Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: A visual search inspired computational model for ship detection in optical satellite images
Abstract: In this letter, we propose a novel computational model for automatic ship detection in optical satellite images. The model first selects salient candidate regions across entire detection scene by using a bottom-up visual attention mechanism. Then, two complementary types of top-down cues are employed to discriminate the selected ship candidates. Specifically, in addition to the detailed appearance analysis of candidates, a neighborhood similarity-based method is further exploited to characterize their local context interactions. Furthermore, the framework of our model is designed in a multiscale and hierarchical manner which provides a plausible approximation to a visual search process and reasonably distributes the computational resources. Experiments over panchromatic SPOT5 data prove the effectiveness and computational efficiency of the proposed model.
Extracted statement — Band: PAN
Research field: Science
Title: Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature
Abstract: Ship detection in high-resolution optical imagery is a challenging task due to the variable appearances of ships and background. This paper aims at further investigating this problem and presents an approach to detect ships in a “coarse-to-fine” manner. First, to increase the separability between ships and background, we concentrate on the pixels in the vicinities of ships. We rearrange the spatially adjacent pixels into a vector, transforming the panchromatic image into a “fake” hyperspectral form. Through this procedure, each produced vector is endowed with some contextual information, which amplifies the separability between ships and background. Afterward, for the “fake” hyperspectral image, a hyperspectral algorithm is applied to extract ship candidates preliminarily and quickly by regarding ships as anomalies. Finally, to validate real ships out of ship candidates, an extra feature is provided with histograms of oriented gradients (HOGs) to generate a hypothesis using AdaBoost algorithm. This extra feature focuses on the gray values rather than the gradients of an image and includes some information generated by very near but not closely adjacent pixels, which can reinforce HOG to some degree. Experimental results on real database indicate that the hyperspectral algorithm is robust, even for the ships with low contrast. In addition, in terms of the shape of ships, the extended HOG feature turns out to be better than HOG itself as well as some other features such as local binary pattern.
Extracted statement — Band: PAN
Research field: Science
Title: Ship detection from optical satellite images based on sea surface analysis
Abstract: Automatic ship detection in high-resolution optical satellite images with various sea surfaces is a challenging task. In this letter, we propose a novel detection method based on sea surface analysis to solve this problem. The proposed method first analyzes whether the sea surface is homogeneous or not by using two new features. Then, a novel linear function combining pixel and region characteristics is employed to select ship candidates. Finally, Compactness and Length-width ratio are adopted to remove false alarms. Specifically, based on the sea surface analysis, the proposed method cannot only efficiently block out no-candidate regions to reduce computational time, but also automatically assign weights for candidate selection function to optimize the detection performance. Experimental results on real panchromatic satellite images demonstrate the detection accuracy and computational efficiency of the proposed method.
Extracted statement — Band: PAN
Research field: Science
Title: Ship detection from high-resolution imagery based on land masking and cloud filtering
Abstract: High resolution satellite images play an important role in target detection application presently. This article focuses on the ship target detection from the high resolution panchromatic images. Taking advantage of geographic information such as the coastline vector data provided by NOAA Medium Resolution Coastline program, the land region is masked which is a main noise source in ship detection process. After that, the algorithm tries to deal with the cloud noise which appears frequently in the ocean satellite images, which is another reason for false alarm. Based on the analysis of cloud noise's feature in frequency domain, we introduce a windowed noise filter to get rid of the cloud noise. With the help of morphological processing algorithms adapted to target detection, we are able to acquire ship targets in fine shapes. In addition, we display the extracted information such as length and width of ship targets in a user-friendly way i.e. a KML file interpreted by Google Earth.
Extracted statement — Band: PAN
Research field: Science
Title: Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images
Abstract: With the development of high-resolution imagery, ship detection in optical satellite images has attracted a lot of research interest because of the broad applications in fishery management, vessel salvage, etc. Major challenges for this task include cloud, wave, and wake clutters, and even the variability of ship sizes. In this letter, we propose an unsupervised ship detection method toward overcoming these existing issues. Visual saliency, which focuses on highlighting salient signals from scenes, is applied to extract candidate regions followed by a homogeneous filter presented to confirm suspected ship targets with complete profiles. Then, a novel descriptor, ship histogram of oriented gradient, which characterizes the gradient symmetry of ship sides, is provided to discriminate real ships. Experimental results on numerous panchromatic satellite images demonstrate the good performance of our method compared to state-of-the-art methods.
Extracted statement — Band: PAN
Research field: Science
Title: Fast ship detection from optical satellite images based on ship distribution probability analysis
Abstract: Automatic ship detection from optical satellite images remains a tough task. In this paper, a novel method of ship detection from optical satellites is proposed by analyzing the ship distribution probability. First, an anomaly detection model is constructed by the sea cluster histogram model; then, the ship distribution based on the ship safety navigational criterion is analyzed to obtain the ship candidates, and obvious non-ship objects are removed by the area properties from ship candidates; finally, a structural continuity descriptor is designed to remove false alarms from the ship candidates. Experiments on numerous satellite images from panchromatic and one band within multispectral sensors are conducted. The results verified that the proposed method outperforms existing methods in both effectiveness and efficiency.
Extracted statement — Band: PAN
Research field: Science
Title: Ship detection in panchromatic images: a new method and its DSP implementation
Abstract: In this paper, a new ship detection method is proposed after analyzing the characteristics of panchromatic remote sensing images and ship targets. Firstly, AdaBoost(Adaptive Boosting) classifiers trained by Haar features are utilized to make coarse detection of ship targets. Then LSD (Line Segment Detector) is adopted to extract the line features in target slices to make fine detection. Experimental results on a dataset of panchromatic remote sensing images with a spatial resolution of 2m show that the proposed algorithm can achieve high detection rate and low false alarm rate. Meanwhile, the algorithm can meet the needs of practical applications on DSP (Digital Signal Processor).
Extracted statement — Band: PAN
Research field: Science
Title: Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature
Abstract: Automatic ship detection from optical satellite imagery is a challenging task due to cluttered scenes and variability in ship sizes. This letter proposes a detection algorithm based on saliency segmentation and the local binary pattern (LBP) descriptor combined with ship structure. First, we present a novel saliency segmentation framework with flexible integration of multiple visual cues to extract candidate regions from different sea surfaces. Then, simple shape analysis is adopted to eliminate obviously false targets. Finally, a structure-LBP feature that characterizes the inherent topology structure of ships is applied to discriminate true ship targets. Experimental results on numerous panchromatic satellite images validate that our proposed scheme outperforms other state-of-the-art methods in terms of both detection time and detection accuracy.
Extracted statement — Band: PAN
Research field: Science
Title: PEGASIS: power efficient gathering in sensor informa- tion systems
Abstract: Sensor webs consisting of nodes with limited battery power and wireless communications are deployed to collect useful information from the field. Gathering sensed information in an energy efficient manner is critical to operate the sensor network for a long period of time. In W. Heinzelman et al. (Proc. Hawaii Conf. on System Sci., 2000), a data collection problem is defined where, in a round of communication, each sensor node has a packet to be sent to the distant base station. If each node transmits its sensed data directly to the base station then it will deplete its power quickly. The LEACH protocol presented by W. Heinzelman et al. is an elegant solution where clusters are formed to fuse data before transmitting to the base station. By randomizing the cluster heads chosen to transmit to the base station, LEACH achieves a factor of 8 improvement compared to direct transmissions, as measured in terms of when nodes die. In this paper, we propose PEGASIS (power-efficient gathering in sensor information systems), a near optimal chain-based protocol that is an improvement over LEACH. In PEGASIS, each node communicates only with a close neighbor and takes turns transmitting to the base station, thus reducing the amount of energy spent per round. Simulation results show that PEGASIS performs better than LEACH by about 100 to 300% when 1%, 20%, 50%, and 100% of nodes die for different network sizes and topologies.
Extracted statement — Protocol: PEGASIS
Research field: Science
Title: The role of cytogenetic abnormalities as a prognostic marker in primary myelofibrosis: applicability at the time of diagnosis and later during disease course
Abstract: Although cytogenetic abnormalities are important prognostic factors in myeloid malignancies, they are not included in current prognostic scores for primary myelofibrosis (PMF). To determine their relevance in PMF, we retrospectively examined the impact of cytogenetic abnormalities and karyotypic evolution on the outcome of 256 patients. Baseline cytogenetic status impacted significantly on survival: patients with favorable abnormalities (sole deletions in 13q or 20q, or trisomy 9 +/- one other abnormality) had survivals similar to those with normal diploid karyotypes (median, 63 and 46 months, respectively), whereas patients with unfavorable abnormalities (rearrangement of chromosome 5 or 7, or > or = 3 abnormalities) had a poor median survival of 15 months. Patients with abnormalities of chromosome 17 had a median survival of only 5 months. A model containing karyotypic abnormalities, hemoglobin, platelet count, and performance status effectively risk-stratified patients at initial evaluation. Among 73 patients assessable for clonal evolution during stable chronic phase, those who developed unfavorable or chromosome 17 abnormalities had median survivals of 18 and 9 months, respectively, suggesting the potential role of cytogenetics as a risk factor applicable at any time in the disease course. Dynamic prognostic significance of cytogenetic abnormalities in PMF should be further prospectively evaluated.
Extracted statement — Disease: PMF
Research field: Science
Title: Robust Face Landmark Estimation under Occlusion
Abstract: Human faces captured in real-world conditions present large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.g. food). Current face landmark estimation approaches struggle under such conditions since they fail to provide a principled way of handling outliers. We propose a novel method, called Robust Cascaded Pose Regression (RCPR) which reduces exposure to outliers by detecting occlusions explicitly and using robust shape-indexed features. We show that RCPR improves on previous landmark estimation methods on three popular face datasets (LFPW, LFW and HELEN). We further explore RCPR's performance by introducing a novel face dataset focused on occlusion, composed of 1,007 faces presenting a wide range of occlusion patterns. RCPR reduces failure cases by half on all four datasets, at the same time as it detects face occlusions with a 80/40% precision/recall.
Extracted statement — Methods: RCPR
Research field: Science
Title: Dynamic modeling and optimal control of batch reactors, based on structure approaching hybrid neural networks
Abstract: A novel Structure Approaching Hybrid Neural Network (SAHNN) approach to model batch reactors is presented. The Virtual Supervisor−Artificial Immune Algorithm method is utilized for the training of SAHNN, especially for the batch processes with partial unmeasurable state variables. SAHNN involves the use of approximate mechanistic equations to characterize unmeasured state variables. Since the main interest in batch process operation is on the end-of-batch product quality, an extended integral square error control index based on the SAHNN model is applied to track the desired temperature profile of a batch process. This approach introduces model mismatches and unmeasured disturbances into the optimal control strategy and provides a feedback channel for control. The performance of robustness and antidisturbances of the control system are then enhanced. The simulation result indicates that the SAHNN model and model-based optimal control strategy of the batch process are effective.
Extracted statement — Types: SAHNN
Research field: Science
Title: Supervised descent method and its applications to face alignment
Abstract: Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface.
Extracted statement — Methods: SDM
Research field: Science
Title: SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks
Abstract: We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.
Extracted statement — Protocol: SEP
Research field: Science
Title: Page grammars and page parsing. A syntactic approach to document layout recognition
Abstract: Describes a syntactic approach to deducing the logical structure of printed documents from their physical layout. Page layout is described by a two-dimensional grammar, similar to a context-free string grammar, and a chart parser is used to parse segmented page images according to the grammar. This process is part of a system which reads scanned document images and produces computer-readable text in a logical mark-up format such as SGML. The system is briefly outlined, the grammar formalism and the parsing algorithm are described in detail, and some experimental results are reported.<<ETX>>
Extracted statement — Output Representation: SGML
Research field: Science
Title: Vision based autonomous vehicle navigation with self-organizing map feature matching technique
Abstract: Vision is becoming more and more common in applications such as localization, autonomous navigation, path finding and many other computer vision applications. This paper presents an improved technique for feature matching in the stereo images captured by the autonomous vehicle. The Scale Invariant Feature Transform (SIFT) algorithm is used to extract distinctive invariant features from images but this algorithm has a high complexity and a long computational time. In order to reduce the computation time, this paper proposes a SIFT improvement technique based on a Self-Organizing Map (SOM) to perform the matching procedure more efficiently for feature matching problems. Experimental results on real stereo images show that the proposed algorithm performs feature group matching with lower computation time than the original SIFT algorithm. The results showing improvement over the original SIFT are validated through matching examples between different pairs of stereo images. The proposed algorithm can be applied to stereo vision based autonomous vehicle navigation for obstacle avoidance, as well as many other feature matching and computer vision applications.
Extracted statement — Algorithm: SIFT
Research field: Science
Title: SLSA: A Sentiment Lexicon for Standard Arabic
Abstract: Sentiment analysis has been a major area of interest, for which the existence of highquality resources is crucial. In Arabic, there is a reasonable number of sentiment lexicons but with major deficiencies. The paper presents a large-scale Standard Arabic Sentiment Lexicon (SLSA) that is publicly available for free and avoids the deficiencies in the current resources. SLSA has the highest up-to-date reported coverage. The construction of SLSA is based on linking the lexicon of AraMorph with SentiWordNet along with a few heuristics and powerful back-off. SLSA shows a relative improvement of 37.8% over a state-of-theart lexicon when tested for accuracy. It also outperforms it by an absolute 3.5% of F1-score when tested for sentiment analysis.
Extracted statement — Lexicon: SLSA
Research field: Science
Title: Reliability Modeling for SOA Systems
Abstract: Service-oriented architecture (SOA) is a popular paradigm for development of distributed systems by composing the functionality provided by the services exposed on the network. In effect, the services can use functionalities of other services to accomplish their own goals. Although such an architecture provides an elegant solution to simple construction of loosely coupled distributed systems, it also introduces additional concerns. One of the primary concerns in designing a SOA system is the overall system reliability. Since the building blocks are services provided by various third parties, it is often not possible to apply the well established fault removal techniques during the development phases. Therefore, in order to reach desirable system reliability for SOA systems, the focus shifts towards fault prediction and fault tolerance techniques. In this paper an overview of existing reliability modeling techniques for SOA-based systems is given. Furthermore, we present a model for reliability estimation of a service composition using directed acyclic graphs. The model is applied to the service composition based on the orchestration model. A case study for the proposed model is presented by analyzing a simple Web Service composition scenario.
Extracted statement — Area of use: SOA
Research field: Science
Title: Automatic Reliability Management in SOA-based critical systems
Abstract: A well-known concept for the design and development of distributed software systems is service-orientation. In SOA, an interacting group of autonomous services realize a dynamic adaptive heterogenous distributed system. Because of its flexibility, SOA allows an easy adaptation of new business requirements. This also makes the serviceorientation idea a suitable concept for development of critical software systems. Reliability is a central parameter for developing critical software systems. SOA brings some additional requirements to the usual reliability models currently being used for standard software solutions. In order to fullfil all requirements and guarantee a certain degree of reliability, a generic reliability management model is needed for SOA based software systems. This article defines research challenges in this area and gives an approach to solve this problem.
Extracted statement — Area of use: SOA
Research field: Science
Title: Estimating Reliability Of Service-Oriented Systems: A Rule- Based Approach”
Abstract: In service-oriented architecture (SOA), the entire software system consists of an interacting group of autonomous services. In order to make such a system reliable, it should inhibit guarantee for basic service, data flow, composition of services, and the complete workflow. This paper discusses the important factor of SOA and their role in the entire SOA system reliability. We focus on the factors that have the strongest effect of SOA system reliability. Based on these factors, we used a fuzzy-based approach to estimate the SOA reliability. The proposed approach is implemented on a database obtained for SOA application, and the results obtained validate and confirm the effectiveness of the proposed fuzzy approach. Furthermore, one can make trade-off analyses between different parameters for reliability.
Extracted statement — Area of use: SOA