Successive Mean Quantization Transform Research Articles

Fast face detection using a unified architecture for unconstrained and infrared face images

Human face detection along with its localization is a difficult task when the face is presented in the cluttered scene in an unconstrained scenario that might be with arbitrary pose variations, occlusions, random backgrounds and infrared (IR) environment. This paper proposes a novel face detection method which can address some of these issues and challenges quite successfully during face detection in unconstrained as well as infrared environments. It makes use of Fast Successive Mean Quantization Transform (FastSMQT) features for image enhancement and feature representation to deal with illumination and sensor insensitiveness. A split up Sparse Network of Winnows (SNoW) with Winnow updating rule is then exploited to speed up the original SNoW classifier. Finally, the features and classifiers are combined together with skin detection algorithm for face detection in crowd image and head orientation correction of near infrared faces. The proposed face detector is robust in handling pose, occlusion, illumination, blur and low image resolution. The experiment is performed on six challenging and publicly available databases, viz. BIOID, LFW, FDDB, UFI, WIDER FACE and IIT Delhi near infrared. The experimental results depict that the proposed method outperforms traditional as well as some advanced methods in detecting unconstrained and infrared faces under challenging situations.

Effect of shadow removal by gamma correction in SMQT algorithm in environmental application

Environmental data can be achieved from remote sensing. Shadows are main problems in achieving environmental data by remote sensing. In this research, an algorithm is proposed for preparing environmental data from shadow-covered objects. The proposed algorithm provides some information about digital number (DN) of shadow-covered objects by reducing the shadow effect. The successive mean quantization transform (SMQT) algorithm, gamma correction and combination of images form the basis of the proposed algorithm. SMQT algorithm eliminates gain and bias features by indicating the data structure. The data structure remains unchanged, and the differences between terrains are highlighted by compressing the dynamic range and stretching the histogram of the image in parts with different terrains of same DN. The output of the SMQT algorithm is a gray image. A new image is obtained by combining this image with the original image through HSV color space. In the second step, gamma correction is applied to the entire image according to brightness and contrast. But the gamma correction rate is not the same in all parts of an image. As a result, gamma correction should be done locally. However, local gamma correction and the use of a kernel of a specific dimension increase computation time. In addition, if there is a noise in the image, it will cause a significant deviation in the correction. To solve this problem, the output of SMQT algorithm is combined with the image obtained from gamma correction. Shadows in the input image cause the image histogram to be compressed in areas near zero. After performing the process in the study, the histogram was stretched and the maximum histogram reached from 190 to 250.

A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means

Digital watermarking is a process of giving security from unauthorized use. To protect the data from any kind of misuse while transferring, digital watermarking is the most popular authentication technique. This paper proposes a novel digital watermarking scheme for biomedical images. In the model, initially, the biomedical image is preprocessed using improved successive mean quantization transform (SMQT) which uses the Otsu’s threshold value. In the next phase, the image is segmented using Otsu and Fuzzy c-means. Afterwards, the watermark is embedded in the image using discrete wavelet transform (DWT) and inverse DWT (IDWT). Finally, the watermark is extracted from the biomedical image by executing the inverse operation of the embedding process. Experimental results exhibit that the proposed digital watermarking scheme outperforms the typical models in terms of effectiveness and imperceptibility while maintaining robustness against different attacks by demonstrating a lower bit error rate (BER), a cross-correlation value closer to one, and higher values of structural similarity index measures (SSIM) and peak signal-to-noise ratio (PSNR).

An improved image enhancement algorithm

To solve the problems of noise, detail loss and poor contrast in the successive mean quantization transform (SMQT), a new SMQT algorithm based on Otsu algorithm is proposed. In this algorithm, we integrate the optimal threshold selected by the Otsu algorithm into the SMQT algorithm, then obtain the successive mean quantization of the binary tree. By this algorithm, an enhanced image is output with a higher quality. From both subjective visual effect and objective quality evaluation, the experimental results show that the improved algorithm reduces noise, improves contrast and makes the image details more clear.

An adaptive approach for heartbeat detection based on S-transform in seismocardiograms.

This study presents a new technique which allows identification of individual heartbeats from seismocardiograms (SCG) with high accuracy. Our method is electrocardiogram (ECG) independent and designed based upon S-transform and Shannon energy. The S-transform which is a time-frequency (TF) representation first provides frequency-dependent resolution while preserving a direct relationship with Fourier spectrum. Subsequently, individual heartbeats are detected in the time domain by calculating the Shannon energy (SSE) of each obtained local spectrum and employing other techniques such as successive mean quantization transform (SMQT) and adaptive thresholding. A total of 30 recordings were analysed in this study by measuring SCG and simultaneous electrocardiogram (ECG) in supine position. The performance of the algorithm was tested using the standard ECGs obtained from each test subject. The obtained results were as follows (sensitivity, precision, and detection error rate): (98.0%, 98.4% and 0.2%). In conclusion, the results confirmed that combination of S-transform, Shannon energy, and other techniques considerably enhanced the efficiency for the heartbeat detection in seismocardiograms.

FIVDL: Fingerprint Image Verification using Dictionary Learning

Fingerprints are used for identification in forensics and are classified into Manual and Automatic. Automatic fingerprint identification system is classified into Latent and Exemplar. A novel Exemplar technique of Fingerprint Image Verification using Dictionary Learning (FIVDL) is proposed to improve the performance of low quality fingerprints, where Dictionary learning method reduces the time complexity by using block processing instead of pixel processing. The dynamic range of an image is adjusted by using Successive Mean Quantization Transform (SMQT) technique and the frequency domain noise is reduced using spectral frequency Histogram Equalization. Then, an adaptive nonlinear dynamic range adjustment technique is utilized to determine the local spectral features on corresponding fingerprint ridge frequency and orientation. The dictionary is constructed using spatial fundamental frequency that is determined from the spectral features. These dictionaries help in removing the spurious noise present in fingerprints and reduce the time complexity by using block processing instead of pixel processing. Further, dictionaries are used to reconstruct the image for matching. The proposed FIVDL is verified on FVC database sets and Experimental result shows an improvement over the state-of-the-art techniques. (C) 2015 The Authors. Published by Elsevier B.V.

Enhanced Face Detection Technique Based on Color Correction Approach and SMQT Features

Face detection is considered as a challenging problem in the field of image analysis and computer vision. There are many researches in this area, but because of its importance, it needs to be further developed. Successive Mean Quantization Transform (SMQT) for illumination and sensor insensitive operation and Sparse Network of Winnow (SNoW) to speed up the original classifier based face detection technique presented such a good result. In this paper we use the Mean of Medians of CbCr (MMCbCr) color correction approach to enhance the combined SMQT features and SNoW classifier face detection technique. The proposed technique is applied on color images gathered from various sources such as Internet, and Georgia Database. Experimental results show that the face detection performance of the proposed method is more effective and accurate compared to SFSC method.