LINEAR FEATURE EXTRACTION FOR SATELLITE IMAGES USING CNLS (CONTEXTUAL NONLINEAR SMOOTHING) ALGORITHM

The satellite images present a great variety of features due to the trouble what returns their treatment is little delicate. The automated extraction of linear features from remotely sensed imagery has been the subject of extensive research over several decades. Recent studies show promise for extraction of feature information for applications such as updating geographic information systems (GIS). Research has been stimulated by the increase in available imagery in recent years following the launch of several airborne and satellite sensors. All the satellite images, which are going to be used in the present work, are going to be processed in the computer vision, for which the existing researchers are interested to analyze the synthetic images by feature extraction. These images contain many types of features. Indeed, the features are classified in 1-D feature such as step, roof and 2-D features such as corners, edges, and blocks. The satellite images present a great variety of features due to the trouble what returns their treatment is little delicate. In this we present a method for edge segmentation of satellite images based on 2-D Phase Congruency (PC) model. The proposed approach is composed by two steps: The contextual nonlinear smoothing algorithm (CNLS) is used to smooth the input images. Then, the 2D stretched Gabor filter (S-G filter) based on proposed angular variation is developed in order to avoid the multiple responses

The Removal of Random Valued Impulse Noise Using Contrast Enhancement and Decision Based Filter

Digital images are transmitted in noisy environment and it will frequently affected by impulse noise .To remove this noise from the image is a fundamental problem of image processing. There are various types of noise in an image especially salt and pepper noise and random valued impulse noise. This paper introduces a new filtering scheme based on contrast enhancement filter and decision based filter for removing the random valued impulse noise. The application of a nonlinear function to increasing the difference between noise pixels and noise-free and results in efficient detection of noisy pixels. As the performance of a filtering system, in general, depends on the number of iterations used, the effective stopping criterion based on noisy image characteristics to determine the number of iterations is also proposed. This proposed method removes only the corrupted pixel by its neighboring pixel values. As a result of this, the proposed method removes the noise effectively and preserves the edges without any loss up to 80% of noise level

Algorithms for max and min filters with improved worst-case performance

Published versio

A class of adaptive directional image smoothing filters

Cataloged from PDF version of article.The gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions. The idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates aline segment in that direction. Hence, the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance. In this paper we describe a class of adaptive directional image smoothing filters based on generalized Gaussian distributions. We propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized Gaussian distribution. Several experimental results indicate a significant improvement compared to some standard filters. Copyright (C) 1996 Pattern Recognition Society

Performance evaluation of a feature-preserving filtering algorithm for removing additive random noise in digital images

We evaluate the performance of a feature-preserving filtering algorithm over a range of images corrupted by typical additive random noise against three common spatial filter algorithms: median, sigma and averaging. The concept of the new algorithm is based on a corrupted-pixel identification methodology over a variable subimage size. Rather than processing every pixel indiscriminately in a digital image, this corrupted-pixel identification algorithm interrogates the image in variable-sized subimage regions to determine which are the corrupted pixels and which are not. As a result, only the corrupted pixels are being filtered, whereas the uncorrupted pixels are untouched. Extensive evaluation of the algorithm over a large number of noisy images shows that the corrupted-pixel identification algorithm exhibits three major characteristics. First, its ability in removing additive random noise is better visually (subjective) and has the smallest mean-square errors (objective) in all cases compared with the median filter, averaging filter and sigma filter. Second, the effect of smoothing introduced by the new filter is minimal. In other words, most edge and line sharpness is preserved. Third, the corrupted-pixel identification algorithm is consistently faster than the median and sigma filters in all our test cases. © 1996 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

Morphological erosions and openings: fast algorithms based on anchors

Several efficient algorithms for computing erosions and openings have been proposed recently. They improve on VAN HERK's algorithm in terms of number of comparisons for large structuring elements. In this paper we introduce a theoretical framework of anchors that aims at a better understanding of the process involved in the computation of erosions and openings. It is shown that the knowledge of opening anchors of a signal f is sufficient to perform both the erosion and the opening of f. Then we propose an algorithm for one-dimensional erosions and openings which exploits opening anchors. This algorithm improves on the fastest algorithms available in literature by approximately 30% in terms of computation speed, for a range of structuring element sizes and image content

Improving an algorithm for filtering multiplicative noise

In this paper we suggest an improved algorithm for filtering multiplicative noise . We opted for a simple • method to evaluate local statistics and we supposed that the local distribution of the original image was uniform in order to express our Jack of knowledge about the original probability function. The algorithm is based on spatial homomorphic development . The proposed algorithm is effective for high noise as well as for low .Nous proposons un algorithme amélioré de filtrage du bruit multiplicatif, en traitement d'images numériques basé sur un développement homomorphique spatial. Nous avons adopté une approche simple pour évaluer la statistique locale et supposé que la distribution locale de l'image originale est uniforme, ceci afin de traduire une méconnaissance de la probabilité originale qui peut être quelconque . L'algorithme proposé est efficace aussi bien pour des bruits importants que pour des bruits faibles

Klasifikasi Tulisan Tangan Pada Resep Obat Menggunakan Convolutional Neural Network

Obat merupakan bahan kimia yang dapat merepresentasikan tubuh secara fisiologi dan psikologi ketika dikonsumsi. Obat sebagai alat bantu untuk menyembuhkan dari berbagai macam penyakit. Dengan berkembangnya zaman dan bertambahnya wawasan, menyebabkan bertambah juga jenis obat-obatan yang memiliki banyak manfaat dan kegunaanya. Penelitian ini bertujuan untuk mendeteksi nama obat dalam resep dokter menggunakan Convolutional Neural Network (CNN) dengan transfer learning. Metode transfer learning merupakan metode yang popular dalam mengklasifikasi gambar digital yang berguna untuk mempercepat proses klasifikasi. Penelitian ini membandingkan lima artistektur transfer learning yaitu VGG16, Resnet, Xception, LeNet, dan GoogleNet. Penelitian ini juga menggunakan grayscaling, resizing, dan median filter pada tahap preprocessing. Preprocessing digunakan untuk meningkatkan kualitas citra pada citra resep obat dan menghilangkan noise pada citra. ResNet-50 merupakan arsitektur terbaik untuk mengklasifikasi nama obat. Pada percobaan menggunakan ResNet-50, mendapatkan F1 score tertinggi yaitu sebesar 97,56% dan waktu training rata-rata 0,25 detik setiap epoch. Dapat disimpulkan Resnet merupakan arsitektur terbaik untuk mengklasifikasikan nama obat dalam citra resep dokter serta dapat mendeteksi nama obat secara akurat