nils-herrmann/clean_scirepeval huggingface dataset:
SAMS: A Seamless and Authorized Multimedia Streaming Framework for WMSN-Based IoMT

An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for a long-term usage. These nodes are capable of capturing both multimedia and nonmultimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, underlying routing protocols need to provide an acceptable level of Quality of Service (QoS) support for multimedia traffic. In this paper, we propose a Seamless and Authorized Streaming (SAMS) framework for a cluster-based hierarchical WMSN. The SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their Cluster Heads (CHs). Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its CH. This waiting may result in an excessive packet-loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for an intercluster communication. In the case of a buffer overflow, a member node in one cluster switches to a neighboring CH provided that the latter has an available channel for allocation. The experimental results show that the SAMS provides an acceptable level of QoS and enhances security of an underlying network.
ECCH: A novel color coocurrence histogram

In this paper, a novel color cooccurrence histogram method, named eCCH which stands for color cooccurrence histogram at edge points, is proposed to describe the spatial-color joint distribution of images. Unlike all existing ideas, we only investigate the color distribution of pixels located at the two sides of edge points on gradient direction lines. When measuring the similarity of two eCCHs, the Gaussian weighted histogram intersection method is adopted, where both identical and similar color pairs are considered to compensate color variations. Comparative experimental results demonstrate the performance of the proposed eCCH in terms of robustness to color variance and small computational complexity.
Notice of Retraction Study on generalized fractal algorithm of global optimization

To promote a fractal algorithm from being suitable for global optimization only on three dimensional spaces to an n dimensional space, this paper presents a simple and convenient method for dividing an n-dimensional hypercube. A key problem is then solved to develop the fractal algorithm in a high dimensional space so that the fractal algorithm becomes a generalized global optimization algorithm. The theoretical foundation of the algorithm is set up. Simulations show the generalized fractal algorithm is effective.
Mean shift for accurate license plate localization

This paper presents a region-based algorithm for accurate license plate localization, where mean shift is utilized to filter and segment color vehicle images into candidate regions. Three features are extracted in order to decide whether a candidate region represents a real license plate, namely, rectangularity, aspect ratio, and edge density. Then, the Mahalanobis classifier is used with respect to above three features to classify license plate regions and non-license plate regions. Experimental results show that the proposed algorithm produces high robustness and accuracy.
The Extended Co-learning Framework for Robust Object Tracking

Recently, object tracking has been widely studied as a binary classification problem. Semi-supervised learning is particularly suitable for improving classification accuracy when large quantities of unlabeled samples are generated (just like tracking procedure). The purpose of this paper is to fulfill robust and stable tracking by using collaborative learning, which belongs to the scope of semi-supervised learning, among three classifiers. Different from [1], random fern classifier is incorporated to deal with 2bitBP feature newly added and certain constraints are specially implemented in our framework. Besides, the way for selecting positive samples is also altered by us in order to achieve more stable tracking. Algorithm proposed in this paper is validated by tracking pedestrian and cup under occlusion. Experiments and comparison show that our algorithm can avoid drifting problem to some degree and make tracking result more robust and adaptive.
An approach of canny edge detection with virtual hexagonal image structure

Edge detection plays an important role in the areas of image processing, multimedia and computer vision. Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that, in the human vision system, the edge points always appear where the gradient magnitude assumes a maximum. Hexagonal structure is an image structure alternative to traditional square image structure. The geometrical arrangement of pixels on a hexagonal structure can be described as a collection of hexagonal pixels. Because all the existing hardware for capturing image and for displaying image are produced based on square structure, an approach that uses bilinear interpolation and tri-linear interpolation is applied for conversion between square and hexagonal structures. Based on this approach, an edge detection method is proposed. This method performs Gaussian filtering to suppress image noise and computes gradients on the hexagonal structure. The pixel edge strengths on the square structure are then estimated before Canny's edge detector is applied to determine the final edge map. The experimental results show that the proposed method improves the edge detection accuracy and efficiency.
An Approach to Edge Detection on a Virtual Hexagonal Structure

Hexagonal structure is another image structure alternative to traditional square image structure for image processing and computer vision. The geometrical arrangement of pixels on a hexagonal structure can be described as a collection of hexagonal pixels. Because all the existing hardware for capturing image and for displaying image are produced based on square structure, it becomes important to find a proper software approach to mimic hexagonal structure so that images represented on the traditional square structure can be smoothly converted from or to the images on hexagonal structure. For accurate image processing, it is critical to best maintain the image resolution during the image conversion. In this paper, a bilinear interpolation algorithm that is used to convert an image from square structure to hexagonal structure is presented. Based on this, an edge detection method is proposed. Our experimental results show that the bilinear interpolation improves the edge detection accuracy.
A Sybil Attack Detection Scheme for a Centralized Clustering-Based Hierarchical Network

Wireless Sensor Networks (WSNs) have experienced phenomenal growth over the past decade. They are typically deployed in remote and hostile environments for monitoring applications and data collection. Miniature sensor nodes collaborate with each other to provide information on an unprecedented temporal and spatial scale. The resource-constrained nature of sensor nodes along with human-inaccessible terrains poses various security challenges to these networks at different layers. In this paper, we propose a novel detection scheme for Sybil attack in a centralized clustering-based hierarchical network. Sybil nodes are detected prior to cluster formation to prevent their forged identities from participating in cluster head selection. Only legitimate nodes are elected as cluster heads to enhance utilization of the resources. The proposed scheme requires collaboration of any two high energy nodes to analyze received signal strengths of neighboring nodes. The simulation results show that our proposed scheme significantly improves network lifetime in comparison with existing clustering-based hierarchical routing protocols.
Counting People Based on Linear, Weighted, and Local Random Forests

Recently, many works have been published for counting people. However, when being applied to real-world train station videos, they have exposed many limitations due to problems such as low resolution, heavy occlusion, various density levels and perspective distortions. In this paper, following the recent trend of regression-based density estimation, we present a linear regression approach based on local Random Forests for counting either standing or moving people on station platforms. By dividing each frame into sub-windows and extracting features with ground truth densities as well as learned weights, we perform a linear transformation for counting people to overcome the perspective problems of the existing patch-based approaches. We present improvements against several recent baselines on the UCSD dataset and a dataset of CCTV videos taken from a train station. We also show improvements in speed compared with the state-of-the-art models based on detection and Deep Learning.
Geo-Location Oriented Routing Protocol for Smart Dynamic Mesh Network

Wireless Mesh Network is an emerging technology with great potential to become a Self-Sustained Network. Unlike the traditional networks that dominate the current communication system and rely on a large and expensive setup of wired/wireless access points to provide connection between users, the Wireless Mesh Network is formed by the user devices (referred as Nodes) which connect to each other to form a network. However, due to the use of legacy/traditional network models for mesh networks, there exist various limitations towards its implementation. This paper presents a new approach towards the Wireless Mesh Network, incorporating a new routing scheme based on the Geo-Location of the devices. It puts forward the structure, working principle and its performance during the first implementation.
Scalable switch for bi-directional MultiRing network

MultiRing is a network of 2/sup n/ nodes which can be configured into different ring networks of 2n-1 different configurations when ring direction being taken into account. It supports a wide variety of algorithms, such as algorithms for parallel image processing. In this paper, a MultiRing is implemented using a star topology with a MultiRing switch at the centre. We present a hierarchical design of the MultiRing switch. We demonstrate that the construction of the switch is economical in terms of gate count and port numbers. Our design preserves the need that all nodes can communicate simultaneously and independently in a ring configuration. We list the advantages of our design such as scalability and feasibility when comparing with other switch designs.
Gaussian Weighted Histogram Intersection for License Plate Classification

The conventional histogram intersection (HI) algorithm computes the intersected section of the corresponding color histograms in order to measure the matching rate between two color images. Since this algorithm is strictly based on the matching between bins of identical colors, the final matching rate can be easily affected by color variation caused by various environment changes. In this paper, a Gaussian weighted histogram intersection (GWHI) algorithm is proposed to facilitate the histogram matching via taking into account matching of both identical and similar colors. The weight is determined by the distance between two colors. The algorithm is applied to license plate classification. Experimental results show that the proposed algorithm produces a much lower intra-class distance and a much higher inter-class distance than previous HI algorithms for tested images which are captured under various illumination conditions
A method based on orientation field for skew correction of license plate

Character recognitions are generally very sensitive to skew in the automatic license plate recognition (ALPR) system. The skew correction of license plate thus is an important step in ALPR. In this paper, we propose an orientation-based method for skew detection. The license plate image is firstly divided into a set of 5×5 non-overlapping blocks. The local orientation of each black is estimated by gradients of pixels in the block. Next, the direction histogram which can reveal the overall orientation information in the license plate image is counted. The skew angle of license plate is detected by the local maximum of the direction histogram. The experimental results demonstrate the great robustness and efficiency of our method.
Local Binary Patterns for Human Detection on Hexagonal Structure

Local binary pattern (LBP) was designed and has been widely used for efficient texture classification. LBP provides a simple and effective way to represent texture patterns. Uniform LBPs play an important role for LBP-based pattern/object recognition as they include majority of LBPs. On the other hand, Human detection based on Mahalanobis distance map (MDM) recognizes appearance of human based on geometrical structure. Each MDM shows a clear texture pattern that can be classified using LBPs. In this paper, we compute LBPs of MDMs on a hexagonal structure. The circular pixel arrangement in hexagonal structure results in higher accuracy for LBP representation than on square structure. Chi-square as a measure is used for human detection based on uniform LBPs obtained. We show that our method using LBPs built on MDMs has a higher human detection rate and a lower false positive rate compared to the method merely based on MDMs. We will also show using experimental results that LBPs on hexagonal structure lead to more robust human classification.
Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.
Enhanced affine invariant shape description by ESPRIT

Affine transform best describes the shape change of an image that is viewed in different angles. Affine invariant shape descriptor provides us a means to recognize the object in an image regardless of shape change of the object. In this paper, three affine invariant shape descriptors are presented, which are all based on Fourier transform. To suppress the inherent shortage of Fourier transform, estimation of signal parameters via rotational invariance techniques (ESPRIT) is adopted to improve the discriminability of shape descriptor.
Image clustering using Particle Swarm Optimization

This paper proposes an image clustering algorithm using Particle Swarm Optimization (PSO) with two improved fitness functions. The PSO clustering algorithm can be used to find centroids of a user specified number of clusters. Two new fitness functions are proposed in this paper. The PSO-based image clustering algorithm with the proposed fitness functions is compared to the K-means clustering. Experimental results show that the PSO-based image clustering approach, using the improved fitness functions, can perform better than K-means by generating more compact clusters and larger inter-cluster separation.
Image Matching Using Colour Edge Cooccurrence Histograms

In this paper, a novel colour edge cooccurrence histogram (CECH) method is proposed to match images by measuring similarities between their CECH histograms. Unlike the previous colour edge cooccurrence histogram proposed by Crandall and Luo (2004 ) we only investigate those pixels which are located at the two sides of edge points in their gradient direction lines and at a distance away from the edge points. When measuring similarities between two CECH histograms, a newly proposed Gaussian weighted histogram intersection (GWHI) method is extended for this purpose. Both identical colour pairs and similar colour pairs are taken into account in our algorithm, and the weights are decided by the larger distance between two colour pairs involved in matching. The proposed algorithm is tested for matching vehicle number plate images captured under various illumination conditions. Experimental results demonstrate that the proposed algorithm can be used to compare images in real-time, and is robust to illumination variations and insensitive to the model images selected.
Learning-Based License Plate Detection Using Global and Local Features

This paper proposes a license plate detection algorithm using both global statistical features and local Haar-like features. Classifiers using global statistical features are constructed firstly through simple learning procedures. Using these classifiers, more than 70% of background area can be excluded from further training or detecting. Then the AdaBoost learning algorithm is used to build up the other classifiers based on selected local Haar-like features. Combining the classifiers using the global features and the local features, we obtain a cascade classifier. The classifiers based on global features decrease the complexity of the system. They are followed by the classifiers based on local Haar-like features, which makes the final classifier invariant to the brightness, color, size and position of license plates. The encouraging detection rate is achieved in the experiments
Number Plate Recognition Based on Support Vector Machines

Automatic number plate recognition method is required due to increasing traffic management. In this paper, we first briefly review some knowledge of Support Vector Machines (SVMs). Then a number plate recognition algorithm is proposed. This algorithm employs an SVM to recognize numbers. The algorithm starts from a collection of samples of numbers from number plates. Each character is recognized by an SVM, which is trained by some known samples in advance. In order to recognize a number plate correctly, all numbers are tested one by one using the trained model. The recognition results are achieved by finding the maximum value between the outputs of SVMs. In this paper, experimental results based on SVMs are given. From the experimental results, we can make the conclusion that SVM is bettr than others such as inductive learning-based number recognition
Web Service Locating Unit in RFID-Centric Anti-counterfeit System

The problem of piracy has disturbed people’s daily life for hundreds of years and has not been relieved until now, though many existing anti-counterfeit solutions have been applied. However, due to the emergences of Radio Frequency IDentification (RFID) technologies, there is a more reliable alternative solution to construct authentication system. On the other hand, there arises another issue of how to simplify the deployment of RFID-centric anti-counterfeit system over the Internet. In this article, we propose an approach, Web Service Locating Unit (WSLU), to achieve this goal to manage numbers of RFID-centric authentication services (relied on web services).
An overcomplete pyramid representation for improved gsm image denoising

Removing noise from a digital image is a challenging problem. Application of Gaussian Scale Mixtures (GSM) in the wavelet domain has been reported to be one of the most effective denoising algorithms, published to date. In this paper we investigate the impact of overcomplete wavelet image representations on the GSM image denoising algorithm. We explore the desirable local characteristics of wavelet coefficients that can enhance the efficiency of GSM denoising and based on the findings, we devise an improved over-complete pyramid representation to enhance the GSM denoising performance. We present the experimental denoising results using the proposed pyramid representation, and they outperform state-of-the-art GSM denoising results reported in the literature.
Evaluation of billing and charging architecture for the Internet service provisioning

This article develops a charging scheme that is simple and easily usable for the users and provides them with the incentives to use only the resources they require. Our scheme has been developed and based on the use of Internet resource and demonstrates how the contributing providers can share the total charge earned by each mobile and wireless services in a fair way. We made a comparison of our architecture with existing architectures and demonstrated that our architecture adopts an accommodating approach for customer which is economically viable for the ISP provider.
Basic Transformations on Virtual Hexagonal Structure

Hexagonal structure is different from the traditional square structure for image representation. The geometrical arrangement of pixels on hexagonal structure can be described in terms of a hexagonal grid. Hexagonal structure provides an easy way for image translation and rotation transformations. However, all the existing hardware for capturing image and for displaying image are produced based on square architecture. It has become a serious problem affecting the advanced research based on hexagonal structure. In this paper, we introduce a new virtual hexagonal structure. Based on this virtual structure, a more flexible and powerful image translation and rotation are performed. The virtual hexagonal structure retains image resolution during the process of image transformations, and does not introduce distortion. Furthermore, images can be smoothly and easily transferred between the traditional square structure and the hexagonal structure
Subspace Analysis Methods plus Motion History Image for Human Action Recognition

This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History Image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting of several frames for recognition, our method uses only a MHI per action sequence for recognition. Obviously, our method avoids the complexity as well as the large computation in sequence matching based methods. Encouraging experimental results on a widely used database demonstrate the effectiveness of the proposed method.
Face recognition based on modified LBP

Face recognition in unconstrained, natural conditions still remains a challenging task. As a powerful local descriptor, Local Binary Patterns has shown the advantage of representation and performance. However, it is still affected by robustness and accuracy. In this paper, a novel method is presented to improve the performance of automatic face recognition under uncontrolled conditions. We modify the conventional Local Binary Pattern and use it as a new feature descriptor. Partial Hausdorff Distance is applied as a dissimilarity measurement. Experimental results show that the proposed algorithm outperforms the traditional LBP approach in terms of accuracy rate and robustness. It can reduce the sensitivity caused by illumination variation, pose variation, occlusion etc.