With the recent explosion of interest in microarray technology, large amounts of microarray images are being produced currently. Since there is no standard method for information extracting, the storage and the transmission of this type of data are becoming increasingly challenging. Here we present a new segmentation template extracted method and propose a new lossless compression scheme. Our segmentation scheme is based on mean shift filtering and morphological H-reconstruction that can accurately segment microarray images. Based on the extracted segmentation template, our compression scheme divides image into foreground regions and background region and code each region separately. Particularly, two 16-bit images sharing one segmentation template and the segmentation template are compressed into one file. Experimental results and comparison with Gzip that commonly used in microarray management showed that our scheme is efficient and also can greatly facilitate the downstream information extraction and analysis.
Offline handwritten chinese character recognition (HCCR) is still a difficult problem because of its large stroke changes, writing anomaly, and the difficulty for obtaining its stroke ranking information. Generally, offline HCCR can be divided into two procedures: feature extraction for capturing handwritten Chinese character information and feature classifying for character recognition. In this paper, we proposed a new chinese character recognition algorithm. In feature extraction part, we adopted elastic mesh dividing method for extracting the block features and its relative fuzzy features that utilized the relativities between different strokes and distribution probability of a stroke in its neighbor sub-blocks. In recognition part, we constructed a classifier based on a supervisory competitive learning algorithm to train competitive learning neural network with the extracted features set. Experimental results show that the performance of our algorithm is encouraging and can be comparable to other algorithms.
DNA microarray are an experimental technology which consists in arrays of thousands of discrete DNA sequences that are printed on glass microscope slides and allows the monitoring of expressions for tens of thousands of genes simultaneously. Image analysis is an important aspect for microarray experiments that can affect subsequent analysis such as identification of differentially expressed genes. The aim of this step is to extract the gene expression data included in the spots image. Imge processing for microarray images includes three tasks: spot gridding, segmentation and information extraction. In this study, we address the segmentation and information extraction problems, and propose a new segmentation method and a new background and foreground segmentation correction method for accurate information extraction. The initial segmentation is based on minimum error thresholding under the assumption that the probability density distribution of spot image and background image satisfies Gaussian and the final results is obtained through refining initial segmentation by Bayes decision theory. The advantage of our method is that it does not have any restrictions on the spot shape. We compare our experimental results with those obtained from the widely used software GenePix.
In this paper, a novel post-processing method is proposed in wavelet domain for the suppression of blocking artifacts in compressed images. The novelty of new method is that we can obtain soft-threshold values based on the difference between the wavelet transform coefficients of image blocks and the coefficients of the entire image and threshold high frequency wavelet coefficients in different subbands using different values and strategies. The threshold value is made adaptive to different images and characteristics of blocking artifacts. In particular, the new method is robust, fast and works remarkably well for different DCT based compressed images at low bit rate. The method is nonlinear, computationally efficient and spatially adaptive. Another advantage of the new method is that it retains sharp features in the images after it removes artifacts. Experimental results show that the proposed method can achieve a significantly improved visual quality in the images, and also increase PSNR in the output image. The algorithm can be used for real-time post-processing in DCT-based encoders and decoders.
In this paper, we presented an algorithm that implements the automatic detection of faces and facial features in color images. First, we use the chroma chart technique to determine the skin regions, then separate the touching regions apart using morphological operators; Second, we extract the candidate facial feature regions using morphological valley operators, for each skin region; Third, we match the candidate facial features with the model face we proposed to determine the optimal facial feature combination of eyes and mouth, thus we get the final description of the faces' geometric information. In this work, we improved the performance of the chroma chart, introduced morphological operators into the skin region analysis and facial feature detection, put forward our matching scheme that's fast and stable. The whole process is completely automatic, and is robust to lighting conditions, shading, tilt, multiple faces, etc.
The block discrete cosine transform (BDCT) is the most widely used technique for the compression of both still and moving images, a major problem related with the BDCT techniques is that the decoded images, especially at low bit rate, exhibit visually annoying blocking effects. In this paper, based on Mallets multiscale edge detection, we proposed an efficient deblocking algorithm to further improved the coding performance. The advantage of our algorithm is that it can efficiently preserve texture structure in the original decompressed images. Our method is similar to that of Z. Xiong's, where the Z.Xiong's method is not suitable for images with a large portion of texture; for instance, the Barbara Image. The difference of our method and the Z.Xiong's is that our method adopted a new thresholding scheme for multi-scale edge detection instead of exploiting cross-scale correlation for edge detection. Numerical experiment results show that our scheme not only outperforms Z.Xiong's for various images in the case of the same computational complexity, but also preserve texture structure in the decompressed images at the same time. Compared with the best iterative-based method (POCS) reported in the literature, our algorithm can achieve the same peak signal-to-noise ratio (PSNR) improvement and give visually very pleasing images as well.
The basic idea of theory of Marr's image edge-detecting is firstly to smooth original image with Gaussian function, then obtain the zero-cross map of Laplacian's transform of smoothed image. However, the residual between the original image and smoothed image remain s some feature points that may not be detected. Therefore, this paper firstly proposed a new smoothing operator which has low-pass characteristics similar to a Butterworth filter and limited spatial extent similar to a Gaussian function, then we constructed a class of edge-detecting operator that can be controlled more easily using Laplace's transform. The new edge-detecting operator also has closed forms that facilitate implementation, and allows us flexibility control feature- detecting accuracy compared to Marr's operator. In addition, Marr's edge-detecting operator is a special formulation of a new operator. Practical numerical experimental results showed that hose edge-detecting operators have some practical effect and reference value.