In this paper we present a method of segmentation using a multiagent system, and an application to fish otolith growth ring detection. The otoliths images are composed of alternative concentric dark and light rings, the number of which increases with the age of the fish. Up to now, the identification of growth rings, for age estimation, is routinely achieved by human readers, but this task is tedious and depends on the reader's subjectivity. The system proposed here is composed of several agents whose individual task is to detect local extremes on a grayscale image. For this aim the agents are provided with sensors on the gray levels of the image. By computing the mean gray level of two sensors placed in front of it, the agent, if it searches for light rings (respectively dark) will decide to turn in the direction of the lighter (respectively darker) sensor. The path of the agents has been tested as a roof edge detector, using the Canny criteria: good detection, good localization, and low multiple response, in order to choose the best parameters ruling the agents behavior, according to the image structures. Tests have been first achieved on synthetic images, and then on otoliths images.
In this article, we present a parallel image processing system based on the concept of reactive agents. This means that, in our system, each agent has a very simple behavior which allows it to take a decision (find out an edge, a region, ...) according to its position in the image and to the information enclosed in it. Our system lies in the oRis language, which allows to describe very finely and simply the agents' behaviors. In fact, oRis is an interpreted and dynamic multiagent language. First of all, oRis is an object language with the use of classes regrouping attributes and methods. The syntax is close to the C++ language and includes notions of multiple inheritance, oRis is also an agent language: every object with a method `main()' becomes an agent. This method is cyclically executed by the system scheduler and corresponds to the agent behavior. We also present an application made with oRis. This application allows to detect concentric striae located on different natural `objects' (age-rings of tree, fish otolith growth rings, striae of some minerals, ...). The stopping of the multiagent system is implemented through a technique issued from immunology: the apoptosis.
We present a method for detecting concentric strias which can be found in different natural 'objects'. One of the major problems encountered during an automatic image processing is the lack of continuity perception in strias. We propose an approach to this continuity perception based on a multiagent system made up of reactive agents. These agents can move around on their environment which consists of an image made up of light and dark rings set out concentrically. Our multiagent system is made up of a set of agents named darkening agents and lightening agents. These agents follow either the light rings or the dark rings and act on the image. Their actions aim to reinforce the rings by stressing the contrasts allowing, thus, a reliable detection of these rings, even if they are discontinuous. Each agent has three sensors allowing it to obtain information about the environment. The sensor ar made up of unit sensors returning the value of a pixel. The three sensors of an agent are: (1) a unit sensor allowing the agent to know if it is located on an already detected stria, (2) two disk-shaped sensors made up of unit sensors. These two sensors return the sum of their unit sensors. They are located in front of the agent and distant one from the other. Those two disk-shaped sensors are used for determining the movements of the agent and, therefore, for detecting the rings. An agent, indeed, always tries to move where the values returned by the senors is minimal. The suggested multiagent system has shown great robustness and adaptability, for it can detect strias, even if they are discontinuous and the image noisy.
In this article, we would like to detect boundaries of objects with the help of a multiagent system made up of reactive agents. The reactivity being very important, the agents' behavior is very simple (perception-action): they have the capacity, nevertheless, to adapt locally to what they consider their environment, that is to say the image. An agent can move and has its own position in its environment. The basic behavior for an agent consists of following the highest brightness gradient and inscribing its path, if estimating to be on an edge, in all the agents' shared memory. Its path thus corresponds to edges which are found in the image. Please note that, in order to be noise resistant, the path is actually stored in the shared memory only if it is long enough. The notion of shared memory is very important because it allows the interaction among agents and the coordination of their actions. The agents actually use already found edges for finding new ones or complete those already detected. We have tested this system on different gray scale images scenes, but as well on synthetic scenes allowing analysis of thus obtained results. The results are promising and especially fast. Our multiagent system has been tested on a single-processor computer, and it has been noted that the number of agents in a simulation neither affects the quality of the result nor CPU time necessary for segmentation of a given scene. We think that this approach is original in its use of agents and may be used to implement parallel image processing by assigning, for instance, an agent to each processor.
This work is related to a project of medical robotics applied to surgical endoscopy, led in collaboration with Doctor Berreni from the Saint Roch nursing-home in Perpignan (France). After taking what Doctor Berreni advises, two aspects of endoscopic color image processing have been brought out: (1) The help to the diagnosis by the automatic detection of the sick areas after a learning phase. (2) The 3D reconstruction of the analyzed cavity by using a zoom.
This work is related to medical robotics applied to endoscopic image processing. When operating, the surgeon uses both an endoscope and a color video camera but does not get any 3-D information of the analyzed cavity. The use of only one camera has led us to use the concept of axial stereovision. In this paper, we present a matching process using color information in order to recover the 3-D shapes of non-polyhedric objects (such as organs) with a zoom sensor.
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