Search results for "pattern recognition"
showing 10 items of 2301 documents
Linear Feature Extraction for Ranking
2018
We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix. We try to optimize a transformation matrix, so that a new matrix (dataset) can be generated as the product of the original matrix and a transformation matrix. The transformation matrix projects high-dimensional document vectors into lower dimensions. Theoretically, there could be very large transformation matrices, each leading to a new generated matrix. In LifeRank, we produce a transformation matrix so that the generat…
A Dataset of Annotated Omnidirectional Videos for Distancing Applications
2021
Omnidirectional (or 360°) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360° videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360° image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some point…
Distributed optimal power flow for islanded microgrids: An application to the Smart Polygeneration Microgrid of the Genoa University
2016
In this work, the application of an original distributed optimal power flow method to test a microgrid in the Savona area, Italy is proposed. The microgrid shows different types of Distributed Energy Resources (DERs) and is connected to the main grid through a fixed power bus. Due to the high computational speed, the applied distributed Optimal Power Flow can be performed almost in real time, i.e. every 5 minutes or less. The operating solution found for generators, simply using local information, corresponds to a suboptimal condition with reduced losses, bus voltages and line currents within constrained intervals. The distributed optimization algorithm is iterative, but also fast. It is ba…
Regularization Preserving Localization of Close Edges
2007
International audience; In this letter, we address the problem of the influence of neighbor edges and their effect on the edge delocalization while extracting a neighbor contour by a derivative approach. The properties to be fulfilled by the regularization operators to minimize or suppress this side effect are deduced, and the best detectors are pointed out. The study is carried out in 1-D for discrete signal. We show that among the derivative filters, one of them can correctly detect our model edges without being influenced by a neighboring transition, whatever their separation distance is and their respective amplitude is. A model of contour and close transitions is presented and used through…
Principal Component and Neural Network Analyses of Face Images: What Can Be Generalized in Gender Classification?
1998
We present an overview of the major findings of the principal component analysis (pca) approach to facial analysis. In a neural network or connectionist framework, this approach is known as the linear autoassociator approach. Faces are represented as a weighted sum of macrofeatures (eigenvectors or eigenfaces) extracted from a cross-product matrix of face images. Using gender categorization as an illustration, we analyze the robustness of this type of facial representation. We show that eigenvectors representing general categorical information can be estimated using a very small set of faces and that the information they convey is generalizable to new faces of the same population and to a l…
A Method Based on Multi-source Feature Detection for Counting People in Crowded Areas
2019
We propose a crowd counting method for multisource feature fusion. Image features are extracted from multiple sources, and the population is estimated by image feature extraction and texture feature analysis, along with for crowd image edge detection. We count people in high-density still images. For instance, in the city’s squares, sports fields, subway stations, etc. Our approach uses a still image taken by a camera on a drone to appraise the count in the population density image, using a kind of sources of information: HOG, LBP, CANNY. We furnish separate estimates of counts and other statistical measurements through several types of sources. Support vector machine SVM, classification an…
Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination
2015
This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive…
Fuzzy-based Kernel Regression Approaches for Free Form Deformation and Elastic Registration of Medical Images
2009
In modern medicine, a largely diffused method for gathering knowledge about organs and tissues is obtained by means of merging information from several datasets. Such data are provided from multimodal or sequential acquisitions. As a consequence, a pre-processing step that is called “image registration” is required to achieve data integration. Image registration aims to obtain the best possible spatial correspondence between misaligned datasets. This procedure is also useful to correct distortions induced by magnetic interferences with the acquisition equipment signals or the ones due patient’s involuntary movements such as heartbeat or breathing. The problem can be regarded as finding the …
Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Tech…
2020
Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period …
Use of electronic nose to determine defect percentage in oils. Comparison with sensory panel results
2010
Abstract An electronic nose based on an array of 6 metal oxide semiconductor sensors was used, jointly with linear discriminant analysis (LDA) and artificial neural network (ANN) method, to classify oils containing the five typical virgin olive oil (VOO) sensory defects (fusty, mouldy, muddy, rancid and winey). For this purpose, these defects, available as single standards of the International Olive Council, were added to refined sunflower oil. According to the LDA models and the ANN method, the defected samples were correctly classified. On the other hand, the electronic nose data was used to predict the defect percentage added to sunflower oil using multiple linear regression models. All …