A Neural-Fuzzy Description of Ambiguous Figures in Visual Message

Transcription

1 A Neural-Fuzzy Description of Ambiguous Figures in Visual Message Chi-Wei Lee*, Jalin Ko Tsung Huang** *Yuan Ze University C/o Department of Information Communication 135 Yuan-Tung Rd., Chung-Li, Taoyuan 320, Taiwan ** Yuan Ze University C/o Department of Information Communication 135 Yuan-Tung Rd., Chung-Li, Taoyuan 320, Taiwan Abstract: Message design has been a complicated issue in recent year, not only because the transferring mode can be used to convey a whole body of information, but also because the receiving perceptions operate within the meaningful and cultural boundaries. Visual message, which involves visible format of information, commonly are carried by graphic design. The design of thinking with visual messages, however, can easily be distorted from its original designated meaning into a contrary one, and its power to communicate can therefore be twisted. In visual graphic, the contained messages delivered to audiences have traditionally been highly dependent on individual experience. From a graphical-oriented perspective, designed messages have not usually been concerned with audience-centered problems. Visual language can only reflect the designer s own aesthetics, so that the audience is sometimes considered secondary to the artwork itself. Beyond conveying meaningful visual languages in the designed objective, it is an intention matter in this information-exploring environment. Based on the perspective of message design, this paper is going to present the research of ambiguous figures in visual message. Using fuzzy description of illusion scheme, the content elements of graphics can be explained by represented certain inferences. And, these visual grasps reflect the perception of meaningful information. The study possibly provides a better understanding of graphic approaches in designing messages while we plan meaningful information in the future. Key words: ambiguous figure, fuzzy theory, neural network, perception, cognition 1. Introduction Visual information processing and design is an important research topic in cognitive science that are interdisciplinary. It has been investigated in psychology, artificial intelligence, robotics, physiology and biophysics. Connectionism mechanism such as neural network provides a methodology for cognitivists and engineers to work and think together about the integration of cognition and artificial intelligence. Visual message or information, when conveyed from the designer to the receiver, faces problems of avoiding distorted and ambiguous information or producing ambiguous information on purpose (ambiguous figures in optical illusions). To study the problems, approaches in artificial intelligence such as fuzzy theory and neural network are introduced in this paper. The computational analysis of visual perception is also proposed to provide message designers a procedural tool to examine and interpret multiple meaning when designing messages.

2 1.1. Computational Cognitivism Cognitive science is the interdisciplinary study of cognition. Some cognitive scientists consider that the mind is to the brain as software is to hardware; mental states and processes are like computer programs implemented in brain states and processes. According to the computational view of cognitive science, (1) there are mental states and processes intervening between input stimuli and output responses, (2) these mental states and processes either are computations or else are computable, and hence (3) in contrast to behaviorism, mental states and processes are capable of being investigated scientifically. (Rapaport, 1998) Even cognitive scientists who disagree about the computational view of the mind are usually willing to agree that computer programs force cognitive scientists to make intuitions explicit and to translate vague terminology into concrete proposals; they provide a secure test of the consistency of a theory ; they are working models whose behavior can be directly compared with human performance (Johnson-Laird, 1981; Pylyshyn, 1985). That is, the proper methodology of cognitive science is to express one s theories about (human) cognition in a computer program and then to compare the program s behavior with (human) cognitive behavior (Rapaport, 1998) Neural Network The first step toward artificial neural networks came in 1943 when Warren McCulloch, a neuro-physiologist, and a young mathematician, Walter Pitts, wrote a paper on how neurons might work. They modeled a simple neural network with electrical circuits. Frank Rosenblatt, a neuro-biologist, began to work on the perceptron in The perceptron, which resulted from this research, was built in hardware and is the oldest neural network still in use today. In 1959, Bernard Widrow and Marcian Hoff developed a model based on their MP neuron, which was the first neural network to be applied to a real world problem. It is an adaptive filter which eliminates echoes on phone lines. This neural network is still in commercial use. (Anderson & McNeil, 2003) Rosenblatt summarizes the basis of his work on perceptron theory perceptron theory in the following assumptions: (Rosenblatt, 1958) 1. The physical connections of the nervous system which are involved in learning and recognition are not identical from one organism to another. At birth, the construction of the most important networks is largely random, subject to a minimum number of genetic constraints. 2. The original system of connected cells is capable of a certain amount of plasticity; after a period of neural activity, the probability that a stimulus applied to one set of cells will cause a response in some other set is likely to change, due to some relatively long-lasting changes in the neuron themselves. 3. Through exposure to a large sample of stimuli, those which are most "similar" will tend to form pathways to the same sets of responding cells. Those which are markedly "dissimilar" will tend to develop connections to different sets of responding cells. 4. The application of positive and/or negative reinforcement may facilitate or hinder whatever formation of connections is currently in progress. 5. Similarity is represented at some level of the nervous system by a tendency of similar stimuli to activate the same sets of cells Connectionism Connectionism is a computational approach to modeling the brain which relies on the interconnection of many

3 simple units to produce complex behavior. Connectionism mechanism provides a methodology for cognitivists and engineers to work and think together about the integration of cognition and artificial intelligence. The connectionist (or neural network, or parallel distributed processing) approach to artificial intelligence and computational cognitive science can be seen as one way for a system to behave intelligently without being a symbol system and yet be computational. On this approach, large numbers of very simple processors ( nodes ) are connected in multiple ways by communication links of varying strengths. Input nodes receive information from the external world. The information is propagated along the links to and among intermediate (or hidden ) nodes, finally reaching output nodes. If the output is not what was expected, the strengths of the links are adjusted. This process is repeated until the system settles down into a stable configuration that exhibits the desired (cognitive) behavior. Connectionist systems and techniques have been developed for learning features of natural language, for aspects of visual perception, and for a number of other cognitive (as well as non-cognitive) phenomena. Rather than having intelligence programmed into the system using explicit rules and representations, intelligence is sometimes held to emerge from the organization of the nodes and links. (Rapaport, 1998) 2. Fuzzy Theory 2.1. The Concept of Fuzzy In using our everyday natural language to impart knowledge and information, there is a great deal of imprecision and vagueness. Such statements as John is tall and Marry is young are examples expressing fuzziness. Traditional Von Neuman computers are capable of processing precise commands like Start the air-conditioner if the temperature is over 30 C. However, the control mechanism is too strict since there is no obvious difference between 30 C and 31 C for human. To deal with the vagueness in this kind of information for computers, L. A. Zaldeh proposed the concept of fuzzy theory in 1965 (Zaldeh, 1965). Fuzzy theory is a mathematical tool to be used to make machines that mimic human reasoning, which is usually based on uncertain and imprecise information, has captured the attention of many scientists and cognitivists. Related academic topics such as fuzzy set, fuzzy logic, fuzzy inference, etc., were developed and fulfilled in both theory and practice (Yan, etc., 1994; Buckley & Feuring, 1999). Now, fuzzy theory is embedded in many products. It is broadly adapted to computer chips, washing machines, air-conditioners, refrigerators, automobile brakes and other industrial designs. With the success of automatic control and intelligent systems, we are now witnessing and endorsement of fuzzy concepts in technology Fuzzy Logic In a traditional Von Neuman computer, the process function is based on two-valued logic, which is either one (true) or zero (false). An example of two-valued logic process function of young can be seen in Fig. 1. The degree of membership function is exactly equals to one (true) if the age of a person is no more than thirty and equals to zero (false) otherwise. The figure can not represent the concept of matter of degree in the meaning of the expression young.

4 1 young 1 young old Membership function Membership function Age Age Fig. 1 Membership function of two-valued logic definition of young Fig. 2 Membership function of multi-valued logic definition of young For multi-valued logic, which is the basic concept of fuzzy theory, the definition of young is more natural and realistic. In Fig. 2, the degree of membership function of young decreases and that of old increases as age increases. We can say that the degree of young of a thirty-year-old person is larger than that of a forty-year-old person. And a thirty-five-year-old person is 50% young and 50% old Fuzzy Cognitive Map Cognition can be considered as mental interpretation of an environment involves fuzzy factors. The fuzzy factors themselves have fuzzy relationships. Kosko (1993) described the fuzzy relationships among fuzzy factors in a fuzzy cognitive map. Fig. 3 is the fuzzy cognitive map for evaluating driving speed on a freeway. It indicates important fuzzy factors and how they affect the driving speed of a driver. We can see in the figure that freeway congestion increases some auto accidents. Due to increased auto accidents, the probability of freeway congestion often gets higher and reduces much patrol frequencies. If patrol frequencies is high enough, maybe auto accidents can be reduced a little. In addition, freeway congestion and own risk aversion can slow down own driving speed. FREEWAY CONGESTION VERY MUCH BAD WEATHER ALWAYS - SOME OFTEN OWN RISK AVERSION SOME OWN DRIVING SPEED A LITTLE - USUALLY AUTO ACCIDENTS - ALWAYS PATROL FREQUENCIES Fig. 3 Fuzzy cognitive map for evaluating driving speed on a freeway MUCH A LITTLE - 3. Fuzzy Description of Illusion Scheme 3.1. Ambiguous Figures An ambiguous figure is a figure that represents multiple meanings in human cognition. Ambiguous figures have long fascinated artists, children, and others who enjoy surprises. The characteristic of ambiguity challenges normal conceptions of truth. On the other hand, it provides the basis for an appreciation of the creativity inherent in all humans. It also represents an important connection between scientific and other ways of making

5 sense of the world in which humans find themselves. One of the most challenging questions of human visual perception is how the brain creates a unified perspective based on various visual signals. Some visual figures are interestingly ambiguous and can be seen in two perspectives. These ambiguous figures can be used to learn how the human brain forms a perspective. Rubin vase (Fig. 4) and Young girl or old lady? (Fig. 5) are well-known examples of ambiguous figures. Fig. 4 Rubin vase Fig. 5 Young girl or old lady? 3.2. Visual Information Processing Preattentive processing of visual information is performed automatically on the entire visual field detecting basic features of objects in the display. Such basic features include colors, closure, line ends, contrast, tilt, curvature and size (Treisman, 1986). These simple features are extracted from the visual display in the preattentive system and later joined in the focused attention system into coherent objects (Treisman, 1985). Preattentive processing is done quickly, effortlessly and in parallel without any attention being focused on the display. Treisman (1986) also found support for the theory that some properties are coded as values of deviation from a null or a reference value. Thus, the more features an object has, the more likely it will pop out. Gestalt psychologists Max Westheimer, Kurt Koffka and Wolfgang Kohler derived Gestalt laws to explain perception. The Gestalt laws describe the influence of global context. Elements tend be perceptually grouped and made salient if they are close to each other (proximity), similar to one another (similarity), from a continuous contour (continuity), form a closed contour (closure), have spatial symmetry (symmetry), or have figure and ground relationship (figure and ground) (Ware, 2000). Though many researchers pointed out the figure-and-ground property in ambiguous figures, detailed perception mechanisms are still unclear. Machine-based computational cognitivists and engineers are struggling to make computers smarter in visual reasoning and perception Ambiguous Figures in Visual Message Fuzziness arises when we look at the figure and the interpreted images in our brains may occur simultaneously or in turn. To explain the fuzzy perception of an ambiguous figure, the mental interpretation can be considered as computational cognition of evaluating the total influence of key fuzzy areas in the figure. A key fuzzy area is a part of the figure having vague meaning that plays an important key in the mental interpretation process than the other parts of the figure. Take Rubin vase (Fig. 6) for example, area A is the key fuzzy area. When a person is looking at area A, if his/her attention is focused on the white part, then the figure has more probability to be perceived as a vase. On the other hand, if his/her attention is focused on the black part, he/she sees two faces. The membership function of

6 the key fuzzy area in Rubin vase indicating the degree of similarity is shown in Fig. 7. Since the focus may be switched between white part and black part of the figure, the perception of the key fuzzy area can be either a vase or two faces. 1 vase faces A Membership function 0 Focus on white Focus on black Focus Fig. 6 Fuzzy key areas in Rubin vase Fig. 7 Fuzzy key areas in Rubin vase The fuzzy cognitive map of Rubin vase is shown in Fig. 8. The key fuzzy area A plays a key role in determining the perception result. If a certain person, in a certain period of time, perceives area A as vase with 0.65 degree of similarity and two faces with 0.35 degree of similarity, then the result of perception will be a vase since it has higher degree of similarity. Either one of the two interpretations of area A has negative effect on its opponent interpretation. The interpretation vase causes more negative effect than two faces because interpretation vase receives higher degree of similarity. In this case, vase perception prevents further interpretation of the entire image as two faces. Finally the perception result is more likely to be perceived as a vase by the person in that period of time. However, if the person changes his/her viewpoint (perhaps influenced by somebody or a hint) in another period of time, the degree of similarity may vary and the perception result differs. Young girl or old lady? in Fig. 9 is a more complicated example. There are several fuzzy key areas involving the perception process: area A (can be perceived as cheek or nose), B (ear or eye), C (necklace or mouth) and D (chest or jaw). It takes longer to unravel the two images in the ambiguous figure. Since the influence mechanism in the fuzzy cognitive map is more complex, it is reasonable for a person to take more time to adjust the degree of similarity for all fuzzy key factors in mind so that both images perceived become stable and clear. In Fig. 10, a person in a certain period of time perceives fuzzy key areas A, B, C and D as cheek, ear, necklace and jaw with 0.9, 0.7, 0.6 and 0.8 degree of similarity respectively. Though the perception of area D as jaw supports the perception result as old lady with higher degree of similarity (0.8) as compared to young girl (0.2), the final perception result is determined by the summation of degree of similarity of all fuzzy key factors instead of a single factor. In this case, the total degree of similarity of young girl and old lady are 2.4 and 1.6. The person perceives the ambiguous figure as a young girl at that moment. In most cases, the total degree of similarity of two images in an ambiguous figure may be very close. Both images will be perceived when a person tries to interpret the figure. Furthermore, the perception of fuzzy key areas for a person tend to be dynamically biased by hints following the figure, explanation by other people or experience in daily life, thus the perception result varies and unstable. A A: 0.65 vase 0.35 A: two faces Result Fig. 8 Fuzzy cognitive map of Rubin vase

7 A C A: cheek A: nose A B B C B: ear B: eye C: necklace C: mouth Young girl Old lady Result D D D: chest D: jaw Fig. 9 Fuzzy key areas in Young girl or old lady? Fig. 10 Fuzzy cognitive map of Young girl or old lady? 4. Conclusions Based on the perspective of message design, this paper presents the research of ambiguous figures in visual message. Using fuzzy description of illusion scheme, the content elements of graphics can be explained by represented certain inferences. And, these visual grasps reflect the perception of meaningful information. This study provides a visual design approach combining cognitivism and computational model for intelligent machine analysis and production of visual messages with illusion scheme. The two research fields, cognitive science and computer science, merged together possibly introduce an interesting and perhaps a better understanding of graphic approaches in designing messages in planning meaningful information in the future. References 1. Anderson D. & McNeil G., (2003). 2. Johnson-Laird, Philip N. & Ruth M. J. Deduction. NJ: Lawrence Erlbaum Associates (1991). 3. Kosko B. Fuzzy thinking. New York: Hyperion (1993). 4. Pylyshyn Z. Computation and Cognition: Toward a Foundation for Cognitive Science; 2 nd edition (1985). 5. Rapaport W. J. Cognitive Science. Encyclopedia of Computer Science, 4 th edition, New York (1998). 6. Rosenblatt F. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Cornell Aeronautical Laboratory, Psychological Review, 65(6), (1958). 7. Treisman A. Preattentive processing in vision. Graphics- and image-processing, 31(2), (1985). 8. Treisman A. Features and objects in visual processing. Scientific American, 255(5), (1986). 9. Ware C. Information visualization: perception for design, California: Academic Press (2000). 10. Zadeh L. A. Fuzzy sets. Information & Control, 8, (1965). 11. Yan J., Ryan M. & Power J. Using fuzzy logic. New Jersey: Prentice Hall Inc (1994). 12. Buckley J. J. & Feuring T. Fuzzy and neural: interactions and applications (1999).