nils-herrmann/clean_scirepeval huggingface dataset:
Objective Quality Assessment and Perceptual Compression of Screen Content Images

Screen content image (SCI) has recently emerged as an active topic due to the rapidly increasing demand in many graphically rich services such as wireless displays and virtual desktops. SCIs are often composed of pictorial regions and computer generated textual/graphical content, which exhibit different statistical properties that often lead to different viewer behaviors. Inspired by this, we propose an objective quality assessment approach for SCIs that incorporates both visual field adaptation and information content weighting into structural similarity based local quality assessment. Furthermore, we develop a perceptual screen content coding scheme based on the newly proposed quality assessment measure, targeting at further improving the SCI compression performance. Experimental results show that the proposed quality assessment method not only better predicts the perceptual quality of SCIs, but also demonstrates great potentials in the design of perceptually optimal SCI compression schemes.
Contextually adaptive signal representation using conditional principal component analysis

The conventional method of generating a basis that is optimally adapted (in MSE) for representation of an ensemble of signals is principal component analysis (PCA). A more ambitious modern goal is the construction of bases that are adapted to individual signal instances. Here we develop a new framework for instance-adaptive signal representation by exploiting the fact that many real-world signals exhibit local self-similarity. Specifically, we decompose the signal into multiscale subbands, and then represent local blocks of each subband using basis functions that are linearly derived from the surrounding context. The linear mappings that generate these basis functions are learned sequentially, with each one optimized to account for as much variance as possible in the local blocks. We apply this methodology to learning a coarse-to-fine representation of images within a multi-scale basis, demonstrating that the adaptive basis can account for significantly more variance than a PCA basis of the same dimensionality.
Research on computer immune system with multilayer recovery

There are many excellent characteristics in the nature immune system that can be cited in the computer immune system. Until now, some of them are being used in the safe of the computer great. But now in the computer immune system, the methods of the detection in some stage are singly. For this problem, a sort of computer immune system with multilayer recovery is designed here. It has three layers to protect the system and different detections are used in them according to different conditions and stages. All this work can improve the rate of detection and reduce the rate of the wrong catch.
An Integrated Method to Generate a Cloud-Free Image Automatically Based on Landsat5 Data

How to remove cloud and shadow completely through several (more than 2) images is considered in this paper. Based on landsat5 data, when enter several images, it can automatically find one image with the least cloud and shadow as the base image. Then mosaic with other images which are taken in different time for the same area, an image without any cloud and shadow is produced.
Measuring Intra- and Inter-Observer Agreement in Identifying and Localizing Structures in Medical Images

Inter- and intra-observer variability exists in any measurements made on medical images. There are two sources of variability. The first occurs when the observers identify and localize the object of interest, and the second happens when the observers make appropriate measurement on the object of interest. A number of statistical methods are available to quantify the degree of agreement between measurements made by different observers. However, little has been done to develop metrics for quantifying the variability in identifying and localizing the objects of interest prior to measurement. In this paper, we propose to use the complex wavelet structural similarity index (CW-SSIM) method to measure the variability in identifying and localizing structures on images. Performance comparisons using simulated images as well as real mammography images demonstrate the effectiveness and robustness of the CW-SSIM method.
Iterative Signal Processing for Coded LSTF Architectures

For high speed orthogonal frequency division multiplexing (OFDM) plus multiple-input multiple-output (MIMO) multiplexing, a new coded layered space-time-frequency (LSTF) architecture with iterative signal processing at the receiver is proposed, where each independent codeword is threaded in the three-dimensional (3D) space-time-frequency transmission resource array. The iterative receiver structure is adopted consisting of a joint minimum-mean-square-error soft-interference-cancellation (MMSE-SIC) detector and the maximum a posteriori (MAP) convolutional decoders. Simulation results show that the proposed LSTF architecture can achieve almost the same performance as the LSTF (i.e., LSTF-a) where coding is applied across the whole information stream. However, due to its structure of multiple parallel lower speed encoders/decoders with shorter codeword length, the proposed LSTF architecture can be more easily implemented than the LSTF-a.
Palmprint Verification using Complex Wavelet Transform

Palmprint is a unique and reliable biometric characteristic with high usability. With the increasing demand of automatic palmprint authentication systems, the development of accurate and robust palmprint verification algorithms has been attracting a lot of interests. The relative translation, rotation and distortion between two palmprint images will introduce much error in palmprint matching. However, an accurate registration of palmprint images is too time-consuming. In this paper, we propose a modified complex wavelet structural similarity index (CW-SSIM) to compute the matching score and hence identify the input palmprint. Since CW-SSIM is robust to translation, small rotation and distortion, a fast rough alignment of palmprint images is sufficient. CW-SSIM is also insensitive to luminance and contrast changes. Our experimental results show that the proposed scheme outperforms the state-of-the-art methods by achieving a higher genuine acceptance rate and a lower false acceptance rate simultaneously.
General-purpose reduced-reference image quality assessment based on perceptually and statistically motivated image representation

Divisive normalization has been recognized as a successful approach to model the perceptual sensitivity of biological vision. It also provides a useful image representation that is well-matched to the statistical properties of natural images. Here we propose a reduced- reference image quality assessment method in the divisive normalization transform domain, where the quality of an image is evaluated based on a set of reduced-reference features extracted from a divisive normalization representation of the image. The proposed method is general-purpose, in the sense that no assumption is made about the types of distortions occurred in the image being evaluated. The proposed method is trained and tested using the LIVE database and demonstrates good performance for a wide range of distortions.
A practical adaptive packet scheduling algorithm with single user fairness guarantee over the forward link of 3G cellular data services

An efficient practical packet scheduling algorithm for high data rate cellular services has to tackle two challenges: bandwidth efficiency and fairness, which are conflicting with each other. Existing methods, e.g., HSDPA, usually fail to strike an optimal balance of the two aspects. Recently, a highly practical scheme called MTA-ISIR has been suggested. While the MTA-ISIR scheme is considered to be a promising method for practical use, we find that it can be further improved. In this paper, we propose a new approach in which the power level threshold used for handling the fairness is dynamically adjusted. Our new approach also makes use of the "opportunistic scheduling" concept which enables the scheduler to optimize the bandwidth efficiency without sacrificing fairness. Simulation results indicate that our proposed method outperforms MTA-ISIR in various practical situations.
Supervised-learning based face hallucination for enhancing face recognition

This paper presents a two-step supervised face hallucination framework based on class-specific dictionary learning. Since the performance of learning-based face hallucination relies on its training set, an inappropriate training set (e.g., an input face image is very different from the training set) can reduce the visual quality of reconstructed high-resolution (HR) face significantly. To address this problem, we propose to utilize supervised learning to learn a set of class-specific dictionaries so that one of the learned dictionaries can well fit the global and local characteristics of an input low-resolution (LR) face image. Besides, the representative coefficients of the input LR face image may be unreliable due to insufficient information contained in the LR input image. To resolve this issue, we propose a maximum a posteriori estimator to infer the global HR face. Experimental results demonstrate that our method cannot only effectively enhance the visual quality of a reconstructed HR face, but also significantly improves the accuracy of face recognition compared to existing hallucination methods.
Saliency-based visualization for image search

In this paper, we propose a novel algorithm for improving and visualizing image search results. The proposed algorithm improves user's image search experience by three steps: (1) re-rank the initial image search results by the random walk refinement based on visual consistency and saliency cues, (2) project the re-ranked images into a 2-dimentional panel according to their saliency information and correlations, (3) detect and extract the saliency regions in each image for final visualization. To evaluate the performance of our algorithm, user study has been conducted. Experimental results demonstrate that our visualization algorithm provides more pleasing image search experience than the conventional image search methods.
Eye corner detection with texture image fusion

The localization of eye corner is of great importance since it offers crucial information in various face-related applications including face tracking, gaze estimation, and facial expression recognition. In this paper, a new approach is proposed which localizes eye corners in a precise and robust way. In our approach, we first estimate a rough location about an eye corner. Then, a set of texture images are derived from regions around the estimated eye corner location. These regions are then fused together to decide the reliability of the estimated eye corner location, and an eye corner's location will be re-estimated if the current estimated eye corner is evaluated as unreliable. Finally, a local refinement process is also applied which refines the location of the estimated eye corner to create a more satisfactory result. Experiments show that our approach can achieve better detection results than the existing methods.
Inferring users' image-search goals with pseudo-images

The analysis of user search goals for a query can be very useful in improving search engine relevance and user experience. Although the research on inferring user goals or intents for text search has received much attention, little has been proposed for image search with visual information. In this paper, we propose a novel approach to capture user search goals in image search by exploring pseudo-images which are extracted by mining single sessions in user click-through logs to reflect user information needs. Moreover, we also propose a novel evaluation criterion to determine the number of user search goals for a query. Experimental results demonstrate the effectiveness of the proposed method.
Real Time Learning Evaluation Based on Gaze Tracking

In this paper, we present a system that extracts the information implied by eye movements and use this information to analyze students' learning behavior. Our system uses a common webcam to capture students' facial image sequences when they are learning in front of monitors. We then process these images and establish sequences of changing location of iris, which represent the movements of eyes. With the eye movement sequences, we train a HMM classifier that can analyze their pattern and generate learning status for any given moment in the lesson. These statuses could help the computers to get a better understanding about the students' intention and behavior during online learning. The status sequences of those who view the same lesson could also be used as a reference for teaching quality assessment.
An enhanced covariance spectrum sensing technique based on stochastic resonance in cognitive radio networks

In this paper, a novel covariance spectrum sensing approach used in cognitive radio (CR) networks which is based on the dynamical stochastic resonance (SR) technique is proposed. When the optimal SR technique is introduced as the pre-processing method for the covariance-based detection and after it has been realized, it can increase the signal-to-noise ratio (SNR) of the primary user (PU) signal and accordingly increase the mean value of the decision statistic of the covariance-based detection, so that the detection probability of the proposed approach can be improved under constant false alarm rate (CFAR). Computer simulation results verify the effectiveness of the proposed approach compared with the traditional spectrum sensing methods.
Visual Similarity Based Anti-phishing with the Combination of Local and Global Features

Phishing uses a fake Web page to steal personal sensitive information such as credit card numbers and passwords. Generally, the fake Web page is visually similar to the legitimate target Web page. The phishers can obtain financial benefits through these information. Anti-phishing is very important for a variety of applications such as phishing attacks, online transaction security, and user privacy protection. In this paper, we propose a novel and effective visual similarity based phishing detection approach that compares the snapshot image pair of the suspected Web page and the protected Web page. The proposed approach is based on the key insight that both the local and the global features of the Web page image can be used to represent the visual characteristics of the Web page together. This approach is purely on the image level, and thus can effectively deal with the non-text phishing tricks including images or Flashes objects in the HTML contents. For the local feature, the existence of the target logo is detected. For the global feature, the similarity of the visible part of the Web page is considered. We implemented and evaluated the proposed approach on a large scale dataset consisting of 2,129 real world phishing Web pages and 1,367 irrelevant legitimate Web pages. The experimental results show that the proposed approach can achieve over 90.00% true positive rate and 97.00% true negative rate. Our approach has been applied in the anti-phishing project of a major Internet Service Provider and gives a periodical reports to the potential users.
Distributed optimal power control for multicarrier cognitive systems

In this paper, the power optimization of the multicarrier cognitive system underlying the primary network is investigated. We consider the interference coupled cognitive network under individual secondary user's power constraint and primary user's rate constraint. A multicarrier discrete distributed (MCDD) algorithm based on Gibbs sampler is proposed. Although the problem is nonconcave, MCDD is proved to converge to the global optimal solution. To reduce the computational complexity and convergence time, the Gibbs sampler based Lagrangian algorithm (GSLA) is proposed to get a near optimal solution. We also provide simulation results to show the effectiveness of the proposed algorithms.
A new algorithm for improving Deblurring effects and addressing spatially-variant blur problems for image motion deblurring

Motion deblurring techniques have played important roles in many image processing applications. In this paper, a new algorithm is proposed for motion deblurring from a single image. The proposed algorithm introduces an anisotropic Patiral Differential Equation (PDE) method for latent image prediction and employs an adaptive optimization model in the blind deconvolution iterative step. Furthermore, in order to solve the spatially-variant blur problems, we also propose a Saliency-based Deblurring (SD) approach by introducing saliency extraction together with a compensate method for image deblurring. Experimental results have demonstrated the effectiveness of the proposed algorithm.
A loose-strategy-based Complexity Scalable Motion Estimation method for video coding

In this paper, a new Complexity Scalable Motion Estimation (CSME) method is proposed which can perform Motion Estimation adaptively under different computation or power budgets while keeping high coding performance. We first propose to use a new class-based method to measure the Macroblock (MB) importance. Based on the new MB importance measure, a complete CSME framework is then proposed. The proposed method allocates computation in a one-pass flow and is based on a loose strategy where frames can borrow computation from other frames to achieve better performance under the same computation budget. Experimental results demonstrate that the proposed method can allocate computation more accurately than previous methods.
Capacity of Multicarrier Multilayer Broadcast and Unicast Hybrid Cellular System with Independent Channel Coding over Subcarriers

In this paper, we discuss the hybrid capacity region of a generic multicarrier multilayer broadcast and unicast cellular system with independent channel coding over subcarriers. In particular, we analytically derive the capacity region and provide conditions to achieve its boundary. The simulation results show that the hybrid capacity regions are considerably higher than those of the traditional time division multiplexing scheme.
A new approach for extracting and summarizing abnormal activities in surveillance videos

In this paper, we propose a new approach to detect abnormal activities in surveillance videos and create suitable summary videos accordingly. The proposed approach first introduces a blob sequence optimization process which integrates spatial, temporal, size, and motion correlation among objects to extract suitable abnormal blob sequences. With this process, blob extraction errors due to occlusion or background interferences can be effectively avoided. Then, we also propose an abnormality-type-based method which creates short-period summary videos for long-period input surveillance videos by properly arranging abnormal blob sequences according to their activity types. Experimental results show that our proposed approach can effectively create satisfying summary videos from input surveillance videos.
A new package-group-transmission-based algorithm for human activity recognition in videos

In this paper, a new package-group-transmission-based algorithm is proposed for human activity recognition in videos. The proposed algorithm first models the entire scene as a network where each node in the network corresponds to a segmentation of the scene. Based on this network, we further model people in the scene as groups of packages. Thus, various human activities can be modeled as the process of “package group transmission” in the network and these activities can be efficiently recognized by suitably analyzing the “package transmission” process. Our proposed algorithm can not only detect activities under the challenging multiple camera scenario, but also be able to recognize various complex group activities among people. Experimental results demonstrate the effectiveness of our proposed algorithm.
A Coarse to Fine Facial Key Landmark Points Locating Algorithm Based on Active Shape Model

In this paper, we proposed an improved coarse to fine improved algorithm to enhance the accuracy of facial key landmark points locating. Based on the analysis of PCA, the proposed algorithm redesigns the parameter update rule through adding a monotonically decreasing inert factor function to the traditional ASM iterations (D-ASM). The new rule could update parameters at a finer process. Besides, we compare the performances of different types of inert factor functions and select the suitable one. Furthermore, we further design a classifier-based algorithm for the more accurate locating of 2D key corner points. Finally, local D-ASM is constructed and the inner landmarks are further fitting with corner points fixed. Experimental results on various faces demonstrate the effectiveness and rationality of our proposed algorithm.
An efficient framework for analyzing periodical activities in sports videos

In this paper, we propose a new and efficient framework for analyzing periodical activities in sports videos. The proposed framework first locates the motion patterns (MP) in each frame. By analyzing these located motion patterns with the proposed MP-analysis algorithm, the periodical sports activities can be effectively detected and counted. Furthermore, the qualified and unqualified actions can also be efficiently differentiated. Experimental results on various sports videos including dumbbell and weightlifting demonstrate the effectiveness of our proposed framework.
Recognizing Human Group Behaviors with Multi-group Causalities

Human group behaviors are usually composed of several sub-groups. Considering the interaction between groups, this paper presents an algorithm to recognize human group behavior with multi-group causalities. It has two main contributions: (1) we introduce inter-group causality to reflect the interaction between human groups, (2) an improved coding scheme (i.e. Locality-constrained Linear Coding) is used for encoding the causality to go beyond Vector Quantization (VQ). Finally, a simple linear SVM is adopted to learn this model. Our experiment results demonstrate that inter-group causality feature and LLC methods can significantly boost behavior recognition performance.
Inferring User Image-Search Goals Under the Implicit Guidance of Users

The analysis of user search goals for a query can be very useful in improving search engine relevance and user experience. Although the research on inferring user goals or intents for text search has received much attention, little has been proposed for image search. In this paper, we propose to leverage click session information, which indicates high correlations among the clicked images in a session in user click-through logs, and combine it with the clicked images' visual information for inferring user image-search goals. Since the click session information can serve as past users' implicit guidance for clustering the images, more precise user search goals can be obtained. Two strategies are proposed to combine image visual information with the click session information. Furthermore, a classification risk based approach is also proposed for automatically selecting the optimal number of search goals for a query. Experimental results based on a popular commercial search engine demonstrate the effectiveness of the proposed method.