Automatic instrument localization in laparoscopic surgery

This paper presents a tracking algorithm for automatic instrument localization in robotically assisted laparoscopic surgery. We present a simple and robust system that doesn't need the presence of artificial marks, or special colours to distinguish the instruments. So, the system enables the robot to track the usual instruments used in laparoscopic operations. Since the instruments are normally the most structured objects in laparoscopic scenes, the algorithm uses the Hough transform to detect straight lines in the scene. In order to distinguish among different instruments or other structured elements present in the scene, motion information is also used. We give in this paper a detailed description of all stages of the system

Detection of image structures using the Fisher information and the Rao metric

In many detection problems, the structures to be detected are parameterized by the points of a parameter space. If the conditional probability density function for the measurements is known, then detection can be achieved by sampling the parameter space at a finite number of points and checking each point to see if the corresponding structure is supported by the data. The number of samples and the distances between neighboring samples are calculated using the Rao metric on the parameter space. The Rao metric is obtained from the Fisher information which is, in turn, obtained from the conditional probability density function. An upper bound is obtained for the probability of a false detection. The calculations are simplified in the low noise case by making an asymptotic approximation to the Fisher information. An application to line detection is described. Expressions are obtained for the asymptotic approximation to the Fisher information, the volume of the parameter space, and the number of samples. The time complexity for line detection is estimated. An experimental comparison is made with a Hough transform-based method for detecting lines

One-shot learning of object categories

Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by maximum likelihood (ML) and maximum a posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully

An Original Approach for a Better Remote Control of an Assistive Robot

Many researches have been done in the field of assistive robotics in the last few years. The first application field was helping with the disabled people\\u27s assistance. Different works have been performed on robotic arms in three kinds of situations. In the first case, static arm, the arm was principally dedicated to office tasks like telephone, fax... Several autonomous modes exist which need to know the precise position of objects. In the second configuration, the arm is mounted on a wheelchair. It follows the person who can employ it in more use cases. But if the person must stay in her/his bed, the arm is no more useful. In a third configuration, the arm is mounted on a separate platform. This configuration allows the largest number of use cases but also poses more difficulties for piloting the robot. The second application field of assistive robotics deals with the assistance at home of people losing their autonomy, for example a person with cognitive impairment. In this case, the assistance deals with two main points: security and cognitive stimulation. In order to ensure the safety of the person at home, different kinds of sensors can be used to detect alarming situations (falls, low cardiac pulse rate...). For assisting a distant operator in alarm detection, the idea is to give him the possibility to have complementary information from a mobile robot about the person\\u27s activity at home and to be in contact with the person. Cognitive stimulation is one of the therapeutic means used to maintain as long as possible the maximum of the cognitive capacities of the person. In this case, the robot can be used to bring to the person cognitive stimulation exercises and stimulate the person to perform them. To perform these tasks, it is very difficult to have a totally autonomous robot. In the case of disabled people assistance, it is even not the will of the persons who want to act by themselves. The idea is to develop a semi-autonomous robot that a remote operator can manually pilot with some driving assistances. This is a realistic and somehow desired solution. To achieve that, several scientific problems have to be studied. The first one is human-machine-cooperation. How a remote human operator can control a robot to perform a desired task? One of the key points is to permit the user to understand clearly the way the robot works. Our original approach is to analyse this understanding through appropriation concept introduced by Piaget in 1936. As the robot must have capacities of perceptio

Image objects detection based on the feature points matching

Tato práce se týká oboru počítačového vidění. Konkrétně se věnuje extrakci významných bodů z obrazu jako prostředek pro srovnání obrazů a vyhledávání objektů. Jsou zde zmíněny 4 metody, které jsou porovnávány z hlediska účinnosti a využití. Jako hlavní algoritmy jsou zde zmíněny SIFT a SURF, které jsou v poslední době nejčastěji využívané. Jsou zde popsány taky metody popisu významných bodů a jejich porovnávání. Jsou zde vloženy testovací obrazy pro primární testování implementovaného algoritmu. Nakonec je popsána implementace metody SURF a je otestována z hlediska několika nejvýznamnějších parametrů.This paper is concerned in branch of computer vision. Methods for extracting feature points are presented as tools for image comparison and finding objects in images. Four methods are metioned which are compared with respect to their effectiveness and utilization. Algorythms SIFT and SURF are described as a state-of-the-arts. This paper also mentions methods for describing feature points and their comparison. Testing images are inserted as a tool for first testing of implemented algorythm. Finally, the implemented method SURF is described and tested with respect to several most relevant parameters.

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

The development of generative Bayesian models for classification of cell images

A generative model for shape recognition of biological cells in images is developed. The model is designed for analysing high throughput screens, and is tested on a genome wide morphology screen. The genome wide morphology screen contains order of 104 images of fluorescently stained cells with order of 102 cells per image. It was generated using automated techniques through knockdown of almost all putative genes in Drosphila melanogaster. A major step in the analysis of such a dataset is to classify cells into distinct classes: both phenotypic classes and cell cycle classes. However, the quantity of data produced presents a major time bottleneck for human analysis. Human analysis is also known to be subjective and variable. The development of a generalisable computational analysis tool is an important challenge for the field. Previously cell morphology has been characterized by automated measurement of user-defined biological features, often specific to one dataset. These methods are surveyed and discussed. Here a more ambitious approach is pursued. A novel generalisable classification method, applicable to our images, is developed and implemented. The algorithm decomposes training images into constituent patches to build Bayesian models of cell classes. The model contains probability distributions which are learnt via the Expectation Maximization algorithm. This provides a mechanism for comparing the similarity of the appearance of cell phenotypes. The method is evaluated by comparison with results of Support Vector Machines at the task of performing binary classification. This work provides the basis for clustering large sets of cell images into biologically meaningful classes