Xiaoqing Lu

Detection of Overlapped Quadrangles in Plane Geometric Figures

Digital plane geometric figures (PGFs) are important resources of digital education, especially in mathematical pedagogy. The related applications, such as recognition and retrieval system on geometric figure images, have not been fully exploited. Special quadrangles, including rectangles, parallelograms, and trapezoids, are important components of PGFs, and extracting these special quadrangles is a prerequisite task. In this paper, we focus on the detection of overlapped quadrangles in PGFs using the proposed one-pass detection algorithm based on the geometric symmetrical property of involved shapes. We also introduce a method to refine the detected geometric primitives by significance analysis in order to optimize the shape descriptor. Experimental results show that the proposed method for special quadrangle detection is accurate and efficient, and is ready for further use in PGF retrieval systems.

Plane Geometry Figure Retrieval with Bag of Shapes

Digital education is serving an increasingly important function in most educational institutions, thus resulting in the production of a large number of digital documents online for education purposes. However, convenient ways to retrieve mathematic geometry questions are lacking because current retrieval systems largely rely on keywords instead of geometry figure images. This study focuses on plane geometry figure (PGF) image retrieval with the aim of retrieving relevant geometry images that contain more structural information than a question text stem. To fully use geometrical properties, a Bag-of-shapes (BoS) method is proposed to build the feature descriptor of an image. The BoS method contains either basic geometric primitives or dual-primitive structures along with several specific geometrical features for shape description. Based on the BoS feature descriptor, we apply cosine similarity with group feature weight as vector similarity measure for ranking to achieve high efficiency. For a PGF image query, the retrieval results are provided in an appropriate ranking order, which has high visual similarity with respect to human perception. Retrieval experiments and evaluation results show the effectiveness and efficiency of the proposed BoS shape descriptor.

A robust circle detection algorithm based on top-down least-square fitting analysis

In this paper, we propose a robust and efficient circle detector, which achieves accurate results with a controlled number of false detections and requires no parameter tuning. The proposed algorithm consists of three steps as follows. First, we propose a novel edge point chaining method to extract Canny edge segments (i.e., contiguous and sequential chains of Canny edge points). Second, we split each edge segment into several smooth sub-segments, and detect candidate circles within each obtained sub-segment based on top-down least-square fitting analysis. Third, we employ Desolneux et al.s method to reject the false detections. Experimental results demonstrate that the proposed method is efficient and more robust than the state-of-the-art algorithm EDCircles.

Structure analysis for plane geometry figures

As there are increasing numbers of digital documents for education purpose, we realize that there is not a retrieval application for mathematic plane geometry images. In this paper, we propose a method for retrieving plane geometry figures (PGFs), which often appear in geometry books and digital documents. First, detecting algorithms are applied to detect common basic geometry shapes from a PGF image. Based on all basic shapes, we analyze the structural relationships between two basic shapes and combine some of them to a compound shape to build the PGF descriptor. Afterwards, we apply matching function to retrieve candidate PGF images with ranking. The great contribution of the paper is that we propose a structure analysis method to better describe the spatial relationships in such image composed of many overlapped shapes. Experimental results demonstrate that our analysis method and shape descriptor can obtain good retrieval results with relatively high effectiveness and efficiency.

Unsupervised Speech Text Localization in Comic Images

Localizing speech texts in comic images is a crucial step for catering the growing needs of reading comics on mobile devices. For example, automatically reading speech texts while adding sound effects alongside can not only render comic contents vividly but also help visually impaired readers. Unlike conventional text localization methods, we present an effective unsupervised speech text localization method in this paper that is free of training data. The proposed method consists of two major stages: (1) based on the concurrence of characters, the first stage of our method is to generate some of the character strings (a row or column of characters that align horizontally or vertically) from the comic images while the fonts and gaps of the adjacent characters within the character string are also obtained, (2) in the second stage, the obtained fonts and gaps of adjacent characters are used to detect rest of the character strings within the comic image via Bayesian classifier. The proposed method is tested on a dataset consists of 1000 comic images from ten printed comic series and provide satisfactory results.

Plane Geometry Figure Retrieval Based on Bilayer Geometric Attributed Graph Matching

With the development of computer-aided education and digital library, there have emerged large numbers of digital documents online for education purposes. However, it is far from convenient to retrieve mathematic geometry questions because current retrieval systems largely rely on keywords instead of geometry figure images. We focus on plane geometry figure (PGF) image retrieval aiming at retrieving relevant geometry images that hold more similar geometric attributes and structure properties than a question text stem. Motivated by Attribute Graph (AG), and aiming to catch more delicate local geometric attributes and overall structure layout, we propose a Bilayer Geometric Attributed Graph (Bilayer-GAG) matching method to retrieve the relevant PGF images. The root node of Bilayer-GAG catches the spatial relationships among its children - the graph elements of the second layer, the second layer contains curvilinear geometric primitives and linear nested AGs that consist of nodes and edges with geometric signatures. Then we calculate the overall matching cost in three perspectives and finally retrieve top-k relevant Bilayer-GAGs. For a PGF image query, the retrieval results are shown in an appropriate ranking order, which has high visual similarity with respect to human perception. Retrieval experiments results show the effectiveness and efficiency of the proposed Bilayer-GAG.

Review of chart recognition in document images

As an effective information transmitting way, chart is widely used to represent scientific statistics datum in books, research papers, newspapers etc. Though textual information is still the major source of data, there has been an increasing trend of introducing graphs, pictures, and figures into the information pool. Text recognition techniques for documents have been accomplished using optical character recognition (OCR) software. Chart recognition techniques as a necessary supplement of OCR for document images are still an unsolved problem due to the great subjectiveness and variety of charts styles. This paper reviews the development process of chart recognition techniques in the past decades and presents the focuses of current researches. The whole process of chart recognition is presented systematically, which mainly includes three parts: chart segmentation, chart classification, and chart Interpretation. In each part, the latest research work is introduced. In the last, the paper concludes with a summary and promising future research direction.

Stroke-Based Character Segmentation of Low-Quality Images on Ancient Chinese Tablet

Ancient Chinese tablets are invaluable in terms of historical and aesthetic value. Automatic character segmentation of images from degraded tablets poses a challenging problem. Therefore, this paper proposes a new character segmentation method that utilizes an enhanced stroke filter and an energy propagation process based on local layout information. A ground-truth dataset was established to evaluate the accuracy of the algorithm adopted by the proposed segmentation method. Experimental results indicate that the proposed method can effectively extract characters from low-quality ancient Chinese tablet images.

Evaluation of Fonts for Digital Publishing and Display

Advances in digital technology have greatly facilitated the design of new type fonts. Today, hundreds of thousands of fonts can be found in various visual appearances or styles, which are used in digital publishing and information display. As a result, it has become important to find ways of evaluating their impact on our daily lives: (1) ease in reading, (2) comprehension of the texts, and (3) eye-strain. This paper summarizes an in-depth inquiry into the following topics: (a) impact of fonts on digital publishing and display, (b) the influence of typographic features on reading, (c) the role of fonts in reading, (d) effect of spacing on reading speed and comprehension, and (e) machine reading of early styles of ancient Chinese characters. Several insightful questions on this subject are asked, and answers have been provided through this paper and the oral presentations. A comprehensive list of references is included at the end of each section for further studies and research.

A diagram retrieval method with multi-label learning

In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.