The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform

Xiang Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Finding and Extracting Academic Information from Conference Web Pages
    Social Media Retrieval and Mining, 2013
    Co-Authors: Peng Wang, Xiang Zhang, Fengbo Zhou
    Abstract:

    This paper proposes a method for finding and extracting academic information from conference Web pages. The main contributions include: (1) A lightweight topic crawling method based on search engine is used to crawl academic conference Web pages. (2) An new vision-based page segmentation algorithm is proposed to improve the result of classical VIPS algorithm by introducing complete tree. This algorithm can divide Web pages into text blocks. (3) Using bayesian network classifier, all text blocks are classified as 10 categories according to its vision features, key-word features and text content features. The Initial Classification results have 75 % precision and 67 % recall. (4) The context information of text blocks are employed to repair and refine Initial Classification results, which are improved to 96 % precision and 98 % recall. Finally, academic information is easily extracted from the classified text blocks. Experimental results on real-world datasets show that our method is effective and efficient for finding and extracting academic information from conference Web pages.

  • ICTAI - A New Vision-Based Method for Extracting Academic Information from Conference Web Pages
    2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 2012
    Co-Authors: Peng Wang, Mingqi Zhou, Xiang Zhang
    Abstract:

    This paper proposes a new vision-based method for extracting academic information from conference Web pages. The main contributions include: (1) An new vision-based page segmentation algorithm is proposed to improve the result of classical VIPS algorithm. This algorithm can divide pages into text blocks. (2) All text blocks are classified as 10 categories according to vision features, keyword features and text content features. The Initial Classification results have 75% precision and 67% recall. (3) The context information of text blocks are employed to repair and refine Initial Classification results, which are improved to 96% precision and 98% recall. Finally, academic information is extracted from classified text blocks. Our experimental results on real-world datasets show that the proposed method is effective and efficient for extracting academic information from conference Web pages.

  • A New Vision-Based Method for Extracting Academic Information from Conference Web Pages
    2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 2012
    Co-Authors: Peng Wang, Mingqi Zhou, Xiang Zhang
    Abstract:

    This paper proposes a new vision-based method for extracting academic information from conference Web pages. The main contributions include: (1) An new vision-based page segmentation algorithm is proposed to improve the result of classical VIPS algorithm. This algorithm can divide pages into text blocks. (2) All text blocks are classified as 10 categories according to vision features, keyword features and text content features. The Initial Classification results have 75% precision and 67% recall. (3) The context information of text blocks are employed to repair and refine Initial Classification results, which are improved to 96% precision and 98% recall. Finally, academic information is extracted from classified text blocks. Our experimental results on real-world datasets show that the proposed method is effective and efficient for extracting academic information from conference Web pages.

Bir Bhanu - One of the best experts on this subject based on the ideXlab platform.

  • ICPR - Temporal dynamics of tip fluorescence predict cell growth behavior in pollen tubes
    2016 23rd International Conference on Pattern Recognition (ICPR), 2016
    Co-Authors: Asongu L. Tambo, Bir Bhanu
    Abstract:

    In the sexual reproductive life cycle of flowering plants, the growth of the pollen tube plays a vital role. The pollen tube grows towards the ovary of the flower where it delivers male reproductive material. This growth often involves twists and turns as the pollen tube navigates towards the ovary. Current growth models are a collection of mathematical equations to explain observable linear growth behavior in pollen tubes. However, there are few studies on the relationship between the fluorescence signal at the tip of the cell and the growth behavior (straight vs. turning). In this paper, we propose a method of extracting features from the tip fluorescence signal which will be used to distinguishing between straight vs. turning growth behavior. The tip signal is obtained as a ratio of the average membrane-to-cytoplasm fluorescence values over time. A two-stage scheme is used to automatically detect individual growth intervals/cycles from the tip signal and split the experimental video into growth segments. In each growth segment, we extract relevant features. An Initial Classification uses structure-based features to distinguish between straight vs. turning growth cycles. The signal-based features are then used to train a Naive Bayes classifier to refine the miss-Classifications of the Initial Classification. Our results show that this two-stage process yields good Classification results.

  • Temporal dynamics of tip fluorescence predict cell growth behavior in pollen tubes
    2016 23rd International Conference on Pattern Recognition (ICPR), 2016
    Co-Authors: Asongu L. Tambo, Bir Bhanu
    Abstract:

    In the sexual reproductive life cycle of flowering plants, the growth of the pollen tube plays a vital role. The pollen tube grows towards the ovary of the flower where it delivers male reproductive material. This growth often involves twists and turns as the pollen tube navigates towards the ovary. Current growth models are a collection of mathematical equations to explain observable linear growth behavior in pollen tubes. However, there are few studies on the relationship between the fluorescence signal at the tip of the cell and the growth behavior (straight vs. turning). In this paper, we propose a method of extracting features from the tip fluorescence signal which will be used to distinguishing between straight vs. turning growth behavior. The tip signal is obtained as a ratio of the average membrane-to-cytoplasm fluorescence values over time. A two-stage scheme is used to automatically detect individual growth intervals/cycles from the tip signal and split the experimental video into growth segments. In each growth segment, we extract relevant features. An Initial Classification uses structure-based features to distinguish between straight vs. turning growth cycles. The signal-based features are then used to train a Naive Bayes classifier to refine the miss-Classifications of the Initial Classification. Our results show that this two-stage process yields good Classification results.

Peng Wang - One of the best experts on this subject based on the ideXlab platform.

  • Finding and Extracting Academic Information from Conference Web Pages
    Social Media Retrieval and Mining, 2013
    Co-Authors: Peng Wang, Xiang Zhang, Fengbo Zhou
    Abstract:

    This paper proposes a method for finding and extracting academic information from conference Web pages. The main contributions include: (1) A lightweight topic crawling method based on search engine is used to crawl academic conference Web pages. (2) An new vision-based page segmentation algorithm is proposed to improve the result of classical VIPS algorithm by introducing complete tree. This algorithm can divide Web pages into text blocks. (3) Using bayesian network classifier, all text blocks are classified as 10 categories according to its vision features, key-word features and text content features. The Initial Classification results have 75 % precision and 67 % recall. (4) The context information of text blocks are employed to repair and refine Initial Classification results, which are improved to 96 % precision and 98 % recall. Finally, academic information is easily extracted from the classified text blocks. Experimental results on real-world datasets show that our method is effective and efficient for finding and extracting academic information from conference Web pages.

  • ICTAI - A New Vision-Based Method for Extracting Academic Information from Conference Web Pages
    2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 2012
    Co-Authors: Peng Wang, Mingqi Zhou, Xiang Zhang
    Abstract:

    This paper proposes a new vision-based method for extracting academic information from conference Web pages. The main contributions include: (1) An new vision-based page segmentation algorithm is proposed to improve the result of classical VIPS algorithm. This algorithm can divide pages into text blocks. (2) All text blocks are classified as 10 categories according to vision features, keyword features and text content features. The Initial Classification results have 75% precision and 67% recall. (3) The context information of text blocks are employed to repair and refine Initial Classification results, which are improved to 96% precision and 98% recall. Finally, academic information is extracted from classified text blocks. Our experimental results on real-world datasets show that the proposed method is effective and efficient for extracting academic information from conference Web pages.

  • A New Vision-Based Method for Extracting Academic Information from Conference Web Pages
    2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 2012
    Co-Authors: Peng Wang, Mingqi Zhou, Xiang Zhang
    Abstract:

    This paper proposes a new vision-based method for extracting academic information from conference Web pages. The main contributions include: (1) An new vision-based page segmentation algorithm is proposed to improve the result of classical VIPS algorithm. This algorithm can divide pages into text blocks. (2) All text blocks are classified as 10 categories according to vision features, keyword features and text content features. The Initial Classification results have 75% precision and 67% recall. (3) The context information of text blocks are employed to repair and refine Initial Classification results, which are improved to 96% precision and 98% recall. Finally, academic information is extracted from classified text blocks. Our experimental results on real-world datasets show that the proposed method is effective and efficient for extracting academic information from conference Web pages.

Asongu L. Tambo - One of the best experts on this subject based on the ideXlab platform.

  • ICPR - Temporal dynamics of tip fluorescence predict cell growth behavior in pollen tubes
    2016 23rd International Conference on Pattern Recognition (ICPR), 2016
    Co-Authors: Asongu L. Tambo, Bir Bhanu
    Abstract:

    In the sexual reproductive life cycle of flowering plants, the growth of the pollen tube plays a vital role. The pollen tube grows towards the ovary of the flower where it delivers male reproductive material. This growth often involves twists and turns as the pollen tube navigates towards the ovary. Current growth models are a collection of mathematical equations to explain observable linear growth behavior in pollen tubes. However, there are few studies on the relationship between the fluorescence signal at the tip of the cell and the growth behavior (straight vs. turning). In this paper, we propose a method of extracting features from the tip fluorescence signal which will be used to distinguishing between straight vs. turning growth behavior. The tip signal is obtained as a ratio of the average membrane-to-cytoplasm fluorescence values over time. A two-stage scheme is used to automatically detect individual growth intervals/cycles from the tip signal and split the experimental video into growth segments. In each growth segment, we extract relevant features. An Initial Classification uses structure-based features to distinguish between straight vs. turning growth cycles. The signal-based features are then used to train a Naive Bayes classifier to refine the miss-Classifications of the Initial Classification. Our results show that this two-stage process yields good Classification results.

  • Temporal dynamics of tip fluorescence predict cell growth behavior in pollen tubes
    2016 23rd International Conference on Pattern Recognition (ICPR), 2016
    Co-Authors: Asongu L. Tambo, Bir Bhanu
    Abstract:

    In the sexual reproductive life cycle of flowering plants, the growth of the pollen tube plays a vital role. The pollen tube grows towards the ovary of the flower where it delivers male reproductive material. This growth often involves twists and turns as the pollen tube navigates towards the ovary. Current growth models are a collection of mathematical equations to explain observable linear growth behavior in pollen tubes. However, there are few studies on the relationship between the fluorescence signal at the tip of the cell and the growth behavior (straight vs. turning). In this paper, we propose a method of extracting features from the tip fluorescence signal which will be used to distinguishing between straight vs. turning growth behavior. The tip signal is obtained as a ratio of the average membrane-to-cytoplasm fluorescence values over time. A two-stage scheme is used to automatically detect individual growth intervals/cycles from the tip signal and split the experimental video into growth segments. In each growth segment, we extract relevant features. An Initial Classification uses structure-based features to distinguish between straight vs. turning growth cycles. The signal-based features are then used to train a Naive Bayes classifier to refine the miss-Classifications of the Initial Classification. Our results show that this two-stage process yields good Classification results.

Yi-chen Wang - One of the best experts on this subject based on the ideXlab platform.

  • Semantic Classification of Heterogeneous Urban Scenes Using Intrascene Feature Similarity and Interscene Semantic Dependency
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Xiuyuan Zhang, Shihong Du, Yi-chen Wang
    Abstract:

    Semantic Classification of urban scenes aims to classify scenes composed of many different types of objects into predefined semantic classes. To learn the association between urban scenes and semantic classes, five tasks are needed: 1) segmenting the image into scenes; 2) establishing semantic classes of scenes; 3) extracting and transforming features; 4) measuring the intrascenes feature similarity; and 5) labeling each scene by a semantic Classification method. Despite many efforts on these tasks, most existing works consider only visual features with inconsistent similarity measurement, while ignore semantic features inside scenes and the interactions between scenes, leading to poor Classification results for high heterogeneous scenes. To solve these problems, this study combines intrascene feature similarity and interscene semantic dependency to form a two-step Classification approach. For the first step, visual and semantic features are first optimized to be invariant to affine transformation, and then are employed in K-Nearest Neighbor to Initially classify scenes. For the second step, multinomial distribution is presented to model both the spatial and semantic dependency between scenes, and then used to improve the Initial Classification results. The implementations conducted in two study areas indicate that the proposed approach produces better results for heterogeneous scenes than visual interpretation, as it can discover and model the hidden information between scenes which is often ignored by existing methods. In addition, compared with the Initial Classification, the optimized step improves accuracies by 3.6% and 5% in the two study areas, respectively.