The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents an enhanced method of partitioning a dataset into clusters when dealing with the handwritten signature recognition problem. The goal of the present system is improving the performance of two previously developed systems. In the first version of our system we dealt with data extraction from signature images and obtained a recognition rate of 91.04% using the Naïve Bayes classifier...
Artificial neural network (ANN) is an important part of artificial intelligence, it has been widely used in remote sensing classification research field. Wetlands remote sensing classification based on ANN is difficult, because of the complex feature of wetlands areas. The purity of training samples for remote sensing image supervised classification is difficult to guarantee that will affect the classification...
We propose to use energy minimization in MRFs for matching-based image recognition tasks. To this end, the Tree-Reweighted Message Passing algorithm is modified by geometric constraints and efficiently used by exploiting the guaranteed monotonicity of the lower bound within a nearest-neighbor based classification framework. The constraints allow for a speedup linear to the dimensionality of the reference...
This paper proposes an evolutionary RBF network classifier for polar metric synthetic aperture radar ( SAR) images. The proposed feature extraction process utilizes the full covariance matrix, the gray level co-occurrence matrix (GLCM) based texture features, and the backscattering power (Span) combined with the H/α/A decomposition, which are projected onto a lower dimensional feature space using...
This paper presents scratch restoration method that can deal with scratches of various lengths and widths in old film. The proposed method consists of detection and reconstruction. The detection is performed using texture and shape properties of the scratches: first, each pixel is classified as scratches and non-scratches using a neural network (NN)-based texture classifier, and then some false alarms...
The ways distances are computed or measured enable us to have different representations of the same objects. In this paper we want to discuss possible ways of merging different sources of information given by differently measured dissimilarity representations. We compare here a simple averaging scheme [1] with dissimilarity forward selection and other techniques based on the learning of weights of...
Matching near-infrared (NIR) face images to visible light (VIS) face images offers a robust approach to face recognition with unconstrained illumination. In this paper we propose a novel method of heterogeneous face recognition that uses a common feature-based representation for both NIR images as well as VIS images. Linear discriminant analysis is performed on a collection of random subspaces to...
This paper presented a new method for scene images classification via Partially Connected Neural Network. The neural network has a mesh structure in which each neuron maintain a fixed number of connections with other neurons. In training, the evolutionary computation method was used to optimize the connection target neurons and its connection weights. The model is able to receive a large number of...
In this paper, we propose a three-layer spatial sparse coding (TSSC) for image classification, aiming at three objectives: naturally recognizing image categories without learning phase, naturally involving spatial configurations of images, and naturally counteracting the intra-class variances. The method begins by representing the test images in a spatial pyramid as the to-be-recovered signals, and...
A key feature in population based optimization algorithms is the ability to explore a search space and make a decision based on multiple solutions. In this paper, an incremental learning strategy based on a dynamic particle swarm optimization (DPSO) algorithm allows to produce heterogeneous ensembles of classifiers for video-based face recognition. This strategy is applied to an adaptive classification...
In this paper, a supervised learning strategy based on a Multi-Objective Particle Swarm Optimization (MOPSO) is introduced for ARTMAP neural networks. It is based on the concept of neural network evolution in that particles of a MOPSO swarm (i.e., network solutions) seek to determine user-defined parameters and network (weights and architecture) such that generalisation error and network resources...
Searching a required image based on the content and visual meaning of the image is challenging. One of the complex groups of images is sport image since a sport image may contain various relevant information such as players' postures, textures and color of players' clothes, and complicated background ground. Achieving high recognition rate depends upond the features extracted from the image. In this...
Fabric defect detection and classification plays a very important role for the automatic detection in fabrics. This study refers to the four common seen defects of stretch knitted fabrics: laddering, end-out, hole, and oil spot. First of all, wavelet transfer is applied to obtain its wavelet energy to take them as defect features of this image, and then the back-propagation neural network (BPNN) was...
Nowadays very large archives of digital images can be easily produced thanks to the availability of digital cameras as standalone devices, or embedded into a number of portable devices. Each personal computer is typically a repository for thousands of images, while the Internet can be seen as a very large repository. One of the most severe problems in the classification and retrieval of images from...
Discrimination between corn seedlings and weeds is an important and necessary step to implement spatially variable herbicides application. This paper proposed a method of weed identification by using the technique of image processing and probabilistic neural network. Otsu's method for automatic threshold was applied to segment weeds images based on the modified excess green feature, it could distinguish...
The key to surgical planning for breast conservation is tumor localization. An accurate localization of the breast tumor is essential to guide the surgeon to the lesion, and ensure its correct and adequate removal with satisfactory excision margins. Current breast tumor localization techniques are invasive and often result in a cosmetic disfigurement. In this paper, we use the ultrawide band radar-based...
Aiming at the existing problems in pattern recognition of surface defect images of steel strips, a RBF neural network classification and recognition method based on principal component analysis (PCA) is proposed to solve them. Using PCA to extract the main characteristics of the sample data which computed by the image of strip surface defects to achieve the optimal sample characteristics data compression,...
Wound characterization is important task in chronic wounds treatment, because changes of the wound size and tissue types are indicators of the healing progress. Developed color image processing software analyze digital wound image and based on learned tissue samples performs tissue classification. Implemented statistical pattern recognition algorithm classifies individual pixels of the wound image...
In this paper we are using Devanagari script OCR for recognition. The handwritten data set is created by us and for printed characters we have used ISM font. Here we are using gradient and curvature based feature extraction method. We have compared Nearest Neighbor, K-Nearest Neighbor, Euclidian Distance-based K-NN, Cosine Similarity -based K-NN, Condensed Nearest Neighbor, Reduced Nearest neighbor,...
Illumination and expression variation are the major challenges in the face recognition. This paper presents comparative analysis of two normalization techniques namely, DCT in Log domain and 2-point normalization method.. The DCT is employed to compensate for illumination variations in the logarithm domain. Since illumination variation lies mainly in the low frequency band, an appropriate number of...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.