A Novel Approach for Skin Region Extraction in Color Images

2007 ◽  
Author(s):  
Ehsan Namjoo ◽  
Ali Aghagolzadeh ◽  
M. Shahram Moin ◽  
A. Akbari
Keyword(s):  
Color Images ◽  
Skin Region ◽  
Novel Approach ◽  
Region Extraction

scholarly journals Morphological sharpening of color images

2016 ◽  
Vol 64 (1) ◽  
pp. 103-113
Author(s):  
S. Skoneczny
Keyword(s):  
Color Images ◽  
Total Order ◽  
Experimental Results ◽  
Morphological Operations ◽  
Gray Level ◽  
Low Contrast ◽  
Novel Approach ◽  
Multivalued Images ◽  
Theoretical Considerations ◽  
Gray Level Images

Abstract This paper presents a novel approach to morphological contrast sharpening of image using the multilevel toggle operator. The concept presented here is a generalization of toggle based contrast operator for gray-level images. The multilevel toggle operator is used to enhance the contrast of multivalued images. In order to perform necessary morphological operations the modified pairwise ordering (MPO) algorithm is proposed. It gives the total order of color pixels. For comparison four other ordering methods are used. The main advantage of the proposed sharpener is its significant contrast enhancing ability when using MPO. Theoretical considerations as well as practical results are shown. Experimental results show its applicability to low-contrast color images.

scholarly journals A Revisit Histogram of Oriented Descriptor for Facial Color Image Classification Based on Fusion of Color Information

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huy Nguyen-Quoc ◽  
Vinh Truong Hoang
Keyword(s):  
Color Image ◽  
Real Life ◽  
Selection Method ◽  
Color Images ◽  
Human Detection ◽  
Maximum Magnitude ◽  
Color Component ◽  
Novel Approach ◽  
The Face ◽  
Benchmark Datasets

Histogram of Oriented Gradient (HOG) is a robust descriptor which is widely used in many real-life applications, including human detection, face recognition, object counting, and video surveillance. In order to extract HOG descriptor from color images whose information is three times more than the grayscale images, researchers currently apply the maximum magnitude selection method. This method makes the information of the resulted image is reduced by selecting the maximum magnitudes. However, after we extract HOG using the unselected magnitudes of the maximum magnitude selection method, we observe that the performance is better than using the maximum magnitudes in several cases. Therefore, in this paper, we propose a novel approach for extracting HOG from color images such as Color Component Selection and Color Component Fusion. We also propose the extended kernels in order to improve the performance of HOG. With our new approaches in the color component analysis, the experimental results of several facial benchmark datasets are enhanced with the increment from 3 to 10% of accuracy. Specifically, a 95.92% of precision is achieved on the Face AR database and 75% on the Georgia Face database. The results are better more than 10 times compared with the original HOG approach.

scholarly journals Sparse Approach in Filtering of Color Images Corrupted by Mixture Noises

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Noise Suppression ◽  
Impulsive Noise ◽  
Median Filter ◽  
Color Images ◽  
Subjective Perception ◽  
Transform Domain ◽  
Second Stage ◽  
Novel Approach ◽  
Impulsive Noises ◽  
Impulsive Noise Suppression

A novel filtering approach is presented in denoising in the color images contaminated by mixture of additive-impulsive noises. Novel framework consists of three principal stages: impulsive noise suppression that is performed detecting pixels corrupted by impulsive noise and then, filtering found spikes by a variant of median filter; during second stage, original additive noise suppression filter is employed in Wavelet transform domain via a sparse representation and 3D-filtering; finally, nondesirable effects obtained in an image during previous stages are processed to correct fine details. In case of multiplicative noise suppression, the designed denoising scheme uses 3D homomorphic sparse processing stage and post-filtering procedure. Evaluation of novel approach in denoising complex distortions has been performed using objective criteria (PSNR and SSIM measures) and subjective perception via human visual system confirming their better performance in comparison with state-of-theart techniques.

A Novel Approach of Edge Detection Based on Vector Morphology

2014 ◽  
Vol 511-512 ◽  
pp. 545-549
Author(s):  
Qiang Chen
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Color Image ◽  
Detection Algorithm ◽  
Difficult Problem ◽  
Color Images ◽  
Edge Detection Algorithm ◽  
Morphological Gradient ◽  
Novel Approach ◽  
Image Edges

Edge detection of color image is a difficult problem in image processing. Although a lot of corresponding to methods have been proposed, however, none of them can effectively detect image edges while suppressing noises. In this paper, a novel edge detection algorithm of color images based on mathematical morphology is proposed. Through designing a new anti-noise morphological gradient operators, we can obtain better edge detection results. The proposed gradient operators are applied to detect edge for three components of a color image. An then, the final edge can be obtained by fusing the three edge results. Experimental results show that the feasibility and effectiveness of the proposed algorithm. Moreover, the proposed algorithm has better effect of preserving the edge details and better robustness to noises than traditional methods.

scholarly journals Merge and Split approach incolor image Steganography using Run Length Encoding and LSB Techniques

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Rajput G ◽  
◽  
Ramesh Chavan ◽  
Keyword(s):  
Communication Channel ◽  
Third Parties ◽  
Color Images ◽  
Audio Signals ◽  
Multiple Images ◽  
Run Length ◽  
Video Frames ◽  
Novel Approach ◽  
Other Information ◽  
And Performance

The purpose of steganography is to communicate secret messages between the sender and intended recipient in such a way that no one suspects the very existence of the message. The techniques aim to protect the secret information from third parties by embedding them into other information such as text, audio signals, images, and video frames. In this paper we propose a novel approach of hiding secret messages in multiple images (a cover image)using run length encoding and LSB techniques and communicate the message to intended person over the communication channel by transmitting individual images.Experiments are performed on a set of color images and performance of the proposed system is presented.

scholarly journals An Automatic Face Detection System for RGB Images

2011 ◽  
Vol 6 (1) ◽  
pp. 21 ◽  
Author(s):  
Tudor Barbu
Keyword(s):  
Face Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Detection System ◽  
Color Images ◽  
Human Face ◽  
Skin Region ◽  
Detection Approach ◽  
Rgb Images ◽  
Digital Color Images

We propose a robust face detection approach that works for digital color images. Our automatic detection method is based on image skin regions, therefore a skin-based segmentation of RGB images is provided first. Then, we decide for each skin region if it represents a human face or not, using a set of candidate criteria, an edge detection process, a correlation based technique and a threshold-based method. A high face detection rate is obtained using the proposed method.

scholarly journals Stone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images

2016 ◽  
Vol 6 (3) ◽  
pp. 1152
Author(s):  
Palnati Vijay Kumar ◽  
Pullela S V V S R Kumar ◽  
Nakkella Madhuri ◽  
M Uma Devi
Keyword(s):  
Image Classification ◽  
Texture Classification ◽  
Digital Camera ◽  
Color Images ◽  
Present Approach ◽  
Visual Patterns ◽  
Novel Approach ◽  
Wide Range ◽  
Standard Classification

Texture classification is widely used in understanding the visual patterns and has wide range of applications. The present paper derived a novel approach to classify the stone textures based on the patterns occurrence on each sub window. The present approach identifies overlapped nine 5 bit T-patterns (O5TP) on each 5×5 sub window stone image. Based the number of occurrence of T-patterns count the present paper classify the stone images into any of the four classes i.e. brick, granite, marble and mosaic stone images.  The novelty of the present approach is that no standard classification algorithm is used for the classification of stone images. The proposed method is experimented on Mayang texture images, Brodatz textures, Paul Bourke color images, VisTex database, Google color stone texture images and also original photo images taken by digital camera. The outcome of the results indicates that the proposed approach percentage of grouping performance is higher to that of many existing approaches.

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