nils-herrmann/clean_scirepeval huggingface dataset:
Improved block truncation coding using optimized dot diffusion.

Block truncation coding (BTC) has been considered a highly efficient compression technique for decades. However, its inherent artifacts, blocking effect and false contour, caused by low bit rate configuration are the key problems. To deal with these, an improved BTC, namely dot-diffused BTC (DDBTC), is proposed in this paper. Moreover, this method can provide excellent processing efficiency by exploiting the nature parallelism advantage of the dot diffusion, and excellent image quality can also be offered through co-optimizing the class matrix and diffused matrix of the dot diffusion. According to the experimental results, the proposed DDBTC is superior to the former error-diffused BTC in terms of various objective image quality assessment methods as well as processing efficiency. In addition, the DDBTC also shows a significant image quality improvement comparing with that of the former ordered-dither BTC.
Joint Compression/Watermarking Scheme Using Majority-Parity Guidance and Halftoning-Based Block Truncation Coding

In this paper, a watermarking scheme, called majority-parity-guided error-diffused block truncation coding (MPG-EDBTC), is proposed to achieve high image quality and embedding capacity. EDBTC exploits the error diffusion to effectively reduce blocking effect and false contour which inherently exhibit in traditional BTC. In addition, the coding efficiency is significantly improved by replacing high and low means evaluation with extreme values substitution. The proposed MPG-EDBTC embeds a watermark simultaneously during compression by evaluating the parity value in a predefined parity-check region (PCR). As documented in the experimental results, the proposed scheme can provide good robustness, image quality, and processing efficiency. Finally, the proposed MPG-EDBTC is extended to embed multiple watermarks and achieves excellent image quality, robustness, and capacity. Nowadays, most multimedia is compressed before it is stored. It is more appropriate to embed information such as watermarks during compression. The proposed method has been proved to solve effectively the inherent problems in traditional BTC, and provide excellent performance in watermark embedding.
Two-layer features extracted from essential areas for facial expression recognition

This work presents a novel two-layer feature extraction method on essential facial areas. First, each original image is divided into three local images (first-layer area) according to the facial organs carried significant information. Second, the local images are down-sampled to images of lower resolutions (second-layer area). Then the texture and frequency features are individually extracted by the Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) operators. Finally, the Support Vector Machine (SVM) is adopted to classify seven facial expressions with Japanese female facial expression (JAFFE) database. Experimental results shows that the second layer of the proposed scheme can significantly boost the recognition rate by around 10%. As a result, the proposed scheme can obtain the highest recognition rate among various former methods. Moreover, the proposed method can achieve a better recognition rates with a lower resolution than those methods in the literature with higher resolutions.
High-capacity data hiding in halftone images using minimal error bit searching

In this paper, a high capacity data hiding is proposed for embedding a large amount of information into halftone images. The embedded watermark can be distributed into several error-diffused images by the proposed minimal error bit searching technique (MEBS). The method can be generalized to self-decoding mode with dot diffusion or color halftone images. From the experiments, the embedded capacity from 33% up to 50% and good quality result are achieved. Furthermore, the proposed MEBS method is also extended for robust watermarking to against the degradation from printing-and-scanning and several kinds of distortions.
Accurate Facial Landmark Extraction

Facial landmark extraction system is crucial in various applications, including face recognition, expression analysis, face tracking, and face animation. This letter aims to improve the performance of an existing landmark extraction method proposed by Ren et al. in terms of error rate. Specifically, the Gaussian blur filter is applied on the input image to reduce noise interference and the theta-based split rule is deployed to strengthen the performance of the random forests. Then, global linear regression is applied instead of treating each landmark independently. Experimental results demonstrate that the proposed modified facial landmark extraction algorithm outperforms the conventional methods for both the LFPW and Helen databases.
License Plate Localization and Character Segmentation with feedback self-learning and hybrid-binarization techniques

License Plate Localization (LPL) and Character Segmentation (CS) play key roles in License Plate Recognition System (LPRS). In this study, we dedicate ourselves in these two issues. In LPL, the histogram equalization is employed to solve the low contrast and dynamic range problem; the texture properties, e.g., aspect ratio, and color similarity are used to locate the License Plate (LP). In CS, the hybrid- binarization technique is proposed to effectively segment the characters in the dirt LP. The feedback self-learning procedure is also employed to adjust the parameters in the system. As documented in the experiments, good localization and segmentation results are achieved with the proposed algorithms.
Alteration detection and image recovery using Halftone Replacement Prior Watermark Embedding

This study presents a watermarking scheme, namely Halftone Replacement Prior Watermark Embedding (HRPWE), for medical and surveillance systems applications. The proposed method provides better image quality and higher robustness to withstand attacks. Besides, EDF-based inverse halftoning approach is also proposed to detect alteration region and then recover the original image with low complexity advantage. Hence, it could be used in cooperating with HRPWE for surveillance images to withstand compression or unauthorized modification, and for medical images to detect and recover unauthorized modification. This approach is proved to be of excellent performances with extensive experimental results.
Data Hiding in Halftone Images Using Adaptive Noise-Balanced Error Diffusion and Quality-Noise Look Up Table

This study presents a reasonable computational complexity watermarking algorithm to embed hidden pattern into two or more halftone images with Adaptive Noise-Balanced Error Diffusion (ANBEDF). One halftone image is obtained by traditional error diffusion, and the others are obtained by ANBEDF. The visual decoded hidden pattern can be detected when the similar error-diffused images are overlaid each other. Better decoded result can be obtained by a simple XNOR operation. The proposed method employs the trained Quality-Noise Look Up Table (QNLUT) and the optimized multipliers to control the adaptive noise strength according to the local variance value. The experimental results show that higher decoding rate is available under the same image quality performance as former approaches reported in the literature.
Vehicle Verification Using Gabor Filter Magnitude with Gamma Distribution Modeling

This letter presents a new method to derive the image feature descriptor for vehicle verification. The effectiveness of the proposed feature descriptor is based on the nature of the Gabor filter magnitude that tends to obey the Gamma distribution. The statistical parameters of the Gabor magnitude are computed using the Maximum Likelihood Estimation (MLE), which is later utilized to construct the feature descriptor. Conventionally, the Gabor magnitude is simply modeled by using Gaussian distribution, and thus the image descriptor consists of mean, standard deviation, and skewness values of the Gabor filter magnitude. However, recent investigations found that the skewness parameter is not contributing towards class separation. Based on our observation, the Gamma distribution provides a better statistical fitting to represent the Gabor filter magnitude when compared to the Gaussian distribution. As documented in the experimental results, the proposed feature descriptor yields higher accuracy for vehicle verification when compared to the conventional schemes.
Parallel and element-reduced Error-Diffused Block Truncation Coding

Block Truncation Coding (BTC) is an efficient compression technique for its inherent simple coding strategy. However, the annoying blocking effect and false contour accompanied in high coding gain configurations make the applications relatively limited compares to some up-to-date compression schemes. For this, Error-Diffused Block Truncation Coding (EDBTC) is proposed to solve these problems and obtain satisfactory results. Unfortunately, the EDBTC sacrifices the parallel advantage of traditional BTC. Moreover, the number of diffused directions of EDBTC can be reduced to obtain higher efficiency. For these, the Interlaced Error-Diffused Block Truncation Coding (IEDBTC) is proposed in this work to claim back the parallel advantage. In addition, the diffused elements are also reduced from four to two with the proposed optimization procedure while preserving the image quality.
Panoramic Face Recognition

Normally, modeling 3D face is an effective way for pose-invariant recognition, yet its expensive computation significantly discourages potential applications. In this paper, a simple and fully automatic panoramic image-based pose-invariant face recognition method is proposed to present excellent accuracy with low complexity. In this paper, a face shape model with local morphing treatment is first constructed and considered as the alignment standard to deal with all of the possible geometric distortion problems. During the recognition phase, a proposed systematically designed algorithm with morphing and the selection function are both utilized to significantly ease the negative effects of various poses within ±45° in yaw and ±22.5° in pitch. As demonstrated in experimental results, a similar accuracy as that of the 3D start-of-the-arts is achieved with much less computational complexity.
Quality Compressed Steganography Using Hidden Referenced Halftoning

Block truncation coding is an efficient compression technique while offering good image quality. Nonetheless, the blocking effect inherent in BTC causes severe perceptual artifact in high compression ratio applications. In this paper, an error-diffused block truncation coding (EDBTC) is proposed to solve this problem. According to the EDBTC, the error caused by the difference between the original grayscale pixel value and the correspondingly high or low mean substitute is diffused to the predefined neighborhood, and hence the average grayscale will be maintained invariably. In addition, since the compressed data are widely distributed in the internet transmission, the extra message delivering in a secret way also highly raises attention recently. In this paper, we propose the compressed steganography using Hidden Referenced Halftoning (CSHRH), which cooperates with error diffusion and ordered dithering to achieve the objective of secret communication in BTC images. As documented in the experimental results, a low complexity with good image quality approach is obtained. Moreover, CSHRH is extended to secret-sharing steganography (SSS) and color extension steganography (CES). The SSS is able to distribute message into multiple host images and hence improves the security. The CES is able to deliver secure message via color embedded CSHRH image. Both extensions are also with an extra benefit of achieving high capacity message convection.
Statistical modeling of the Gabor filter magnitude using Gamma distribution for effectively vehicle verification

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.  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.
Sample Space Dimensionality Refinement for Symmetrical Object Detection

Formerly, dimensionality reduction techniques are effective ways for extracting statistical significance of features from their original dimensions. However, the dimensionality reduction also induces an additional complexity burden which may encumber the real efficiency. In this paper, a technique is proposed for the reduction of the dimension of samples rather than the features in the former schemes, and it is able to additionally reduce the computational complexity of the applied systems during the reduction process. This method effectively reduces the redundancies of a sample, in particular for those objects which possess partially symmetric property, such as human face, pedestrian, and license plate. As demonstrated in the experiments, based upon the premises of faster speeds in training and detection by a factor of 4.06 and 1.24, respectively, similar accuracies to the ones without considering the proposed method are achieved. The performance verifies that the proposed technique can offer competitive practical values in pattern recognition related fields.
Inverse Halftoning Based on the Bayesian Theorem

This study proposes a method which can generate high quality inverse halftone images from halftone images. This method can be employed prior to any signal processing over a halftone image or the inverse halftoning used in JBIG2. The proposed method utilizes the least-mean-square (LMS) algorithm to establish a relationship between the current processing position and its corresponding neighboring positions in each type of halftone image, including direct binary search, error diffusion, dot diffusion, and ordered dithering. After which, a referenced region called a support region (SR) is used to extract features. The SR can be obtained by relabeling the LMS-trained filters with the order of importance. Moreover, the probability of black pixel occurrence is considered as a feature in this work. According to this feature, the probabilities of all possible grayscale values at the current processing position can be obtained by the Bayesian theorem. Consequently, the final output at this position is the grayscale value with the highest probability. Experimental results show that the proposed method offers better visual quality than that of Mese-Vaidyanathan's and Chang 's methods in terms of human-visual peak signal-to-noise ratio (HPSNR). In addition, the memory consumption is also superior to Mese-Vaidyanathan's method.
Multiple license plates recognition system

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 Naïve 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.
Effective Image Retrieval System Using Dot-Diffused Block Truncation Coding Features

This paper presents a new approach to derive the image feature descriptor from the dot-diffused block truncation coding (DDBTC) compressed data stream. The image feature descriptor is simply constructed from two DDBTC representative color quantizers and its corresponding bitmap image. The color histogram feature (CHF) derived from two color quantizers represents the color distribution and image contrast, while the bit pattern feature (BPF) constructed from the bitmap image characterizes the image edges and textural information. The similarity between two images can be easily measured from their CHF and BPF values using a specific distance metric computation. Experimental results demonstrate the superiority of the proposed feature descriptor compared to the former existing schemes in image retrieval task under natural and textural images. The DDBTC method compresses an image efficiently, and at the same time, its corresponding compressed data stream can provide an effective feature descriptor for performing image retrieval and classification. Consequently, the proposed scheme can be considered as an effective candidate for real-time image retrieval applications.
Ocular Recognition for Blinking Eyes.

Ocular recognition is expected to provide a higher flexibility in handling practical applications as oppose to the iris recognition, which only works for the ideal open-eye case. However, the accuracy of the recent efforts is still far from satisfactory at uncontrollable conditions, such as eye blinking which implies any poses of eyes. To address these issues, the skin texture, eyelids, and additional geometrical features are employed. In addition, to achieve higher accuracy, sequential forward floating selection is utilized to select the best feature combinations. Finally, the non-linear support vector machine is applied for identification purpose. Experimental results demonstrate that the proposed algorithm achieves the best accuracy for both open eye and blinking eye scenarios. As a result, it offers greater flexibility for the prospective subjects during recognition as well as higher reliability for security.
Enhanced block truncation coding image using digital multitone screen

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.
Hybrid pixel-based data hiding and block-based watermarking for error-diffused halftone images

A low computational complexity noise-balanced error diffusion (NBEDF) technique is proposed for embedding watermarks into error-diffused images. The visual decoding pattern can be perceived when two or more similar NBEDF images are overlaid, even in a high activity region. Also, with the modified improved version of NBEDF, two halftone images can be made from two totally different gray-tone images, and still provide a clear and sharp visual decoding pattern. With self-decoding techniques, we can also decode the pattern with only one NBEDF image. However, the NBEDF method is not so robust to damage due to printing or other distortions. Thus, a kernels-alternated error diffusion (KAEDF) technique is proposed. By using them alternately in the halftone process, we find that two well-known kernels (Jarvis, J.F. et al., 1976; Stucki, P., 1981) are compatible. In the decoder, because the spectral distributions of Jarvis and Stucki kernels are different in the 2D fast Fourier transform domain, we use the cumulative squared Euclidean distance criterion to determine each cell in a watermarked halftone image belonging to either Jarvis or Stucki, and then decode the watermark. Furthermore, because the detailed textures of Jarvis and Stucki patterns are somewhat different in the spatial domain, the lookup table (LUT) technique is also used for fast decoding. From simulation results, the correct decoding rates for both techniques are high and extremely robust, even after printing and scanning processes. Finally, we extend the hybrid NBEDF and KAEDF algorithms to two color EDF halftone images, where 8 independent KAEDF watermarks and 16 NBEDF watermarks can be inserted and still achieve a high-quality result.
Hand gesture recognition using codebook model and Pixel-Based Hierarchical-Feature Adaboosting

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.
Hierarchical pedestrian detection under low resolution scenario

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.