Gabor Filter and Rough Clustering Based Edge Detection

This paper introduces an efficient edge detection method based on Gabor filter and rough clustering. The input image is smoothed by Gabor function, and the concept of rough clustering is used to focus on edge detection with soft computational approach. Hysteresis thresholding is used to get the actual output, i.e. edges of the input image. To show the effectiveness, the proposed technique is compared with some other edge detection methods.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201

Analysis of grating cell features for texture discrimination, An

Department Head: L. Darrell Whitley.2010 Summer.Includes bibliographical references (pages 65-67).The design of artificial vision systems has been influenced by knowledge of the early stages of processing in the human vision system. The discovery of directionally sensitive cells in the human visual cortex lead to the theory of edge detection in computer vision, and the discovery that simple cell receptive fields can be modeled as Gabor filters has led to the development and use of Gabor jets. In this thesis, we evaluate a low-level image feature inspired by "grating" cells found in the human visual cortex. These cells, and the features based on them, detect spatial gratings–repeated patterns of light and dark bars–in their receptive fields. We evaluate the utility of grating cell model features to distinguish different textures using Fisher’s linear discriminant. It will be shown that the grating cell features contain significantly more distinguishing information than another standard Gabor-filter-based image feature

Implementation of Pre-processing and Efficient Blood Vessel Segmentation in Retinopathy Fundus Image

The human retina is a light receptive tissue and its enormously rich in blood vessels for its high physiological stress and dysfunction of the retinal vasculature can effect from several diseases. Diabetic retinopathy is caused due to complications of diabetes, which can eventually develop new blood vessels at the back of the retina and it can lead to blur vision or loss of vision. This work describes the problems of retinopathy associated with diabetic patients and premature babies. We propose methods for the preprocessing and efficient segmentation method to support measurement of the openness of the MTA, including image enhancement techniques like morphological operations, efficient luminance component construction and bank of Gabor filters to segment retinal blood vessels. Finally an image cropping is used to separate inferior and superior part of this segmented image for the effective and detailed analysis of the vascular structure in the fundus eye images. Certain retinal disorders, if not detected in time, can cause serious problems like blur vision and blindness in patients. The implementation and the performance of the various edge detection methods like Canny, Sobel and Gabor filters are based on visual perception. It has been concluded that in case of natural images such as retinal fundus image a Gabor filter yielded better results in segmentation of blood vessels as compared to edge detection methods of Canny and Sobel. DOI: 10.17762/ijritcc2321-8169.15066

Feature detection from echocardiography images using local phase information

Ultrasound images are characterized by their special speckle appearance, low contrast, and low signal-to-noise ratio. It is always challenging to extract important clinical information from these images. An important step before formal analysis is to transform the image to significant features of interest. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant and thus suitable for ultrasound images. We extend the previous local phase-based method to detect features using the local phase computed from monogenic signal which is an isotropic extension of the analytic signal. We apply our method of multiscale feature-asymmetry measurement and local phase-gradient computation to cardiac ultrasound (echocardiography) images for the detection of endocardial, epicardial and myocardial centerline

A biologically inspired spiking model of visual processing for image feature detection

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images