The Experts below are selected from a list of 107319 Experts worldwide ranked by ideXlab platform
Xiao-gang Wang - One of the best experts on this subject based on the ideXlab platform.
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Single-pedestrian detection aided by two-pedestrian detection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015Co-Authors: Wanli Ouyang, Xingyu Zeng, Xiao-gang WangAbstract:In this paper, we address the challenging problem of detecting pedestrians who appear in groups. A new approach is proposed for single-pedestrian detection aided by two-pedestrian detection. A mixture model of two-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single- and two-pedestrian detectors, and to refine the single-pedestrian detection result using two-pedestrian detection. The two-pedestrian detector can integrate with any single-pedestrian detector. Twenty-five state-of-the-art single-pedestrian detection approaches are combined with the two-pedestrian detector on three widely used public Datasets: Caltech, TUD-Brussels, and ETH. Experimental results show that our framework improves all these approaches. The average improvement is 9 percent on the Caltech-Test Dataset, 11 percent on the TUD-Brussels Dataset and 17 percent on the ETH Dataset in terms of average miss rate. The lowest average miss rate is reduced from 37 to percent on the Caltech-Test Dataset, from 55 to 50 percent on the TUD-Brussels Dataset and from 43 to 38 percent on the ETH Dataset.
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CVPR - Single-Pedestrian Detection Aided by Multi-pedestrian Detection
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013Co-Authors: Wanli Ouyang, Xiao-gang WangAbstract:In this paper, we address the challenging problem of detecting pedestrians who appear in groups and have interaction. A new approach is proposed for single-pedestrian detection aided by multi-pedestrian detection. A mixture model of multi-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby multiple pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single-and multi-pedestrian detectors, and to refine the single-pedestrian detection result with multi-pedestrian detection. It can integrate with any single-pedestrian detector without significantly increasing the computation load. 15 state-of-the-art single-pedestrian detection approaches are investigated on three widely used public Datasets: Caltech, TUD-Brussels and ETH. Experimental results show that our framework significantly improves all these approaches. The average improvement is 9% on the Caltech-Test Dataset, 11% on the TUD-Brussels Dataset and 17% on the ETH Dataset in terms of average miss rate. The lowest average miss rate is reduced from 48% to 43% on the Caltech-Test Dataset, from 55% to 50% on the TUD-Brussels Dataset and from 51% to 41% on the ETH Dataset.
Wanli Ouyang - One of the best experts on this subject based on the ideXlab platform.
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Single-pedestrian detection aided by two-pedestrian detection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015Co-Authors: Wanli Ouyang, Xingyu Zeng, Xiao-gang WangAbstract:In this paper, we address the challenging problem of detecting pedestrians who appear in groups. A new approach is proposed for single-pedestrian detection aided by two-pedestrian detection. A mixture model of two-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single- and two-pedestrian detectors, and to refine the single-pedestrian detection result using two-pedestrian detection. The two-pedestrian detector can integrate with any single-pedestrian detector. Twenty-five state-of-the-art single-pedestrian detection approaches are combined with the two-pedestrian detector on three widely used public Datasets: Caltech, TUD-Brussels, and ETH. Experimental results show that our framework improves all these approaches. The average improvement is 9 percent on the Caltech-Test Dataset, 11 percent on the TUD-Brussels Dataset and 17 percent on the ETH Dataset in terms of average miss rate. The lowest average miss rate is reduced from 37 to percent on the Caltech-Test Dataset, from 55 to 50 percent on the TUD-Brussels Dataset and from 43 to 38 percent on the ETH Dataset.
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CVPR - Single-Pedestrian Detection Aided by Multi-pedestrian Detection
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013Co-Authors: Wanli Ouyang, Xiao-gang WangAbstract:In this paper, we address the challenging problem of detecting pedestrians who appear in groups and have interaction. A new approach is proposed for single-pedestrian detection aided by multi-pedestrian detection. A mixture model of multi-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby multiple pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single-and multi-pedestrian detectors, and to refine the single-pedestrian detection result with multi-pedestrian detection. It can integrate with any single-pedestrian detector without significantly increasing the computation load. 15 state-of-the-art single-pedestrian detection approaches are investigated on three widely used public Datasets: Caltech, TUD-Brussels and ETH. Experimental results show that our framework significantly improves all these approaches. The average improvement is 9% on the Caltech-Test Dataset, 11% on the TUD-Brussels Dataset and 17% on the ETH Dataset in terms of average miss rate. The lowest average miss rate is reduced from 48% to 43% on the Caltech-Test Dataset, from 55% to 50% on the TUD-Brussels Dataset and from 51% to 41% on the ETH Dataset.
Xingyu Zeng - One of the best experts on this subject based on the ideXlab platform.
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Single-pedestrian detection aided by two-pedestrian detection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015Co-Authors: Wanli Ouyang, Xingyu Zeng, Xiao-gang WangAbstract:In this paper, we address the challenging problem of detecting pedestrians who appear in groups. A new approach is proposed for single-pedestrian detection aided by two-pedestrian detection. A mixture model of two-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single- and two-pedestrian detectors, and to refine the single-pedestrian detection result using two-pedestrian detection. The two-pedestrian detector can integrate with any single-pedestrian detector. Twenty-five state-of-the-art single-pedestrian detection approaches are combined with the two-pedestrian detector on three widely used public Datasets: Caltech, TUD-Brussels, and ETH. Experimental results show that our framework improves all these approaches. The average improvement is 9 percent on the Caltech-Test Dataset, 11 percent on the TUD-Brussels Dataset and 17 percent on the ETH Dataset in terms of average miss rate. The lowest average miss rate is reduced from 37 to percent on the Caltech-Test Dataset, from 55 to 50 percent on the TUD-Brussels Dataset and from 43 to 38 percent on the ETH Dataset.
Salih Gunes - One of the best experts on this subject based on the ideXlab platform.
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detection of ecg arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine
Applied Mathematics and Computation, 2007Co-Authors: Kemal Polat, Salih GunesAbstract:Abstract Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this study, we have detected on ECG Arrhythmias using principal component analysis (PCA) and least square support vector machine (LS-SVM). The approach system has two stages. In the first stage, dimension of ECG Arrhythmias Dataset that has 279 features is reduced to 15 features using principal component analysis. In the second stage, diagnosis of ECG Arrhythmias was conducted by using LS-SVM classifier. We took the ECG Arrhythmias Dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) machine learning database. Classifier system consists of three stages: 50–50% of training-Test Dataset, 70–30% of training-Test Dataset and 80–20% of training-Test Dataset, subsequently, the obtained classification accuracies; 96.86%, 100% ve 100%. The end benefit would be to assist the physician to make the final decision without hesitation. This result is for ECG Arrhythmias disease but it states that this method can be used confidently for other medical diseases diagnosis problems, too.
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a new method to medical diagnosis artificial immune recognition system airs with fuzzy weighted pre processing and application to ecg arrhythmia
Expert Systems With Applications, 2006Co-Authors: Kemal Polat, Seral şahan, Salih GunesAbstract:Abstract Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has showed an effective and intriguing performance on the problems it was applied. Previously, AIRS was applied a range of problems including machine-learning benchmark problems and medical classification problems like breast cancer, diabets, liver disorders classification problems. The conducted medical classification task was performed for ECG arrhythmia data taken from UCI repository of machine-learning. Firsly, ECG Dataset is normalized in the range of [0,1] and is weighted with fuzzy weighted pre-processing. Then, weighted input values obtained from fuzzy weighted pre-processing is classified by using AIRS classifier system. In this study, fuzzy weighted pre-processing, which can be improved by ours, is a new method and firstly, it is applied to ECG Dataset. Classifier system consists of three stages: 50–50% of traing-Test Dataset, 70–30% of traing-Test Dataset and 80–20% of traing-Test Dataset, subsequently, the obtained classification accuries: 78.79, 75.00 and 80.77%.
Giseli Rabello Lopes - One of the best experts on this subject based on the ideXlab platform.
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an entity relatedness Test Dataset
International Semantic Web Conference, 2017Co-Authors: Jose Eduardo Talavera Herrera, Marco A Casanova, Bernardo Pereira Nunes, Luiz Andre Paes P Leme, Giseli Rabello LopesAbstract:A knowledge base stores descriptions of entities and their relationships, often in the form of a very large RDF graph, such as DBpedia or Wikidata. The entity relatedness problem refers to the question of computing the relationship paths that better capture the connectivity between a given entity pair. This paper describes a Dataset created to support the evaluation of approaches that address the entity relatedness problem. The Dataset covers two familiar domains, music and movies, and uses data available in IMDb and last.fm, which are popular reference Datasets in these domains. The paper describes in detail how sets of entity pairs from each of these domains were selected and, for each entity pair, how a ranked list of relationship paths was obtained.
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International Semantic Web Conference (2) - An Entity Relatedness Test Dataset
Lecture Notes in Computer Science, 2017Co-Authors: Jose Eduardo Talavera Herrera, Marco A Casanova, Bernardo Pereira Nunes, Luiz André P. Paes Leme, Giseli Rabello LopesAbstract:A knowledge base stores descriptions of entities and their relationships, often in the form of a very large RDF graph, such as DBpedia or Wikidata. The entity relatedness problem refers to the question of computing the relationship paths that better capture the connectivity between a given entity pair. This paper describes a Dataset created to support the evaluation of approaches that address the entity relatedness problem. The Dataset covers two familiar domains, music and movies, and uses data available in IMDb and last.fm, which are popular reference Datasets in these domains. The paper describes in detail how sets of entity pairs from each of these domains were selected and, for each entity pair, how a ranked list of relationship paths was obtained.
