nils-herrmann/clean_scirepeval huggingface dataset:
Keypoint-Based Keyframe Selection

Keyframe selection has been crucial for effective and efficient video content analysis. While most of the existing approaches represent individual frames with global features, we, for the first time, propose a keypoint-based framework to address the keyframe selection problem so that local features can be employed in selecting keyframes. In general, the selected keyframes should both be representative of video content and containing minimum redundancy. Therefore, we introduce two criteria, coverage and redundancy, based on keypoint matching in the selection process. Comprehensive experiments demonstrate that our approach outperforms the state of the art.
Regression and classification based distance metric learning for medical image retrieval

Better utilizing the vast amount of valuable information stored in the medical imaging databases is always an interesting research area, and one way is to retrieve similar images as a reference dataset to assist the diagnosis. Distance metric is a core component in image retrieval; and in this paper, we propose a new learning-based distance metric design, based on regression and classification techniques. We design a weight learning approach by classifying the similar-dissimilar data samples, and a further optimization with a sparsity-constraint regression algorithm for feature selection. The learned distance metric is generally applicable for medical image retrievals. We evaluate the proposed method on clinical PET-CT images, and demonstrate clear performance improvements.
EAGLE: A novel descriptor for identifying plant species using leaf lamina vascular features

The timely and accurate identification of plant species is a persistent challenge as pressure from human activity threatens global flora biodiversity. Most existing research on computer based plant species identification has focused on using leaf contour, signature and spectral analysis techniques alongside textural properties of the leaf lamina. However, these global feature based methods often suffer from limited discriminative ability and scalability, particularly on mobile devices for use in the field. In this paper, we propose a novel descriptor named EAGLE and employ the popular Bag of Visual Words (BoVW) model to achieve scalable and effective species identification suitable for implementation on mobile devices. EAGLE exploits the vascular structure of a leaf within a spatial context, where the edge patterns among neighbouring regions characterise the overall venation structure and are represented in a histogram of angular relationships. Experimental results on the widely used Swedish leaf image database demonstrated the EAGLE descriptor is able to boost the performance of the effective SURF local descriptor by 6%.
Enhancing home area networks to facilitate telehealth services: Test-bed scenario with video-consultation-calls (VCC)

Recent years have witnessed rapid development of Telehealth applications and the integration of portable devices to facilitate services such as Voice-over-IP (VoIP), Video-over-IP and distribution of other data types e.g., medical images and health sensor data. In line with Telehelath, Home Area Network (HAN) has also seen broad deployments and has expanded conventional households to support latest electronic devices that are equipped with network connectivity. Telehealth applications are starting to be designed to take advantages of the HAN environment and are providing healthcare at the patients home, i.e., facilitates hospital at home via video-consultation-call (VCC). However, some of these Telehealth applications are highly delay sensitive and require higher bandwidth priority and a network load-balancing system in order to produce best quality of service (QoS). We observed that typical HAN is established without bandwidth priority and network-load-balancing systems. This lack of management causes HAN to produce unacceptable QoS for Telehealth applications. In this scenario, we propose a scheme to enhance HAN while providing better quality of VCC to support Telehealth services. Our scheme controls the Internet traffic within the HAN network infrastructure environment. In our experimental setting, we implemented a real network test-bed based on HAN where two-way Skype VCC was established and the quality of packet-loss was measured. Skype was chosen for its wide availability and support on all types of computing devices (PC, mobile, laptop etc.). Our results indicate that packet-loss fluctuates between ~5-16% when user datagram protocol (UDP) traffic is utilised concurrently by other devices in HAN. It also demonstrated that our scheme could enhance HAN performance for VCC as it reduced packet-loss down to ~0.3 %.
Retinal Vessel Segmentation Using Minimum Spanning Superpixel Tree Detector

The retinal vessel is one of the determining factors in an ophthalmic examination. Automatic extraction of retinal vessels from low-quality retinal images still remains a challenging problem. In this paper, we propose a robust and effective approach that qualitatively improves the detection of low-contrast and narrow vessels. Rather than using the pixel grid, we use a superpixel as the elementary unit of our vessel segmentation scheme. We regularize this scheme by combining the geometrical structure, texture, color, and space information in the superpixel graph. And the segmentation results are then refined by employing the efficient minimum spanning superpixel tree to detect and capture both global and local structure of the retinal images. Such an effective and structure-aware tree detector significantly improves the detection around the pathologic area. Experimental results have shown that the proposed technique achieves advantageous connectivity-area-length (CAL) scores of 80.92% and 69.06% on two public datasets, namely, DRIVE and STARE, thereby outperforming state-of-the-art segmentation methods. In addition, the tests on the challenging retinal image database have further demonstrated the effectiveness of our method. Our approach achieves satisfactory segmentation performance in comparison with state-of-the-art methods. Our technique provides an automated method for effectively extracting the vessel from fundus images.
Classification of dementia from FDG-PET parametric images using data mining

It remains a challenge to identify the different types of dementia and separate these from various subtypes from the normal effects of ageing. In this paper the potential of parametric images from FDG-PET studies to aid the classification of dementia using data mining techniques was investigated. Scalar, joint, histogram and voxel-level features were used in the investigation with principal component analysis (PCA) for dimensionality reduction. The logistic regression model and the additive logistic regression model were applied in the classification. The results show that cerebral metabolic rate of glucose consumption (CMRGlc) was efficient in the classification of dementia and data mining using voxel-level features with PCA and the logistic regression model method achieving the best classification.
Object localization in medical images based on graphical model with contrast and interest-region terms

In this paper, we propose a novel method for object localization, generally applicable to medical images in which the objects can be distinguished from the background mainly based on feature differences. We design a new CRF model with additional contrast and interest-region potentials, which encode the higher-order contextual information between regions, on the global and structural levels. We also propose a sparse-coding based classification approach for the interest-region detection with discriminative dictionaries, to serve as a second opinion for more accurate region labeling. We evaluate our object localization method on two medical imaging applications: lesion dissimilarity on thoracic PET-CT images, and cell segmentation on microscopic images. Our evaluations show higher performance when comparing to recently reported approaches.
Transmission and Coupling Characteristics of Ultra-Broadband Cr-Doped Multimode Fibers

We report the results of experimental and theoretical investigations of the transmission and coupling characteristics of an in-line multimode Cr-doped fiber (MMCDF) in a single mode fiber (SMF) optical link. This is the situation involved in the employment of a broadband MMCDF optical amplifier in an optical network. The excitation of several modes in the MMCDF can lead to a severe insertion loss. By selecting a proper core diameter of the MMCDF, it is possible to achieve mode-matching at the junctions between the SMF and the MMCDF. This leads to the excitation of predominantly the fundamental LP01 mode in a step-index MMCDF, result in a minimization of the insertion loss at both ends of the MMCDF. Using an MMCDF with a length of 12.5 cm and an average core diameter of 13.5 mum, we obtained a coupling efficiency of 64.6% ~ 74.1% in the spectral region from 1300 to 1600 nm. The measured coupling efficiencies are in good agreement with our theoretical modeling. The result indicates that the low insertion loss of MMCDF may be used as an inline ultra-broadband optical amplifier in an optical link for the entire 1300 to 1600 nm spectral range of low-loss windows of silica fibers. Our results are presented and discussed.
A fast algorithm for computing field radiated by an insulated dipole antenna in dissipative medium

A fast algorithm for determining the near-field characteristics of an insulated dipole antenna (IDA) embedded in a homogenous dissipative medium is described in this paper. A thin-wire-approximation type of analysis is followed here. In this case, radiation is considered to originate from a filamentary current flowing along the axis of the dipole, which is surrounded immediately by the ambient dissipative medium. The translational symmetry inherent in the resultant radiation integrals is then exploited to speed up the computation. In one case studied, the basic thin-wire approach that uses no symmetry property is found to yield accurate results in approximately 380 times less CPU time than the traditional King-Casey approach. In another case, use of symmetry property further reduces the CPU time by a factor of 7; additional reduction in CPU time is possible by taking into account the near-field nature of the problem.
Multiscale registration for noisy medical images

a multiscale deformable registration framework is proposed by combining edge preserving scale space with the free form deformation (FFD) for robust registration of noisy medical images. The edge preserving scale space derived from the total variation model with the L1 norm (TV-L1) has two advantages for registration: the first one is removing noise from the image pairs to be registered and the second one is selecting edges and contours of an image according to their geometric sizes. After being denoised, the selected edges and contours are sufficient enough to drive deformation using the FFD grid at each scale. Then the deformation fields are gained accurately in a coarse-to-fine manner. The proposed method is effective where one or both of the images to be registered with significant level of noise. Experiments on both artificially deformed medical images with ground truth and clinical CBCT images demonstrate the accuracy and robustness of our proposed multiscale deformable registration techniques.
Texture segmentation using local morphological multifractal exponents

This paper deals with the problem of segmenting various textures. For this purpose, we have applied mathematical morphology for the multifractal analysis of images. The digital gray level image is treated as a 3D surface whose multifractal measures are calculated by performing dilations on this surface. Plotting the acquired measures against the size of the structuring element, the local morphological multifractal exponents can be estimated, based on which the unsupervised fuzzy C-means clustering method is used to segment a texture image into the desired number of classes. Randomly choosing 12 natural textures from the Brodatz album, 66 mosaics of 2 textures and 495 mosaics of 4 textures are used to test the new segmentation approach and other two techniques, where the multifractal features are extracted by the box-counting based methods. The comparison results demonstrate that the proposed approach can differentiate texture images more effectively and provide more robust segmentation results.
Data processing and presentation for a personalised, image-driven medical graphical avatar

With the continuing digital revolution in the healthcare industry, patients are being confronted with the difficult task of managing their digital medical data. Current personal health record (PHR) systems are able to store and consolidate this data, but they are limited in providing tools to facilitate patients' understanding and management of the data. One reason for this stems from the limited use of contextual information, especially in presenting spatial details such as in volumetric images and videos, as well as time-based temporal data. Further, lack of meaningful visualisation techniques exist to represent the data stored in PHRs. In this paper we propose a medical graphical avatar (MGA) constructed from whole-body patient images, and a navigable timeline of the patient's medical records. A data mapping framework is presented that extracts information from medical multimedia data such as images, video and text, to populate our PHR timeline, while also embedding spatial and textual annotations such as regions of interest (ROIs) that are automatically derived from image processing algorithms. We developed a prototype to process the various forms of PHR data and present the data in a graphical avatar. We analysed the usefulness of our system under various scenarios of patient data use and present preliminary results that indicate that our system performs well on standard consumer hardware.
DSD: Depth Structural Descriptor for Edge-Based Assistive Navigation

Structural edge detection is the task of finding edges between significant surfaces in a scene. This can underpin many computer vision tasks such as sketch recognition and 3D scene understanding, and is important for conveying scene structure for navigation with assistive vision. Identifying structural edges from a depth image can be challenging because surface structure that differentiates edges is not well represented in this format. We derive a depth input encoding, the Depth Surface Descriptor (DSD), that captures the first order properties of surfaces, allowing for improved classification of surface geometry that corresponds to structural edges. We apply the DSD feature to salient edge detection on RGB-D images using a fully convolutional neural network with deep supervision. We evaluate our method on both a new RGB-D dataset containing prosthetic vision scenarios, and the SUNRGBD dataset, and show that our approach produces improved performance compared to existing methods by 4%.
A New Approach for Readout of Resistive Sensor Arrays for Wearable Electronic Applications

Resistive sensor arrays have been increasingly adopted in wearable electronic applications, which require low-complexity and low-energy circuits. However, current readout strategies for resistive sensor arrays require additional electrical components, such as transistors, diodes, multiplexers, op-amps, switches, current sources, and A/D converters, leading to a considerable increase in circuit complexity, power consumption, system instability, and crosstalk error. To address the problem, this paper proposes a new approach, which determines sensor resistance values by establishing and solving resistance matrix equations of sensor arrays. Unlike conventional approaches, it allows crosstalk currents in arrays to avoid additional components that are originally used for eliminating crosstalk currents and minimizing crosstalk error. Meanwhile, it takes advantage of on-chip resources of wearable platforms, thereby reducing redundant chips. It was implemented on a prototype of 10 × 10 textile resistive sensor array, which was taken in a sensing cushion for sitting pressure monitoring of chair bound people. Experimental results on this array platform showed the new approach achieved a satisfactory accuracy (0.61% ± 0.41%), as well as a low crosstalk error (2.77% ± 0.61%). The fabricated sensing cushion also exhibited a relatively low pressure measurement error (6.30% ± 0.75%). Compared with other approaches, the proposed approach demonstrated the lowest circuit complexity on a microcontroller based wearable platform, and a sufficient sensor capacity. It is ideal for a wide range of applications like wearable or implantable sensing, presenting a reference for the design of low-complexity and low-crosstalk error wearable systems based on resistive sensor arrays.
Structural and Optical Characteristics of Strain-mediated InGaAs Quantum Dots

Self-assembled In<sub>x</sub>Ga<sub>1-x</sub>As quantum dots, with In(Ga)As capping layers to mediate the strain distribution were studied by x-ray reciprocal space mapping, TEM, and PL. The In<sub>0.75</sub>Ga<sub>0.25</sub>As QDs capped with In<sub>0.1</sub>Ga<sub>0.9</sub>As has a PL emission at lambda = 1.31 mum.
A hyper-sphere SVM introduced the margin

Binary hyper-sphere support vector machine (SVM) is a new method for data description. Its weakness is that the margin between two classes of samples is zero or an uncertain value, which affects the classifier's generalization performance to some extent. So a generalized hyper-sphere SVM (GHSSVM) is provided in this paper. By introducing the parameter n and b (n>b), the margin which is greater than zero may be obtained. The experimental results show the proposed classifier may have better generalization performance and the less experimental risk than the hyper-sphere SVM in the references.
Reduction of Multimode Interference in 300-nm Broadband Cr-Doped Fibers

Reduction of multimode interference in 300-nm broadband Cr-doped fibers (CDFs) using mode-field matched center launching technique is demonstrated. With a matched CDF core diameter around 15.5 μm, the measured broadband coupling efficiencies are 60.3%~ 87.5%. This study may open the possibility for utilizing the CDFs as a new broadband fiber amplifier to cover 300 nm bandwidths.
Automated saliency-based lesion segmentation in dermoscopic images

The segmentation of skin lesions in dermoscopic images is considered as one of the most important steps in computer-aided diagnosis (CAD) for automated melanoma diagnosis. Existing methods, however, have problems with over-segmentation and do not perform well when the contrast between the lesion and its surrounding skin is low. Hence, in this study, we propose a new automated saliency-based skin lesion segmentation (SSLS) that we designed to exploit the inherent properties of dermoscopic images, which have a focal central region and subtle contrast discrimination with the surrounding regions. The proposed method was evaluated on a public dataset of lesional dermoscopic images and was compared to established methods for lesion segmentation that included adaptive thresholding, Chan-based level set and seeded region growing. Our results show that SSLS outperformed the other methods in regard to accuracy and robustness, in particular, for difficult cases.
Location classification of lung nodules with optimized graph construction

The locations of lung nodules relative to the other lung anatomical structures are important hints of malignant cancers. In this paper, we propose a fully automatic method to identify if a lung nodule is well-circumscribed, juxta-vascular, juxta-pleural or pleural tail in computed tomography (CT) images. First, we design an optimized graph model, introducing new global and region-based energy terms, to label each voxel as background or foreground in a single graph cut algorithm. Then, the texture features of a lung nodule are extracted based on the voxel labeling outputs, and its location information is inferred. We evaluate the proposed method on low-dose CT images, and demonstrate highly effective nodule classification results comparatively.
A Bag-of-Importance Model With Locality-Constrained Coding Based Feature Learning for Video Summarization

Video summarization helps users obtain quick comprehension of video content. Recently, some studies have utilized local features to represent each video frame and formulate video summarization as a coverage problem of local features. However, the importance of individual local features has not been exploited. In this paper, we propose a novel Bag-of-Importance (BoI) model for static video summarization by identifying the frames with important local features as keyframes, which is one of the first studies formulating video summarization at local feature level, instead of at global feature level. That is, by representing each frame with local features, a video is characterized with a bag of local features weighted with individual importance scores and the frames with more important local features are more representative, where the representativeness of each frame is the aggregation of the weighted importance of the local features contained in the frame. In addition, we propose to learn a transformation from a raw local feature to a more powerful sparse nonlinear representation for deriving the importance score of each local feature, rather than directly utilize the hand-crafted visual features like most of the existing approaches. Specifically, we first employ locality-constrained linear coding (LCC) to project each local feature into a sparse transformed space. LCC is able to take advantage of the manifold geometric structure of the high dimensional feature space and form the manifold of the low dimensional transformed space with the coordinates of a set of anchor points. Then we calculate the l2 norm of each anchor point as the importance score of each local feature which is projected to the anchor point. Finally, the distribution of the importance scores of all the local features in a video is obtained as the BoI representation of the video. We further differentiate the importance of local features with a spatial weighting template by taking the perceptual difference among spatial regions of a frame into account. As a result, our proposed video summarization approach is able to exploit both the inter-frame and intra-frame properties of feature representations and identify keyframes capturing both the dominant content and discriminative details within a video. Experimental results on three video datasets across various genres demonstrate that the proposed approach clearly outperforms several state-of-the-art methods.
Dual-modal visibility metrics for interactive PET-CT visualization

Dual-modal positron emission tomography and computed tomography (PET-CT) imaging enables the visualization of functional structures (PET) within human bodies in the spatial context of their anatomical (CT) counterparts, and is providing unprecedented capabilities in understanding diseases. However, the need to access and assimilate the two volumes simultaneously has raised new visualization challenges. In typical dual-modal visualization, the transfer functions for the two volumes are designed in isolation with the resulting volumes being fused. Unfortunately, such transfer function design fails to exploit the correlation that exists between the two volumes. In this study, we propose a dual-modal visualization method where we employ `visibility' metrics to provide interactive visual feedback regarding the occlusion caused by the first volume on the second volume and vice versa. We further introduce a region of interest (ROI) function that allows visibility analyses to be restricted to subsection of the volume. We demonstrate the new visualization enabled by our proposed dual-modal visibility metrics using clinical whole-body PET-CT studies of various diseases.