Yingle Fan

Overlapped Cell Image Segmentation Based on Distance Transform

In micro cell images, several cells overlapped with each other often appears which causes difficulty to automatic cell estimation. How to separate overlapped cells to individual cells becomes the prerequisite of analyzing the micro cell images. In this paper, by introducing the theory of distance transform combining with watershed algorithm, a new algorithm of overlapped cell segmentation is proposed. This algorithm adopts distance transform to turn the position information of pixels to grey level information, and then adopts the watershed algorithm to realize the search and segmentation of the adhesion border of cell. The main characteristic of this method is that it can reduce the requisition of the quality of image, such as grey difference or noise level and so on. At the same time, it can calculate fast with high accuracy and steady result. So it is suitable for most overlapped cell images segmentation

An Environmental Automatic Monitoring System for Heavy Metals

An automatic monitoring system was developed for simultaneous determination of trace zinc, cadmium, lead, copper, iron and arsenic in environmental aqueous media using electrochemical stripping voltammetry. The sensor was mercury-film silver-based electrode. With a potentiostat, several pumps and valves controlled by computer, the system realized in-situ real-time detection of the six heavy metal ions mentioned above without manual operation. Quantitative heavy metals analysis at parts-per-billion level was performed by standard addition method. One measurement can be completed in 20 min with only 10 ml sample.

EEG Nonlinear Feature Detection in Brain-Computation Interface

Brain-computer interface research focused on using electroencephalogram(EEG) from the scalp over sensorimotor cortex to control outer device. The studies seek to improve the classification accuracy by improving the selection of signal features based on non-linear methods. Since EEG signals may be considered chaotic, chaos theory may supply effective quantitative descriptors of EEG dynamics and of underlying chaos in the brain. The complexity of the chaotic system can be characterized by complexity measure computed from the signals generated by the system.Two new features of EEG, Kolmogorov and CO complexity measure are presented for analyzing EEG signals in BCI system. The experiments proved that the method is effective; the accuracy of the system reaches 90.3%.

A new unit layer linear rate control algorithm for H.264 based on PID controller

Abstract This paper presents a new unit layer rate control algorithm for H.264 combining PID controller with linear rate model. In various video coding standards, like MPEG-2, H.263, MPEG-4 and H. 264, it has been reported that the linear rate control algorithm can achieve more accurate and robust rate control. In fact, human is not only sensitive to spatial quality, but also to temporal quality. In order to have better tradeoff between spatial and temporal quality and obtain more consistent quality, the PID controller is inducted into unit layer linear rate control algorithm for H. 264. The scene changes are also effectively dealt with. Experimental results show that the proposed rate control algorithm can not only track target bit rate more accurately and achieve significantly smaller bit rate estimation error, like that of linear rate control algorithm, but also improve the temporal quality while keeping high spatial quality, and the quality of frames with scene changes can obviously be improved.

Fibre-optic Chemical Sensor Based on Characteristic Spectrum Recognition and its Application

In this paper, a characteristic spectrum recognition method based on artificial neural network is proposed to improve the sensitivity and selectivity of the chemical sensor. The requirements on the selectivity and the sensitivity of the chemical sensing materials can be lowered while the sensing function and sensing range raised effectively, which will be benefit to the practical realization and application of the chemical sensing system.

Texture Image Segmentation Based on Description Complexity

From fractal simulation theory, texture images can be reproduced by some texture sets through a nonlinear plural dynamical system. This paper generalizes the KC complexity measure, which is often used in analyzing the complexity of one-dimension time sequence into two-dimension image. The tests prove that the complexity description based on the KC complexity measure is effective. An improved measure method based on the spatial redundancy, is proposed to reduce the sensitivity to noises and to improve the robustness. Comparing with other usual algorithms of texture segmentation, the proposed algorithm has the advantages of less computation and better segmentation performance.

Image segmentation based on the local minium cross-entropy and quad-tree

With maximum entropy principle, satisfactory segmentation can be attained in dealing with the various sizes of objects. However, for some inhomogeneous images, due to the factors of inhomogeneous illumination, the global threshold cannot be used to segment all objects. On the basis of the current threshold algorithms and with the deduction of the relationships between entropy of the original set and ones of subsets, this article develops an image segmentation method based on local minimum cross-entropy, so to meet the requirements of inhomogeneous cell images. Moreover, the article presents a realization process of the algorithm that is combined with the quad-tree model, which has the advantageous of less computation and better segmentation effect, in comparison with other algorithms of adaptive threshold method.

A New Type of Photochemistry Instrumentation Based on Multi-sensor and Multi-spectrum

Up to now, the main obstacle in developing photochemistry instrumentation is the lack of satisfactory sensitivity and selectivity of chemical sensor. Many efforts have been made to solve this problem, for instance, by composing sensing array with different materials and data treatment algorithm based on artificial neural networks. These efforts, however, are quite limited due to the fact that the excitation signal on single or fixed frequency is not suitable for the sensors with different optical characteristics. In this paper, a new type of photochemistry instrumentation based on multi-sensor and multi-spectrum is proposed. The multi-sensor is composed of sensor array made by non-selective sensing materials and excited by a continuous spectrum. As the continuous spectrum covers a relatively wide frequency scope, the sensor array can describe different optical characteristics and create abundant sensing information. The adaptive sampling procedure of the spectrum and data processing algorithms is also given for the universal pattern recognition. Experimental results show that the proposed system not only works well on-line but also reduces the requirement of sensitivity and selectivity of the sensor. Therefore, the proposed system is helpful for the practical realization of photochemistry instrumentation.

A New Coding Scheme for CT Image Using Integer-to-integer Shape Adaptive Discrete Wavelet Transforms

This paper proposes an integer-to-integer shape adaptive discrete wavelet transform(ISA-DWT) coding scheme for CT(computed tomography) image. The scheme consists of (1) extraction of shape information of the foreground of CT image; (2) integer-to-integer shape adaptive discrete wavelet transform; (3) the modified SPIHT algorithm. In CT image, the foreground contains useful clinical information and the background contains noise. In our scheme, only the foreground is compressed losslessly to avoid the unnecessary bit stream consumption. Experimental results indicate that the proposed scheme results in obviously improved performance as compared to the conventional integer-to-integer discrete wavelet transform.

Unsupervised Texture Segmentation Using Permutation Entropy and Grey-level Feature

A new method based on permutation entropy and grey level feature is provided in this paper. Permutation entropy is a new complexity measure for time series based on comparison of neighbouring values. The definition applies to describe the texture feature of image. The new complexity measure feature combines with the grey-scale mean and grey-scale deviation, construct multi-dimension feature vector. Then, apply the fuzzy c-means algorithm as the classifier to cluster the feature vectors, get the texture segmentation results. Experiments show that the method is particularly useful in the presence of dynamical or observational noise and the advantages of the method are its simplicity, extremely fast calculation, its robustness