Pre-Attentive Segmentation in the Primary Visual Cortex

Stimuli outside classical receptive fields have been shown to exert significant influence over the activities of neurons in primary visual cortexWe propose that contextual influences are used for pre-attentive visual segmentation, in a new framework called segmentation without classification. This means that segmentation of an image into regions occurs without classification of features within a region or comparison of features between regions. This segmentation framework is simpler than previous computational approaches, making it implementable by V1 mechanisms, though higher leve l visual mechanisms are needed to refine its output. However, it easily handles a class of segmentation problems that are tricky in conventional methods. The cortex computes global region boundaries by detecting the breakdown of homogeneity or translation invariance in the input, using local intra-cortical interactions mediated by the horizontal connections. The difference between contextual influences near and far from region boundaries makes neural activities near region boundaries higher than elsewhere, making boundaries more salient for perceptual pop-out. This proposal is implemented in a biologically based model of V1, and demonstrated using examples of texture segmentation and figure-ground segregation. The model performs segmentation in exactly the same neural circuit that solves the dual problem of the enhancement of contours, as is suggested by experimental observations. Its behavior is compared with psychophysical and physiological data on segmentation, contour enhancement, and contextual influences. We discuss the implications of segmentation without classification and the predictions of our V1 model, and relate it to other phenomena such as asymmetry in visual search.

Visual Segmentation without Classification in a Model of the Primary Visual Cortex

Stimuli outside classical receptive fields significantly influence the neurons' activities in primary visual cortex. We propose that such contextual influences are used to segment regions by detecting the breakdown of homogeneity or translation invariance in the input, thus computing global region boundaries using local interactions. This is implemented in a biologically based model of V1, and demonstrated in examples of texture segmentation and figure-ground segregation. By contrast with traditional approaches, segmentation occurs without classification or comparison of features within or between regions and is performed by exactly the same neural circuit responsible for the dual problem of the grouping and enhancement of contours.

Cognitive abilities, sociocultural background and academic achievement

Resumen tomado de la publicaci??n

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

Family background, adolescent's achievement and aspirations, and young adult's enrolment in Australian Universities

Resumen tomado de la publicaci??n

Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

JavaScript Background References

Background material for learning JavaScript, including User Guide and Reference manual plus the JavaScript Shell and the JQuery library.

Changing text and background colours in Adobe Acrobat

A 4-minute video that shows how students with dyslexia or visual stress can change the text and background colours in Adobe Acrobat Reader to suit their needs.

Cirugía bariatrica : gastrogastroplastia laparoscopica y bypass gástrico en pacientes con obesidad mórbida

La obesidad es un problema de salud global siendo la cirugía bariatrica el mejor tratamiento demostrado. El Bypass gástrico (BGYR) es el método más utilizado que combina restricción y malabsorcion; sin embargo los procedimientos restrictivos se han popularizado recientemente. La Gastro-gastroplastia produce restricción gástrica reversible por medio de un pouch gástrico con anastomosis gastrogástrica y propusimos su evaluación Métodos: Estudio retrospectivo no randomizado que evaluó archivos de pacientes con GG y BGYR laparoscópicos entre febrero de 2008 y Abril de 2011 Resultados: 289 pacientes identificados: 180 GG y 109 BGYR de los cuales 138 cumplieron criterios de inclusión, 77 (55.8%) GG y 61 (44,2%) BGYR, 18 (13%) hombres y 120 (87%) mujeres. Para GG la mediana del peso inicial fue 97,15 (± 17,3) kg, IMC inicial de 39,35 (± 3,38) kg/m2 y exceso de peso de 37,1 (±11,9). La mediana de IMC a los 1, 6 y 12 meses fue 34,8 (±3,58) kg/m2, 30,81 (±3,81) kg/m2, 29,58 (±4,25) kg/m2 respectivamente. La mediana de % PEP 1, 6 y 12 meses fue 30,9 (±14,2) %, 61,88 (±18,27) %, 68,4 (±19,64) % respectivamente. Para BGYR la mediana del peso inicial fue 108,1 (± 25,4) kg, IMC inicial 44,4 (± 8,1) y exceso de peso de 48,4 (±15,2) %. La mediana de IMC a los 1, 6 y 12 meses fue 39 (±7,5) kg/m2, 33,31 (±4,9) kg/m2, 30,9 (±4,8) kg/m2 respectivamente. La mediana de % PEP 1, 6 y 12 meses fue 25,9 (±12,9) %, 61,87 (±18,62) %, 71,41 (±21,09) % respectivamente. Seguimiento a un año Conclusiones: La gastro-gastroplastia se plantea como técnica restrictiva, reversible, con resultados óptimos en reducción de peso y alternativa quirúrgica en pacientes con obesidad. Son necesarios estudios a mayor plazo para demostrar mantenimiento de cambios en el tiempo

Smart Meetings - background reading

Background reading for coursework to prepare a technical report as part of the orientation phase. These items are business documents (i.e. grey literature) which might be read as a prelude or complement to finding information in peer reviewed academic publications. grey literature links and articles to be used in preparation of technical report. See also overview guidance document for this assignment http://www.edshare.soton.ac.uk/8017/

Green IT (includes High Powered Computing) - background reading

Background reading for coursework to prepare a technical report as part of the orientation phase. These items are business documents (i.e. grey literature) which might be read as a prelude or complement to finding information in peer reviewed academic publications. grey literature links and articles to be used in preparation of technical report. See also overview guidance document for this assignment http://www.edshare.soton.ac.uk/8017/