Expert systems for image processing: Knowledge-based composition of image analysis processes

doi:10.1016/0734-189X(89)90103-5

Computer Vision, Graphics, and Image Processing

Volume 48, Issue 1, October 1989, Pages 22-49

Computer Vision, Gra...

https://doi.org/10.1016/0734-189X(89)90103-5 Get rights and content

Abstract

Recently several expert systems for image processing were proposed to facilitate the development of image analysis processes. They use the knowledge about image processing techniques to compose complex image analysis processes from primitive image processing operators. In this paper, we classify them into the following four categories and discuss their objectives, knowledge representation, reasoning methods, and future problems: (1) consultation system for image processing, (2) knowledge-based program composition system, (3) rule-based design system for image segmentation algorithms, and (4) goal-directed image segmentation system. In the latter half of the paper, we emphasize the importance of image analysis strategies in realizing effective image analysis: analysis using the pyramid (multi-resolution) data structure, combination of edge-based and region-based analyses, and so on. We propose two methods of representing image analysis strategies: one from a software engineering viewpoint and the other from a knowledge representation viewpoint. Several examples are given to demonstrate the effectiveness of these methods.

References (28)

H Tamura
Design and implementation of SPIDER—A transportable image processing software package
Comput. Vision Graphics Image Process.
(1983)
M Nagao
Control structures in pattern analysis
Pattern Recognit.
(1984)
R.A Brooks
Symbolic reasoning about 3-D models and 2-D images
Artif. Intell.
(1981)
D.L Milgram
Region extraction using convergent evidence
Comput. Graphics Image Process.
(1979)
N Sueda et al.
An expert system for designing image analysis programs based on software package
T Toriu et al.
An expert system for image processing
Fujitsu Sci. Tech. J.
(1987)
H Tamura
DIA-Expert system and its knowledge representation scheme
J. ISP Japan
(1988)
K Sakaue et al.
Automatic generation of image processing programs
A.M Nadif et al.
Low level image segmentation: An expert system
IEEE Trans. Pattern Anal. Mach. Intell.
(1984)
T Matsuyama et al.
LLVE: An expert system for top-down image segmentation
J. IPS Japan
(1986)

J Hasegawa

IMPRESS: A system for image processing procedure construction based on sample-figure presentation

Trans. IEICE Japan

(1987)

Special Issue on Expert Systems for Image Processing

J. ISP Japan

(1988)

P.G Selfridge

Reasoning about Success and Failure in Aerial Image Understanding

T.O Binford

Survey of model-based image analysis systems

Int. J. Robotic Res.

(1982)

Cited by (86)

Trustworthy remote sensing interpretation: Concepts, technologies, and applications
2024, ISPRS Journal of Photogrammetry and Remote Sensing
Geographic spaces is a vast and complex system involving multiple elements and nonlinear interactions of these elements, and rich in geographical phenomena, processes and patterns. Artificial intelligence methods (AI) are increasingly utilized to extract information of interest, patterns and insights from massive remote sensing (RS) images. Among them, two representative paradigms for RS interpretation are knowledge-driven symbolism and data-driven connectionism. Knowledge-driven approaches are certain and theoretically sound, which consider the regularities, formulae with accompanying theorems, and expert knowledge. However, it is difficult to address huge-scale RS data and complex nonlinear problems. The data-driven paradigm has stronger data learning and representation ability, yet poor interpretability, low trustworthiness of interpretation results, and reliance on massive labeled data. To address these limitations, we argue that current RS intelligent interpretation requires the guidance of geographic ideas and a unified theoretical framework rather than the independent use of remotely sensed data, duplicative development of interpretation models, or isolated applications of geographic information extraction. Thus, we introduce the concepts of trustworthy RS interpretation (TRSI) with multi-granularity of space, time, and attribute to facilitate the understanding of the morphology, types, and indicators (visual perception) and the perspective of the structure, evolution, and trends (geographical cognition) of complex geographical space. Second, integrated technologies of the TRSI with multiple scales and stages are developed, including a pixel-level visual perception, a geo-parcel-level quantitative inversion, and a scene-level geographical cognition. Finally, a RS big data interpretation system with quantitative semantic parsing is designed to support precise applications with cloud–edge-end collaboration. Rather than a simple overview and accumulation of traditional RS intelligent interpretation methods, this paper analyzes current challenges in RS interpretation and proposes new concepts, technologies, and applications of TRSI. It will empower us to perceive the complex geographical space comprehensibly and reveal its structural composition and evolutionary mechanism, serving geospatial decision applications such as evaluation, planning, and forecasting.
Deep learning with satellite imagery to enhance environmental enforcement
2021, Data Science Applied to Sustainability Analysis
In this chapter we highlight how rapid advances in computer vision and the increasing availability of high-resolution satellite imagery have facilitated more accurate, efficient, and scalable environmental monitoring and regulation. First, we highlight the range of potential use cases of remote sensing with satellite imagery in environmental enforcement. Second, we describe the methodological evolution from manual learning on satellite imagery, to model-based inference largely based on pixel-by-pixel classification, to deep learning. Third, we provide an in-depth case study, illustrating how deep learning with satellite imagery can solve a problem that has vexed the Environmental Protection Agency for decades: the identification of Concentrated Animal Feeding Operations (CAFO), which pose substantial environmental risk. Last, we highlight the data infrastructure, modeling, and capacity challenges that must be overcome to facilitate this profound shift in the evidence base for environmental enforcement.
Human-computer interaction for the generation of image processing applications
2011, International Journal of Human Computer Studies
Citation Excerpt :
Constraints may be associated with the task to fine-tune its scope. For example, a request for the Multimission VICAR Planner (MVP) system is just “radiometric correction” (Chien and Mortensen, 1996) and a request for the Low-Level Vision Expert (LLVE) system is more detailed “find a rectangle whose area size is between 100 and 200 pixels” (Matsuyama, 1989). Specification by example.
The development of customized image processing applications is time consuming and requires high level skills. This paper describes the design of an interactive application generation system oriented towards producing image processing software programs. The description is focused on two models which constitute the core of the human–computer interaction. First, the formulation model identifies and organizes information that is assumed necessary and sufficient for developing image processing applications. This model is represented as a domain ontology which provides primitives for the formulation language. Second, the interaction model defines ways to acquire such information from end-users. The result of the interaction is an application ontology from which a suitable software is generated. This model emphases the gradual emergence of a semantics of the problem through purely symbolic representations. Based on these two models, a prototype system has been implemented to conduct experiments.
Learning-based algorithm selection for image segmentation
2005, Pattern Recognition Letters
Segmentation of nontrivial images is one of the most important tasks in image processing. It is easy for human being, but extremely difficult for computers. With the purpose of finding optimal segmentation algorithm for every image through learning from human experience, this paper investigates the manual segmentation process and thus presents a performance prediction based algorithm selection model to bridge the knowledge gap between images and segmentation algorithms. Derived from that model, a framework of learning-based algorithm selection system is proposed to automatically segment all images in a large database. A simulation system is designed to select the optimal segmentation algorithm from four candidates for synthetic images. The system is tested on 9000 images by comparing with the manual algorithm selection. The best algorithms are selected for 85% of the cases. If we also regard the second best algorithm as acceptable, more than 97% of images can be properly segmented. The satisfied result demonstrated that this study has provided a promising approach to achieve automated image segmentation.
Multi-agent segmentation of IVUS images
2004, Pattern Recognition
A novel multi-agent image interpretation system has been developed which is markedly different from previous approaches in especially its elaborate high-level knowledge-based control over low-level image segmentation algorithms. Agents dynamically adapt segmentation algorithms based on knowledge about global constraints, contextual knowledge, local image information and personal beliefs. Generally agent control allows the underlying segmentation algorithms to be simpler and be applied to a wider range of problems with a higher reliability.
The agent knowledge model is general and modular to support easy construction and addition of agents to any image processing task. Each agent in the system is further responsible for one type of high-level object and cooperates with other agents to come to a consistent overall image interpretation. Cooperation involves communicating hypotheses and resolving conflicts between the interpretations of individual agents.
The system has been applied to IntraVascular UltraSound (IVUS) images which are segmented by five agents, specialized in lumen, vessel, calcified-plaque, shadow and sidebranch detection. IVUS image sequences from 7 patients were processed and vessel and lumen contours were detected fully automatically. These were compared with expert-corrected semiautomatically detected contours. Results show good correlations between agents and expert with r=0.84 for the lumen and r=0.92 for the vessel cross-sectional areas, respectively.
Object extraction and revision by image analysis using existing geodata and knowledge: Current status and steps towards operational systems
2004, ISPRS Journal of Photogrammetry and Remote Sensing
The paper focuses mainly on extraction of important topographic objects, like buildings and roads, that have received much attention the last decade. As main input data, aerial imagery is considered, although other data, like from laser scanner, SAR and high-resolution satellite imagery, can be also used. After a short review of recent image analysis trends, and strategy and overall system aspects of knowledge-based image analysis, the paper focuses on aspects of knowledge that can be used for object extraction: types of knowledge, problems in using existing knowledge, knowledge representation and management, current and possible use of knowledge, upgrading and augmenting of knowledge. Finally, an overview on commercial systems regarding automated object extraction and use of a priori knowledge is given. In spite of many remaining unsolved problems and need for further research and development, use of knowledge and semi-automation are the only viable alternatives towards development of useful object extraction systems, as some commercial systems on building extraction and 3D city modelling as well as advanced, practically oriented research have shown.

View all citing articles on Scopus

^☆: An earlier version of this paper was presented at 9th International Conference on Pattern Recognition, Rome, Italy, November 1988.

^†: Current address: Department of Information Technology, Okayama University, Okayama, 700, Japan.

View full text

Expert systems for image processing: Knowledge-based composition of image analysis processes☆

Abstract

Comput. Vision Graphics Image Process.

Pattern Recognit.

Artif. Intell.

Comput. Graphics Image Process.

An expert system for designing image analysis programs based on software package

An expert system for image processing

Fujitsu Sci. Tech. J.

DIA-Expert system and its knowledge representation scheme

J. ISP Japan

Automatic generation of image processing programs

Low level image segmentation: An expert system

IEEE Trans. Pattern Anal. Mach. Intell.

LLVE: An expert system for top-down image segmentation

J. IPS Japan

IMPRESS: A system for image processing procedure construction based on sample-figure presentation

Trans. IEICE Japan

Special Issue on Expert Systems for Image Processing

J. ISP Japan

Reasoning about Success and Failure in Aerial Image Understanding

Survey of model-based image analysis systems

Int. J. Robotic Res.