Search results for "methodologies"
showing 10 items of 2106 documents
Tecsis: Low-Cost Methodology To Distinguish Archaeological Findings
2006
The automatic or semi-automatic research of archaeological findings includes some methodologies and algorithms of the Computer Vision. Reconstruction of a scene is one of the key step to get the solution to that challenge. This paper will address a methodology to reconstruction underwater scenes with mosaicing techniques. The reconstruction of scene will be the video-mosaic of sea bottom landscapes starting from single video frames. The methodology is based on the evaluation of the optic °ow in between frames, and its motion estimation has been evaluated on the extracted features from the common areas of consecutive pairs frames. This approach carried out the motion model from a geometric p…
Design and Implementation of a Low-cost Embedded Iris Recognition System on a Dual-core Processor Platform
2012
Abstract Design of a low-cost embedded iris recognition system is described in this paper. Firstly, we develop a simple and effective iris image acquisition unit, which is cheap and easy to use. This is achieved by both of hardware design and image evaluation algorithm development. Secondly, the iris recognition algorithm is introduced, including iris segmentation, image normalization, feature extraction, and code matching. The algorithm implementation architecture is based on an embedded dual-core processor platform, Texas Instruments TMS320DM6446 evaluation module (Davinci), which contains an ARM core and a DSP core in one chip. Thirdly, the evaluation experiments are performed on the est…
A Nonlinear Label Compression and Transformation Method for Multi-label Classification Using Autoencoders
2016
Multi-label classification targets the prediction of multiple interdependent and non-exclusive binary target variables. Transformation-based algorithms transform the data set such that regular single-label algorithms can be applied to the problem. A special type of transformation-based classifiers are label compression methods, which compress the labels and then mostly use single label classifiers to predict the compressed labels. So far, there are no compression-based algorithms that follow a problem transformation approach and address non-linear dependencies in the labels. In this paper, we propose a new algorithm, called Maniac (Multi-lAbel classificatioN usIng AutoenCoders), which extra…
A label compression method for online multi-label classification
2018
Abstract Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally…
Multi-label classification using boolean matrix decomposition
2012
This paper introduces a new multi-label classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them.
Instance-Based Multi-Label Classification via Multi-Target Distance Regression
2021
Interest in multi-target regression and multi-label classification techniques and their applications have been increasing lately. Here, we use the distance-based supervised method, minimal learning machine (MLM), as a base model for multi-label classification. We also propose and test a hybridization of unsupervised and supervised techniques, where prototype-based clustering is used to reduce both the training time and the overall model complexity. In computational experiments, competitive or improved quality of the obtained models compared to the state-of-the-art techniques was observed. peerReviewed
Author Correction: The FAAH inhibitor URB597 suppresses hippocampal maximal dentate afterdischarges and restores seizure-induced impairment of short …
2018
A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.
On Spatio-Temporal Saliency Detection in Videos using Multilinear PCA
2016
International audience; Visual saliency is an attention mechanism which helps to focus on regions of interest instead of processing the whole image or video data. Detecting salient objects in still images has been widely addressed in literature with several formulations and methods. However, visual saliency detection in videos has attracted little attention, although motion information is an important aspect of visual perception. A common approach for obtaining a spatio-temporal saliency map is to combine a static saliency map and a dynamic saliency map. In this paper, we extend a recent saliency detection approach based on principal component analysis (PCA) which have shwon good results wh…
Metadata-Oriented Language Model in Translingual Retrieval of Digital Data
2015
Translingual retrieval relies on processing a source language to retrieve digital document content in a target language. From the perspective of successful browsing digital catalogues, probability of retrieving the full text document in a language other than the query language is close to zero owning to the fact that it is not only the library collection, but especially a problem of matching the index terms with the query keywords which are assumed to be their translation equivalents. In addition, hardly any digital library system is incorporated with a translation component. As a result, such a matching is rather coincidental. Our approach to the translingual document retrieval problem is …
A Neural Network-Based Algorithm for 3D Multispectral Scanning Applied to Multimedia
2005
We describe a new stereoscopic system based on a multispectral camera and an LCD-Projector. The novel concept we want to show consists in the use of multispectral information for 3D-scenes reconstruction. Each 3D point is linked to a curve representing the spectral reflectance. This latter is a physical representation of the matter and presents the advantage over color information, which is perceptual, that it is independent from both illuminant and observer. We first present an easy methodology to geometrically and spectrally calibrate such a system. We then describe an algorithm for recovering 3D coordinates based on triangulation and an algorithm for reflectance curves reconstruction bas…