Morphological Document Recovery in HSI Space

2013 ◽  
pp. 241-250 ◽  
Author(s):  
Ederson Marcos Sgarbi ◽  
Wellington Aparecido Della Mura ◽  
Nikolas Moya ◽  
Jacques Facon
Keyword(s):  
Hsi Space

Fuzzy Model for Human Color Perception and Its Application in E-Commerce

2016 ◽  
Vol 24 (Suppl. 2) ◽  
pp. 47-70 ◽  
Author(s):  
Pakizar Shamoi ◽  
Atsushi Inoue ◽  
Hiroharu Kawanaka
Keyword(s):  
Image Retrieval ◽  
Color Perception ◽  
Fuzzy Model ◽  
Visual Features ◽  
Perceptual Categorization ◽  
Purchasing Behavior ◽  
User Query ◽  
Hsi Space ◽  
Underlying Mechanisms ◽  
High Level

Although image retrieval for e-commerce field has a huge commercial potential, e-commerce oriented content-based image retrieval is still very raw. Modern online shopping systems have certain limitations. In particular, they use conventional tag-based retrieval and lack making use of visual content. The paper presents a methodology to retrieve images of shopping items based on fuzzy dominant colors. People regard color as an aesthetic issue, especially when it comes to choosing the colors of their clothing, apartment design and other objects around. No doubt, color inuences purchasing behavior — to a certain extent, it is a reection of human's likes and dislikes. The fuzzy color model that we are proposing represents the collection of fuzzy sets, providing the conceptual quantization of crisp HSI space having soft boundaries. The proposed method has two parts: assigning a fuzzy colorimetric profile to the image and processing the user query. We also use underlying mechanisms of attention from a theory of visual attention, like perceptual categorization. Subjectivity and sensitivity of humans in color perception and bridging the semantic gap between low-level color visual features and high-level concepts are major issues that we plan to tackle in this research.

Color image segmentation in HSI space for automotive applications

2008 ◽  
Vol 3 (4) ◽  
pp. 311-322 ◽  
Author(s):  
Calin Rotaru ◽  
Thorsten Graf ◽  
Jianwei Zhang
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Automotive Applications ◽  
Hsi Space

Color image encryption combining a reality-preserving fractional DCT with chaotic mapping in HSI space

2015 ◽  
Vol 75 (11) ◽  
pp. 6605-6620 ◽  
Author(s):  
Yaru Liang ◽  
Guoping Liu ◽  
Nanrun Zhou ◽  
Jianhua Wu
Keyword(s):  
Image Encryption ◽  
Color Image ◽  
Color Image Encryption ◽  
Chaotic Mapping ◽  
Hsi Space

A Fuzzy-Like Approach for Smoothing and Edge Detection in Color Images

1998 ◽  
Vol 12 (06) ◽  
pp. 801-816 ◽  
Author(s):  
A. Moghaddamzadeh ◽  
D. Goldman ◽  
N. Bourbakis
Keyword(s):  
Edge Detection ◽  
Image Understanding ◽  
Detection Algorithm ◽  
Color Images ◽  
Color Contrast ◽  
Edge Detection Algorithm ◽  
Edge Contrast ◽  
Hsi Space ◽  
Fuzzy Like ◽  
Processing Steps

Edge detection is one of the most important image processing steps towards image understanding. It is desired that edges be continuous and that the resultant regions or segments be completely isolated from their neighbors. Initially, images must first be smoothed to remove noise. In this paper, a novel fuzzy-like smoother algorithm is presented which removes camera noise and enhances edge contrast. The edge detection algorithm, which is applied on the smoothed image, is then presented. In this algorithm normalized hue in HSI space and color contrast in RGB space are combined using an aggregate operator. Pixels considered to be at least "nearly" locally maximum (defined within) are then found for all edge directions and the results are combined.

A Monitoring System for Abnormal Water Surface Based on HSI Space

2014 ◽  
Vol 687-691 ◽  
pp. 994-997 ◽  
Author(s):  
You Fu Wu ◽  
Peng Yang ◽  
Jing Wu ◽  
Zu Sheng Chen
Keyword(s):  
Monitoring System ◽  
Real World ◽  
Water Surface ◽  
Event Analysis ◽  
Water Based ◽  
Hsi Space ◽  
Surface Monitoring ◽  
The Moment ◽  
Moment Features ◽  
High Level

Classing object is an important step for the high-level visions processing tasks, such as security managing, and abnormality event analysis. In this paper, we address these challenges of abnormal water surface monitoring in real-world unconstrained environments where the background is complex and dynamic. In the algorithm proposed, we extract the moment features of water surface in HSI space, and a technique is developed to monitor the abnormal surface of water based on moment features. Experimental results show that our algorithm works efficiently and robustly.

scholarly journals Image Defogging Algorithm Based on Sparse Representation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Di Fan ◽  
Xinyun Guo ◽  
Xiao Lu ◽  
Xiaoxin Liu ◽  
Bo Sun
Keyword(s):  
Sparse Representation ◽  
Color Space ◽  
Image Brightness ◽  
Low Contrast ◽  
Image Defogging ◽  
Degraded Image ◽  
Hsi Space ◽  
Definition Of ◽  
Nonlinear Stretching ◽  
Sparse Features

Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.

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