nils-herrmann/clean_scirepeval huggingface dataset:
Skin lesion segmentation using co-operative neural network edge detection and colour normalisation

Lesion segmentation is an important step in analysing dermoscopic skin lesion images. In this paper we achieve accurate lesion segmentation using a co-operative neural network-based edge detection approach coupled with a pre-processing step that enhances colour information and contrast of the images. Extensive experiments are carried out on a dataset of 100 dermoscopic images with known ground truths obtained from three expert dermatologists. The results show that our approach is capable of providing good segmentation performance while the colour enhancement step leads to improved segmentation accuracy.
An Integrative Semantic Framework for Image Annotation and Retrieval

Most public image retrieval engines utilise free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. Our semantic retrieval technology is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. We also present our efforts in further improving the recall of our retrieval technology by deploying an efficient query expansion technique.
A reliability improvement method for reconfigurable information and oil production control systems

Fault tolerance, a way to obtain system dependability, is extremely important in domains such as aircraft and spacecraft industries, energy plants, and oil processing and oil producing industries. For information and control systems (ICSs) with performance redundancy, fault tolerance is provided by a special reconfiguration procedure which relates to the monitoring and control task distribution among the operable computational nodes. At the same time, the reliability function of the ICS depends on the computational node load intensity: as the node load (and temperature) increases, the failure rate also increases. This is the cornerstone of the reliability improvement method we present in this paper: to control and improve the reliability function of the system by the configuration formation process. We describe our approach in detail, present a formal model of the ICS configuration formation problem, and give and discuss experimental results.
Content-based retrieval from image databases: Colour, compression, and browsing

Content-based image retrieval (CBIR) has been an active research area for the last two decades and much progress has been made in that time. However, there are still many challenges to be overcome and in this paper we highlight some of these together with some approaches that we have developed to address these problems. In particular, we focus on the issues of colour (or more precise colour variance), image compression, and image database browsing. While colour features are the most widely used image descriptors for CBIR, colour is not necessarily a stable cue as it also depends on various image capture conditions. Colour invariants are features designed to be robust with respect to these confounding factors. Image compression, which is typically applied to most images in use, leads to both processing overheads but also to a small but noticeable drop in retrieval performance. To address these problems, we have developed image retrieval techniques that operate directly in the compressed domain, yet provide better retrieval performance than many standard CBIR techniques. Finally, we look at browsing systems as an alternative approach to dealing with large image databases. The hue sphere browsing system that we have developed organises images on a spherical visualisation space so that visually similar images are located close to each other, and provides powerful interaction tools that aid in exploring the visualised image collections.
GA-based optimisation of fuzzy rule bases for pattern classification

Many decision making problems can be formulated as pattern classification problems. Therefore, high performing classification algorithms are highly sought after. Rule based pattern classification algorithms have an advantage that they do not appear to the user just as a “black box” but may provide additional insight based on the generated rules. In this paper, we focus on fuzzy rule based approaches which employ concepts from fuzzy logic theory to encode input patterns in a non-binary way. Starting with a basic fuzzy classifier we show that, through a simple modification, it can be turned into a cost sensitive classification method, and that classification performance can be improved through an error correction learning approach. Importantly, since rule-based classifiers are prone to rule explosion, we then show that a compact yet powerful rule base can be generated through an optimisation approach based on genetic algorithms.
Interactive browsing of large image databases

With image databases expanding at a rapid rate, effective and efficient methods of managing and querying these collections are highly sought after. In this paper, we present image browsing as an alternative to retrieval based systems. Image browsers provide a visualisation of a complete image database together with tools for interactive and intuitive exploration of the dataset. As examples we give an introduction to our Hue Sphere Image Browser system which provides an intuitive hierarchical approach to navigating large image repositories, while we also present ports to mobile devices and large multi-touch screens.
A hardware approach for organisation of software defined network switches based on FPGA

In this paper, we present a novel method for a hardware-based search in the rule tables of software defined network (SDN) switches. We describe the design features of modern SDNs based on the open standard OpenFlow, and highlight the main problems of modern OpenFlow switches. We then propose a new approach for a flow table search based on field-programmable gate arrays (FPGAs). The proposed method allows searching by means of a mask in all bits of the header of incoming packages and therefore to expand the usability of SDNs.
A cost-sensitive ensemble classifier for breast cancer classification

Breast cancer is the most commonly diagnosed form of cancer in women. Pattern classification approaches often have difficulties with breast cancer related datasets as the available training data are typically imbalanced with many more benign cases recorded than malignant ones, leading to a bias in the classification and insufficient sensitivity. In this paper, we present an ensemble classification algorithm that addresses this problem by employing cost-sensitive decision trees as base classifiers which are trained on random feature subspaces to ensure diversity, and an evolutionary algorithm for simultaneous classifier selection and fusion. Experimental results on two different breast cancer datasets confirm our approach to work well and to provide boosted sensitivity compared to various other state-of-the-art ensembles.
Hidden semantic concept discovery in region based image retrieval

This work addresses content based image retrieval (CBIR), focusing on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented to region; associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse region-based representation is achieved. With this representation a probabilistic model based on statistical-hidden-class assumptions of the image database is obtained, to which expectation-maximization (EM) technique is applied to analyze semantic concepts hidden in the database. An elaborated retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative example, to the discovered semantic concepts. The proposed approach has a solid statistical foundation and the experimental evaluations on a database of 10,000 general-purposed images demonstrate its promise of the effectiveness.
Efficient and effective regularised ART for computed tomography

Algebraic Reconstruction Technique (ART) is a widely employed method in computed tomography since it has certain advantages, such as allowing reconstruction of data with missing projections in some angle ranges, over other techniques such as Filtered Back Projection (FBP). Recently, a regularisation technique for ART, RegART, was introduced which provides greatly reduced noise levels. However, a serious drawback of both ART and RegART is the computational complexity of the methods. In this paper, we present a fast version of RegART, which makes use of nVidia's CUDA technology, and show that this approach performs favourably compared to FBP.
Fuzzy Classification of Gene Expression Data

Microarray expression studies measure, through a hybridisation process, the levels of genes expressed in biological samples. Knowledge gained from these studies is deemed increasingly important due to its potential of contributing to the understanding of fundamental questions in biology and clinical medicine. One important aspect of microarray expression analysis is the classification of the recorded samples which poses many challenges due to the vast number of recorded expression levels compared to the relatively small numbers of analysed samples. In this paper we show how fuzzy rule-based classification can be applied successfully to analyse gene expression data. The generated classifier consists of an ensemble of fuzzy if-then rules which together provide a reliable and accurate classification of the underlying data. Experimental results on several standard microarray datasets confirm the efficacy of the approach.
GA-based optimization of fuzzy rule bases for pattern classification

Many problems can be cast as pattern classification problems. Consequently, developing effective classifiers has become an important research area. Various techniques have been proposed to produce classifiers, however many of these appear to the user as “black boxes” which merely give a decision without any additional insight. In this lecture, the focus will be on fuzzy rule-based classification systems which generate simple if-then rules that can thus also be interpreted by the user. Since rule-based classifiers are prone to rule explosion, It will be presented, in particular, optimization approaches to rule base generation that are based on genetic algorithms and a shown to result in a compact yet effective set of rules. In addition, through a simple modification, the resulting classifier can be made cost-sensitive which is in particular useful for applications in medical diagnosis. Example applications will include the classification of gene expression data and the use of classifiers for breast cancer diagnosis.
Enhancement of nailfold capillaroscopy images

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold. It is particularly useful for early detection of scleroderma spectrum disorders and evaluation of Raynaud's phenomenon. While diagnosis based on NC is typically performed by manual inspection, computerised nailfold capillaroscopy can help to reduce the inherent ambiguity present in human judgement while greatly reducing the time for diagnosis. A crucial step in automated image analysis not only of NC images but of any kind of medical images is an image enhancement process. In this paper, we evaluate the performance of six image enhancement/noise removal techniques for NC images, as a pre-cursor to edge detection aimed at identifying capillaries. Results on a variety of NC images show that bilateral filters and enhancers provide the best image quality for subsequent capillary detection.
Breast Thermogram Analysis Using Classifier Ensembles and Image Symmetry Features

Breast cancer is the most commonly diagnosed form of cancer in women. Thermography, which uses cameras with sensitivities in the thermal infrared, has been shown to provide an interesting alternative to the standard modality of mammography for detecting breast cancer as it is able to detect smaller tumors and hence can lead to earlier diagnosis. In this paper, we present an approach to breast thermogram analysis that extracts features describing bilateral symmetries from an image and then utilizes a classifier ensemble for decision making. Importantly, our classification approach addresses the problem of imbalanced class distribution that is common in medical data analysis. We do this by constructing feature subspaces from balanced data subsets and train different classifiers on different subspaces. To combine the individual classifiers, we investigate two different strategies. The first dynamically assigns classifier weights based on an evolutionary algorithm, while the second uses a neural network for classifier fusion. Both approaches are shown to work well and to lead to significantly improved performance compared to canonical classification systems.
Credibility investigation of newsworthy tweets using a visualising Petri net model

Investigating information credibility is an important problem in online social networks such as Twitter. Since misleading information can get easily propagated in Twitter, ranking tweets according to their credibility can help to detect rumors and identify misinformation. In this paper, we propose a Petri net model to visualise tweet credibility in Twitter. We consider the uniform resource locator (URL) as an effective feature in evaluating tweet credibility since it is used to identify the source of tweets, especially for newsworthy tweets. We perform an experimental evaluation on about 1000 tweets, and show that the proposed model is effective for assigning tweets to two classes: credible and incredible tweets, which each class being further divided into two sub-classes (“credible” and “seem credible” and “doubtful” and “incredible” tweets, respectively) based on appropriate features.
Human Authentication Using Face and Fingerprint Biometrics

Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this paper, we present a multimodal biometric system that is based on the fusion of face and fingerprint biometrics. For face recognition, we employ uniform local binary patterns (ULBP), while minutiae extraction is used for fingerprint recognition. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.
Flickr Retriever -- Fast Retrieval of Flickr Photos

With image repositories growing at an exponential rate, efficient methods for querying such databases are highly sought after. In this demo paper, we present Flickr Retriever, a software program that performs fast retrieval of images from the photo sharing site Flickr using content-based image retrieval concepts. Our methods allows retrieval of JPEG compressed images using only information stored in the header of the files. In particular, we make use of the Huffman tables in the JPEG headers and the fact that during photo upload Flickr optimises these Huffman tables in an image adaptive way. Flickr Retriever can be downloaded from our website.
Cellular neural network based algorithms in the early detection of hand osteoarthritis

Cellular Neural Network (CNN) algorithms have been successfully used in a plethora of image processing applications including the medical imaging domain. Analogic CNN algorithms use CNN templates combined with logic operations to perform operations such as blurring and thresholding for image processing. In this paper we apply CNN based techniques incorporating image enhancement, region segmentation and line detection for detecting the manifestations of osteoarthritis, a metabolic disease afflicting the elderly population caused by wear and tear of cartilage surrounding weight bearing bone joints like the human hand. The two main indicators of osteoarthritis that we examine are the cystic regions, and osteophytes or bony spurs in the vicinity of the joints, produced by the rubbing together of bones due to joint space narrowing.
Approaches for interactive browsing of large image datasets

With the number of digital images growing at a rapid rate, effective approaches for organising large image repositories are highly sought after. In this paper, we summarise a number of such approaches that allow for intuitive browsing of image databases. Based on visual features extracted from the images, complete image libraries can be visualised in such a way that similar images are located close to each other in the visualisation. This, coupled with appropriate browsing operations, allows for interactive exploration of even large scale image collections as demonstrated by the Hue Sphere Image Browser and Honeycomb Image Browser approaches.
Effective classification of HEp-2 cells using fuzzy texture descriptors

Indirect immunofluorescence (IIF) imaging is an important technique for detecting antinuclear antibodies in HEp-2 cells and therefore employed in the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells are often categorised into six groups (homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and centromere cells), which in turn give indications on different autoimmune diseases. While traditionally this classification is performed manually thus representing a subjective and laborous task, recently there is significant interest in computer vision based approaches to automatically categorise HEp-2 cell images and thus provide an objective and fast alternative. Various algorithms have been proposed for this purpose in which texture information often plays a dominant role. In this paper, we also employ texture descriptors for automated classification of HEp-2 cells but choose descriptors that allow to take into account the fuzzy nature of the images and the noise present in them. In particular, we employ a fuzzy version of the well known local binary pattern (LBP) paradigm, coupled with support vector machine (SVM) based classification. We benchmark our approach on the ICPR 2012 HEp-2 contest benchmark dataset and show it to provide excellent classification performance, outperforming not only conventional LBP features but also all algorithms that were entered in the competition as well as the performance of a human expert.
Compact Multi-dimensional LBP Features for Improved Texture Retrieval

Content-based image retrieval has become an important research area and consequently well performing retrieval algorithms are highly sought after. Texture features are often crucial for retrieval applications to achieve high precision, while local binary pattern (LBP) based texture descriptors have been shown to work well in this context. LBP features decsribe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture description. Furthermore, local contrast information can be integrated into LBP leading to LBP variance (LBPV) features. In conventional LBP methods, the histograms corresponding to different radii are simply concatenated resulting in a loss of information between different resolutions and added ambiguity. In this paper, we show that multi-dimensional LBP and LBPV histograms, which preserve the relationships between scales, provide improved texture retrieval performance. To cope with the exponential increase in terms of feature length, we show that application of principal component based feature reduction leads to very compact texture descriptors with high retrieval accuracy.
Molecular classification of Newcastle disease virus based on degree of virulence

Newcastle disease (ND) is one of the most serious infectious diseases of poultry, which have an important economic impact on poultry sector production. The causative agent of the disease is Newcastle disease virus (NDV). NDV strains can be classified into two types according to virulence, namely highly virulent (velogenic) and low virulent (lentogenic) based on their pathogenicity in chickens. In this paper, we address the problem of classifying the new isolated sequence of the newcastle disease virus according to the fusion protein. In order to classify the degree of virulence of the Newcastle virus, we propose a new approach based on the rotation forest algorithm. The performance of our approach is evaluated and compared with three benchmark algorithms. The results show that our proposed algorithm is able to achieve perfect recognition on the benchmark dataset.
An improved algorithm for skeletonisation of nailfold capillaries

Nailfold capillaroscopy (NC) is a medical imaging modality used to assess the characteristics and morphology of micro-blood vessels in the nailfold. NC is of particular importance in diagnosing diseases that lead to morphological changes of capillaries such as scleroderma, Raynaud's phenomenon and other connective tissue diseases. In order to provide a computer-aided diagnosis approach to analysing NC images, in this paper, we present a skeletonisation algorithm of captured nailfold capillaries. Our approach first enhances the image using an a-trimmed filter, and then performs a binarisation step based on difference of Gaussian filtering and thresholding, followed by iterative thinning to extract the capillary skeletons. We show that the proposed algorithm works well and gives significantly superior performance compared to previous approaches.