nils-herrmann/clean_scirepeval huggingface dataset:
A Multiview Joint Sparse Representation with Discriminative Dictionary for Melanoma Detection

Great efforts have been made over the last few decades to promote the development of computer aided diagnosis systems for melanoma detection. However, most of these systems employ multiple descriptors with bag-of-features model to represent the lesions. Since the melanoma and nevi share a large number of common patterns, the representation of original features are not sufficiently discriminative. Besides, the intrinsic relationship among different descriptors has also been ignored. In this paper, the representation of lesions is formulated as a multiview joint reconstruction problem with the learned dictionary. To better characterize the lesion images, a special_common dictionary learning method is proposed which explicitly takes both shared common pattern and a set of class-specific patterns between melanomas and nevi into account. In addition, group sparsity and locality constraints are further integrated to preserve the relationship and structure among local features. Furthermore, in order to enhance the combination capability of multiview, the spare coefficients of multiple features are jointly learned in a unified framework. The experimental results on both dermoscopic and conventional digital image databases demonstrate that our method is able to achieve better diagnosis accuracy than state-of-the-art ones.
Pathology-centric medical image retrieval with hierarchical contextual spatial descriptor

Content-based image retrieval has been suggested as an aid to medical diagnosis. Techniques based on standard feature descriptors, however, might not represent optimally the pathological characteristics in medical images. In this paper, we propose a new approach for medical image retrieval based on pathology-centric feature extraction and representation; and patch-based local feature extraction and hierarchical contextual spatial descriptor are designed. The proposed method is evaluated on positron emission tomography - computed tomography (PET-CT) images from subjects with non-small cell lung cancer (NSCLC), showing promising performance improvements over the other benchmarked techniques.
Single Image Dehazing with White Balance Correction and Image Decomposition

Single image dehazing has been a challenging problem due to its ill-posed nature. While most of the existing single image based dehazing algorithms address this issue by introducing certain assumptions and priors into the haze imaging model, the imaging process of imaging devices has been seldom taken into account, such as white balance and metering. In general, consumer photos are taken with AWB (Auto White Balance). Hence, color temperature in a foggy scenario may not be correctly detected, which results in color distortion; and the whole scene looks brighter, which leads to under-exposure during the imaging process. In this paper, we propose to handle these two issues by applying white balance correction and decomposing an image into two component images, reflex lightness image and ambience illumination image. We devise an improved dark channel prior based algorithm to dehaze the reflex lightness image and the exposure adjustment is estimated from the ambience illumination image. Finally, a high quality haze- free image is produced by refining the brightness of the preliminarily dehazed image with the estimated exposure adjustment. Experimental results with a benchmark dataset demonstrate that our approach outperforms the state-of-the-art, in terms of contrast and color fidelity.
Thoracic image matching with appearance and spatial distribution

Positron emission tomography — computed tomography (PET-CT) produces co-registered anatomical (CT) and functional (PET) patient information (3D image set) from a single scanning session, and is now accepted as the best imaging technique to accurately stage the most common form of primary lung cancer — non-small cell lung cancer (NSCLC). This paper presents a content-based image retrieval (CBIR) method for retrieving similar images as a reference dataset to potentially aid the physicians in PET-CT scan interpretation. We design a spatial distribution to describe the spatial information of each region-of-interest (ROI), and a pairwise ROI mapping scheme between images to compute the image matching level. Similar images are then retrieved based on the local and spatial information of the detected ROIs, and a learned weighted sum of ROI distances. Our evaluation on clinical data shows good image retrieval performance.
L2,0 constrained sparse dictionary selection for video summarization

The ever increasing volume of video content has created profound challenges for developing efficient video summarization (VS) techniques to access the data. Recent developments on sparse dictionary selection have demonstrated promising results for VS, however, the convex relaxation based solution cannot ensure the sparsity of the dictionary directly and it selects keyframes in a local point of view. In this paper, an L2,0 constrained sparse dictionary selection model is proposed to reformulate the problem of VS. In addition, a simultaneous orthogonal matching pursuit (SOMP) based method is proposed to obtain an approximate solution for the proposed model without smoothing the penalty function, and thus selects keyframes in a global point of view. In order to allow for intuitive and flexible configuration of VS process, a percentage of residuals (POR) criterion is also developed to produce video summaries in different lengths. Experimental results demonstrate that our proposed method outperforms the state-of-the-art.
A novel rate-quality model based H.264/AVC frame layer rate control method

Rate control plays an important role in video coding and transmission. In this paper, instead of using rate- distortion (R-D) model to perform rate control as many existing methods do, a rate-quality (R-Q) model of H.264/AVC has first been proposed to represent the coding characteristics of H.264, where PSNR (peak signal-to-noise ratio) is used to represent the reconstructed video quality. Then a H.264 rate control method at frame layer has been presented based on the proposed R-Q model. In this method, the quantization parameter (i.e. quantization step) of the current frame is determined by the proposed R-Q model according to the coding statistics of the previous coded frames. The experimental results show that, compared with the rate control algorithm of JM86 reference software, the output bit rate achieved by our proposed one is more close to the target bit rate while the reconstructed quality is comparable.
Global context inference for adaptive abnormality detection in PET-CT images

PET-CT is now accepted as the best imaging technique for non-invasive staging of lung cancers, and a computer-based abnormality detection is potentially useful to assist the reading physicians in diagnosis. In this paper, we present a new fully-automatic approach to detect abnormalities in the thorax based on global context inference. A max-margin learning-based method is designed to infer the global contexts, which together with local features are then classified to produce the detection results adaptively. The proposed method is evaluated on clinical PET-CT images from NSCLC studies, and high detection precision and recall are demonstrated.
Interaction and presentation techniques for shake menus in tangible augmented reality

Menus play an important role in both information presentation and system control. We explore the design space of shake menus, which are intended for use in tangible augmented reality. Shake menus are radial menus displayed centered on a physical object and activated by shaking that object. One important aspect of their design space is the coordinate system used to present menu options. We conducted a within-subjects user study to compare the speed and efficacy of several alternative methods for presenting shake menus in augmented reality (world-referenced, display-referenced, and object-referenced), along with a baseline technique (a linear menu on a clipboard). Our findings suggest tradeoffs amongst speed, efficacy, and flexibility of interaction, and point towards the possible advantages of hybrid approaches that compose together transformations in different coordinate systems. We close by describing qualitative feedback from use and present several illustrative applications of the technique.
Trajectory Matching and Classification of Video Moving Objects

Trajectory matching is an important way to describe and classify behaviors of moving objects in a computer visual system. In this paper, we present two trajectory description methods, time-sampling sequence and space-sampling sequence, which can be used in different matching applications. We then propose two general trajectory matching schemes based on Levenshtein distance and relaxation matching respectively. Trajectory Levenshtein distance scheme is a good way to compare the topological shapes and directions of trajectories, and can be performed quickly. Trajectory relaxation matching scheme can gain the statistical optimal matching. Finally, we propose a top-to-bottom hierarchical clustering algorithm to classify trajectories, and several experiments demonstrate that our schemes are efficient in matching and classifying different shape and direction trajectories
Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images

The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and oversegmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions. We further propose a Bayesian framework that better delineates the shape and boundaries of the lesion. We also evaluated our approach on two public datasets comprising 1100 dermoscopic images and compared it to other conventional and state-of-the-art unsupervised (i.e., no training required) lesion segmentation methods, as well as the state-of-the-art unsupervised saliency detection methods. Our results show that our approach is more accurate and robust in segmenting lesions compared to other methods. We also discuss the general extension of our framework as a saliency optimization algorithm for lesion segmentation.
Tree structures with attentive objects for image classification using a neural network

This paper presents an image classification method based on a neural network model dealing with tree structures of attentive objects. Apart from regions provided by image segmentation, attentive objects, which are extracted from a segmented image by an attention-driven image interpretation algorithm, are used to construct the tree structure to represent an image. Three combinations of tree structures are investigated, including “image + attentive-object + segments”, “image + attentive-objects”, as well as “image + segments”. Structure based neural networks are trained to classify the images by using the Back Propagation Through Structure (BPTS) algorithm. Experimental results show that the “image + attentive objects” structure is more favorable, comparing with both the other two structures proposed by us and a start-of-art tree structure reported in the literature, in terms of classification rate and computational time.
A wearable, wireless electronic interface for textile sensors lin shu

Electronic interfaces for wearable sensors require wireless connection, appropriate measurement range, small size, simple and robust structure, insensitivity to noise and comfort to wearers in daily life. A novel wearable, wireless electronic interface for resistive textile sensors is presented, which meets the requirements and has the ability to provide real-time measurement. System configuration, accuracy and resolution have been analyzed in-depth and design rules have been defined. Experimental results show that this electronic interface exhibits less than 1% error in a large measurement range for wearable textile resistive sensors. It also shows a good stability to power supply interference. The interface has been successfully applied in a foot pressure measurement syste.
Deformation estimated using a multiresolution wavelet representation for adaptive radiation therapy

Registration of planning CT (PCT) images with daily cone beam CT (CBCT) images is an important task for adaptive radiation therapy (ART). Considering that both global and local deformation are existing in image pairs to be registered, estimation of deformation in a coarse-to-fine strategy with the optimization process can prevent convergence into local minima traps. In this paper, deformation is estimated using a multiresolution wavelet representation for medical image registration. The wavelet, being an inherently hierarchical representation of a signal, is naturally suitable for such progressive optimization starting from the low resolution approximation of the signal which represents the global deformation, through the details in its different orientations and ending with the finest details of the signal which represent the local deformation. The experiments on PCT and CBCT images show accuracy and robustness when compared to traditional B-spline based free form deformation (FFD) methods.
Non-sparse infinite-kernel learning for automated identification of Alzheimer's disease using PET imaging

Multi-kernel learning machine (MKLM) has recently been introduced to the research of computer-aided dementia identification and pathology progress tracking. Despite its good performance especially in case of using heterogeneous data, such learning schema and its variants usually utilize a L-l norm constraint that promotes sparse solutions, which may cause loss of potentially important information. In this paper, we propose the non-sparse infinite-kernel learning machine (NS-IKLM) for automated identification of Alzheimer cases from normal controls. In our approach, a modified constraint is utilized to promotes non-sparse solutions and kernel parameters are automatically tuned during the learning process. The proposed algorithm has been evaluated on a set of FDG-PET images selected from the Alzheimer's disease neuroimaing initiative (ADNI) cohort. Our results demonstrate that the proposed non-sparse NS-IKLM is able to achieve satisfying dementia identification at a relatively low computational cost.
Learning RGB-D Salient Object Detection Using Background Enclosure, Depth Contrast, and Top-Down Features

In human visual saliency, top-down and bottom-up information are combined as a basis of visual attention. Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong performance on RGB salient object detection, providing an effective mechanism for combining top-down semantic information with low level features. Although depth information has been shown to be important for human perception of salient objects, the use of top-down information and the exploration of CNNs for RGB-D salient object detection remains limited. Here we propose a novel deep CNN architecture for RGB-D salient object detection that utilizes both top-down and bottom-up cues. In order to produce such an architecture, we present novel depth features that capture the ideas of background enclosure, depth contrast and histogram distance in a manner that is suitable for a learned approach. We show improved results compared to state-of-the-art RGB-D salient object detection methods. We also show that the low-level and mid-level depth features both contribute to improvements in results. In particular, the F-Score of our method is 0.848 on RGBD1000, which is 10.7% better than the current best.
Structure and location preserving topological representation with applications on CT segmentation

Contour tree represents the nesting relations of the individual components in the image; however, it neglects the geometric structure of the terrain. In this paper, we propose a new topological representation that provides the nesting and spatial relations of regions for CT image interpretation. The tree is constructed based on the signed distance transformation of binary CT image, and combines intensity based contour tree and a new gradient based topology tree. We also investigate the application of the topological representation as a constraint in target object segmentation. Ten non-small cell lung tumor CT studies were used for segmentation accuracy evaluation. The image representation results showed that the proposed tree structure retained the nesting and spatial relations of the tissues or objects in the CT image. The segmentation results demonstrated its usability in the separation of adjacent objects with similar intensity distributions.
Wireless Media Streaming System over CDMA Networks

In this paper, we propose an end-to-end wireless media streaming system over CDMA networks. The system is deployed at the two network end-points, media server and mobile client, where the mobile client software is developed over BREW (binary runtime environment wireless) platform released by Qualcomm. H.264 is adopted as the video coding method due to its high compression efficiency and network friendly design and PureVoice coding technology offered by BREW platform as audio coding method. Four VCR functionalities, i.e. pause, fast forward, stop and play are supported by this system. The system architecture, functional features and implementation details of the system are described in this paper. The application experiments over the CDMA network of the China Unicom Company show that the system can provide the mobile end-users continuous and smooth playback of the multimedia content at a lower network bandwidth (70 Kbps).
InAs-Based Single-Mode Distributed Feedback Interband Cascade Lasers

InAs-based single-mode distributed feedback (DFB) interband cascade (IC) lasers were investigated. The DFB grating was patterned using interference lithography to etch the cladding-free upper separate confinement layer of the IC laser structure. Single-mode emission with a side mode suppression ratio of ~ 30dB was obtained in continuous wave operation at temperatures up to 180 K near 4.5 μm . A total tuning range of 16 nm could be achieved for a single device, with typical temperature- and current-tuning rates of 0.4 nm/K and 0.016 nm/mA, respectively. The impact of the DFB grating on device performance was evaluated and discussed in comparison with Fabry-Perot lasers.
Embedded singularity detection zerotree wavelet coding

We explore the wavelet coefficient selection and denoising by singularity detection (SD) for the embedded zero-tree wavelet (EZW) coding algorithm in this paper. The EZW coding algorithm exploits the relation between the multi-scale wavelet coefficients that finer scale wavelet coefficients are probably to vanish if the coarse scale wavelet coefficient vanishes. It is true for the parts of image that are regular but not the cases for noise-like features. In other words, the performance of the coding algorithm may be greatly degraded for the latter. In this paper, we investigate to arrange the wavelet coefficients according to the local regularity, by using the computed wavelet coefficients from the encoding filters. The advantage is two folds. For normal coding, we can make the encoded bit-stream first appear with the wavelet coefficients that correspond to the most regular part of the image, and the irregular one's follows. For noisy image encoding, we can remove noises before encoding hence increase the image quality as well as the coding efficiency.
Image Recovery from Broken Image Streams

This paper presents an approach for image recovery from broken image streams based on an image continuity model. The information to be recovered includes the header of an image (width, height, color space etc.) and the data stream which might have been partially damaged. We begin our discussion on common image formats and necessary information for the image recovery. We then define several measures on image contents to facilitate the image recovery. Using these measures, an effective recovery algorithm is proposed. Experimental results show that the algorithm can successfully recover images of different formats, even multi-frame images from broken image streams in most cases.
Foreground Detection With Simultaneous Dictionary Learning and Historical Pixel Maintenance

Foreground detection is fundamental in surveillance video analysis and meaningful toward object tracking and higher level tasks, such as anomaly detection and activity analysis. Nevertheless, existing methods are still limited in accurately detecting the foreground due to the complex scene settings. To robustly handle the diverse background variations and foreground challenges, this paper proposes a Background REpresentation approach With Dictionary Learning and Historical Pixel Maintenance (BREW-DLHPM). Specifically, a dictionary learning problem is formulated at the frame level to adaptively represent the background signals with the varied structure information captured, while a pixel-level maintenance is exploited to grasp the dynamic nature of historical information under the help of the learned background. The simultaneous utilization of dictionary learning and historical pixel maintenance facilitates the accurate description of the background and thus guides a wise foreground detection decision. The proposed BREW-DLHPM has been evaluated on the prestigious change detection challenge data set against 11 state-of-the-art foreground detection approaches and encouraging performances have been achieved by our method.
Multifractal estimation for remote sensing image segmentation

Multifractal analysis can successfully characterize the roughness and self-similarity of textural images. But most popular methods produce less accurate results. In this paper, a novel multifractal estimation algorithm based on mathematical morphology is proposed and a set of new multifractal features, namely the local morphological multifractal exponents (LMME) is defined. A series of cubic structure elements (SE) and iterative morphological operations are utilized so that the computational complexity of the new approach can be tremendously reduced. A quadtree-based multilevel segmentation algorithm is also developed to efficiently apply the presented multifractal features to image segmentation. Both the proposed approach and the box-counting based methods have been assessed on real remote sensing images. The comparison results demonstrate that the morphological multifractal estimation can differentiate texture images more effectively and provide a more robust segmentation result.
Photocatalytic study of zinc oxide with different bismuth doping

The zinc oxides (ZnO) films with different bismuth (Bi) doping were prepared by spray pyrolysis deposition at 450°C with aqueous solution contained zinc acetate and bismuth nitrate precursors. Under ultra-violate light illumination, the photodegradation of methyl orange for these ZnO films were studied. In the singly doped ZnO, the 5% Bi doped ZnO shows the relatively high photocatalytic activity. The reaction rate was achieved and possible reason was discussed. It is found that the raction rate can be enhanced by increasing the surface doping. The absorption coefficient was investigated and possible reason was discussed.