Object recognition: solution of the simultaneous pose and correspondence problem

The use of hypothesis verification is recurrent in the model-based recognition literature. Verification consists in measuring how many model features transformed by a pose coincide with some image features. When data involved in the computation of the pose are noisy, the pose is inaccurate and difficult to verify, especially when the objects are partially occluded. To address this problem, the noise in image features is modeled by a Gaussian distribution. A probabilistic framework allows the evaluation of the probability of a matching, knowing that the pose belongs to a rectangular volume of the pose space. It involves quadratic programming, if the transformation is affine. This matching probability is used in an algorithm computing the best pose. It consists in a recursive multi resolution exploration of the pose space, discarding outliers in the match data while the search is progressing. Numerous experimental results are described. They consist of 2D and 3D recognition experiments using the proposed algorithm.Nous nous intéressons à la reconnaissance d'objets volumiques par mise en correspondance d'indices visuels. Nous supposons que les objets à reconnaître sont représentés à l'aide de modèles tridimensionnels, composés d'indices visuels. Reconnaître un objet signifie, dans ce cas, mettre en correspondance les indices du modèle de cet objet avec des indices extraits de l'image, de manière à ce que ces derniers puissent s'expliquer comme une transformation géométrique des indices du modèle. La recherche de la pose (valeur des paramètres de la transformation alignant le modèle sur l'image) et la recherche des correspondances sont ici traitées simultanément. Cela constitue l'originalité et la force de la méthode que nous proposons. Nous présentons de nombreux résultats expérimentaux illustrant l'utilisation de notre approche pour la reconnaissance d'objets

Three dimensional pattern recognition using feature-based indexing and rule-based search

In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells; This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene; Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage; Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size. This data base organization according to object features facilitates machine learning in the context of a knowledge-base driven recognition algorithm. Lastly, feature-based indexing permits the recognition of 3D objects based on a comparatively small number of stored views, further limiting the size of the feature database; Experiments with real images as well as synthetic images including occluded (partially visible) objects are presented. The experiments show almost perfect recognition with feature-based indexing, if the detected features in the test scene are viewed from the same angle as the view on which the model is based. The experiments also show that the knowledge base is a highly effective and efficient search tool recognition performance is improved without increasing the database size requirements. The experimental results indicate that feature-based indexing in combination with a knowledge-based system will be a useful methodology for automatic target recognition (ATR)

Biologically-inspired hierarchical architectures for object recognition

PhD ThesisThe existing methods for machine vision translate the three-dimensional objects in the real world into two-dimensional images. These methods have achieved acceptable performances in recognising objects. However, the recognition performance drops dramatically when objects are transformed, for instance, the background, orientation, position in the image, and scale. The human’s visual cortex has evolved to form an efficient invariant representation of objects from within a scene. The superior performance of human can be explained by the feed-forward multi-layer hierarchical structure of human visual cortex, in addition to, the utilisation of different fields of vision depending on the recognition task. Therefore, the research community investigated building systems that mimic the hierarchical architecture of the human visual cortex as an ultimate objective. The aim of this thesis can be summarised as developing hierarchical models of the visual processing that tackle the remaining challenges of object recognition. To enhance the existing models of object recognition and to overcome the above-mentioned issues, three major contributions are made that can be summarised as the followings 1. building a hierarchical model within an abstract architecture that achieves good performances in challenging image object datasets; 2. investigating the contribution for each region of vision for object and scene images in order to increase the recognition performance and decrease the size of the processed data; 3. further enhance the performance of all existing models of object recognition by introducing hierarchical topologies that utilise the context in which the object is found to determine the identity of the object. Statement ofHigher Committee For Education Development in Iraq (HCED

Reconhecimento de padrões usando indexação recursiva

Tese (Doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico

大規模観測対象のための幾何および光学情報の統合

University of Tokyo (東京大学

Comparaison des documents audiovisuels<br />par Matrice de Similarité

The work of this thesis relates to the comparison of video documents. The field of digital video is in full expansion. Videos are now present in large quantity even for personal use. The video comparison is a basic analysis operation in complement of classification, extraction and structuring of videos.Traditional approaches of comparison are primarily based on the low-level features of the videos to be compared, considered as multidimensional vectors. Other approaches are based on the similarity of frames without taking into account neither the temporal composition of the video nor the audiolayer. The main disadvantage of these methods is that they reduce the comparison role to a simple operator robust to noise effects. Such operators are generally used in order to identify the various specimens of a same document.The originality of our approach lies in the introduction of the of style similarity notion, taking as a starting point the human criteria into the comparison. These criteria are more flexible, and do not impose a strict similarity of all the studied features at the same time.We define an algorithm of extraction of the similarities between the series of values produced bythe analysis of the audiovisual low-level features. The algorithm is inspired by the dynamic programmingand the time series comparison methods.We propose a representation of the data resulting from these processings in the form of a matrixpattern suitable for the visual and immediate comparison of two videos. This matrix is then used topropose a generic similarity measure. The measure is applicable independently to videos of comparableor heterogeneous contents.We developed several applications to demonstrate the behavior of the comparison method and thesimilarity measure. The experiments concern primarily: - the identification of the structure in acollection/sub-collection of documents, - the description of stylistics elements in a movie, and - theanalysis of the grid of programs from a TV stream.Les travaux de cette thèse concernent la comparaison des documents vidéo. Dans le domaine en pleine expansion de la vidéo numérique, les documents disponibles sont maintenant présents en quantité importante même dans les foyers. Opération de base de tout type d'analyse de contenus, en complément de la classification, de l'extraction et de la structuration, la comparaison dans le domaine de l'audiovisuel est d'une utilité qui n'est pas à démontrer.Des approches classiques de comparaison se basent essentiellement sur l'ensemble des caractéristiquesbas niveaux des documents à comparer, en les considérant comme des vecteurs multidimensionnels. D'autres approches se basent sur la similarité des images composant la vidéo sans tenir compte de la composition temporelle du document ni de la bandeson. Le défaut que l'on peut reprocher à ces méthodes est qu'elles restreignent la comparaison à un simple opérateur binaire robuste au bruit. De tels opérateurs sont généralement utilisés afin d'identifier les différents exemplaires d'un même document. L'originalité de notre démarche réside dans le fait que nous introduisons la notion de la similarité de styleen s'inspirant des critères humains dans la comparaison des documents vidéo. Ces critèressont plus souples, et n'imposent pas une similarité stricte de toutes les caractéristiques étudiéesà la fois.En nous inspirant de la programmation dynamique et de la comparaison des séries chronologiques, nous définissons un algorithme d'extraction des similarités entre les séries de valeurs produites par l'analyse de caractéristiques audiovisuelles de bas-niveau. Ensuite, un second traitement générique approxime le résultat de l'algorithme de la longueur de la PlusLongue Sous-Séquence Commune (PLSC) plus rapidement que ce dernier. Nous proposons une représentation des données issues de ces traitements sous la forme d'un schéma matriciel propre à la comparaison visuelle et immédiate de deux contenus. Cette matrice peut être également utilisée pour définir une mesure de similarité générique, applicable à des documents de même genre ou de genres hétérogènes.Plusieurs applications ont été mises en place pour démontrer le comportement de la méthode de comparaison et de la mesure de similarité, ainsi que leur pertinence. Les expérimentations concernent essentiellement : - l'identification d'une structure organisationnelle en collection / sous-collection d'une base de documents, - la mise en évidence d'élémentsstylistiques dans un film de cinéma, - la mise en évidence de la grille de programmes d'unflux de télévision