Median filter

About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.

A New and Efficient Algorithm for the Removal of High Density Salt and Pepper Noise in Images and Videos

Abstract: A new and efficient algorithm for high-density salt and pepper noise removal in images and videos is proposed. The existing non-linear filter like Standard Median Filter (SMF), Adaptive Median Filter (AMF), Decision Based Algorithm (DBA) and Robust Estimation Algorithm (REA) shows better results at low and medium noise densities. At high noise densities, their performance is poor. A new algorithm to remove high-density salt and pepper noise using modified sheer sorting method is proposed. The new algorithm has lower computation time when compared to other standard algorithms. Results of the algorithm is compared with various existing algorithms and it is proved that the new method has better visual appearance and quantitative measures at higher noise densities as high as 90%.

Noise estimation and filtering using block-based singular value decomposition

Abstract: Preprocessing of image and video sequences with spatial filtering techniques usually improves the image quality and compressibility. We present a block-based, nonlinear filtering algorithm based on singular value decomposition and compression-based filtering. Experiments show that the proposed filter preserves edge details and can significantly improve the compression performance.

Automatic detection of human faces in natural scene images by use of a skin color model and of invariant moments

Abstract: We use a skin color model based on the Muhulanobis metric and a shape analysis based on invariant moments to automatically detect and locate human faces in two-dimensional natural scene images. First, color segmentation of an input image is performed by thresholding in a perceptually plausible hue-saturation color space where the effects of the variability of human skin color and the dependency of chrominance on changes in illumination are reduced. We then group regions of the resulting binary image which have been classified as candidates into clusters of connected pixels. Performing median filtering on the image and discarding the smallest remaining clusters ensures that only a small number of clusters will be used for further analysis. Fully translation-, scale- anti in-plane rotation invariant moments are calculated for each remaining cluster. Finally, in order to distinguish faces from distractors, a multilayer perceptron neural network is used with the invariant moments as the input vector. Supervised learning of the network is implemented with the backpropagation algorithm, at first for frontal views of faces. Preliminary results show the efficiency of the combination of color segmentation and of invariant moments in detecting faces with a large variety of poses and against relatively complex backgrounds.

Color Transform Based Approach for Disease Spot Detection on Plant Leaf

Abstract: In this research, an algorithm for disease spot segmentation using image processing techniques in plant leaf is implemented. This is the first and important phase for automatic detection and classification of plant diseases. Disease spots are different in color but not in intensity, in comparison with plant leaf color. So we color transform of RGB image can be used for better segmentation of disease spots. In this paper a comparison of the effect of CIELAB, HSI and YCbCr color space in the process of disease spot detection is done. Median filter is used for image smoothing. Finally threshold can be calculated by applying Otsu method on color component to detect the disease spot. An algorithm which is independent of background noise, plant type and disease spot color was developed and experiments were carried out on different "Monocot" and "Dicot" family plant leaves with both, noise free (white) and noisy background.

Cell segmentation with median filter and mathematical morphology operation

Abstract: This paper describes a part of current research work on an automated cell tracking (ACT) project with the aim of tracking the movement of representative cells in order to determine the activity of the cell once certain medical substances have been injected. A major requirement for this project is an efficient method to segment cell images. This work shows that the combination methods of the median filter and the mathematical morphology operation such as dilation and erosion are adaptable and can be optimized to improve the segmentation result. The experimental results show that this method is suitable for segmentation based on edge detection of melanomas (tumor cells) and lymphocytes (blood cells) and the performance of this method is comparable to several edge detection methods such as Sobel, Prewitt, Roberts and Laplacian of Gaussian.