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The digital halftoning is widely adopted in printing applications to obtain a high-quality printing output with optimal ink usage. The technique comprises of transforming a continuous tone grayscale image into its approximate binary image. On the other hand, image quality assessment deals with estimating the perceptual quality of the degraded image. The paper deals with exploitation of ordered dithering technique of the halftoning, to improve the performance of the existing image quality indices. The widely used full reference image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are considered for this paper. The main objective aims to improvise the correlation coefficient between the widely used IQM and Human Visual System (HVS). Moreover, the computation demand of the existing indices is also reduced significantly. The well-known LIVE image quality database is used to validate the performance of the proposed approach. To perform correlation analysis, Pearson, Spearman and Kendall correlation coefficients are used. From experimental analysis, it can be validated that the proposed IQM performs with improved accuracy and less computational demand.

The era of intelligent transportation systems is evolving, which amends the way to deploy smart vision systems. One prominent challenge is to maintain the quality of highway roads for the safety issues. The manual road crack inspection is laborious, time-consuming, and inaccurate. A robust and automatic crack detection is needed to overcome these issues. Many deep learning based automatic crack detections have been developed. However, the majority of models suffer from inaccurate boundary detection due to the irregular shapes of the edge. Thus, the edge detection plays an important role to detect the gradient of the images. This study focuses on the refinement process, in which the pre-computed crack detection is refined to enhance the detection result with more accurate boundaries. The proposed refinement involves the image gradient as an additional feature. The vision transformer is adopted as the core layer to refine the crack in patch-wise manner. The multi-head attention in the transformer enhances the ability of the refinement network to find the relation of each processed patch. Extensive experiments have shown that the proposed method is able to generate superior crack boundaries than that of the state-of-the-art methods.

This paper presents a new method for denoising an image corrupted with Additive White Gaussian Noise (AWGN). We adopt the effectiveness of Convolutional Vision Transformer (CvT) to suppress the occurred noise on an image. The proposed method exploits the residual learning approach in order to estimate and reduce the noise on a noisy image. Herein, the model is trained in the end-to-end manner to capture the relation between the noisy image and its noise map. The experimental results show the effectiveness of the proposed method in terms of subjective and objective measurements.

In recent years, the development of document segmentation technology is gaining more and more attention in the area of semantic segmentation, which plays an important role in the task of understanding the structure of documents. Although this demand applying deep learning approaches has undergone continuing advancement, the document segmentation systems still suffer from low accuracy rate. This paper presents a new high-performance document segmentation algorithm, namely Deep Document Segmentation Network (DDSnet), which incorporates advanced end-to-end deep learning methods for segmenting four different types of document features, including background, texts, tables, and figures. To overcome the small receptive fields, the atrous residual block is proposed, which is efficiently boosted by the adoption of multi-branches structure. For the better fine-grained output, the proposed atrous convolution residual block is conducted to achieve high accuracy. Moreover, this paper also releases the brand-new large-scale database, namely PPSD2019, for document segmentation that provides a pixel-level database for another benchmark. As documented in the experimental results, the proposed document segmentation method achieves a superior segmentation rate than that of the former competitive schemes. As a result, the proposed method and database can be considered as a very competitive candidate for the document segmentation applications.

This paper presents a novel deep learning-based technique for reconstructing the Ordered Dithering Block Truncation Coding (ODBTC) decoded image. The proposed method inherits the effectiveness of wavelets transform and Convolutional Neural Networks (CNN). The proposed technique employs the two-dimensional Decimated Wavelet Transform (DWT) to decompose the ODBTC image into low and high frequency sub-bands over various resolution. These image sub-bands are progressively reconstructed with the Pyramid M-Shaped CNN consisting multiple input and output. This scheme considers the ODBTC decoded image as noisy image in Which the information of image can reconstructed by modifying the wavelets sub-bands using residual learning strategy. The produced sub-bands can be combined and transformed back to improve the quality of ODBTC reconstructed image. As documented in experimental results, the proposed method yields promising result for the ODBTC image reconstruction.

Inverse halftoning aims to reconstructs the continuous-tone image, i.e. 255 discrete color levels from its halftone image. The main challenge of inverse halftoning task is on removing the noisy dots over flat areas and, at the same time, restoring the image structures and texture details. To tackle this problem, we propose a novel approach to reconstruct the halftone image in wavelet domain, namely Subbands Aware Inverse Halftoning Network (SAIHN). The proposed framework consists of two sub networks, i.e. the first networks adjust the wavelet sub-bands for image reconstruction, whereas, the second networks refines the quality of reconstructed image by correcting the distortion in pixel domain. The experimental results confirm that the proposed method can reconstruct the halftone image with high quality measured in term of objectively and subjectively assessments. Furthermore, the proposed method demonstrates its superiority on less learnable parameters and more efficient computational complexity in comparison to that of the former existing deep learning-based reconstruction method.

Recent years, fingerprint recognition has a high market share in biometrics, but humans' hands are often covered with oil and sweat that they secrete, rainwater or dirt, which often affect the accuracy of fingerprint recognition. This work proposed a finger vein identification technology for low response time that can be implemented in a cost-efficient embedded system using the binary robust invariant elementary feature. In this method allows the features matching with binary robust invariant elementary feature and verification with multi-quality assessment process. In experimental results, that the EER performance are 0.13% and 0.69%, using homemade and public (FVUSM) datasets when the data were collected with training and testing. The method is very suitable for real-time finger vein recognition applications.

This paper presents a deep learning-based method for effective image companding. The autoencoder inherits the effectiveness of Convolutional Neural Networks (CNN) and residual learning framework to transform High Dynamic Range (HDR) images to Low Dynamic Range (LDR) and its reverse process. Since, the image companding task involves the nondifferentiable operation, thus the encoder and decoder networks are alternately trained using an iterative approach. The experimental result clearly reveals that the proposed method yields a promising result.

This paper proposes a new simple image encryption technique using the Beta chaotic map for performing the confusion and diffusion of input plain image. This image encryption is further applied to the multiple secret sharing (MSS). The former existing scheme in MSS employs the Chinese remainder theorem (CRT) and eXclusive- OR (XOR) operations to convert the secret image to become a set of shared images. The former scheme yields a good result while the number of secret images is even. However, it has slight limitation while the number of secret images is odd. This paper aims to overcome this problem by utilizing the proposed image encryption along with three new MSS approaches: 1) incorporating an additional random images; 2) performing $k$ image encryption; and 3) using two different masking coefficients. As documented in the experimental section, the proposed method performs well in the MSS task. It solves the problem in former scheme on dealing with odd number of secret images. In addition, the proposed method is superior compared with the other competing schemes. The proposed image encryption also improves the security level in the MSS system.

Digital halftone deals with transforming a gray scale image into its printable binary version. In this paper, a generative adversarial network-based model is proposed to perform both halftoning and its structural reconstruction. The GAN model is based on the concept of unpaired image to image translation and the model learns both transformations simultaneously. For optimal training, the model is feed with high structural data and the model architechture is also modified in accordance to this transformation problem. From results, it has been validated that the model performs with consistent accuracy in both transformations.

Halftone based block truncation coding images (H-BTC) are the improved version of BTC images which can offer superior representation and enhanced image quality. The application of the H-BTC images range from image compression and retrieval, indexing, reconstruction, classification and so on. In this paper, a brief introduction to various H-BTC images is presented and their evaluation is carried out in terms of image quality and computational demand. Further, the application prospective of the H-BTC images are also provided. The developed H-BTC database comprises of 30K images comprising of the five types of H-BTC images in which three categories are constructed based on digital halftoning and two types are based on digital multitoning. The database would be very useful to carry out deep learning research and various image processing tasks. The database along with its source code will be made open source for the research and academic purpose.

Nowadays, the computerized electrocardiogram (ECG) interpretation is the best available tool for the detection of heart diseases. The symptoms of atrial fibrillation, one of the heart disease, are the most challenging task of heart disease that relies on the cardiologist to read the ECG. However, this task involves a time-consuming process, leading to fatigue-induced medical errors. In this paper, a novel atrial fibrillation diagnostic algorithm based on deep learning architecture is proposed which incorporates the spectrogram to further improve the accuracy performance. As shown in the experimental results, the proposed method on PhysioNet /CinC Challenge 2017, which contains 8528 of single-lead ECG recordings, achieves the 10-fold cross-validation set performance of 78%.

Multitone Block Truncation Coding (MT-BTC) image is the superior version of halftone based BTC compression methods. The MT-BTC images are developed based on ordered dithering method which utilize multitone dithering mask for image construction. During the transformation, the original image is processed in a block-wise manner and is replaced in terms of the maximum, minimum and their intermediate values of the respective block. In comparison with standard compressions techniques, MT-BTC possess very unique representation and suffers from inherent halftone noises. In this paper, a simplified version of image to image translation architecture is developed based on cGAN's. To begin with, a MT-BTC database is developed using the latest multitone approach, and it comprise of around 10,000 images. Further, the proposed cGAN's model is optimized to perform with minimal layers and reduced parameters. The PatchGAN discriminator is adjusted to judge over the patch size of 64×64 which has good impact over quality improvements. From the comprehensive performance evaluation, it has been validated that the proposed approach can achieve consistent and improved reconstruction quality.

This paper presents a new technique for improving the quality of Error Difussion Block Truncation Coding (EDBTC) decoded image. The proposed method exploits the usability of wavelet transform on decomposing the EDBTC decoded image into low and high frequency subbands. This new technique employs the two dimensional Discrete Wavelet Transform (DWT) and two dimensional Stationary Wavelet Transform (SWT). This scheme considers the EDBTC decoded image as noisy image in which the occurence of noise can be minimized by modifying the wavelet high frequency. As documented in experimental results, the proposed method yields promising result for the EDBTC image reconstruction.

This paper presents a new simple and efficient technique for secret sharing. This technique inherits the effectiveness of Progressive Visual Secret Sharing (PVSS), in which the quality of recovered secret image is improved if more shared images are stacked together using the eXclusive-OR (XOR) operation. The proposed method also extends the usability of PVSS approach into the Friendly Visual Secret Sharing (PVSS). The content of shared image is converted into more friendly appearances rather than in the noise-like shared image. The proposed method offers promising result for grayscale and color image. It also produces recovered secret image in the lossless condition. The proposed method can be regarded as a good candidate for secret sharing.

Image segmentation is one of the popular techniques for vision application, i.e., retrieving object information from an image. Segmentation not only provides the class and location of an object, but the associated contour as well. Recent advances of the segmentation deploy the encoder and decoder architectures with skip connection, and also utilize the edge information to obtain the best contour. This work focuses on maintaining the information flow from skip connection, and also identifies suited feature from the bottom layer. The feature selector can be also deployed in edge information for yielding segmentation contour. Designed filters are also employed to analyze the best feature for the reconstruction purpose. Experiment results show that the proposed scheme can improvise the performance of the simple ENet to achieve a superior IoU from 51.3% to 57.9%.

Facial expression recognition (FER) is an important and challenging problem for automatic inspection of surveillance videos. In recent years, with the progress of hardware and the evolution of deep learning technology, it is possible to change the way of tackling facial expression recognition. In this paper, we propose a sequence-based facial expression recognition framework for differentiating facial expression. The proposed framework is extended to a frame-to-sequence approach by exploiting temporal information with gated recurrent units. In addition, facial landmark points and facial action unit are also used as input features to train our network which can represent facial regions and its components effectively. Based on this, we build a robust facial expression system and is evaluated using two publicly available databases. The experimental results show that despite the uncontrolled factors in the videos, the proposed deep learning-based solution is consistent in achieving promising performance compared to that of the former schemes.

Digital multitoning is an enhanced version of the halftone printing technique which can render multiple tones at each pixel of an image. This superior strategy offers a better printing quality in terms of smooth texture, homogeneity and less artifacts. Considering the potential scope of multitoning, an effective strategy to embed a binary watermark image is proposed. The proposed approach is based on multiple look up table (MLUT) which can alter multitone pattern in correspondence to the watermark tone. The MLUT table consist of adjacent multitone screens which are statistically differentiable and cannot be visually distinguishable. From experimental simulations, it is validated that the proposed technique yields consistent decoding rate, payload capacity and computationally less intensive in comparison with various halftone watermarking strategies.

Digital halftoning is a technique to represent the binary approximation of continuous tone image meant for printing. There are wide variety of halftone methods available based on ordered dithering, error distribution, dot diffusion and iterative approaches. This paper emphasizes on the development of a digital halftone database (DHD) consisting of 23 varieties of halftone methods. Moreover, the detailed elaboration on implementation of each technique and its comparison study is carried out. The critical halftone characteristics such as homogeneity, isotropy, directional artifacts and its impact to the pattern quality are comprehensively analyzed using the stochastic halftone statistics. Finally, a complete performance comparison based on rendered image quality, spatial and spectral statistics of stochastic process parameters are analyzed. The DHD database consisting of more than 4500 halftone images along with some of its source code is made free access for scientific study and purposes.

Digital halftoning is a strategy to convert the color or gray scale image into a printable format. Among several halftoning techniques, error diffusion is one of the conventional and widely adopted method in many printing devices. In further, halftone classification is very important to obtain the perfect reconstruction of binary printed images. The paper attempts to solve this issue by exploiting the intrinsic similarity between the stochastic geometry and halftoning. Feature vectors are constructed using the point process statistic parameters such as directional distribution function and radially averaged power spectral density. Extreme learning machine model is developed and eight varieties of error diffusion halftone images are considered for classification. From the results, it has been validated that the proposed scheme yields better accuracy of 97.5% and it is faster than the existing approaches.

An improved version of Block Truncation Coding (BTC) termed Multitone Block Truncation Coding (MTBTC) is proposed. The presented framework exploits the digital multi toning strategy to obtain a superior quality BTC images. Previously, the halftone techniques such as ordered dithering, error diffusion and dot diffusion are extended to obtain different version of BTC. Though it performs good, there are still many scope for improvements. In recent times digital multitoning is gaining prominence and advanced multitone screens such as direct multibit search and clustered dot screens are developed. This multitone screen can enhance the visual quality significantly and offer more homogenous and noise free output for halftone images. Considering this properties, an upgradation to the halftone based BTC is proposed in this paper through the use of sophisticated multitone screens. From the simulation results, it is concluded that the proposed framework can provide very high quality output than any existing BTC techniques. Another highlighting aspect is that the proposed technique can facilitate higher block size processing and it is computationally very simple. In overall, the proposed scheme is an ideal candidate to implement compression for high resolution images and can be exploited further to adopt in artificial intelligent tasks.

Block truncation coding (BTC) has been considered as a highly efficient compression technique for decades, but the blocking artifact is its main issue. The halftoning-based BTC has significantly eased this issue, yet an apparent impulse noise artifact is accompanied. In this study, an improved BTC, termed adaptive dot-diffused BTC (ADBTC), is proposed to further improve the visual quality. Also, this method provides an additional flexibility on the compression ratios determination in contrast to the former fixed and few number of configuration possibilities. As documented in the experimental results, the proposed method achieves the superior image quality regarding the five various objective IQA methods. As a result, it is a very competitive approach for the needs of both high frame rate and high-resolution image compression.

This paper presents a new image feature descriptor derived from the Direct Binary Search Block Truncation Coding (DBSBTC) data-stream without requiring the decoding process. Three image feature descriptors, namely Color Autocorrellogram Feature (CAF), Legendre Chromaticity Moment Feature (LCMF), and Local Halftoning Pattern Feature (LHPF), are simply constructed from the DBSBTC min quantizer, max quantizer, and its corresponding bitmap image, respectively. The similarity between two images can be measured using these descriptors under specific distance metric. The proposed method yields better image retrieval performance compared to the former Block Truncation Coding (BTC) and existing schemes under the natural and textural image database in the grayscale and color space. The DBSBTC performs well for image compression, at the same time, it gives an effective discriminative feature in the image retrieval task.

Processing efficiency can be a key factor which dominates the value of a commercial product and its value-added application such as security. In this study, a computationally reduced halftoning-based watermarking is proposed. In encoder, the Efficient Direct Binary Search (EDBS) is employed to generate reference table to ensure the output is in halftone format. Subsequently, a number of optimized compressive tables with various texture angles are established for subsequent table lookup. In decoder, the Least-Mean-Square (LMS) enlarges the differences among those phenotypes of the embedded angles and the required number of dimensions for each angle. Finally, the naive Bayes classifier is employed to collect the possibility information for classifying various angles. As documented in the experimental results, good image quality and correct detect rate can be obtained simultaneously. Moreover, a high processing efficiency of 0.6 milliseconds for an image of size 512×512 can also be achieved, which can substantially increase the commercial competitive strength in printing market, in particular the security issue is well addressed.

The Local Binary Pattern (LBP) operator and its variants play an important role as the image feature extractor in the textural image retrieval and classification. The LBP-based operator extracts the textural information of an image by considering the neighboring pixel values. However, the LBP-based feature is not a good candidate in capturing the color information of an image, making it is less suitable for measuring the similarity of color images with rich color information. This work overcomes this problem by adding an additional color feature, namely Color Histogram Feature (CHF), along with the LBP-based feature in the image retrieval domain. Experimental result shows that the hybrid CHF and LBP-based feature presents a promising result and outperforms the existing methods over several image databases.

Block Truncation Coding (BTC) image compression technique is capable of extraordinary computational efficiency for high-framerate configurations under the trend of high image resolution. However, the recent development such as the Dot-Diffused BTC (DDBTC) method still encounters various challenges of visual artifacts, including impulsive noise, false contours and blocking effects. In this study, a new method, the Near-Aperiodic DDBTC (NADDBTC) technique, is proposed to analyze the tradeoff and balance between computational efficiency and visual artifacts. It is mainly because of the quantization levels and the co-optimized Class Matrix (CM) and Diffused Matrix (DM) during image compression procedure. In addition, the tiling method for bitmap generation is optimized for aperiodic compressed results. Experimental results show that this method is capable of excellent image quality and visual perception, as well as processing efficiency, at a level similar to the DDBTC technique by taking advantage of parallel processing in dot diffusion. We propose an adaptive strategy to derive quantization levels.The periodic artifact of former DDBTC is eased.Artifacts of former BTCs such as blocking effect and impulsive noise are eased.Proposed method achieves the best visual quality comparing with existing halftoning-based BTCs.

Error diffusion is an efficient halftone method for mainly being applied on printers. The promising high image quality and processing efficiency endorse it as a popular and competitive candidate in halftoning and multitoning applications. The multitoning is an extension of halftoning, adopting more than three tone levels for improving the similarity between an original image and the converted image. Yet, the banding effect, indicating the areas with only one tone level, disturbs the visual perception, and thus seriously degrades image quality. To cope with the banding effect, the tone replacement strategy is proposed in this study. As documented in the experimental results, excellent tone-similarity as that of the original image and promising reconstructed dot-distribution can be provided simultaneously. Comparing with the former banding-free methods, the apparent improvements/features suggest that the proposed method can be a very competitive candidate for multitoning applications.

Due to a variety of accidents, banknotes may be broken into several fragments. These fragments are usually stained, burned, partially lost, and twisted, which makes banknote reconstruction a hard problem. Since the fragments are always not intact, the traditional edge and texture based fragment assembling methods cannot be applied here. In this paper, we develop a framework for banknote reconstruction. We applied the techniques of SIFT point matching, RANSAC, and feature-based alignment. Moreover, convex quadratic optimization based on maximizing the reconstructed area and avoiding overlapping is adopted. Several simulations are given to demonstrate the effectiveness of our framework.

A new feature descriptor based on the statistical modeling of wavelet output is proposed for vehicle verification problem. The Generalized Gaussian Distribution (GGD) offers accurate statistical fitting for the real and imaginary part of Gabor and Dual-Tree Complex Wavelet Transform (DT-CWT) output. Consequently, the descriptor is simply composed from the GGD parameters of Gabor and DT-CWT output by means of Maximum Likelihood Estimation (MLE) approach under GGD assumption. Experimental result shows that the proposed feature descriptor outperforms the former existing method in the vehicle verification using SVM classifier.

Vehicle verification based on still image feature can be considered as supervised classification problem. An image descriptor is directly derived from the Gabor filtered output statistics of a given image. In general, the magnitude of the Gabor filtered output is modeled as the Gaussian distribution. So that the image descriptor is composed from mean, standard deviation, and skewness value of the Gabor filter magnitude [5, 6, 8]. However, Arrospide et. al. [9] argued that the skewness parameter is not meaningful for the class separation. Then, the feature descriptor is well defined only using mean and standard deviation of Gabor output distribution which leads to lower feature dimensionality. Based on our observation, the magnitude of the Gabor filter has strong tendency to follow the Gamma distribution. We propose a new texture descriptor derived from the maximum likelihood estimation of the Gamma distribution for effectively vehicle verification task. Experimental result shows that the proposed method is superior to the former approach under several classifier techniques.

This paper presents an approach for hand gesture recognition that can be employed to enhance the capability of existing applications, especially in sign language communication. For practical use, the hand posture is taken at the back instead of the front and occurred under unexpected background environment. Unlike the front-hand, the back hand view image is less information than the front-viewed. Thus, the recognition among lack of information is the challenge of this task. Codebook-based foreground detection model is used to detect the hand region under an unexpected background environment. Moreover, the Pixel-Based Hierarchical Feature method is proposed to extract the importance features which are further classified by Adaboosting that yields a high recognition rate. For performance evaluation, we have applied perturbation recognition rate analysis of five alphabet patterns and the experimental results shows that the proposed method provides higher recognition accuracy than existing method.

This paper presents a technique for Content-Based Image Retrieval (CBIR) by exploiting the low complexity advantage of the Ordered-Dither Block Truncation Coding (ODBTC) for generating image content descriptors. The two image features, namely Color Co-occurrence Feature (CCF) and Bit Pattern Features (BPF), are generated from ODBTC encoded data streams (without really performing an image compression or decoding process) to measure the similarity between two images. Experimental results show that the proposed method is superior to the Block Truncation Coding (BTC) image retrieval system and other former methods, and prove that the ODBTC scheme is not only suited for image compression for its simplicity, but also offers a conveniently way for image indexing in the content-based image retrieval system.

Two vulnerable attacks on the wavelet transform (WT) and Singular Value Decomposition (SVD) based image watermarking scheme are presented in this paper. The WT-SVD-based watermarking is robust against various common image manipulations and geometrical attacks; however, it cannot resist against two security attacks, i.e. an attacker attack which successfully claims the real owner's watermarked image, and owner attack which correctly extracts watermark from any arbitrary image. As proved in this study, the SVD watermarking scheme cannot provide trustworthy evidence in rightful ownership protection. In addition, the robustness of the SVD watermarking scheme in is a result of improper algorithm design.

This paper presents a new face gender recognition scheme by enjoying the benefit from the dot diffusion among weak classifiers in recognition phase for a low resolution and non-aligned thumbnail image. The main problem of the former Adaboost approaches is that each weak classifier simply offers a binary decision, which fails to compensate the decision error by diffusing it to the rest weak classifiers. To cope with this, this work exploits the dot-diffused-based Adaboost to solve this problem. As documented in the experimental results, with the examination of Feret and CMU databases, this paper has shown that the proposed scheme is an effective candidate in improving the recognition accuracy rate and the efficiency of the overall system process for face gender recognition.

In this study, a local contrast enhancement method, namely Parametric-Oriented Histogram Equalization (POHE), is proposed to effectively yield enhanced results. In general, the grayscale distribution of a specific region in an image can be modeled with a kernel function such as the Gaussian, and thus the corresponding estimated cumulative distribution function (cdf) can be considered as the transformation function for contrast enhancement. The required parameters, however, still need to access all of the pixels in the corresponding region, and thus consume a huge amount of computations. To cope with this, the concept of integral image is adopted to effectively derive the required parameters. In the experimental results, former well-known speed-oriented methods are adopted for comparison, and the results demonstrate that the proposed methods can provide high practical value for biometric and tracking/detection these active issues who desire high efficiency.

The pedestrian detection is a popular research field in recent years, yet the low-resolution issue is rarely discussed for yielding reasonable response time for drivers. In this study, a hierarchical pedestrian detection system is proposed to cope with this issue. In which, two independent features, orientation and magnitude, are adopted as the descriptors to detect pedestrians. Moreover, to meet the real-time requirement, the proposed Probability-based Pedestrian Mask Pre-Filtering (PPMPF) is adopted to initially filter out lots of non-pedestrian regions while retaining as more true pedestrian as possible. In addition, the concept of integral image is also adopted to simplify the calculations of the adopted features. In experimental results, some popular features such as the Haar-like feature and the edgelet feature are adopted for comparison. The results demonstrate that the proposed system offers better performance as well as high processing efficiency, and thus it can be a very competitive candidate for intelligent surveillance applications.

In this study, the current state-of-the-art in moving objects segmentation for intelligent video surveillance has been surveyed. An efficient Modified Directional Lifting-based 9/7 Discrete Wavelet Transform (MDLDWT) structure is proposed to further reduce the computational cost and preserve the fine shape information in low resolution image. Although perfect moving object detection in a practical environment is a challenging task due to the vague object shape issues in the low resolution configuration, the experimental results document that the proposed low complexity MDLDWT scheme can provide more precise detection rate for multiple moving objects, and the fine shape information can be effectively preserved for the real-time video surveillance applications in both indoor and outdoor environments.

Look-Up Table (LUT) halftoning is an efficient way to construct halftone images, and approximately simulate the dot distribution of the learned halftone image set. In this study, a general mechanism named Multiple Look-Up Table (MLUT) halftoning is proposed to generate the halftones of Direct Binary Search (DBS), while the high efficient characteristic of the LUT is still preserved. In the MLUT, the standard deviation is adopted as an important feature to classify various tables. Moreover, the proposed Quick Standard Deviation Evaluation (QSDE) is employed to yield an extremely low computational complexity in calculating the standard deviation. In the parameter optimization, the autocorrelation is adopted since it can fully characterize the periodicity of dot distribution. Experimental results demonstrate that the visual quality of the proposed method can approximate to that of the DBS which is considered as the best halftoning in terms of image quality, which enable the proposed scheme as a very competitive candidate in coping printing industry.

In this paper, an improved system which can detect multiple license plates in high resolution applications is proposed. First, the Symmetric Mask-based Discrete Wavelet Transform (SMDWT) is utilized to speed up the license plates detection. Later on, the proposed Hierarchical AdaBoost (HA) realizes the detection for multiple targets, in which the classifiers simply require 10 minutes off-line training. In the character segmentation phrase, a projection-based approach considering the relationship among characters is also established. Finally, 13 structural/directional features with the Naive Bayes are applied for character recognition. Experimental results conclusively ensure that the proposed technique is effective for freeway's multiple toll station and transportation management applications contributed from excellent reliability and processing efficiency in big frame size.

In this paper, the impact of the lips for facial recognition is investigated. In the first stage of the proposed system, a Fast Box Filtering (FBF) is proposed to generate a noise-free source with high processing efficiency. Afterward, five various mouth corners are detected though the proposed system, in which it is also able to resist beard and rotation problems. For the feature extraction, two geometric ratios and 10 parabolic related parameters are adopted for further recognition through the Support Vector Machine (SVM). Experimental results demonstrate that when the number of subjects is fewer or equal to 36, the Correct Accept Rate (CAR) is greater than 98%, and the False Accept Rate (FAR) is smaller than 0.064% (CAR>95.6%, FAR