Search results for "Pattern recognition"

Part-of-Speech Induction by Singular Value Decomposition and Hierarchical Clustering

2006

Part-of-speech induction involves the automatic discovery of word classes and the assignment of each word of a vocabulary to one or several of these classes. The approach proposed here is based on the analysis of word distributions in a large collection of German newspaper texts. Its main advantage over other attempts is that it combines the hierarchical clustering of context vectors with a previous step of dimensionality reduction that minimizes the effects of sampling errors.

Masked priming effects are modulated by expertise in the script.

2010

In a recent study using a masked priming same–different matching task, García-Orza, Perea, and Muñoz (2010) found a transposition priming effect for letter strings, digit strings, and symbol strings, but not for strings of pseudoletters (i.e., [Formula: see text] produced similar response times to the control pair [Formula: see text]). They argued that the mechanism responsible for position coding in masked priming is not operative with those “objects” whose identity cannot be attained rapidly. To assess this hypothesis, Experiment 1 examined masked priming effects in Arabic for native speakers of Arabic, whereas participants in Experiments 2 and 3 were lower intermediate learners of Arabi…

Are transposition effects specific to letters?

2010

Recent research has consistently shown that pseudowords created by transposing two letters are perceptually similar to their corresponding base words (e.g., jugde–judge). In the framework of the overlap model (Gomez, Ratcliff, & Perea, 2008), this effect is due to a noisy process in the localization of the “objects” (e.g., letters, kana syllables). In the present study, we examine whether this effect is specific to letter strings or whether it also occurs with other “objects” (namely, digits, symbols, and pseudoletters). To that end, we conducted a series of five masked priming experiments using the same–different task. Results showed robust effects of transposition for all objects, ex…

2014

Codebook is an effective image representation method. By clustering in local image descriptors, a codebook is shown to be a distinctive image feature and widely applied in object classification. In almost all existing works on codebooks, the building of the visual vocabulary follows a basic routine, that is, extracting local image descriptors and clustering with a user-designated number of clusters. The problem with this routine lies in that building a codebook for each single dataset is not efficient. In order to deal with this problem, we investigate the influence of vocabulary sizes on classification performance and vocabulary universality with the kNN classifier. Experimental results in…

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography

2019

Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal d…

Optimal imaging of multi-channel EEG features based on a novel clustering technique for driver fatigue detection

2020

Abstract Fatigue may cause a decrease in mental and physical performance capacity, which is a serious safety risk for the drivers in the transportation system. Recently, various studies have demonstrated the deviations of electroencephalogram (EEG) indicators from normal vigilant state during fatigue in time and frequency domains. However, when considering spatial information, these feature descriptors are not satisfying the demand for reliable detection due to the well-known challenge of signal mixing. In this paper, we propose a novel approach based on clustering on brain networks (CBNs) to alleviate the problem to improve the performance of driver fatigue detection. The clustering algori…

Estimation of Leaf Area in Bell Pepper Plant using Image Processing techniques and Artificial Neural Networks

2021

Measurement and estimation of physical properties of plant leaves have always been considered as important requirements for monitoring and optimizing of plant growth. This study aimed at utilization of image processing and artificial intelligence techniques for non-invasive and non-destructive estimation of bell pepper leaves properties in the first month of growth. Physical properties of bell pepper plant leaves were extracted from RGB images. The algorithm makes use of gradient magnitude and watershed image. Leaf area as the most important index of growth was estimated as a function of other physical parameters including leaf length, width, perimeter etc. Using stereo imaging, the leaf di…

Prefiltering for pattern recognition using wavelet transform and neural networks

2003

Publisher Summary Neural networks are built from simple units interlinked by a set of weighted connections. Generally, these units are organized in layers. Each unit of the first layer (input layer) corresponds to a feature of a pattern that is to be analyzed. The units of the last layer (output layer) produce a decision after the propagation of information. Before feeding the computational data to neural networks, the signal must undergo a preprocessing in order to (1) define the initial transformation to represent the measured signal, (2) retain important features for class discrimination and discard that is irrelevant, and (3) reduce the volume of data to be processed, for example, data …

Hydro-Acoustic Target Detection

2014

This chapter presents an example of utilization of the discrete–time wavelet packets, which are described in Sect. 9.1, to classification of acoustic signals and detection of a target. The methodology based on wavelet packets is applied to a problem of detection of a boat of a certain type when other background noises are present. The solution is obtained via analysis of boat’s hydro-acoustic signature against an existing database of recorded and processed hydro-acoustic signals. The signals are characterized by the distribution of their energies among blocks of wavelet packet coefficients.

An Improved Forecasting Model from Satellite Imagery Based on Optimum Wavelet Bases and Adam Optimized LSTM Methods

2021

This paper proposes a new hybrid approach I-WT-LSTM (i.e., Improved Wavelet Long Short-Term Memory (LSTM) Model) for forecasting non-stationary time series (TS) from satellite imagery. The proposed approach consists of two steps: The first step aims at decomposing TS using Multi-Resolution Analysis wavelet (MRA-WT) into inter-and intra-annual components using 18 different mother wavelets (MW). Then, the energy to Shannon entropy ratio criterion is calculated to select the best MW. The second step is based on the LSTM model using Adam optimizer to predict the future. The proposed approach is tested using TS derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2001 t…