nils-herrmann/clean_scirepeval huggingface dataset:
Segmentation of Liver Vasculature From Contrast Enhanced CT Images Using Context-Based Voting

A novel vessel context-based voting is proposed for automatic liver vasculature segmentation in CT images. It is able to conduct full vessel segmentation and recognition of multiple vasculatures effectively. The vessel context describes context information of a voxel related to vessel properties, such as intensity, saliency, direction, and connectivity. Voxels are grouped to liver vasculatures hierarchically based on vessel context. They are first grouped locally into vessel branches with the advantage of a vessel junction measurement and then grouped globally into vasculatures, which is implemented using a multiple feature point voting mechanism. The proposed method has been evaluated on ten clinical CT datasets. Segmentation of third-order vessel trees from CT images (0.76 × 0.76 × 2.0 mm) of the portal venous phase takes less than 3 min on a PC with 2.0 GHz dual core processor and the average segmentation accuracy is up to 98%.
Polar Embedding for Aurora Image Retrieval

Exploring the multimedia techniques to assist scientists for their research is an interesting and meaningful topic. In this paper, we focus on the large-scale aurora image retrieval by leveraging the bag-of-visual words (BoVW) framework. To refine the unsuitable representation and improve the retrieval performance, the BoVW model is modified by embedding the polar information. The superiority of the proposed polar embedding method lies in two aspects. On the one hand, the polar meshing scheme is conducted to determine the interest points, which is more suitable for images captured by circular fisheye lens. Especially for the aurora image, the extracted polar scale-invariant feature transform (polar-SIFT) feature can also reflect the geomagnetic longitude and latitude, and thus facilitates the further data analysis. On the other hand, a binary polar deep local binary pattern (polar-DLBP) descriptor is proposed to enhance the discriminative power of visual words. Together with the 64-bit polar-SIFT code obtained via Hamming embedding, the multifeature index is performed to reduce the impact of false positive matches. Extensive experiments are conducted on the large-scale aurora image data set. The experimental result indicates that the proposed method improves the retrieval accuracy significantly with acceptable efficiency and memory cost. In addition, the effectiveness of the polar-SIFT scheme and polar-DLBP integration are separately demonstrated.
Optimal regulation of linear discrete-time systems with multiplicative noises

This paper studies optimal regulation problem for networked linear discrete-time systems with fading channel. The uncertainties in fading channels are modeled as multiplicative noises. The regulation performance is measured by a quadratic function. The optimal state feedback is designed by the mean-square stabilization solution to a modified Algebraic Riccati equation (MARE). The necessary and sufficient condition to the existence of the mean-square stabilization is presented in terms of the inherent characterizations of the systems. It is a nature extension for the result in standard optimal discrete-time linear quadratic regulation (LQR) problem. We also show that this optimal state feedback design problem is an eigenvalue problem (EVP). And then a design algorithm is developed for this optimal control problem.
A statistical approach for object motion estimation with MPEG motion vectors

We propose a statistical approach to estimate object motion with MPEG motion vectors. A model with two normal distribution terms is applied to represent the simplified object motion. One term models the noise embedded in the motion vector field produced at the encoding stage and the other term models the randomness of the true object motion. Experiments with vehicle motion estimation from MPEG traffic video are used to evaluate the proposed algorithm. The influence of time window, frame size and reference frame distance are investigated. The vehicle speeds can be estimated with a high accuracy of up to 85-92%
A QoS constrained dynamic priority scheduling algorithm for process engine

In this paper, a task scheduling algorithm in process engine is proposed to not only maximize overall customer satisfaction but also guarantee QoS requirement. This new algorithm dynamically assigns priority to a task based on the weighted utility value considering the predicted business value and left time to execute the process instance the task belongs to. The business value of each kind of process instance is modeled as a utility function of response time which reflects the inverse proportional relationship between the customer satisfaction and response time. Experiments show that the proposed algorithm promotes the total value of utility function with better QoS achievement compared to traditional algorithms.
Weakly supervised sparse coding with geometric consistency pooling

Most recently the Bag-of-Features (BoF) representation has been well advocated for image search and classification, with two decent phases named sparse coding and max pooling to compensate quantization loss as well as inject spatial layouts. But still, much information has been discarded by quantizing local descriptors with two-dimensional layouts into a one-dimensional BoF histogram. In this paper, we revisit this popular “sparse coding + max pooling” paradigm by “looking around” the local descriptor context towards an optimal BoF. First, we introduce a Weakly supervised Sparse Coding (WSC) to exploit the Classemes-based attribute labeling to refine the descriptor coding procedure. It is achieved by learning an attribute-to-word co-occurrence prior to impose a label inconsistency distortion over the ℓ1 based coding regularizer, such that the descriptor codes can maximally preserve the image semantic similarity. Second, we propose an adaptive feature pooling scheme over “superpixels” rather than over fixed spatial pyramids, named Geometric Consistency Pooling (GCP). As an effect, local descriptors enjoying good geometric consistency are pooled together to ensure a more precise spatial layouts embedding in BoF. Both of our phases are unsupervised, which differ from the existing works in supervised dictionary learning, sparse coding and feature pooling. Therefore, our approach enables potential applications like scalable visual search. We evaluate in both image classification and search benchmarks and report good improvements over the state-of-the-arts.
Discovering Discriminative Graphlets for Aerial Image Categories Recognition

Recognizing aerial image categories is useful for scene annotation and surveillance. Local features have been demonstrated to be robust to image transformations, including occlusions and clutters. However, the geometric property of an aerial image (i.e., the topology and relative displacement of local features), which is key to discriminating aerial image categories, cannot be effectively represented by state-of-the-art generic visual descriptors. To solve this problem, we propose a recognition model that mines graphlets from aerial images, where graphlets are small connected subgraphs reflecting both the geometric property and color/texture distribution of an aerial image. More specifically, each aerial image is decomposed into a set of basic components (e.g., road and playground) and a region adjacency graph (RAG) is accordingly constructed to model their spatial interactions. Aerial image categories recognition can subsequently be casted as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by comparing all their respective graphlets. Because the number of graphlets is huge, we derive a manifold embedding algorithm to measure different-sized graphlets, after which we select graphlets that have highly discriminative and low redundancy topologies. Through quantizing the selected graphlets from each aerial image into a feature vector, we use support vector machine to discriminate aerial image categories. Experimental results indicate that our method outperforms several state-of-the-art object/scene recognition models, and the visualized graphlets indicate that the discriminative patterns are discovered by our proposed approach.
Analytical Model of Magnetic Force for Axial Stack Permanent-Magnet Bearings

Based on magnetic field of plane point magnetic charge, virtual work principle, and superposition theorem, the magnetic force analytical model for axial stack permanent-magnet bearings (PMB) is established. The aim is to solve the problem of the magnetic force analytical model's lack of being convenient in engineering design calculation of axial stack PMB. The relationship between magnetic force and structure parameters of PMB is analyzed. ANSYS simulation is adopted to test the validity of the magnetic force analytical model. The results indicate that the magnetic force of axial stack PMB is proportional to the magnetic annular average perimeter and the square of residual magnetism induction density, and that the magnetic force increases with an increment in the magnetic annular radial width and magnetic annular number. In the case of similar geometric parameters, the magnetic force of the same direction magnetization axial stack PMB is minimum: when the number of stack magnetic ring is 4, the maximum magnetic force of reversal magnetization axial stack PMB is approximately 4 times as much as the maximum magnetic force of equi-directional magnetization axial stack PMB; when the axial offset is only half of the axial length of single magnetic ring, the axial magnetic force is the maximum. Accordingly, the maximum magnetic force of rotational magnetization axial stack PMB is roughly 1.5 times as much as the maximum magnetic force of reversal magnetization axial stack PMB. When the axial deflection is equal to the axial length of single magnetic ring, the axial magnetic force is the maximum.
The blind periodic estimation of the pseudo noise sequence in multi-rate DS/CDMA transmissions

In this paper, we study on the blind periodic estimation of the pseudo-noise (PN) sequence in variable-spreading-length (VSL) multi-rate direct-sequence code division multiple access (DS/CDMA) systems. The reprocessing of the power spectrum method which is used to estimate the period of general direct-sequence spread spectrum signals is extended to solve the periodic estimation of the PN sequence in multi-rate DS/CDMA signals. We sample the received signals and estimate its power spectrum, then looks upon the estimated power spectrum as input signals and computes its power spectrum again. The derivation can prove that the power spectrum reprocessing of the multi-rate DS/CDMA signals will arise varied amplitude sharp pulse with intervals equaling to the period of these different rates users' PN sequence, accordingly, we can acquire all the periods of the different rates users' PN sequence. The computer simulation shows that the method is effective in low SNR.
Edge-SIFT: Discriminative Binary Descriptor for Scalable Partial-Duplicate Mobile Search

As the basis of large-scale partial duplicate visual search on mobile devices, image local descriptor is expected to be discriminative, efficient, and compact. Our study shows that the popularly used histogram-based descriptors, such as scale invariant feature transform (SIFT) are not optimal for this task. This is mainly because histogram representation is relatively expensive to compute on mobile platforms and loses significant spatial clues, which are important for improving discriminative power and matching near-duplicate image patches. To address these issues, we propose to extract a novel binary local descriptor named Edge-SIFT from the binary edge maps of scale- and orientation-normalized image patches. By preserving both locations and orientations of edges and compressing the sparse binary edge maps with a boosting strategy, the final Edge-SIFT shows strong discriminative power with compact representation. Furthermore, we propose a fast similarity measurement and an indexing framework with flexible online verification. Hence, the Edge-SIFT allows an accurate and efficient image search and is ideal for computation sensitive scenarios such as a mobile image search. Experiments on a large-scale dataset manifest that the Edge-SIFT shows superior retrieval accuracy to Oriented BRIEF (ORB) and is superior to SIFT in the aspects of retrieval precision, efficiency, compactness, and transmission cost.
A General Framework for Linear Distance Preserving Hashing

Binary hashing approaches the approximate nearest neighbor search problem by transferring the data to Hamming space with explicit or implicit distance preserving constraint. With compact data representation, binary hashing identifies the approximate nearest neighbors via very efficient Hamming distance computation. In this paper, we propose a generic hashing framework with a new linear pairwise distance preserving objective and pointwise constraint. In our framework, the direct distance preserving objective aims to keep the linear relationship between the Euclidean distance and the Hamming distance of data points. On the other hand, to impose the pointwise constraint, we instantiate the framework from three different perspectives with pseudo-supervised, unsupervised, and supervised clues and obtain three different hashing methods. The first one is a pseudo-supervised hashing method, which adopts a certain existing unsupervised hashing method to generate binary codes as pseudo-supervised information. For the second one, we get an unsupervised hashing method by considering the quantization loss. The third one, as a supervised hashing method, learns the hash functions in a two-step paradigm. Furthermore, we improve the above-mentioned framework by constraining the global scope of the proposed linear distance preserving objective to a local range. We validate our framework on four large-scale benchmark data sets. The experiments demonstrate that our pseudo-supervised method achieves consistent improvement over the state-of-the-art unsupervised hashing methods, while our unsupervised and supervised methods achieve promising performance compared with the state-of-the-art algorithms.
A Novel OpenGL-Based MoM/SBR Hybrid Method for Radiation Pattern Analysis of an Antenna Above an Electrically Large Complicated Platform

An efficient open graphics library (OpenGL)-based method of moments (MoM)-shooting and bouncing ray (SBR) hybrid method is proposed in this paper to analyze the radiation pattern of an antenna above an electrically large, complicated platform. This study takes advantage of the outstanding rendering technique of OpenGL to accelerate the tracing process of rays from antenna. To improve the accuracy of shadow determination and decrease the number of triangular patches, an OpenGL-based subdivision algorithm is deeply investigated. In addition, some optimization schemes based on OpenGL are also proposed to further boost the algorithm efficiency. Accordingly, this novel MoM-SBR hybrid method can yield more efficient performance compared with the conventional MoM-SBR method. Moreover, some simulation results of the proposed method are verified by comparing with those of FEKO-MLFMM. Finally, examples of realistic airplane and warship models illustrate that our codes perform well even at electrically large complex targets. Simulated 3-D radiation patterns clearly demonstrate disturbance from the platform, and such patterns are of great interest for real antenna design.
$\mathcal {L}_p$ -Norm IDF for Scalable Image Retrieval

The inverse document frequency (IDF) is prevalently utilized in the bag-of-words-based image retrieval application. The basic idea is to assign less weight to terms with high frequency, and vice versa. However, in the conventional IDF routine, the estimation of visual word frequency is coarse and heuristic. Therefore, its effectiveness is largely compromised and far from optimal. To address this problem, this paper introduces a novel IDF family by the use of Lp-norm pooling technique. Carefully designed, the proposed IDF considers the term frequency, document frequency, the complexity of images, as well as the codebook information. We further propose a parameter tuning strategy, which helps to produce optimal balancing between TF and pIDF weights, yielding the so-called Lp-norm IDF (pIDF). We show that the conventional IDF is a special case of our generalized version, and two novel IDFs, i.e., the average IDF and the max IDF, can be defined from the concept of pIDF. Further, by counting for the term-frequency in each image, the proposed pIDF helps to alleviate the visual word burstiness phenomenon. Our method is evaluated through extensive experiments on four benchmark data sets (Oxford 5K, Paris 6K, Holidays, and Ukbench). We show that the pIDF works well on large scale databases and when the codebook is trained on irrelevant data. We report an mean average precision improvement of as large as +13.0% over the baseline TF-IDF approach on a 1M data set. In addition, the pIDF has a wide application scope varying from buildings to general objects and scenes. When combined with postprocessing steps, we achieve competitive results compared with the state-of-the-art methods. In addition, since the pIDF is computed offline, no extra computation or memory cost is introduced to the system at all.
Statistical analysis of split spectrum processing for multiple target detection

This work provides a statistical analysis of the performance of split spectrum processing (SSP) for the detection of multiple targets using data consisting of simulated flaw signals added to experimentally obtained backscattered grain noise. The investigation is performed under two conditions: known a priori target spectral characteristics (i.e., center frequency and bandwidth) which, in turn, identifies the optimal spectral range for processing, and adaptively obtaining the processing frequencies using group delay moving entropy. The group delay moving entropy method was introduced to select the optimal frequency regions for SSP when detecting multiple targets. The effectiveness of this technique is statistically demonstrated in this paper. The performance is measured in terms of normalized signal-to-noise ratio (SNR) and probability of target detection. SSP with known target information yields a slightly higher probability of detection compared to SSP using group delay moving entropy, while both cases achieve comparable SNR enhancement. The SSP results were also compared with the corresponding bandpass filter outputs, which show superior performance for SSP for a wide range of simulation parameters.
Semantic Subspace Projection and Its Applications in Image Retrieval

One of the most challenging problems for image retrieval applications is to find the optimal mapping between high-level semantic concept and low-level features. Traditional approaches often assume that images with same semantic label share strong visual similarities and should be clustered together to facilitate modeling and classification. Our research indicates this assumption is inappropriate in many cases. Instead we model the images as lying on nonlinear image subspaces embedded in the high-dimensional feature space and find that multiple subspaces may correspond to one semantic concept. By intelligently utilizing the similarity and dissimilarity information in semantic and geometric (image) domains, we propose an optimal semantic subspace projection (SSP) that captures the most important properties of the subspaces with respect to classification. Theoretical analysis proves that the well-known linear discriminant analysis (LDA) could be formulated as a special case of SSP. To capture the semantic concept dynamically, SSP can integrate relevance feedback efficiently through incremental learning. Kernel SSP is further proposed to handle nonlinearly separable data. Extensive experiments have been designed and conducted to compare our proposed method to the state-of-the-art techniques such as LDA, locality preservation projection (LPP), local linear embedding (LLE), local discriminant embedding (LDE) and their variants. The results show the superior performance of SSP.
Local Motion Analysis and Its Application in Video based Swimming Style Recognition

In this paper we study the problem of local motion analysis and apply it to swimming style recognition in broadcast sports video. Local motion analysis is challenging for two reasons: 1) local motion is usually buried in clutters involving complex motion from multiple objects; and 2) the process is more sensitive to noises compared to the recovery of global motion. However, an effective approach to local motion analysis is significant for understanding human activity from image sequences. In this work, we firstly extract the object-induced local motion by utilizing robust motion estimation and salient color. The object motion is accordingly characterized by compensated motion vectors and confidence measurement. Beyond a single image, we attempt to capture the motion periodicity over the local motion sequence. For each period, we locate a so-called salient frame within which we derive a compact representation to distinctly characterize an image sequence with repeated actions. Finally, we employ a hierarchical classifier to distinguish local motion based on periodicity and salient frames. Promising results have been achieved on swimming style recognition in broadcast sports video
Face pose analysis from mpeg compressed video for surveillance applications

Here, we propose a hierarchical approach for face pose analysis, which works with MPEG compressed video of head-and-shoulders style. The proposed approach consists of two layers: coarse human body tracker & face locator and fine face tracker. To robustly estimate the parameters of face pose, we use transform domain features to detect and track the reliable parts of the human body for removing distractive factors (e.g. skin-toned nonface objects, random cluttered caused by moving objects). Within the tracked body part, we apply the high-precision face tracker to estimate the pose parameters. The proposed approach is found to be useful particularly for automated transaction service (ATS) surveillance, where clear face is an important visual cue when searching and browsing a video database. The frontal face is practically considered a clear one. A user-study is conducted to show the effectiveness of clear faces selection on automated teller machine (ATM) testing clips.
A New Iris Region Segmentation Method

Iris edge detection is a key step for iris boundary localization in iris recognition system, because there are many edge points caused by influences in iris image such as eyelashes, eyelid and light spots, it causes that edge detection and localization are generally computationally expensive. In order to improve the speed of iris boundaries localization, a new edge detection method for iris images is presented in this article. This method use morphological operators and fuzzy membership functions to select edge points by sharpening edge gradient magnitude. The results of simulation show that the method is effectiveness for decreasing invalid iris edge points and improving iris boundaries localization speed.