Image segmentation based on hierarchical belief propagation with variable weighting parameters

The belief propagation (BP) algorithm is an efficient way to minimize the MRF energy for image segmentation. This paper proposes a hierarchical BP algorithm with variable weighting parameters (HBP-VW) to improve the segmentation accuracy of the BP-based algorithms. In the HBP-VW, two variable weighting parameters are introduced, the global parameter and the local parameter. The global parameter is used to overall adjust the influence of each part in the message update rule. The local parameter is designed to describe the local texture pattern for each site. Texture, remote sensing, and nature images are employed to test the proposed algorithm. Experimental results illustrate a better segmentation accuracy compared with other BP-based algorithms.     

The Study of Full Light Speech Signal Collection System

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Unsupervised Image Segmentation Using Hierarchical Clustering

In the analysis of a medical image database aimed at formulating useful knowledge for image diagnosis requires an unsupervised image processing technique without preconceived knowledge. In this paper, we propose a method for unsupervised image segmentation, which is suitable for finding the features contained in an image. A small region around each pixel is considered as a pattern vector, and the set of pattern vectors acquired from the whole image is classified using the hierarchical clustering technique. In hierarchical clustering, the classification of pattern vectors is divided into two clusters at each node according to the statistical criterion based on the entropy in thermodynamics. Results of the test image generated by the Markov random field (MRF) model and real medical images photomicrographs of a colon tumor are shown.     

Breathing muscle activity and subglottal pressure dynamics in singing and speech

Previous studies have indicated that the diaphragm may contribute to the accomplishment of the quick and precise subglottal pressure changes required during singing. The present investigation compares data collected from simultaneously recorded electromyograms from breathing muscles and transdiaphragmatic pressure during singing as well as during nonsense and emphatic speech.     

Insight into 3D micro‐CT data: exploring segmentation algorithms through performance metrics

Three‐dimensional (3D) micro‐tomography (µ‐CT) has proven to be an important imaging modality in industry and scientific domains. Understanding the properties of material structure and behavior has produced many scientific advances. An important component of the 3D µ‐CT pipeline is image partitioning (or image segmentation), a step that is used to separate various phases or components in an image. Image partitioning schemes require specific rules for different scientific fields, but a common strategy consists of devising metrics to quantify performance and accuracy. The present article proposes a set of protocols to systematically analyze and compare the results of unsupervised classification methods used for segmentation of synchrotron‐based data. The proposed dataflow for Materials Segmentation and Metrics (MSM) provides 3D micro‐tomography image segmentation algorithms, such as statistical region merging (SRM), k‐means algorithm and parallel Markov random field (PMRF), while offering different metrics to evaluate segmentation quality, confidence and conformity with standards. Both experimental and synthetic data are assessed, illustrating quantitative results through the MSM dashboard, which can return sample information such as media porosity and permeability. The main contributions of this work are: (i) to deliver tools to improve material design and quality control; (ii) to provide datasets for benchmarking and reproducibility; (iii) to yield good practices in the absence of standards or ground‐truth for ceramic composite analysis.     

改进的多分辨纹理图像分割算法

提出了一种改进的有监督纹理图像的分割算法。基于实际纹理图像是分割图像叠加了不规则噪声的假设,用被污染的高斯分布描述待分割的图像,并且利用多分辨模型得到代分辨层上的模型参数,从而实现由粗到细直到纹理图像的每个像素的分割。另外在禽域关联为先验信息利用上更为合理。所以这种方法不仅计算量小,而且分割结果也较为精确。     

Recent advances of variational model in medical imaging and applications to computer aided surgery

In this paper, we review some mathematical models in medical image processing. Due to the superiority in modeling and computation, variational methods have been proven to be powerful techniques, which have been extremely popular and dramatically improved in the past two decades. On one hand, many models have been proposed for nearly all kinds of applications. On the other hand, a lot of models can be globally optimized and also many computation tools have been introduced. Under the variational framework, we focus on two basic problems in medical imaging: image restoration and segmentation, which are core components for kinds of specific tasks. For image restoration, we discuss some models on both additive and multiplicative noises. For image segmentation, we review some models on both whole image segmentation and specific target delineation, with the later being a key step in computer aided surgery. Additionally, we present some models on liver delineation and give their applications to living donor liver transplantation.     

Multimodal Interfaces for Cell Phones and Mobile Technology

By modeling users' natural spoken and multimodal communication patterns, more powerful and highly reliable interfaces can be designed that support emerging mobile technology. In this paper, we highlight three different examples of research that is advancing state-of-the-art mobile technology. The first is the development of fusion-based multimodal systems, such as ones that combine speech and pen or touch input, which are substantially improving the robustness and stability of system recognition. The second is modeling of multimodal communication patterns to establish open-microphone engagement techniques that work in challenging multi-person mobile settings. The third is new approaches to adaptive processing, which are able to transparently guide user input to match system processing capabilities. All three research directions are contributing to the design of more reliable, usable, and commercially promising mobile systems of the future.