Sistemas de aeronaves no tripuladas, UAS y RPAS

The simplest way of combining signal and symbolic information is to make them both available within a single system which gives the user access to both but which makes no attempt to derive symbolic information from signal data. Examples of such system are image databases with knowledge-guided retrieval tools and decision support systems which also provide access to images and image processing.

3.3.1.1 AXON: a knowledge-based image retrieval system

This system [Cohn 1990] is a knowledge-guided image retrieval system. The idea is to allow a database of images to be used as a decision support tool by facilitating the retrieval of relevant images. A knowledge base of information about the domain and a set of search heuristics is used to identify a subset of relevant images in response to a query from the user.

Images and image processing

The system works with digitised chest X-rays. There is no image processing.

Symbolic knowledge

A hierarchy of frames is used to represent the necessary information about the medical domain. The most specific level in the hierarchy, the leaf nodes, represent specific instances, that is to say the individual cases and actual medical images included in the database. A set of search heuristics completes the symbolic knowledge.

Operation

Processing is initiated when the user enters a query. Keyword matching is performed to identify a set of relevant images. Matches are sought by searching along

four “axes of clinical relevance” which map onto the frame hierachies. Search heuristics are used to control the widening of the search if insufficient images are identified. There is no processing of images, the aim is simply to guide the retrieval of images.

Combining symbols and signal data

Terms in the symbolic knowledge base are linked to images in the image database. Thus terms in the knowledge base are understood in part through association with a set of images.

3.3.1.2 I^C: a database for retrieving images on the basis of visual content

Orphanoudakis et al. [Orphanoudakis 1994] describe a system in which automatic and user-guided image processing are used to derive representations of image content, representations which can be used to retrieve images from the database.

Images and image processing

The system is designed to work with different classes of image and with different approaches to processing images. The published papers refer to a database of brain MRI images and use edge detection methods to derive a description of imaged objects in terms of contours, edge segments and points.

Symbolic knowledge

The representation of the symbolic knowledge is based on the notion of classes. Images are organised into classes on the basis of primary criteria - such as modality, the part of the anatomy, orientation, plane of cut - and secondary criteria which may be derived from the clinical interpretation of the image or assigned by a machine learning algorithm. The system maintains an encapsulated object for each class, which stores general information about each image type and about image description algorithms.

Each image in the database is also associated with the description obtained by applying the relevant image-processing operations. These stored descriptions are called ‘logical im ages’.

Operation

When an image is added to the database the default description type builds a logical image. In the implemented prototype the description is a hierarchical structure at each level of which attributes relating to the statistical and geometric properties of contours and segments are stored together with an estimate of their importance. Two forms of content-based query are permitted: image queries and sketch queries. In both cases inappropriate candidates are filtered out on the basis of gross characteristics of image segments and then detailed matches are sought for the midpoints and other characteristic features of polygonal approximations to the image segments in the query.

Combining symbols and signal data

The system contains symbolic information about the clinical interpretation of an image, information which is stored in the class object associated with an image. Thus, as with AXON, a link between symbolic information and signal data is made in the database. I^C, however, also uses image processing to generate a ‘logical image’. This logical image is a symbolic description which is derived from signal data and which can be matched with similar descriptions derived from other signal data.

3.3.1.3 VIA-RAD: an interactive problem-solver

The system [Rogers 1995], intended to assist radiologists interpreting medical images, is based on a psychological model of image interpretation which is used to control diagnostic reasoning and visual interpretation and also the application of relevant image enhancement operators.

Images and image processing

The system was developed to assist in the interpretation of digitised chest X- rays. It includes a limited number of image-processing operators which perform image enhancement tasks, one such operator being histogram equalization.

Symbolic knowledge

The symbolic knowledge in the system consists of four basic components describing the domain: landmarks, findings, features and diagnoses. The landmarks frame provides a representation of all the anatomical objects which are visible or are expected to be visible in the image, of the possible aggregations o f landmarks and of their identifiable components. The findings are represented at three different levels of inference from the perceptual data: facts about the image (a lucency), radiological findings (a mass) and diagnoses (a tumour). Features are the concepts which are used to describe findings. The diagnoses frame represents the associations between findings and diagnoses and the information required to calculate the appropriate degree o f belief for the hypothesised diagnoses. In addition to these components, the system contains knowledge about diagnostic strategies and image enhancement operations. The five diagnostic strategies represented are pursue, rule-in-rule-out, not-enough-information, conflict and deliberate-landmark-search. The knowledge about image enhancement operations is associated with the representation of image features.

Operation

The system is based on a blackboard on which are posted visual hypotheses about what is present in an image and reasoning hypotheses about explanations for objects and collections of objects. The user is viewed as an additional information source, reading from and writing to the blackboard in an attempt to solve the problem. The processing is controlled by three modules, all identified in highly anthropomorphic terminology: the ‘hypothesis manager’, the ‘attention director’ and the ‘strategy selector’. The hypothesis manager controls the hypothesis-related information sources

account. The attention director alters the image and posts information about what to look for. The strategy selector decides how attention should be focused, making judge­ ments about the relative merits of the suggestions from the attention director.

Combining symbols and signal data

The representation of features includes information about image enhancement techniques, which may be posted to the blackboard. The attention direction module can invoke these operators to alter the appearance of the image. So symbolic terms are associated with image-processing operators but not with signal data.