Qixiang Ye

Fast and robust text detection in images and video frames

Text in images and video frames carries important information for visual content understanding and retrieval. In this paper, by using multiscale wavelet features, we propose a novel coarse-to-fine algorithm that is able to locate text lines even under complex background. First, in the coarse detection, after the wavelet energy feature is calculated to locate all possible text pixels, a density-based region growing method is developed to connect these pixels into regions which are further separated into candidate text lines by structural information. Secondly, in the fine detection, with four kinds of texture features extracted to represent the texture pattern of a text line, a forward search algorithm is applied to select the most effective features. Finally, an SVM classifier is used to identify true text from the candidates based on the selected features. Experimental results show that this approach can fast and robustly detect text lines under various conditions.

Text Detection and Recognition in Imagery: A Survey

This paper analyzes, compares, and contrasts technical challenges, methods, and the performance of text detection and recognition research in color imagery. It summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems. Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including text localization, verification, segmentation and recognition. Special issues associated with the enhancement of degraded text and the processing of video text, multi-oriented, perspectively distorted and multilingual text are also addressed. The categories and sub-categories of text are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. This review provides a fundamental comparison and analysis of the remaining problems in the field.

A configurable method for multi-style license plate recognition

Despite the success of license plate recognition (LPR) methods in the past decades, few of them can process multi-style license plates (LPs), especially LPs from different nations, effectively. In this paper, we propose a new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format. In the recognition procedure these four parameters are managed by relevant algorithms, i.e., plate rotation, plate line segmentation, character recognition and format matching algorithm, respectively. To recognize special style LPs, users can configure the method by defining corresponding parameter values, which will be processed by the relevant algorithms. In addition, the probabilities of the occurrence of every LP style are calculated based on the previous LPR results, which will result in a faster and more precise recognition. Various LP images were used to test the proposed method and the results proved its effectiveness.

Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)

When appearance variation of object and its background, partial occlusion or deterioration in object images occurs, most existing visual tracking methods tend to fail in tracking the target. To address this problem, this paper proposes a new approach for visual object tracking based on Sample-Based Adaptive Sparse Representation (AdaSR), which ensures that the tracked object is adaptively and compactly expressed with predefined samples. First, the Sample-Based Sparse Representation, which selects a subset of samples as a basis for object representation by exploiting L1-norm minimization, improves the representation adaptation to partial occlusion for tracking. Second, to keep the temporal consistency and adaptation to appearance variation and deterioration in object images during the tracking process, the objects Sample-Based Sparse Representation is adaptively evaluated based on a Kalman filter, obtaining the AdaSR. Finally, the candidate holding the most similar Sample-Based Sparse Representation to the AdaSR of the tracked object will be regarded as the instantaneous tracking result. In addition, we can easily extend the AdaSR for multi-object tracking by integrating the sample set of each tracked object (named Common Sample-Based Adaptive Sparse Representation Analysis (AdaSRA)). AdaSRA fully analyses Adaptive Sparse Representation similarity for object classification. Our experiments on public datasets show state-of-the-art results, which are better than those of several representative tracking methods.

A robust text detection algorithm in images and video frames

In this paper, an algorithm for detecting text in images and video frames is proposed. The algorithm contains two steps: initial detection and verification. In the first step, edge feature and morphology operation are employed to locate edge-dense image blocks. Empirically rules are applied on these blocks to get candidate text. In the second step, wavelet-based features are employed to represent the texture property of text. A SVM classifier is used to identify text from the candidate ones. Experiments show that this algorithm has 93.9% detection rate for English text and a 92.4% detection rate for Chinese text. The algorithm is robust to language, font-color and size.

Color image segmentation using density-based clustering

Color image segmentation is an important but still open problem in image processing. We propose a method for this problem by integrating the spatial connectivity and color features of the pixels. Considering that an image can be regarded as a dataset in which each pixel has a spatial location and a color value, color image segmentation can be obtained by clustering these pixels into different groups of coherent spatial connectivity and color. To discover the spatial connectivity of the pixels, density-based clustering is employed, which is an effective clustering method used in data mining for discovering spatial databases. The color similarity of the pixels is measured in Munsell (HVC) color space whose perceptual uniformity ensures the color change in the segmented regions is smooth in terms of human perception. Experimental results using the proposed method demonstrate encouraging performance.

Playfield detection using adaptive GMM and its application

Playfield detection is a key step in sports video content analysis, since many semantic clues could be inferred from it. In this paper we propose an adaptive GMM based algorithm for playfield detection. Its advantages are twofold. First, it can update model parameters by the incremental expectation maximization (IEM) algorithm, which enables the model to adapt to the playfield variation with time; Second, online training is performed, which saves buffer for training samples. Then, the playfield detection results are applied in recognizing the key zone of the current playfield in soccer video, in which a fast algorithm based on playfield contour and least square is proposed. Experimental results show that the proposed algorithms are encouraging.

A new method to segment playfield and its applications in match analysis in sports video

With the growing popularity of digitized sports video, automatic analysis of them need be processed to facilitate semantic summarization and retrieval. Playfield plays the fundamental role in automatically analyzing many sports programs. Many semantic clues could be inferred from the results of playfield segmentation. In this paper, a novel playfield segmentation method based on Gaussian mixture models (GMMs) is proposed. Firstly, training pixels are automatically sampled from frames. Then, by supposing that field pixels are the dominant components in most of the video frames, we build the GMMs of the field pixels and use these models to detect playfield pixels. Finally region-growing operation is employed to segment the playfield regions from the background. Experimental results show that the proposed method is robust to various sports videos even for very poor grass field conditions. Based on the results of playfield segmentation, match situation analysis is investigated, which is also desired for sports professionals and longtime fanners. The results are encouraging.

1079.5- and 1341.4-nm: larger energy from a dual-wavelength Nd:YAlO 3 pulsed laser

On the basis of oscillation conditions of simultaneous multiple-wavelength lasing that we have established, a larger-energy (1079.5 and 1341.4 nm) dual-wavelength Nd:YAlO(3) pulsed laser has been developed. Output energies of 3.71 and 1.39 J with efficiencies of 1.29% and 0.48% for the 1341.4-and 1079.5-nm wavelengths, respectively, have been achieved. To our knowledge, this is the best result among simultaneous dual-wavelength solid-state lasers to date. The temporal and spatial distributions of these beams obtained from a free-running dual-wavelength Nd:YAlO3 pulsed laser have also been measured. Experimental results show that the temporal and spatial overlap of the two beams is quite good for this type of laser.

Visual abnormal behavior detection based on trajectory sparse reconstruction analysis

Abnormal behavior detection has been one of the most important research branches in intelligent video content analysis. In this paper, we propose a novel abnormal behavior detection approach by introducing trajectory sparse reconstruction analysis (SRA). Given a video scenario, we collect trajectories of normal behaviors and extract the control point features of cubic B-spline curves to construct a normal dictionary set, which is further divided into Route sets. On the dictionary set, sparse reconstruction coefficients and residuals of a test trajectory to the Route sets can be calculated with SRA. The minimal residual is used to classify the test behavior into a normal behavior or an abnormal one. SRA is solved by L1-norm minimization, leading to that a few of dictionary samples are used when reconstructing a behavior trajectory, which guarantees that the proposed approach is valid even when the dictionary set is very small. Experimental results with comparisons show that the proposed approach improves the state-of-the-art.