This paper presents a new approach for 2D object segmentations using an automatic method applied on images with severe non random noise conditions. Results from biomedical images (cytologies) are presented. The proposed methodology works from the output of an edge detector, which is processed to obtain an approximation of the shape of the object from partial information. The nal estimation of t...

This paper briefly describes an artificial neural network for preattentive visual processing. The network is capable of determiuing image motioll in a type of stimulus which defeats most popular methods of motion detect.ion a subset of second-order visual motion stimuli known as drift-balanced stimuli(DBS). The processing st.ages of the network described in this paper are integratable into a mo...

In this paper we use a new logarithmic model of image representation, developed in [1,2], for edge detection. In fact, in the framework of the new model we obtain the formulas for computing the "contrast of a pixel" and the "contrast" image is just the "contour" or edge image. In our setting the range of values is preserved and the quality of the contour is good for high as well as for low lumi...

Color edge detection is approached in this paper using vector order statistics. Based on the R-ordering method, a class of color edge detectors is defined. These detectors function as vector operators as opposed to component-wise operators. Specific edge detectors can be obtained as special cases of this class. Various such detectors are defined and analyzed. Experimental results show the noise...

This paper addresses a special problem with historical document images where handwritten characters from the reverse side appear as noise on the front side and even interfere with the front side characters. A novel method to extract clear textual images from interfering and overlapping areas of text is presented here. The proposed algorithm is interesting in that, with an observation that the e...

In this paper we will present a fuzzy edge detector, FEDGE. It is based on learning fuzzy edges by the method of Fuzzy Categorization and Classification (FCC). A set of images were used as examples for the definition of a fuzzy edge. FCC will try to recognize edges within a new image by collecting evidence from these examples. FEDGE demonstrates that FCC can be used homogeneously from pixel-lev...

Vision analysis with low or no illumination is gaining more and more attention recently, especially in the fields of security surveillance and medical diagnosis. In this paper, a real time sobel square edge detector is developed as a vision enhancer in order to render clear shapes of object in targeting scenes, allowing further analysis such as object or human detection, object or human trackin...

Gaussian filter has an important role in edge detection. In this paper, a new methodology for edge detection using Gaussian second order derivative filters is proposed. Channel separation for Retinal Colour Fundus image is used followed by Gaussian filter. The performance of our proposed method has been assessed on 23 images representing the publicly available dataset; High-Resolution Fundus (H...

We treat the problem of edge detection as one of statistical inference. Local edge cues, implemented by filters, provide information about the likely positions of edges which can be used as input to higher-level models. Different edge cues can be evaluated by the statistical effectiveness of their corresponding filters evaluated on a dataset of 100 pre-segmented images. We use information theor...