Search results for "Pixel"
showing 10 items of 421 documents
MLP Neural Network Implementation on a SIMD Architecture
2002
An Automatic Road Sign Recognition System {A(RS)2} is aimed at detection and recognition of one or more road signs from realworld color images. The authors have proposed an A(RS)2 able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using Multi-Layer Perceptron neural networks. Although system performances are good in terms of both sign detection and classification rates, the entire process requires a large computational time, so real-time applications are not allowed. In this paper we present the implementation of the neural layer on the Georgia Institute of Technology …
Implementation of pattern recognition algorithm based on RBF neural network
2002
In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital Signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240x320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.
NIR and Visible Image Fusion for Improving Face Recognition at Long Distance
2014
Face recognition performance achieves high accuracy in close proximity. However, great challenges still exist in recognizing human face at long distance. In fact, the rapidly increasing need for long range surveillance requires a passage from close-up distances to long distances which affects strongly the human face image quality and causes degradation in recognition accuracy. To address this problem, we propose in this paper, a multispectral pixel level fusion approach to improve the performance of automatic face recognition at long distance. The main objective of the proposed approach is to formulate a method to enhance the face image quality as well as the face recognition rate. First, v…
Multiscale Edges Detection by Wavelet Transform for Model of Face Recognition
1996
Publisher Summary The linear auto-associator is a particular case of the linear-associator. The goal of this network is to associate a set of stimuli to itself, which could be used to store and retrieve face images and it also could be applied as a pre-processing device to simulate some psychological tasks—such as categorizing face according to their gender. A technique of learning based on the wavelet transform can improve recognition capability when the pattern images are with a great noise. One of the ways to store and recall face images uses the linear auto-associative memory. This connectionist model is in conjunction with a pixel-based coding of the faces. The image processing using t…
Assessing forest landscape structure using geographic windows.
2001
Landscape structure, interpreted as indicator of functional processes, has become a main attribute of multiresource forest inventories, enhancing its value with respect to society needs. This approach implies effective use of earth observation techniques and geographic information systems to obtain a global view of the inventoried landscapes and to understand the ecological functions of large spatially-heterogeneous landscape mosaics. Landscape structure often reveal extremely complex patterns that can only be very roughly characterized by methods of Euclidean geometry. Conversely, fractals can be applied to adequately describe many of the irregular, fragmented patterns found in nature. In …
Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine
2021
For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine …
CHRIS/PROBA toolbox for hyperspectral and multiangular data exploitations
2009
The project CHRIS/Proba Toolbox for BEAM (CHRIS-Box) has been developed in order to support users of data from the CHRIS sensor onboard of ESA's Proba platform. BEAM and the CHRIS-Box are user tools which ESA/ESRTN are providing free of charge to the Earth Observation Community. The CHRIS-Box software provides extensions for BEAM that allows accomplishing the following tasks: a) Noise reduction to remove the vertical striping and other noise present in CHRIS response-corrected images; b) Cloud screening to mark cloudy pixels in CHRIS noise-corrected images; the cloud screening algorithm provides cloud probability and abundances for each pixel; c) Atmospheric correction that provides surface…
Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over …
2009
Abstract: In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using Vegetation Indices (VIs), in particular the Normalized Difference Vegetation Index (NDVI) and the Variable Atmospherically Resistant Index (VARI). The second methodology is based on the Spectral Mixture Analysis (SMA) technique, in which a Linear Spectral Unmixing model has been considered in order to retrieve the abundance of the different constituent materials within pixe…
A massive lesion detection algorithm in mammography
2004
A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by avera…
Smart cameras on a chip: using complementary metal-oxide-semiconductor (CMOS) image sensors to create smart vision chips
2020
Abstract: In this chapter, we introduce the fundamental concept of smart cameras on a chip or smart vision chips that simultaneously integrate the same die image capture capability and highly complex image processing. Successive technology scaling has made possible the integration of specific processing elements designed at chip level, at column level or at pixel level. To illustrate this continuous evolution, we survey three different categories of vision chips, exploring first the pioneering works on artificial retinas, then describing the most significant computational chips, and finally presenting the most recent image processing chips able to perform complex algorithms at a high frame …