Marios Anthimopoulos

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This paper proposes a hybrid system for text detection in video frames. The system consists of two main stages. In the first stage text regions are detected based on the edge map of the image leading in a high recall rate with minimum computation requirements. In the sequel, a refinement stage uses an SVM classifier trained on features obtained by a new(More)
This paper proposes a two-stage system for text detection in video images. In the first stage, text lines are detected based on the edge map of the image leading in a high recall rate with low computational time expenses. In the second stage, the result is refined using a sliding window and an SVM classifier trained on features obtained by a new Local(More)
Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved(More)
This paper proposes an algorithm for detecting artificial text in video frames using edge information. First, an edge map is created using the Canny edge detector. Then, morphological dilation and opening are used in order to connect the vertical edges and eliminate false alarms. Bounding boxes are determined for every non-zero valued connected component,(More)
Textual information in images and video frames constitutes a valuable source of high-level semantics for multimedia indexing and retrieval systems. Text detection is the most crucial step in a multimedia text extraction system and although it has been extensively studied the past decade still, it does not exist a generic architecture that would work for(More)
—This paper proposes a performance evaluation method for text detection in color images. The method, contrary to previous approaches, is not based on the inexplicitly defined text bounding boxes for the evaluation of the text detection result but considers only the text pixels detected by binarizing the image and applying a color inversion if needed.(More)