Foreword to the Special Issue on. Recognition ... Special Issue give a flavor of ongoing work. .... used as a chair (such as a rock that someone is sitting on), but.
IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 9, NO. 1, FEBRUARY 2001
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Guest Editorial— Foreword to the Special Issue on Recognition Technology
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UZZY systems are beginning to play significant roles in pattern recognition systems. These systems go beyond laboratory experiments performed on computers with small data sets; they involve large-scale developments on very large data sets from real systems. The research and development activities that are undertaken to produce such recognition systems often involve interaction between universities, industries, and government agencies. Fuzzy sets have moved from small-scale experimentation using data sets with size on the order of hundreds of samples to real-world applications with significant data sets containing tens or hundreds of thousands of samples. The papers in this Special Issue give a flavor of ongoing work. This type of work is very experimental and does not necessarily involve developing new theories. It can be thought of as applied or experimental research in fuzzy sets and is very important in understanding how fuzzy sets can help solve recognition problems. Experimental research in fuzzy sets is also time-consuming and labor-intensive. From my own experience, I can say that creating and managing experiments with data sets consisting of tens of thousands of handwritten characters and words, or hundreds of thousands of Ground Penetrating Radar measurements for land mine detection, takes a very large and continuous effort. I believe that we as a community must realize that experimental work in recognition technology is useful and important, and that it requires a different level of standard in terms of publishing. Planning and conducting experiments on large-scale data sets requires careful thought and methodology; months can be lost because of bad decisions. Methodological considerations, and not just new theoretical developments, become a large part of what reviewers should consider when judging a manuscript in this area. I hope that we can see more papers on applied fuzzy set research in the next few years. Professor Lotfi Zadeh has graciously provided us with a Forward to the Special Issue. His role as a visionary in the world of fuzzy sets that he created several decades ago continues to this day. He coined the phrase “Recognition Technology” and defined it as referring to current or future systems that have the potential to provide a “quantum jump in the capabilities of today’s recognition systems.” Zadeh states that this can occur as a result of three converging developments: 1) major advances in sensor technology; 2) major advances in sensor data processing technology; and 3) the use of soft computing techniques to infer a conclusion from observed data.
Publisher Item Identifier S 1063-6706(01)01373-X.
Some of the papers in this Issue address all three converging developments whereas others address a subset. Recognition of shapes in images or two-dimensional arrays is a primary topic of interest in Recognition Technology. Lazzerini and Marcelloni describe an automated method for building a fuzzy description of shapes. They apply it to two real-world problems: recognition of odors by electronic noses and handwriting recognition. Handwriting recognition systems are one of the best examples of application of fuzzy sets to recognition systems because fuzzy systems naturally model much of the ambiguity present in written language. The authors use a well-known large data set collected from writers across the United States by National Institute of Standards and Technology (NIST). Electronic noses are important new technologies that can play a role in many systems, including the detection of buried unexploded ordnance (UXO) and land mines. Interestingly enough, recognition systems under development for those problem domains are described in two other papers in this Issue. Collins et al. describe a variety of methods for classifying buried objects as either UXO or “clutter” (buried objects that will not explode). The data they analyze are acquired using Electro-Magnetic Induction and Magnetometer sensors. The authors investigate a variety of methods, including Bayesian methods, neural networks, and fuzzy clustering. The results are interesting and provide insight into the applicability of different methods in real-world applications. The related problem of land mine detection is discussed by Gader et al. In that paper, fuzzy methods are used to combine multiple sources of information. Each information source provides evidence, in terms of a confidence measure, reflecting the confidence that a land mine is buried beneath a certain location on the ground. The information sources are algorithms, relying on soft computing, that are applied to data acquired with a Ground Penetrating Radar sensor. Blind test results compare the use of different methods based on fuzzy logic and fuzzy integrals. Many of the methods for land mine and UXO detection and classification are derived or related to the field of Automatic Target Recognition (ATR). The field of ATR has been widely studied for at least two decades but with no fielded success. The lack of success can be attributed to many reasons including (but not limited to) lack of adequate sensors and the lack of appropriate mechanisms for incorporating expert knowledge. One type of expert knowledge that can be included in an ATR algorithm is the knowledge of which features are most useful for a given application. Pal et al.study this problem in a set of experiments using data acquired from a new type of sensor, referred to as a LAser raDAR (LADAR). The LADAR data was acquired from a flying platform in an outdoor environment and thereby
1063–6706/01$10.00 © 2001 IEEE
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represents difficult, real-world data. Extensive experimentation was performed to analyze the performance of the fuzzy feature set selection methodology. Fuzzy logic provides a mechanism for incorporating expert knowledge into recognition systems. Nelson explores the utility of fuzzy rule bases for detecting vehicles using data acquired from an infrared imaging sensor. Results are provided on a “blind” sequence of images, demonstrating the robustness of the approach. One difficulty with object recognition in images is the inability to “define” the objects in question. The classic example is the object class called “chair.” How does one define what makes a chair a chair? Most of us can recognize a chair when we see one, or we can recognize that an object is being used as a chair (such as a rock that someone is sitting on), but we are unable to come up with a satisfactory crisp definition of chair. The problem of definability is discussed by Zadeh in his Forward to the Special Issue and the readers may relate the specific work of Nelson in “defining” vehicles in infrared images to Zadeh’s general discussion. Fuzzy inference systems were used in a quite different recognition problem by Davidson et al. They seek to build a system that could serve to replace humans in the automated inspection of food products. They require a recognition system that can assign ratings to food that are consistent with those assigned by consumers. Of course, consumers can have a high degree of variability and even conflict in their ratings (what looks like a good cookie to consumer A may not look good to consumer B). Fuzzy
IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 9, NO. 1, FEBRUARY 2001
rules were chosen for investigation because of their ability to handle variability and conflict. Several rule-bases were considered, including Mamdani systems which were developed using a blend of poll distributions and heuristics and Takagi–Sugeno systems developed using ANFIS. The last paper by Martins et al. deals with a different problem: that of recognizing experimental drives. The authors discuss the complexity of the drives and the resulting difficulties in applying traditional modeling techniques. They also discuss their perception of the difference between system identification and modeling of dynamical systems using pattern recognition techniques. A comparison is provided between the modeling capabilities of fuzzy logic and formal language techniques. I thank all the authors who submitted manuscripts to this Special Issue and reviewers for their thorough reviews. The reviewers of the IEEE TRANSACTIONS ON FUZZY SYSTEMS have always set a high standard for publication and this Issue was no exception. All papers were reviewed and the review process was quite intensive; a number of interesting papers did not receive sufficiently high marks for inclusion in the Special Issue.
PAUL D.GADER, Guest Editor University of Missouri-Columbia Department of Computer Engineering and Computer Science Columbia, MO 65211 USA
