11 resultados para transgressive segregation

em SAPIENTIA - Universidade do Algarve - Portugal


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Models of visual perception are based on image representations in cortical area V1 and higher areas which contain many cell layers for feature extraction. Basic simple, complex and end-stopped cells provide input for line, edge and keypoint detection. In this paper we present an improved method for multi-scale line/edge detection based on simple and complex cells. We illustrate the line/edge representation for object reconstruction, and we present models for multi-scale face (object) segregation and recognition that can be embedded into feedforward dorsal and ventral data streams (the “what” and “where” subsystems) with feedback streams from higher areas for obtaining translation, rotation and scale invariance.

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There are roughly two processing systems: (1) very fast gist vision of entire scenes, completely bottom-up and data driven, and (2) Focus-of-Attention (FoA) with sequential screening of specific image regions and objects. The latter system has to be sequential because unnormalised input objects must be matched against normalised templates of canonical object views stored in memory, which involves dynamic routing of features in the visual pathways.

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In this paper we present an improved scheme for line and edge detection in cortical area V1, based on responses of simple and complex cells, truly multi-scale with no free parameters. We illustrate the multi-scale representation for visual reconstruction, and show how object segregation can be achieved with coarse-to-finescale groupings. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only, and final categorization on coarse plus fine scales. Processing schemes are discussed in the framework of a complete cortical architecture.

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Saliency maps determine the likelihood that we focus on interesting areas of scenes or images. These maps can be built using several low-level image features, one of which having a particular relevance: colour. In this paper we present a new computational model, based only on colour features, which provides a sound basis for saliency maps for static images and video, plus region segregation and cues for local gist vision.

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Multi-scale representations of lines, edges and keypoints on the basis of simple, complex and end-stopped cells can be used for object categorisation and recognition (Rodrigues and du Buf, 2009 BioSystems 95 206-226). These representations are complemented by saliency maps of colour, texture, disparity and motion information, which also serve to model extremely fast gist vision in parallel with object segregation. We present a low-level geometry model based on a single type of self-adjusting grouping cell, with a circular array of dendrites connected to edge cells located at several angles.

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Object categorisation is linked to detection, segregation and recognition. In the visual system, these processes are achieved in the ventral \what"and dorsal \where"pathways [3], with bottom-up feature extractions in areas V1, V2, V4 and IT (what) in parallel with top-down attention from PP via MT to V2 and V1 (where). The latter is steered by object templates in memory, i.e. in prefrontal cortex with a what component in PF46v and a where component in PF46d.

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Tese de dout., Engenharia Electrónica e de Computadores, Faculdade de Ciência e Tecnologia, Universidade do Algarve, 2007

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The primary visual cortex employs simple, complex and end-stopped cells to create a scale space of 1D singularities (lines and edges) and of 2D singularities (line and edge junctions and crossings called keypoints). In this paper we show first results of a biological model which attributes information of the local image structure to keypoints at all scales, ie junction type (L, T, +) and main line/edge orientations. Keypoint annotation in combination with coarse to fine scale processing facilitates various processes, such as image matching (stereo and optical flow), object segregation and object tracking.

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In this paper we present an improved model for line and edge detection in cortical area V1. This model is based on responses of simple and complex cells, and it is multi-scale with no free parameters. We illustrate the use of the multi-scale line/edge representation in different processes: visual reconstruction or brightness perception, automatic scale selection and object segregation. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only and final categorization on coarse plus fine scales. We also present a multi-scale object and face recognition model. Processing schemes are discussed in the framework of a complete cortical architecture. The fact that brightness perception and object recognition may be based on the same symbolic image representation is an indication that the entire (visual) cortex is involved in consciousness.

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In his introduction, Pinna (2010) quoted one of Wertheimer’s observations: “I stand at the window and see a house, trees, sky. Theoretically I might say there were 327 brightnesses and nuances of color. Do I have ‘327’? No. I have sky, house, and trees.” This seems quite remarkable, for Max Wertheimer, together with Kurt Koffka and Wolfgang Koehler, was a pioneer of Gestalt Theory: perceptual organisation was tackled considering grouping rules of line and edge elements in relation to figure-ground segregation, i.e., a meaningful object (the figure) as perceived against a complex background (the ground). At the lowest level – line and edge elements – Wertheimer (1923) himself formulated grouping principles on the basis of proximity, good continuation, convexity, symmetry and, often forgotten, past experience of the observer. Rubin (1921) formulated rules for figure-ground segregation using surroundedness, size and orientation, but also convexity and symmetry. Almost a century of research into Gestalt later, Pinna and Reeves (2006) introduced the notion of figurality, meant to represent the integrated set of properties of visual objects, from the principles of grouping and figure-ground to the colour and volume of objects with shading. Pinna, in 2010, went one important step further and studied perceptual meaning, i.e., the interpretation of complex figures on the basis of past experience of the observer. Re-establishing a link to Wertheimer’s rule about past experience, he formulated five propositions, three definitions and seven properties on the basis of observations made on graphically manipulated patterns. For example, he introduced the illusion of meaning by comics-like elements suggesting wind, therefore inducing a learned interpretation. His last figure shows a regular array of squares but with irregular positions on the right side. This pile of (ir)regular squares can be interpreted as the result of an earthquake which destroyed part of an apartment block. This is much more intuitive, direct and economic than describing the complexity of the array of squares.

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The vertebral column and its units, the vertebrae, are fundamental features, characteristic of all vertebrates. Developmental segregation of the vertebral bodies as articulated units is an intrinsic requirement to guarantee the proper function of the spine. Whenever these units become fused either during development or postsegmentation, movement is affected in a more or less severe manner, depending on the number of vertebrae affected. Nevertheless, fusion may occur as part of regular development and as a physiological requirement, like in the tetrapod sacrum or in fish posterior vertebrae forming the urostyle. In order to meet the main objective of this PhD project, which aimed to better understand the molecular and cellular events underlying vertebral fusion under physiological and pathological conditions, a detailed characterization of the vertebral fusion occurring in zebrafish caudal fin region was conducted. This showed that fusion in the caudal fin region comprised 5 vertebral bodies, from which, only fusion between [PU1++U1] and ural2 [U2+] was still traceable during development. This involved bone deposition around the notochord sheath while fusion within the remaining vertebral bodies occur at the level of the notochord sheath, as during the early establishment of the vertebral bodies. A comparison approach between the caudal fin vertebrae and the remaining vertebral column showed conserved features such as the presence of mineralization related proteins as Osteocalcin were identified throughout the vertebral column, independently on the mineralization patterns. This unexpected presence of Osteocalcin in notochord sheath, here identified as Oc1, suggested that this gene, opposing to Oc2, generally associated with bone formation and mature osteoblast activity, is potentially associated with early mineralization events including chordacentrum formation. Nevertheless, major differences between caudal fin region and anterior vertebral bodies considering arch histology and mineralization patterns, led us to use RA as an inductive factor for vertebral fusion, allowing a direct comparison of equivalent structures under normal and fusion events. This fusion phenotype was associated with notochord sheath ectopic mineralization instead of ectopic perichordal bone formation related with increased osteoblast activity, as suggested in previous reports. Additionally, alterations in ECM content, cell adhesion and blood coagulation were discussed as potentially related with the fusion phenotype. Finally, Matrix gla protein, upregulated upon RA treatment and shown to be associated with chordacentrum mineralization sites in regular development, was further described considering its potential function in vertebral formation and pathological fusion. Therefore with this work we propose zebrafish caudal fin vertebral fusion as a potential model to study both congenital and postsegmentation fusion and we present candidate factors and genes that may be further explored in order to clarify whether we can prevent vertebral fusion.