nils-herrmann/clean_scirepeval huggingface dataset:
3-D structure recovery from 2-D observations

In this paper we present a novel method for simultaneously determining three dimensional motion and structure of a non-rigid object from its uncalibrated two dimensional data with Gaussian or non-Gaussian distributions. A non-rigid motion can be treated as a combination of a rigid component and a non-rigid deformation. To reduce the high dimensionality of the deformable structure or shape, we estimate the probability distribution function of the structure through random sampling, integrating an established probabilistic model. The fitting between the observations and the estimated 3-D structure is evaluated using the pooled variance estimator. Applications of the proposed method to both synthetic and real image sequences show promising results.
An Analysis of Properties of Malignant Cases for Imbalanced Breast Thermogram Feature Classification

Medical thermography has been demonstrated an effective and inexpensive method for detecting breast cancer, in particular for tumors in early stages and in dense tissue. Image features can be extracted from breast thermograms and used in a pattern classification stage for automated diagnosis and hence as a second objective opinion or for screening purposes. One of the main challenges for applying machine learning algorithms to this task is the high imbalance ratio between class distributions in the available training data. In this paper, we carefully examine the properties of the malignant minority class in order to gain insight into the nature of the data. We identify different types of minority class samples present in a breast thermogram dataset comprising about 150 cases. Using the gained knowledge, we analyse the performance of three state-of-the-art ensemble classifiers, a cost-sensitive one, one based on over-sampling and one using under-sampling, to evaluate which objects are the most difficult to classify correctly. Experimental analysis shows that there is a strong correlation between the type of minority sample and the performance of specific classifier ensemble types.
Image and Text Fusion for Context-aware Recommendation

In this paper, we propose a system that obtains sensor data including multimedia data such as text messages and image data from mobile phones, and extracts information about the user situation from these data to provide situation-oriented web services to users exploiting the extracted context. Based on the situation awareness of the system, it is attained to increase the precision of content-based image matching by combining the partial context obtained there from other types of data such as textual information.
Scleroderma capillary pattern identification using texture descriptors and ensemble classification

Various connective tissue diseases lead to morphological alternations of blood capillaries. Consequently, observation of the capillaries at the finger nailfold - nailfold capillaroscopy (NC) - is a standard method for diagnosing diseases such as scleroderma or Raynaud's phenomenon. This is typically performed through manual inspection by an expert to lead to a determination of one of the established NC scleroderma patterns (early, active, and late). In this paper, we present an automated method of analysing nailfold capillaroscopy images and categorising them into NC patterns. For this purpose, we extract a carefully chosen set of texture features from the images and employ an ensemble classification approach to arrive at decisions for each captured finger which are then aggregated to form a diagnosis for the patient. Experimental results on a set of 60 NC images from 16 subjects demonstrate the accuracy and usefulness of our presented approach.
Very Fast Image Retrieval Based on JPEG Huffman Tables

In this paper, we present a very fast method for performing content-based image retrieval of JPEG compressed images. Our method works directly in the compressed domain of JPEG but in contrast to previous techniques does not require to undo the entropy coding stages of the compression. The reason for this is that, as we show, image adapted Huffman tables can be directly employed as image descriptors and image similarity defined as similarity between the corresponding tables. To do this, we extract both DC and AC Huffman tables and compare the lengths of assigned prefix codes, which give an indication of the frequencies of the related DC and AC values, and use this to compare images. Since the Huffman tables reside in the header of JPEG images, our approach is extremely fast, as not only does it not require any kind of decompression it also needs reading in only a fraction of the file. We evaluate our method on benchmark databases of varying sizes up to in excess of 1 million images, and show that our approach achieves retrieval performance similar to other techniques, while providing a speedup more than 30-fold compared to JPEG compressed domain algorithms and more than 150-fold compared to common pixel domain techniques for online image retrieval.
Visual appearance based document image classification

In the paper, we present a new method for classifying documents with rigid geometry. Our approach is based on the fast and robust Viola-Jones object detection algorithm. The advantages of our proposed method are high speed, the possibility of automatic model construction using a training set, and processing of raw source images without any pre-processing steps such as draft recognition, layout analysis or binarisation. Furthermore, our algorithm allows not only to classify documents, but also to detect the placement and orientation of documents within an image.
Can Mean Shift Trackers Perform Better?

Many tracking algorithms have difficulties dealing with occlusions and background clutters, and consequently don't converge to an appropriate solution. Tracking based on the mean shift algorithm has shown robust performance in many circumstances but still fails e.g.\ when encountering dramatic intensity or colour changes in a pre-defined neighbourhood. In this paper, we present a robust tracking algorithm that integrates the advantages of mean shift tracking with those of tracking local invariant features. These features are integrated into the mean shift formulation so that tracking is performed based both on mean shift and feature probability distributions, coupled with an expectation maximisation scheme. Experimental results show robust tracking performance on a series of complicated real image sequences.
An efficient edge preserving image interpolation algorithm

Quality degradation and computational complexity are the major challenges for image interpolation algorithms. Advanced interpolation techniques achieve to preserve fine image details but typically suffer from lower computational efficiency, while simpler interpolation techniques lead to lower quality images. In this paper, we propose an edge preserving technique based on inverse gradient weights as well as pixel locations for interpolation. Experimental results confirm that the proposed algorithm exhibits better image quality compared to conventional algorithms. At the same time, our approach is shown to be faster than several advanced edge preserving interpolation algorithms.
A hybrid biometric approach embedding DNA data in fingerprint images

In this paper, we present a robust image watermarking approach to hide DNA sequence data into fingerprint templates for copyright protection based on the discrete wavelet transform. A multi-bit watermark, based on DNA data, is embedded into the low frequency sub-band of the fingerprint image. During the inverse process, we employ DNA decoding to extract the image signature from the watermarked image. We evaluate our proposed method using statistical measures including mean squared error, peak signal-to-noise-ratio, and structural similarity index, and the experimental results confirm that the watermarks generated using our approach are invisible.
Classifier ensembles for imbalanced classification

Pattern classification algorithms learn from training samples to generalise to previously unseen data. In practice, many pattern classification tasks are imbalanced. That is, that there exist (many) more training samples of some classes compared to others. This in turn typically leads to poor classification performance on the minority classes even though often they are the more important ones. In this paper, we consider strategies for addressing class imbalance in the context of ensemble classifiers, i.e. classifiers that employ multiple predictors. In particular, we review approaches based on oversampling, an ensemble whose base classifiers are trained on balanced data subsets, a multiple classifier systems with cost-sensitive base classifiers, and a combination of one-class classifiers.
Monitoring daily variations of atmospheric electric fields using data mining methods

The atmospheric electric field potential gradient is studied during snowfall, and the intensity of snowflakes electrifying depending on weather conditions is estimated. Applying modern data mining methods and high-mountain monitoring, where the anthropogenic factors on the electric field variations is insignificant, enables the identification of the influence of snowfall and snowstorms on the electric field's daily variations. Numerical data of the electrification process in the atmosphere are obtained, and relationships between electric field values and snowfall intensity, wind speed and temperature are demonstrated. Modern neural network techniques for data mining are applied in this context.
Mobile image browsing

Image collections, both private and commercial, are ever growing and therefore visual information is becoming more and more important. Moreover, the classical paradigm of how pictures are typically taken has changed completely, first with the spread of digital cameras and, more recently, with mobile devices equipped with integrated cameras. Clearly, these image repositories need to be managed, and tools for effectively and efficiently searching image databases are highly sought after. In this paper, we present the idea of image database visualisation and image browsing as an intuitive and useful approach for accessing large image collections. After introducing the basic concepts, and some of our earlier (desktop-based) image browsing systems, we then focus on our latest work in this area which is the development of effective image browsing applications for mobile devices such as mobile phones and tablets.
Dominant multi-dimensional local binary patterns

Local binary patterns (LBP) are known as a simple yet powerful texture descriptor encoding local neighbourhood properties. LBP descriptors can be calculated at different radii, leading to a multi-resolution texture characterisation. Multi-dimensional LBP (MD-LBP) utilises this concept, while also maintaining the relationships between the different scales by building a multi-dimensional histogram of LBP features. Although this has been shown to give good discriminatory power, the resulting feature vectors are also rather large. In this paper, we show that Dominant MD-LBP (D-MD-LBP), which utilises only dominant texture bins, provides an effective texture descriptor of reduced dimensionality as our experimental results, run on three benchmark datasets of the Outex test suite, confirm.
An adaptive distance-based edge preserving interpolation algorithm for natural images

In this paper, we propose a new image interpolation method using adaptive weights based on inverse gradients and distances from the pixels used in prediction. Since the weights are based on different spatial locations of the pixels, this allows preservation of important edge information and hence to prevent extensive blurring across edges in the upsampling process. Experimental results on a large test image set show that the proposed algorithm gives better performance compared to conventional algorithms.
Intelligent analysis of prostate ultrasound images

In this paper, we present an intelligent approach to analysing prostrate ultrasound images in order to diagnose prostate cancer. Algorithms based on fuzzy image processing are applied first to enhance the contrast of the original image, to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterising the underlying texture of the regions of interest based wavelet domain features. Finally, a rough neural network, where a neural network and rough set theory are integrated into a hybrid system, is designed for discrimination of different regions of interest to test whether they represent malignant or benign cases. The neural network is built from rough neurons, each of which can be viewed as a pair of sub-neurons, corresponding to the lower and upper bound concepts of rough set theory. Experimental results show that the overall classification accuracy of our approach is high.
Overlay of thermal and visual medical images using skin detection and image registration

Thermography captures the temperature distribution of the human skin and is employed in various medical applications. Often it is useful to cross-reference the resulting thermograms with visual images of the patient, either to see which part of the anatomy is affected by a certain disease or to judge the efficacy of the treatment. An attractive approach to provide this information is to overlay the two image types and show a composite image to the clinician. Producing such an overlay however is a non-trivial task due to differences in image capturing conditions of the two modalities. In this paper we introduce an approach that produces accurate overlays of thermal and visual medical images. First unnecessary background information of the visual part are removed by an image segmentation step based on skin detection. The thermal image is then aligned through an intensity based image registration technique. Experimental results based on an set of visual-thermal image pairs demonstrate the effectiveness of the proposed approach
A novel method of organisation of a software defined network control system

In this paper, we offer a new method for solving control tasks in software-defined networks for use in corporate networks. Its main feature is the ability to increase failure free operation due to employing distributed computing resources available in the corporate network. To achieve the ability of effective usage of personal computers, our proposed method uses a multi-agent approach, where a proactive agent controls every personal computer and the process of task solution is dispatched in a decentralised way through interactions of agents. To solve every control task, the agents of the system collaborate and thus facilitate dispatch and computation.
Objective evaluation of methods for border detection in dermoscopy images

Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, dermoscopy image analysis has become an important research area. Border detection is often the first step in the automated analysis of dermoscopy images. Although numerous methods have been developed for the detection of lesion borders, very few studies were comprehensive in the evaluation of their results. In this paper, we evaluate five recent border detection methods on a set of 90 dermoscopy images using three sets of dermatologist-drawn borders as the ground-truth. In contrast to previous work, we utilize an objective measure, the Normalized Probabilistic Rand Index, which takes into account the variations in the ground-truth images. The results demonstrate that the differences between four of the evaluated border detection methods are in fact smaller than those predicted by commonly used measures.
Development of a novel border detection method for melanocytic and non-melanocytic dermoscopy images

Computer aided diagnosis of dermoscopy images has shown great promise in developing a quantitative, objective way of classifying skin lesions. An important step in the classification process is the lesion segmentation. Many papers have been successful at segmenting melanocytic skin lesions (MSLs) but few have focused on non-melanocytic skin lesions (NoMSLs), since the wide variety of lesions makes accurate segmentation difficult. We developed an automatic segmentation program for the border detection of skin lesions. We tested our method on a set of 107 non-melanocytic lesions and on a set of 319 melanocytic lesions. Our method achieved precision/recall scores of 84.5% and 88.5% for NoMSLs, achieving higher scores than two previously published methods. Our method also achieved precision/recall scores of 93.9% and 93.8% for MSLs which was competitive or better than the two other methods. Therefore, we conclude that our approach is an accurate segmentation method for both melanocytic and non-melanocytic lesions.
Evaluating the effects of image filters in CT Liver CAD system

The main objective of image pre-processing is to improve the quality of an image so that it makes subsequent phases of image analysis like segmentation or recognition easier or more effective. Filtering is a key pre-processing technique used for various effects including contrast stretching, sharpening and smoothing. In this paper, we evaluate and analyse the effect of several image filtering techniques with respect to their computer aided diagnosis (CAD) performance. The techniques we investigate include contrast stretching, convolution, median fitlering, averaging, inverse transformation and logarithm transformation filters. An application of CT liver imaging CAD was chosen and the selected filters were applied to see their ability and accuracy to segment and isolate the liver region of interest using a region growing segmentation approach. The effect of the filtering techniques on the segmentation performance of the CAD system was investigated using mean squared error (MSE) and similarity index (SI). The highest performance was achieved for a contrast stretching filter (MSE = 0.1869, SI = 0.8423) and the combination of contrast stretching and average filter (MSE = 0.17198 and SI = 0.83257).
Strategies for imbalanced pattern classification for digital pathology

Pattern classification tasks in digital pathology, which involves the analysis of high resolution digital slides of tissue samples for medical diagnosis, are, like many other medical decision making processes, often imbalanced. This means that there are (many) more training samples of some classes available compared to others, while it is often the minority class(es) that are of medical interest. In this paper, we present strategies for addressing class imbalance in pattern classification problems including the development of cost-sensitive fuzzy classifiers and the derivation of ensemble classification methods, that is classifiers that employ multiple predictors, dedicated for imbalanced classification.
Nailfold capillaroscopy pattern recognition using texture analysis

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. Diagnosis of NC images involves the recognition of early, active and late patterns, also known as NC patterns or scleroderma (SD) patterns, in the images. In this paper, we propose a holistic method to classify NC images in these well known patterns. In particular, we employ texture analysis to describe the underlying patterns, coupled with a classifier to first identify patterns in fingers, and then, through a voting strategy, reach a decision for a patient. Experimental results on a set of NC images with known ground truth demonstrate the efficacy of our approach.
An Evaluation of LBP Texture Descriptors for the Classification of HEp-2 Cells

Indirect immunofluorescence imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells and consequently important for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which give indications on different autoimmune diseases. In the literature, various algorithms have been proposed for automatic classification of HEp-2 cells based typically on shape features, texture features and classification algorithms. Local binary pattern (LBP) features are simple yet powerful texture descriptors, which encode the neighbours of a pixels into a binary pattern. While over the years a variety of LBP algorithms have been introduced, only a few descriptors are utilised in the context of HEp-2 cell classification. In this paper, we benchmarked eight rotation invariant LBP variants and a total of 16 descriptors on the ICPR 2012 HEp-2 contest benchmark dataset. We found rotation invariant multi-dimensional LBP features to lead to the best classification performance.
Visualisation and Browsing of Flickr Retrieval Results

With the prevalence of digital cameras and increased band with, online photo collections are growing at a rapid rate. Especially larger image collections require user-friendly tools to search and browse the dataset for which standard approaches of organising images are not sufficient. In this demo paper, we present an efficient and effective approach to visualising and browsing image search results. While various approaches to visualisation and browsing of image database have been introduced, these are too slow to be applied to online retrieval results since the complete images would need to be downloaded for feature extraction and exploitation. The method we present here addresses this problem and allows for interactive image database navigation of images retrieved from the photo sharing site Flickr. We do this by utilising solely the headers of JPEG files, in particular the Huffman tables there, for feature extraction coupled with a projection onto a 2-dimensional visualisation space based on principal component analysis. Images are dynamically placed into a grid structure and organised in a tree-like hierarchy for visual browsing.
A new family of order-statistics based switching vector filters

In this paper, we present a family of order-statistics based vector filters for the removal of impulsive noise from color images. These filters preserve the edges and fine image details by switching between the identity (no filtering) operation and a robust order-statistics based filter operation based on the univariate median operator. Experiments on a diverse set of images and comparisons with state-of-the-art filters show that the proposed filters combine simplicity, flexibility, good filtering quality, and low computational requirements.
An uncompressed benchmark image dataset for colour imaging

Virtually all vision and imaging applications and algorithms require validation and evaluation, yet benchmarking and evaluation have proven to be difficult and challenging. This is partly due to the amount of work involved in performing an appropriate and convincing test, but is also often hindered by the fact that there are rather few test datasets that are publicly available. In this paper we present UCID10K, an image database comprising 10,000 colour images. Importantly, all images in the dataset are captured and preserved in uncompressed form allowing its use for benchmarking a variety of imaging applications including image compression, colour quantisation, steganography and image forensics. In addition, a ground truth for the evaluation of content-based image retrieval algorithms is provided enabling the UCID dataset to be used for assessing the performance of image querying techniques but also the testing of compressed-domain retrieval techniques. The UCID10K database is publicly available to fellow researchers.