Search results for "Pattern recognition"
showing 10 items of 2301 documents
Principal polynomial analysis for remote sensing data processing
2011
Inspired by the concept of Principal Curves, in this paper, we define Principal Polynomials as a non-linear generalization of Principal Components to overcome the conditional mean independence restriction of PCA. Principal Polynomials deform the straight Principal Components by minimizing the regression error (or variance) in the corresponding orthogonal subspaces. We propose to use a projection on a series of these polynomials to set a new nonlinear data representation: the Principal Polynomial Analysis (PPA). We prove that the dimensionality reduction error in PPA is always lower than in PCA. Lower truncation error and increased independence suggest that unsupervised PPA features can be b…
A New Set of Quartic Trivariate Polynomial Equations for Stratified Camera Self-calibration under Zero-Skew and Constant Parameters Assumptions
2012
This paper deals with the problem of self-calibrating a moving camera with constant parameters. We propose a new set of quartic trivariate polynomial equations in the unknown coordinates of the plane at infinity derived under the no-skew assumption. Our new equations allow to further enforce the constancy of the principal point across all images while retrieving the plane at infinity. Six such polynomials, four of which are independent, are obtained for each triplet of images. The proposed equations can be solved along with the so-called modulus constraints and allow to improve the performance of existing methods.
Random forests, a novel approach for discrimination of fish populations using parasites as biological tags.
2008
Due to the complexity of host-parasite relationships, discrimination between fish populations using parasites as biological tags is difficult. This study introduces, to our knowledge for the first time, random forests (RF) as a new modelling technique in the application of parasite community data as biological markers for population assignment of fish. This novel approach is applied to a dataset with a complex structure comprising 763 parasite infracommunities in population samples of Atlantic cod, Gadus morhua, from the spawning/feeding areas in five regions in the North East Atlantic (Baltic, Celtic, Irish and North seas and Icelandic waters). The learning behaviour of RF is evaluated in …
Stability and l1-gain analysis for positive 2D T–S fuzzy state-delayed systems in the second FM model
2014
This paper considers the problems of delay-dependent stability and l"1-gain analysis for a class of positive two-dimensional (2D) Takagi-Sugeno (T-S) fuzzy linear systems with state delays described by the second FM model. Firstly, the co-positive type Lyapunov function method is applied to establish sufficient conditions of asymptotical stability for the addressed positive 2D T-S fuzzy system. Then, the l"1-gain performance analysis for the positive 2D T-S fuzzy delayed system is studied. All the obtained results are formulated in the form of linear matrix inequalities (LMIs) which are computationally tractable. Finally, an illustrative example is given to verify the effectiveness of the p…
Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography.
2019
Abstract In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then…
A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance
2011
International audience; Prostate volume estimation from segmented prostate contours in Trans Rectal Ultrasound (TRUS) images aids in diagnosis and treatment of prostate diseases, including prostate cancer. However, accurate, computationally efficient and automatic segmentation of the prostate in TRUS images is a challenging task owing to low Signal-To-Noise-Ratio (SNR), speckle noise, micro-calcifications and heterogeneous intensity distribution inside the prostate region. In this paper, we propose a probabilistic framework for propagation of a parametric model derived from Principal Component Analysis (PCA) of prior shape and posterior probability values to achieve the prostate segmentatio…
Context-dependent minimisation of prediction errors involves temporal-frontal activation
2020
According to the predictive coding model of perception, the brain constantly generates predictions of the upcoming sensory inputs. Perception is realised through a hierarchical generative model which aims at minimising the discrepancy between predictions and the incoming sensory inputs (i.e., prediction errors). Notably, prediction errors are weighted depending on precision of prior information. However, it remains unclear whether and how the brain monitors prior precision when minimising prediction errors in different contexts. The current study used magnetoencephalography (MEG) to address this question. We presented participants with repetition of two non-predicted probes embedded in cont…
CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease
2023
AbstractThis study was aimed to investigate the predictive value of the radiomics features extracted from pericoronaric adipose tissue — around the anterior interventricular artery (IVA) — to assess the condition of coronary arteries compared with the use of clinical characteristics alone (i.e., risk factors). Clinical and radiomic data of 118 patients were retrospectively analyzed. In total, 93 radiomics features were extracted for each ROI around the IVA, and 13 clinical features were used to build different machine learning models finalized to predict the impairment (or otherwise) of coronary arteries. Pericoronaric radiomic features improved prediction above the use of risk factors alon…
3D chain interactive
2014
With constant evolution of technology, 3D scanners are providing more and more data with ever greater precision. However, the substantial increase of the data size is problematic. Files become very heavy and can cause problems in data transmission or data storage. Therefore, most of the time, the data obtained by the 3d scanners will be analyzed, processed and simplify; this is called 3D acquisition chain.This manuscript presents an approach which digitize objects dynamically by adapting point density during the acquisition depending on the object complexity, without informations on object shape. This system allows to avoid the use of the classic 3D chain. This system do not calculate a den…
Minimal learning machine in hyperspectral imaging classification
2020
A hyperspectral (HS) image is typically a stack of frames, where each frame represents the intensity of a different wavelength of light. Each spatial pixel has a spectrum. In the classification of the HS image, each spectrum is classified pixel-by-pixel. In some of the real-time applications, the amount of the HS image data causes performance challenges. Those issues relate to the platforms (e.g. drones) payload restrictions, the issues of the available energy and to the complexity of the machine learning models. In this study, we introduce the minimal learning machine (MLM) as a computationally cheap training and classification machine learning method for the hyperspectral imaging classificatio…