Algorithms of filtering, edge detection, and extraction of details and their implementation using cellular neural networks (CNN) are developed in this paper. The theory of CNN based on universal binary neurons (UBN) is also developed. A new learning algorithm for this type of neurons is carried out. Implementation of low-pass filtering algorithms using CNN is considered. Separate processing of ...

background: breast cancer is currently one of the leading causes of death among women worldwide. the diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. objective: this paper proposes a new algorithm which draws on discrete wavelet transform and adaptive...

image recognition is one of the most important field in image processing that in recent decades had much attention .due to expansion of related fields with image processing and various application of this science in machine vision, military science, geography, aerospace and artificial intelligence and lots of other aspects, out stand the importance of this subject.one of the most important aspe...

Edge detection is one of the most commonly used operations in image analysis, and there are more algorithms in the literature for enhancing and detecting edges. Natural images contain both textured and untextured regions, so the cues of contour and texture are exploited simultaneously. In this paper, we present a new edge detection method for natural images using decomposition model. The main i...

Gravitational edge detection is one of the new edge detection algorithms that is based on the law of gravity. This algorithm assumes that each image pixel is a celestial body with a mass represented by its grayscale intensity and their interactions are based on the Newtonian laws of gravity. In this article, a multispectral version of the algorithm is introduced. The method uses gravitational t...

Edge detection is an important tool in digital image processing applications for extracting information from image. Sobel operator based edge detection algorithm creates an image which highlights edges and transitions. In real-time image processing applications, it is required to process large data of pixels in a given timing constraints. Hence, speed of image processing is a big problem. Recon...

Previous blotch identification algorithms perform identification process for each pixel in current frame, which are time-consuming. To deal with this issue, a blotch identification algorithm based on edge detection is proposed in this work. Above all, edges of current frame are extracted by edge detectors, blotch identification area is limited to edge pixels and pixels nearby. To further reduce...

A new approach used for image edge deduction, segmentation and normalization illumination under varying lighting conditions are presented. Edge detection refers to the process of identifying and locating sharp by applying smooth and noisy clinical technic in an image. It has favorable applications in the fields such as machine vision, pattern recognition, object recognition, motion analysis, pa...

Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. This paper evaluates the performance of Prewitt Edge Detector for detection of edges in digital images corrupted with different kinds of noise. Different kinds of noise are studied in order to evaluate the performance of the Prewitt Edge Det...

anomaly detection (ad) has recently become an important application of hyperspectral images analysis. the goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. one way to improve the performance and runtime of these algorithms is to use dimensionality reduction (dr) techniques. this paper evaluates the effect of thr...