Search results for "Pattern recognition"
showing 10 items of 2301 documents
A challenging dissociation in masked identity priming with the lexical decision task.
2013
Abstract The masked priming technique has been used extensively to explore the early stages of visual-word recognition. One key phenomenon in masked priming lexical decision is that identity priming is robust for words, whereas it is small/unreliable for nonwords. This dissociation has usually been explained on the basis that masked priming effects are lexical in nature, and hence there should not be an identity prime facilitation for nonwords. We present two experiments whose results are at odds with the assumption made by models that postulate that identity priming is purely lexical, and also challenge the assumption that word and nonword responses are based on the same information. Our e…
Subsequent Keyframe Generation for Visual Servoing
2021
International audience; In this paper, we study the problem of autonomous and reliable positioning of a camera w.r.t. an object when only this latter is known but not the rest of the scene. We propose to combine the advantages and efficiency of a visual servoing scheme and the generalization ability of a generative adversarial network. The paper describes how to efficiently create a synthetic dataset in order to train a network that predicts an intermediate visual keyframe between two images. Subsequent predictions are used as visual features to autonomously converge towards the desired pose even for large displacements. We show that the proposed method can be used without any prior knowled…
Comparison of Functional Network Connectivity and Granger Causality for Resting State fMRI Data
2017
Functional network connectivity (FNC) and Granger causality have been widely used to identify functional and effective connectivity for resting functional magnetic resonance imaging (fMRI) data. However, the relationship between these two approaches is still unclear, making it difficult to compare results. In this study, we investigate the relationship by constraining the FNC lags and the causality coherences for analyzing resting state fMRI data. The two techniques were applied respectively to examine the connectivity within default mode network related components extracted by group independent component analysis. The results show that FNC and Granger causality provide complementary result…
Microaneurysm detection with radon transform-based classification on retina images.
2012
The creation of an automatic diabetic retinopathy screening system using retina cameras is currently receiving considerable interest in the medical imaging community. The detection of microaneurysms is a key element in this effort. In this work, we propose a new microaneurysms segmentation technique based on a novel application of the radon transform, which is able to identify these lesions without any previous knowledge of the retina morphological features and with minimal image preprocessing. The algorithm has been evaluated on the Retinopathy Online Challenge public dataset, and its performance compares with the best current techniques. The performance is particularly good at low false p…
Images perceived after chromatic or achromatic contrast sensitivity losses.
2010
Purpose. We simulate how subjects with losses in chromatic and achromatic contrast sensitivity perceive colored images by using the spatiochromatic corresponding pair algorithm. Methods. This is a generalized version of the algorithm by Capilla et al. (J Opt Soc Am (A) 2004;21:176 –186) for simulating color perception of color deviant subjects, which incorporates a simple spatial vision model, consisting of a linear filtering stage, with a band-pass achromatic filter and two low-pass chromatic ones, for the red-green and blue-yellow mechanisms. These filters, except for the global scaling, are the subject’s contrast sensitivity functions measured along the cardinal directions of the color s…
A Neural Architecture for Segmentation and Modelling of Range Data
2003
A novel, two stage, neural architecture for the segmentation of range data and their modeling with undeformed superquadrics is presented. The system is composed by two distinct neural stages: a SOM is used to perform data segmentation, and, for each segment, a multi-layer feed-forward network performs model estimation. The topology preserving nature of the SOM algorithm makes this architecture suited to cluster data with respect to sudden curvature variations. The second stage is designed to model and compute the inside-outside function of an undeformed superquadric in whatever attitude, starting form the (x, y, z) data triples. The network has been trained using backpropagation, and the we…
A vision-based fully automated approach to robust image cropping detection
2020
Abstract The definition of valid and robust methodologies for assessing the authenticity of digital information is nowadays critical to contrast social manipulation through the media. A key research topic in multimedia forensics is the development of methods for detecting tampered content in large image collections without any human intervention. This paper introduces AMARCORD (Automatic Manhattan-scene AsymmetRically CrOpped imageRy Detector), a fully automated detector for exposing evidences of asymmetrical image cropping on Manhattan-World scenes. The proposed solution estimates and exploits the camera principal point, i.e., a physical feature extracted directly from the image content th…
Rotation-Invariant Texture Retrieval via Signature Alignment Based on Steerable Sub-Gaussian Modeling
2008
This paper addresses the construction of a novel efficient rotation-invariant texture retrieval method that is based on the alignment in angle of signatures obtained via a steerable sub-Gaussian model. In our proposed scheme, we first construct a steerable multivariate sub-Gaussian model, where the fractional lower-order moments of a given image are associated with those of its rotated versions. The feature extraction step consists of estimating the so-called covariations between the orientation subbands of the corresponding steerable pyramid at the same or at adjacent decomposition levels and building an appropriate signature that can be rotated directly without the need of rotating the im…
Spherical nonlinear correlations for global invariant three-dimensional object recognition
2007
We define a nonlinear filtering based on correlations on unit spheres to obtain both rotation- and scale-invariant three-dimensional (3D) object detection. Tridimensionality is expressed in terms of range images. The phase Fourier transform (PhFT) of a range image provides information about the orientations of the 3D object surfaces. When the object is sequentially rotated, the amplitudes of the different PhFTs form a unit radius sphere. On the other hand, a scale change is equivalent to a multiplication of the amplitude of the PhFT by a constant factor. The effect of both rotation and scale changes for 3D objects means a change in the intensity of the unit radius sphere. We define a 3D fil…
Classification of SD-OCT Volumes for DME Detection: An Anomaly Detection Approach
2016
International audience; Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binar…