Positron-emission tomography imaging of long-term shape recognition challenges

Long-term visual memory performance was impaired by two types of challenges: a diazepam challenge on acquisition and a sensory challenge on recognition. Using positron-emission tomography regional cerebral blood flow imaging, we studied the effect of these challenges on regional brain activation during the delayed recognition of abstract visual shapes as compared with a baseline fixation task. Both challenges induced a significant decrease in differential activation in the left fusiform gyrus, suggesting that this region is involved in the automatic or volitional comparison of incoming and stored stimuli. In contrast, thalamic differential activation increased in response to memory challenges. This increase might reflect enhanced retrieval attempts as a compensatory mechanism for restoring recognition performance.

The role of automatic shape and position recognitionin streamlining manufacturing

The main features of most components consist of simple basic functional geometries: planes, cylinders, spheres and cones. Shape and position recognition of these geometries is essential for dimensional characterization of components, and represent an important contribution in the life cycle of the product, concerning in particular the manufacturing and inspection processes of the final product. This work aims to establish an algorithm to automatically recognize such geometries, without operator intervention. Using differential geometry large volumes of data can be treated and the basic functional geometries to be dealt recognized. The original data can be obtained by rapid acquisition methods, such as 3D survey or photography, and then converted into Cartesian coordinates. The satisfaction of intrinsic decision conditions allows different geometries to be fast identified, without operator intervention. Since inspection is generally a time consuming task, this method reduces operator intervention in the process. The algorithm was first tested using geometric data generated in MATLAB and then through a set of data points acquired by measuring with a coordinate measuring machine and a 3D scan on real physical surfaces. Comparison time spent in measuring is presented to show the advantage of the method. The results validated the suitability and potential of the algorithm hereby proposed

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

A complex network-based approach for boundary shape analysis

This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has all efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, Curvature, Zernike moments and multiscale fractal dimension). (C) 2008 Elsevier Ltd. All rights reserved.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

A SDK improvement towards gesture support

Human-Computer Interaction have been one of the main focus of the technological community, specially the Natural User Interfaces (NUI) field of research as, since the launch of the Kinect Sensor, the goal to achieve fully natural interfaces just got a lot closer to reality. Taking advantage of this conditions the following research work proposes to compute the hand skeleton in order to recognize Sign Language Shapes. The proposed solution uses the Kinect Sensor to achieve a good segmentation and image analysis algorithms to extend the skeleton from the extraction of high-level features. In order to recognize complex hand shapes the current research work proposes the redefinition of the hand contour making it immutable to translation, rotation and scaling operations, and a set of tools to achieve a good recognition. The validation of the proposed solution extended the Kinects Software Development Kit to allow the developer to access the new set of inferred points and created a template-matching based platform that uses the contour to define the hand shape, this prototype was tested in a set of predefined conditions and showed to have a good success ration and has proven to be eligible for real-time scenarios.

Reconnaissance des actions humaines à partir d'une séquence vidéo

The work done in this master's thesis, presents a new system for the recognition of human actions from a video sequence. The system uses, as input, a video sequence taken by a static camera. A binary segmentation method of the the video sequence is first achieved, by a learning algorithm, in order to detect and extract the different people from the background. To recognize an action, the system then exploits a set of prototypes generated from an MDS-based dimensionality reduction technique, from two different points of view in the video sequence. This dimensionality reduction technique, according to two different viewpoints, allows us to model each human action of the training base with a set of prototypes (supposed to be similar for each class) represented in a low dimensional non-linear space. The prototypes, extracted according to the two viewpoints, are fed to a $K$-NN classifier which allows us to identify the human action that takes place in the video sequence. The experiments of our model conducted on the Weizmann dataset of human actions provide interesting results compared to the other state-of-the art (and often more complicated) methods. These experiments show first the sensitivity of our model for each viewpoint and its effectiveness to recognize the different actions, with a variable but satisfactory recognition rate and also the results obtained by the fusion of these two points of view, which allows us to achieve a high performance recognition rate.

L’impact de la stéréoscopie dans la reconnaissance, la perception et la constance de forme 3D

Les buts des recherches présentées dans cette thèse étaient d’évaluer le rôle de la stéréoscopie dans la reconnaissance de forme, dans la perception du relief et dans la constance de forme. La première étude a examiné le rôle de la stéréoscopie dans la perception des formes visuelles en utilisant une tâche de reconnaissance de formes. Les stimuli pouvaient être présentés en 2D, avec disparité normale (3D) ou avec disparité inversée. La performance de reconnaissance était meilleure avec les modes de présentation 2D et 3D qu’avec la 3D inversée. Cela indique que la stéréoscopie contribue à la reconnaissance de forme. La deuxième étude s’est intéressée à la contribution conjointe de l’ombrage et de la stéréoscopie dans la perception du relief des formes. Les stimuli étaient des images d’une forme 3D convexe synthétique présentée sous un point de vue menant à une ambigüité quant à sa convexité. L’illumination pouvait provenir du haut ou du bas et de la gauche ou de la droite, et les stimuli étaient présentés dichoptiquement avec soit de la disparité binoculaire normale, de la disparité inversée ou sans disparité entre les vues. Les participants ont répondu que les formes étaient convexes plus souvent lorsque la lumière provenait du haut que du bas, plus souvent avec la disparité normale qu’en 2D, et plus souvent avec absence de disparité qu’avec disparité inversée. Les effets de direction d’illumination et du mode de présentation étaient additifs, c’est-à-dire qu’ils n’interagissaient pas. Cela indique que l’ombrage et la stéréoscopie contribuent indépendamment à la perception du relief des formes. La troisième étude a évalué la contribution de la stéréoscopie à la constance de forme, et son interaction avec l’expertise perceptuelle. Elle a utilisé trois tâches de discrimination séquentielle de trombones tordus ayant subi des rotations en profondeur. Les stimuli pouvaient être présentés sans stéréoscopie, avec stéréoscopie normale ou avec stéréoscopie inversée. Dans la première moitié de l’Exp. 1, dans laquelle les variations du mode de présentation étaient intra-sujets, les performances étaient meilleures en 3D qu’en 2D et qu’en 3D inversée. Ces effets ont été renversés dans la seconde moitié de l’expérience, et les coûts de rotation sont devenus plus faibles pour la 2D et la 3D inversée que pour la 3D. Dans les Exps. 2 (variations intra-sujets du mode de présentation, avec un changement de stimuli au milieu de l’expérience) et 3 (variations inter-sujets du mode de présentation), les effets de rotation étaient en tout temps plus faibles avec stéréoscopie qu’avec stéréoscopie inversée et qu’en 2D, et plus faibles avec stéréoscopie inversée que sans stéréoscopie. Ces résultats indiquent que la stéréoscopie contribue à la constance de forme. Toutefois, cela demande qu’elle soit valide avec un niveau minimal de consistance, sinon elle devient stratégiquement ignorée. En bref, les trois études présentées dans cette thèse ont permis de montrer que la stéréoscopie contribue à la reconnaissance de forme, à la perception du relief et à la constance de forme. De plus, l’ombrage et la stéréoscopie sont intégrés linéairement.

Generalization over contrast and mirror reversal, but not figure-ground reversal, in an "edge-based

Baylis & Driver (Nature Neuroscience, 2001) have recently presented data on the response of neurons in macaque inferotemporal cortex (IT) to various stimulus transformations. They report that neurons can generalize over contrast and mirror reversal, but not over figure-ground reversal. This finding is taken to demonstrate that ``the selectivity of IT neurons is not determined simply by the distinctive contours in a display, contrary to simple edge-based models of shape recognition'', citing our recently presented model of object recognition in cortex (Riesenhuber & Poggio, Nature Neuroscience, 1999). In this memo, I show that the main effects of the experiment can be obtained by performing the appropriate simulations in our simple feedforward model. This suggests for IT cell tuning that the possible contributions of explicit edge assignment processes postulated in (Baylis & Driver, 2001) might be smaller than expected.

Methods of sensory augmentation through electro-tactile stimulation of the tongue

This paper details an investigation into sensory substitution by means of direct electrical stimulation of the tongue for the purpose of information input to the human brain. In particular, a device has been constructed and a series of trials have been performed in order to demonstrate the efficacy and performance of an electro-tactile array mounted onto the tongue surface for the purpose of sensory augmentation. Tests have shown that by using a low resolution array a computer-human feedback loop can be successfully implemented by humans in order to complete tasks such as object tracking, surface shape identification and shape recognition with no training or prior experience with the device. Comparisons of this technique have been made with visual alternatives and these show that the tongue based tactile array can match such methods in convenience and accuracy in performing simple tasks.

Attention deficits in dyslexia:Evidence for an automatisation deficit?

Both attentional difficulties and rapid processing deficits have recently been linked with dyslexia. We report two studies comparing the performance of dyslexic and control teenagers on attentional tasks. The two studies were based on two different conceptions of attention. Study 1 employed a design that allowed three key components of attention - focusing, switching, and sustaining - to be investigated separately. One hypothesis under investigation was that rapid processing problems - in particular impaired ability to switch attention rapidly - might be associated with dyslexia. However, although dyslexic participants were significantly less accurate than their controls in a condition where they had to switch attention between two target types, the nature of the deficit suggested that the problem was not in switching attention per se. Thus, in Study 2, we explored an alternative interpretation of the Study 1 results in terms of the classic capacity-limited models of "central" attention. We contrasted two hypotheses: (1) that dyslexic teenagers have reduced cognitive resources versus (2) that they suffer from a general impairment in the ability to automatise basic skills. To investigate the automaticity of the shape recognition component of the task a similar attention paradigm to that used in Study 1 was employed, but using degraded, as well as intact, stimuli. It was found that stimulus degradation led to relatively less impairment for dyslexic than for matched control groups. The results support the hypothesis that dyslexic people suffer from a general impairment in the ability to automatise skills - in this case the skill of automatic shape recognition.

Supervised learning of graph structure

Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present a naïve node-observation model, where we make the important assumption that the observation of each node and each edge is independent of the others, then we propose an EM-like approach to learn a mixture of these models and a Minimum Message Length criterion for components selection. Moreover, in order to avoid the bias that could arise with a single estimation of the node correspondences, we decide to estimate the sampling probability over all the possible matches. Finally we show the utility of the proposed approach on popular computer vision tasks such as 2D and 3D shape recognition. © 2011 Springer-Verlag.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Learning Three-Dimensional Shape Models for Sketch Recognition

Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.