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In this paper, a new watermarking using cell neural network with hyper-chaotic cellular neural network (HCCNN) is proposed. In the scheme, the pixel values of the image are used to the input of the HCCNN. The outputs of the HCCNN are encrypted by the public key system, and then are embedded into the LSBs of the original image. The receiver can verify the suspected image with the signer's public key...
Image segmentation is a popular research topic in the field of image processing. In this paper, we propose a new image segmentation method. To solve the problem of the previous entropic thresholding method using a traditional histogram, grey levels are embedded in the traditional histogram and applied in the entropic thresholding algorithm. Experimental results show that the modified method proposed...
Because there are some defects with the detection of PCB (Printed Circuit Board) location hole with traditional methods, the paper presents a fast and accurate positioning algorithm of location hole based on potential function, which is not interfered by the bright spots within the pad image and also be adaptable to the fuzzy pad image and the deformed pad due to poor quality of circuit board corrosion...
For measurement chips of micro-drill's main lips, a novel data processing was presented. While the line of micro-drill's main lips is fitted, the special structure's Hopfield neural network is designed according to normal equation. The neural network has 2 neurons, the weights of which are elements of normal equation's symmetry matrix, the input numerical value of which are the vector consisted of...
Feature extraction for the dimensionality reduction of hyperspectral data is performed by means of Auto-Associative Neural Networks. The algorithm performance is compared to the Principal Component Analysis and the Maximum Noise Fraction ones. Results of land cover pixel-based maps yielded by the reduced vector and a dedicated neural network classification algorithm are also reported.
Information mining from heavy SAR images is considered from the point of view of the procedure automatization. Two schemes based on Neural Networks are evaluated, one based on the Self Organizing Map method exploiting polarimetric information and oriented to land cover classification, the other based on the Pulse-Coupled Neural Networks aiming at characterizing the imaged buildings.
In this paper, we investigate the performance of pulse-coupled neural networks (PCNNs) to detect the damage caused by an earthquake. PCNN is an unsupervised model in the sense that it does not need to be trained, which makes it an operational tool during crisis events when it is crucial to produce damage maps as soon as the post-event images are available. The damage map resulting from PCNN was validated...
A method for automatic identification of changes using regression with neural networks is presented. The regression is iteratively performed by updating the weights of the pixels. The method is applied to a small subset of two Landsat images and the results indicate that the proposed method produces good results.
Two different unsupervised change detection techniques are here investigated. The first method is based on pulse-coupled neural networks, which show invariance to object scale, shift or rotation. The second method, based on the normalized cross-correlation, is suited to work in an “on-line” processing as more images are made available, for example in case of natural events such as an earthquake or...
Satellite remote sensing is an important tool in the detection and short range forecasting (nowcasting) of fog events. Fog over land develops primarily during the late-night and pre-dawn hours, infrared remote sensing is indispensable in observing fog formation at night, while visible imagery helps to monitor the extent and density of fog after sunrise. Satellite remote sensing is widely used in the...
Image classification is an important task for many aspects of global change studies and environmental applications. This paper emphasizes on the analysis and usage of different advanced image classification techniques like Cloud Basis Functions (CBFs) Neural Networks, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for object based classification to get better accuracy. For comparison,...
Data Fusion refers to extracting useful information from multisource data such as remotely sensed images and GIS datasets. The emergence of ANN (Artificial Neural Network)-based approaches has greatly promote the data fusion technology in remote sensing classifications. But the traditional ANN-based approach still has some drawbacks, especially in the step of weight training. Hence, this paper proposes...
In the field of electronics device assembly, miniaturization of components, denser packing of boards, surface mounting technology, and highly automated assembly equipment make the task of inspecting the defects of soldering joints in the electronics products more critical and more difficult for humans. The automated inspection systems are required for the stable inspection of products. One of the...
This paper describes an approach for extracting words, textlines and text blocks by analyzing the spatial configuration of connected domain and word contour rectangles on a given document image. The basic idea is that connected components of black pixels and contours can be used as computational units in document image analysis. In this paper, we try to find a spatial feature and overlapped relationships...
We propose a multiclass hierarchical abductive learning classifier and apply it to improve the recognition rate of handwritten numerals while reduce the dimensionality of the feature space. For handwritten recognition, there are ten classes. Using 9 binary GMDH-based neural network models structured in a hierarchy has led to improving balance factor of the dataset for each classifier and improving...
Edge detection is a previous step for image recognition systems that helps to extract the most important shapes in an image, ignoring the homogeneous regions and remarking the real object to classify or recognize. Traditional and fuzzy edge detectors can be used, but it's very difficult to demonstrate which one is better before the recognition results are obtained. In this work we present an experiment...
This paper describes an artificial neural network based system for classifying the contents of hyperspectral images that is able to automatically reduce the dimensionality of the data provided by the hyperspectrometers without compromising their efficacy. The data reduction is achieved through the adaptation of the window size and the number of parameters that make up the description of the spectral...
In this paper we propose a feature extraction method based on brightness control of the pixels of an image using a fuzzy logic inference system. The proposed method was used in a hybrid pattern recognition system of the fingerprints, to improve the identification rate. The fuzzy extraction method preprocesses the fingerprint images and adjusts the brightness. The obtained image is input to the ensemble...
A novel Over-Segmentation and Neural Binary Validation (OSNBV) is presented in this paper. OSNBV is a character segmentation strategy for off-line cursive handwriting recognition. Unlike the approaches in the literature, OSNBV is a prioritized segmentation approach. Initially, OSNBV over-segments a handwritten word into primitives. Neural binary validation is iteratively applied to the primitives...
In this study, we propose a space-varying cellular neural network (CNN) designed by Hopfield neural network (Hopfield NN). CNN is classified into two types of system like space-invariant system and space-varying system. Space-invariant means that all cells have identical template. On the other hand, space-varying means that all cells do not have identical template according to the state values of...
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