Search results for "Visual Object Recognition"
showing 10 items of 50 documents
LeSSS: Learned Shared Semantic Spaces for Relating Multi-Modal Representations of 3D Shapes
2015
In this paper, we propose a new method for structuring multi-modal representations of shapes according to semantic relations. We learn a metric that links semantically similar objects represented in different modalities. First, 3D-shapes are associated with textual labels by learning how textual attributes are related to the observed geometry. Correlations between similar labels are captured by simultaneously embedding labels and shape descriptors into a common latent space in which an inner product corresponds to similarity. The mapping is learned robustly by optimizing a rank-based loss function under a sparseness prior for the spectrum of the matrix of all classifiers. Second, we extend …
The what, when, where, and how of visual word recognition
2014
A long-standing debate in reading research is whether printed words are perceived in a feedforward manner on the basis of orthographic information, with other representations such as semantics and phonology activated subsequently, or whether the system is fully interactive and feedback from these representations shapes early visual word recognition. We review recent evidence from behavioral, functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and biologically plausible connectionist modeling approaches, focusing on how each approach provides insight into the temporal flow of information in the lexical system. We conclude that, consistent with interactive a…
Insect brains use image interpolation mechanisms to recognise rotated objects.
2008
Recognising complex three-dimensional objects presents significant challenges to visual systems when these objects are rotated in depth. The image processing requirements for reliable individual recognition under these circumstances are computationally intensive since local features and their spatial relationships may significantly change as an object is rotated in the horizontal plane. Visual experience is known to be important in primate brains learning to recognise rotated objects, but currently it is unknown how animals with comparatively simple brains deal with the problem of reliably recognising objects when seen from different viewpoints. We show that the miniature brain of honeybees…
GESTALT-INSPIRED FEATURES EXTRACTION FOR OBJECT CATEGORY RECOGNITION
2013
International audience; We propose a methodology inspired by Gestalt laws to ex- tract and combine features and we test it on the object cat- egory recognition problem. Gestalt is a psycho-visual the- ory of Perceptual Organization that aims to explain how vi- sual information is organized by our brain. We interpreted its laws of homogeneity and continuation in link with shape and color to devise new features beyond the classical proxim- ity and similarity laws. The shape of the object is analyzed based on its skeleton (good continuation) and as a measure of homogeneity, we propose self-similarity enclosed within shape computed at super-pixel level. Furthermore, we pro- pose a framework to …
Symmetry in Computer Vision
2002
Symmetry properties establish the invariance of a system to a given set of transformations. Physicists assign special meaning whenever symmetry is broken in nature; for example, groups of symmetry have been used to explain and predict the spatial organization of atoms in a crystal. Psychologists consider relevant the property of symmetry in the perception of visual signals. The paper will briefly describe different approaches, introduced in computer vision, to measure symmetry. A review of some applications at the Computer Vision Group (Department of Mathematics and Applications of Palermo University) is presented. They regard attentive visual processing, the analysis of faces, the recognit…
2D geon based generic object recognition
2011
The Recognition by Components(RBC) is a theory in Psychology introduced by Biederman in the late 80s, by which humans perceive scenes through simple 3D objects with regular shapes such as spheres, cubes, cylinders, cones, or wedges, called Geons (geometric ions). Extracting geons from 2D images is a very challenging task as it requires a good segmentation and the recognition of the 3D geons in a 2D space. In this paper, we propose a novel approach for extracting 2D geons from 2D images. The process is composed of three major parts: image preprocessing which includes image background removal and segmentation, arc-geon detection, and polygon-geon detection. We also propose a general procedure…
Object Classification Technique for mmWave FMCW Radars using Range-FFT Features
2021
In this article, we present a novel target classification technique by mmWave frequency modulated continuous wave (FMCW) Radars using the Machine Learning on raw data features obtained from range fast Fourier transform (FFT) plot. FFT plots are extracted from the measured raw data obtained with a Radar operating in the frequency range of 77- 81 GHz. The features such as peak, width, area, standard deviation, and range on range FFT plot peaks are extracted and fed to a machine learning model. Two light weight classification models such as Logistic Regression, Naive Bayes are explored to assess the performance. Based on the results, we demonstrate and achieve an accuracy of 86.9% using Logist…
Innovative modelling techniques in computer vision
1996
Abstract The paper is concerned with two of main research activities currently carried on at the Computer Science and Artificial Intelligence lab of DIE. The first part deals with hybrid artificial vision models, intended to provide object recognition and classification capabilities to an autonomous intelligen system. In this framework, a system recovering 3-D shape information from grey-level images of a scene, building a geometric representation of the scene in terms of superquadrics at the geometric level, and reasoning about the scene at the symbolic level is described. In the second part, attention is focused on automatic indexing of image databases. JACOB, a prototypal system allowing…
Three-dimensional object detection under arbitrary lighting conditions
2006
A novel method of 3D object recognition independent of lighting conditions is presented. The recognition model is based on a vector space representation using an orthonormal basis generated by the Lambertian reflectance functions obtained with distant light sources. Changing the lighting conditions corresponds to multiplying the elementary images by a constant factor and because of that, all possible lighting views will be elements that belong to that vector space. The recognition method proposed is based on the calculation of the angle between the vector associated with a certain illuminated 3D object and that subspace. We define the angle in terms of linear correlations to get shift and i…
Three-dimensional object recognition by Fourier transform profilometry
2008
An automatic method for three-dimensional (3-D) shape recognition is proposed. It combines the Fourier transform profilometry technique with a real-time recognition setup such as the joint transform correlator (JTC). A grating is projected onto the object surface resulting in a distorted grating pattern. Since this pattern carries information about the depth and the shape of the object, their comparison provides a method for recognizing 3-D objects in real time. A two-cycle JTC is used for this purpose. Experimental results demonstrate the theory and show the utility of the new proposed method.