403-413
Author(s):  
Deepa Abin ◽  
Sudeep D. Thepade
Keyword(s):  
Color Spaces ◽  
Outdoor Scenes ◽  
Suppression Method ◽  
Shadow Suppression

scholarly journals Crowd region detection in outdoor scenes using color spaces

2018 ◽  
Vol 09 (02) ◽  
pp. 1850012 ◽  
Author(s):  
Huma Chaudhry ◽  
Mohd Shafry Mohd Rahim ◽  
Tanzila Saba ◽  
Amjad Rehman
Keyword(s):  
Research Community ◽  
Human Detection ◽  
Color Variation ◽  
Surveillance Systems ◽  
Color Spaces ◽  
Region Detection ◽  
Novel Approach ◽  
Outdoor Scenes ◽  
Dense Crowd ◽  
Crowded Scenes

In the last few decades, crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems. While human detection in partially crowded scenarios have achieved many reliable works, a highly dense crowd-like situation still is far from being solved. Densely crowded scenes offer patterns that could be used to tackle these challenges. This problem is challenging due to the crowd volume, occlusions, clutter and distortion. Crowd region classification is a precursor to several types of applications. In this paper, we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity. Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.

A Suppression Method of Higher Harmonics Resonance in Distribution System

2011 ◽  
Vol 131 (9) ◽  
pp. 737-746 ◽  
Author(s):  
Shouji Sugimura ◽  
Tadashi Naitoh ◽  
Atsushi Toyama ◽  
Fumihiko Ohta
Keyword(s):  
Distribution System ◽  
Higher Harmonics ◽  
Suppression Method

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

Fusion Based Edge Detection and Segmentation of Color Spaces

2017 ◽  
Author(s):  
Prof. S. H. Jawale ◽  
Prof. A. B. Bavaskar
Keyword(s):  
Edge Detection ◽  
Color Spaces

Representation, Modeling, and Recognition of Outdoor Scenes

1999 ◽  
Author(s):  
Martin A. Fischler ◽  
Robert C. Bolles
Keyword(s):  
Outdoor Scenes

scholarly journals Disentangled generative adversarial network for low-dose CT

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang
Keyword(s):  
Network Architecture ◽  
Low Dose ◽  
Noise Suppression ◽  
State Of The Art ◽  
Visual Quality ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

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