17 research outputs found
Face image matching using fractal dimension
A new method is presented in this paper for calculating the correspondence between two face images on a pixel by pixel basis. The concept of fractal dimension is used to develop the proposed non-parametric area-based image matching method which achieves a higher proportion of matched pixels for face images than some well-known methods
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An Experimental System for the Integration of Information from Stereo and Multiple Shape From Texture Algorithms
In numerous computer vision applications, there is both the need and the ability to access multiple types of information about the three dimensional aspects of objects or surfaces. When this information comes from different sources the combination becomes non-trivial. This paper describes the present state of ongoing research in Columbia's Vision Laboratory in the integration of multiple visual sensing methodologies which yield three dimensional information, in particular, feature based stereo algorithms, and various shape-from-texture algorithms are already in operation and multi-view shape-from-texture and shape-from shading modules are expected to be incorporated. Unlike most systems for multi-sensor integration, which fuse all the information at one conceptual level, e.g., the surface level, the system under development uses two levels of data fusion, intra-process integration and inter-process integration. The paper discusses intra-process integration techniques for feature-based stereo and shape-from-texture algorithms. It also discusses an inter-process integration technique based on smooth models of surfaces. Examples are presented using camera acquired images
Shape from X: Psychophysics and Computation
This chapter contains sections titled: The Many Routes to Shape, The Need for Integration, Shape From Stereo and Shading (Local Measurements) 1 , Shape from Shading and Texture (Global Measurements), Shape from Disparate Shading (Intensity-Based Stereo), Shape from Highlights 2 , Integration of Depth Modules, A Bayesian Framework for Cue Integration 3 , Final Remarks, Acknowledgments, Appendices, Reference
Mètode d'extracció multiparamètrica de caracterÃstiques de textura orientat a la segmentació d'imatges
Tal com es veurà en el següent capÃtol d'antecedents, existeixen formes molt variades d'afrontar l'anà lisi de textures però cap d'elles està orientada al cà lcul en temps real (video rate). Degut a la manca de mètodes que posin tant d'èmfasi en el temps de processat, l'objectiu d'aquesta tesi és definir i desenvolupar un nou mètode d'extracció de caracterÃstiques de textura que treballi en temps real. Per aconseguir aquesta alta velocitat d'operació, un altre objectiu és presentar el disseny d'una arquitectura especÃfica per implementar l'algorisme de cà lcul dels parà metres de textura definits, aixà com també l'algorisme de classificació dels parà metres i la segmentació de la imatge en regions de textura semblant.En el capÃtol 2 s'expliquen els diversos mètodes més rellevants dins la caracterització de textures. Es veuran els mètodes més importants tant pel que fa als enfocaments estadÃstics com als estructurals. També en el mateix capÃtol se situa el nou mètode presentat en aquesta tesi dins els diferents enfocaments principals que existeixen. De la mateixa manera es fa una breu ressenya a la sÃntesi de textures, una manera d'avaluar quantitativament la caracterització de la textura d'una imatge. Ens centrarem principalment, en el capÃtol 3, en l'explicació del mètode presentat en aquest treball: s'introduiran els parà metres de textura proposats, la seva necessitat i definicions. Al ser parà metres altament perceptius i no seguir cap model matemà tic, en aquest mateix capÃtol s'utilitza una tècnica estadÃstica anomenada anà lisi discriminant per demostrar que tots els parà metres introdueixen suficient informació per a la separabilitat de regions de textura i veure que tots ells són necessaris en la discriminació de les textures.Dins el capÃtol 4 veurem com es tracta la informació subministrada pel sistema d'extracció de caracterÃstiques per tal de classificar les dades i segmentar la imatge en funció de les seves textures. L'etapa de reconeixement de patrons es durà a terme en dues fases: aprenentatge i treball. També es presenta un estudi comparatiu entre diversos mètodes de classificació de textures i el mètode presentat en aquesta tesi; en ell es veu la bona funcionalitat del mètode en un temps de cà lcul realment reduït. S'acaba el capÃtol amb una anà lisi de la robustesa del mètode introduint imatges amb diferents nivells de soroll aleatori. En el capÃtol 5 es presentaran els resultats obtinguts mitjançant l'extracció de caracterÃstiques de textura a partir de diverses aplicacions reals. S'aplica el nostre mètode en aplicacions d'imatges aèries i en entorns agrÃcoles i sobre situacions que requereixen el processament en temps real com són la segmentació d'imatges de carreteres i una aplicació industrial d'inspecció i control de qualitat en l'estampació de teixits. Al final del capÃtol fem unes consideracions sobre dos efectes que poden influenciar en l'obtenció correcta dels resultats: zoom i canvis de perspectiva en les imatges de textura.En el capÃtol 6 es mostrarà l'arquitectura que s'ha dissenyat expressament per al cà lcul dels parà metres de textura en temps real. Dins el capÃtol es presentarà l'algorisme per a l'assignació de grups de textura i es demostrarà la seva velocitat d'operació a video rate.Finalment, en el capÃtol 7 es presentaran les conclusions i les lÃnies de treball futures que es deriven d'aquesta tesi, aixà com els articles que hem publicat en relació a aquest treball i a l'anà lisi de textures. Les referències bibliogrà fiques i els apèndixs conclouen el treball
Modeling, Estimation, and Pattern Analysis of Random Texture on 3-D Surfaces
To recover 3-D structure from a shaded and textural surface image involving textures, neither the Shape-from-shading nor the Shape-from-texture analysis is enough, because both radiance and texture information coexist within the scene surface. A new 3-D texture model is developed by considering the scene image as the superposition of a smooth shaded image and a random texture image. To describe the random part, the orthographical projection is adapted to take care of the non-isotropic distribution function of the intensity due to the slant and tilt of a 3-D textures surface, and the Fractional Differencing Periodic (FDP) model is chosen to describe the random texture, because this model is able to simultaneously represent the coarseness and the pattern of the 3-D texture surface, and enough flexible to synthesize both long-term and short-term correlation structures of random texture. Since the object is described by the model involving several free parameters and the values of these parameters are determined directly from its projected image, it is possible to extract 3-D information and texture pattern directly from the image without any preprocessing. Thus, the cumulative error obtained from each pre-processing can be minimized. For estimating the parameters, a hybrid method which uses both the least square and the maximum likelihood estimates is applied and the estimation of parameters and the synthesis are done in frequency domain. Among the texture pattern features which can be obtained from a single surface image, Fractal scaling parameter plays a major role for classifying and/or segmenting the different texture patterns tilted and slanted due to the 3-dimensional rotation, because of its rotational and scaling invariant properties. Also, since the Fractal scaling factor represents the coarseness of the surface, each texture pattern has its own Fractal scale value, and particularly at the boundary between the different textures, it has relatively higher value to the one within a same texture. Based on these facts, a new classification method and a segmentation scheme for the 3-D rotated texture patterns are develope
Surface diagnosticity predicts the high-level representation of regular and irregular object shape in human vision
The human visual system has an extraordinary capacity to compute three-dimensional (3D) shape structure for both geometrically regular and irregular objects. The goal of this study was to shed new light on the underlying representational structures that support this ability. Observers (N = 85) completed two complementary perceptual tasks. Experiment 1 involved whole–part matching of image parts to whole geometrically regular and irregular novel object shapes. Image parts comprised either regions of edge contour, volumetric parts, or surfaces. Performance was better for irregular than for regular objects and interacted with part type: volumes yielded better matching performance than surfaces for regular but not for irregular objects. The basis for this effect was further explored in Experiment 2, which used implicit part–whole repetition priming. Here, we orthogonally manipulated shape regularity and a new factor of surface diagnosticity (how predictive a single surface is of object identity). The results showed that surface diagnosticity, not object shape regularity, determined the differential processing of volumes and surfaces. Regardless of shape regularity, objects with low surface diagnosticity were better primed by volumes than by surfaces. In contrast, objects with high surface diagnosticity showed the opposite pattern. These findings are the first to show that surface diagnosticity plays a fundamental role in object recognition. We propose that surface-based shape primitives—rather than volumetric parts—underlie the derivation of 3D object shape in human vision
Massively Parallel Approach to Modeling 3D Objects in Machine Vision
Electrical Engineerin
Superquadric Description on Large Arrays of Bit-serial Processors
This study describes the parallel implementation of a new computer vision technique, superquadric description. The use of superquadric primitives to extend the power of Constructive Solid Geometry for Computer Aided Design purposes was first proposed in [BARR 84]. The application of this technique for machine vision purposes was first published by Alex Pentland in [PENTL 86b]. This study developed a parallel least-squares solution technique to solve a slightly modified form of the regression equations originally derived in [PENTL 86b]. This technique is intended for execution on large arrays of bit-serial processors. Several ways have been suggested to interconnect the processing elements in such arrays, therefore the performance of this technique was estimated for three interconnection networks.Electrical and Computer Engineerin