Chung-Cheng Chiu

A Robust Object Segmentation System Using a Probability-Based Background Extraction Algorithm

A video-based monitoring system must be capable of continuous operation under various weather and illumination conditions. Moreover, background subtraction is a very important part of surveillance applications for successful segmentation of objects from video sequences, and the accuracy, computational complexity, and memory requirements of the initial background extraction are crucial in any background subtraction method. This paper proposes an algorithm to extract initial color backgrounds from surveillance videos using a probability-based background extraction algorithm. With the proposed algorithm, the initial background can be extracted accurately and quickly, while using relatively little memory. The intrusive objects can then be segmented quickly and correctly by a robust object segmentation algorithm. The segmentation algorithm analyzes the threshold values of the background subtraction from the prior frame to obtain good quality while minimizing execution time and maximizing detection accuracy. The color background images can be extracted efficiently and quickly from color image sequences and updated in real time to overcome any variation in illumination conditions. Experimental results for various environmental sequences and a quantitative evaluation are provided to demonstrate the robustness, accuracy, effectiveness, and memory economy of the proposed algorithm.

The moving object segmentation approach to vehicle extraction

In this study, we propose a moving object segmentation approach based on an efficient and real-time color background extraction. We use the statistical algorithm to obtain the color background and moving objects information efficiently. Then we perform the run-length algorithm to compensate the segmented objects and update the background real-time. The variation of the illuminative condition is also considered in our work. Several experimental results are illustrated to show the advantage of the proposed work on processing time and the segmented result.

Efficient implementation of several multilevel thresholding algorithms using A combinatorial scheme

Abstract In this study, we present a combinatorial scheme for reducing the computation timings of determining the optimal threshold values in multilevel thresholding. By applying the proposed scheme on criterion-based multilevel thresholding, not only do we effectively avoid the redundant evaluation of threshold sets, but we also substantially suppress the computation cost for each evaluation of each potential threshold set, thereby significantly reducing the computation timings for obtaining the optimal set of threshold values. In addition, this proposed scheme achieves the parameterization of the desired number of thresholds. We have implemented this scheme on multilevel thresholding using the criterion functions of three well-known methods: the between-class variance method, the maximum entropy method, and the minimum error method. Experimental results demonstrate the feasibility and computational efficiency of the proposed scheme on multilevel thresholding. Performance evaluations of these three criterion functions in multilevel thresholding are also presented in the experimental results.

Complex document image segmentation using localized histogram analysis with multi-layer matching and clustering

This paper proposes a new segmentation method to separate the text from various complex document images. An automatic multilevel thresholding method, based on discriminant analysis, is utilized to recursively segment a specified block region into several layered image sub-blocks. Then the multi-layer region based clustering method is performed to process the layered image sub-blocks to form several object layers. Hence character strings with different illuminations, nontext objects and background components are segmented into separate object layers. After performed text extraction process, the text objects with different sizes, styles and illuminations are properly extracted. Experimental results on the extraction of text strings from complex document images demonstrate the effectiveness of the proposed region-based segmentation method.

MULTI-LAYER SEGMENTATION OF COMPLEX DOCUMENT IMAGES

Text is commonly printed on a complex background. Segmenting text is an important part in document analysis. In the past some methods have been shown for the segmentation of texts with images. However, previous studies have not sufficiently addressed complex compound documents. This investigation presents an algorithm for the segmentation of text in various document images. The proposed segmentation algorithm applies a new multilayer segmentation method to separate the text from various compound document images, independent from the text and background overlapping or not. This method solves various problems associated with the complexity of background images. Experimental results obtained using various document images scanned from book covers, advertisements, brochures and magazines, reveal that the proposed algorithm can successfully segment Chinese and English text strings from various backgrounds, regardless of whether the texts are over a simple, slowly varying or rapidly varying background texture.