IDENTIFICATION OF STIMULI

Abstract: Method and device for identification of stimuli, in which one or various identification methods in combination can be applied. Here, stimuli in their original manifestation, as well as stimuli positions and orientations are determined and it is ascertained which stimuli respondents have in their field of vision, from which moment, for how long and under which circumstances, as well as the way in which they handle and manipulate them.

Description: IDENTIFICATION OF STIMULI The invention relates to a method and device for iden¬ tification of stimuli. Such a method is described in NL-A- 1002854, which was filed simultaneously with the present application. In this method, the intention is to identify stimuli as well as stimuli positions and orientations. The error margin in this method is partly connected to the comparison algo¬ rithm applied for said identification, as well as to the determination of the instances of measurement. The object of the invention is to provide a different method by which the correct identification can be realized in nearly all cases. Such a method according to the inventi¬ on is characterized in that one or more different methods of identification can be applied in combination. Methods each having their specific advantages and disadvantages, together yielding a smaller error margin than each on its own. Further, the method according to the invention is characterized in that a flexible measuring frequency is employed, in which the instance of measuring can be made dependent of conditions. Further, the method according to the invention is characterized in that measuring sensors, being part of the device, not only record an image of a stimulus once every instance of measurement, but at the same record semi-continuous stimuli data, so as to immediately and/or afterwards select the data best suitable to be used for stimuli identification. Furthermore, the method according to the invention is characterized in that contours of the stimuli to be identi- fied are determined and are divided in segments which are applied for determining the positions, movements, rotations, orientations and/or distortions of the stimuli. Further, the method is characterized in that the possible identifications determined by the various algorithms together, including the most likely one, are additionally checked by way of a series of conditions relating to logical connections. The final identification concerns the stimulus having the highest position in a probability value order at the same time meeting at least one established part of the tested conditi¬ ons. The stimuli that can be determined practically error- free, concern printed matter, illustrations, photos, texts, instructions, manuals, etc., in printed media, including magazines, dailies, specialist journals, brochures, flyers, free local printed matter, DM-material, books, guides, etc. Yet also products, product components, product concepts, models, e.g. of cars, and designs, as well as packages and other threedimensional objects. The method according to the invention concerns a stimu¬ li classification and read in method, which detects any variant of stimuli exposure beforehand and stores it in such a way, that comparison with a read-in stimulus enables an error-free recognition within a short time, e.g. wihtin one second, as well as an error-free determination of position and orientation of the read-in stimulus. In this way, it is possible to detect e.g. manipulations of stimuli by respon¬ dents, such as with printed matter, the way of holding it and/or flipping the pages. Yet also the handling and manipu¬ lation of objects, e.g. turning objects for the purpose of observing it at various viewing angles. Recognizing stimuli requires previous read in and as a consequence of this, knowledge of stimuli characteristics and subsequently, on the basis of comparison methods and algorithms, recognizing of stimuli presented for identifica¬ tion. With the method according to the invention in particu¬ lar, several different methods, each having their specific advantages and disadvantages, are applied in combination, together resulting in a smaller error margin than each on its own. To that end, the combination of said methods is such, that the disadvantages of the individual methods are compensated, whereas the advantages are combined. Recognizing objects in 3 dimensions is based on the same method in which characteristics of each variant of exposure are read in beforehand. The device according to the method consists of one or more sensors by which characterizing properties of stimuli are recorded. The system operates both on the identification of a stimulus in its entirety and on partly non-visible stimuli, including partly covered stimuli. For example, a partly opened magazine or daily will already sufficient to be able to determine the position, orientation and the number of that specific page with certainty. Rotations up to 180° are tolerated. One or some parts of 3-dimensional objects, exposed completely or partly during manipulation, will be sufficient for identification of said objects. The method according to the invention is an indispensa¬ ble part in the chain of activities by means of which the massive measurement of respondent reactions to massive amounts of stimuli, and parts of stimuli, can be executed. In a general sense, said method can be applied to many types of stimuli, such as previously mentioned and speci¬ fied, including 3-dimensional objects, yet also to the identification of characteristics of human beings and ani¬ mals, such as e.g. faces and postures, also with facial decorations changed by time, such as glasses, moustache and hair, not by applying a most critical identification stand¬ ard, but by a standard relating to the data lying underne¬ ath. The method is also applicable for identification of movements and characteristics of dynamic actions, e.g. movements of human beings and animals, including natural movements and facial expression; but also for mechanically produced dynamics, such as movements of apparatus and acti¬ ons of robots. The thus obtainable data can be applied e.g. for the benefit ergonomic analyses. The method according to the invention can further be applied with all projects, whether or not industrial, where a quick identification of objects, place and orientation is desired, e.g. with automated production. By way of example, a method, which can be followed together with the method and the system according to the invention for identification of stimuli and determination of the positions and orientations of said stimuli, will now be indicated step by step. For the sake of clarity, the steps in the example are illustrated by way of printed matter stimuli in the form of pages as those of a magazine. Reading in images, and producing characteristics, of several views on a three-dimensional object can e.g. be regarded as a ranking of stimuli, such as e.g. the pictures of films, or, for the example given below, as the various pages of a magazine. Steps with reading in reference stimuli beforehand and producing the accompanying characteristics: 1. Positioning said stimuli on a presentation surface such as placing a magazine in a fixed position on a reading table. 2. Leafing through said magazine page by page, in which each page is read in as an individual stimulus and is subsequently recorded and classified, its characteristics being stored in such a way, that later comparison to that of a stimulus to be identified is possible within a short time, e.g. within one second. Steps with semi-continuous recording of images and producing the characteristics of stimuli to be identified: After reading in the characteristics of the identifica- tion stimuli beforehand, e.g. respondents are given the opportunity to leaf through the "learned" magazine. This can be done page by page, from beginning to end, but e.g. also arbitrarily, depending on the preferences and interests of respondents. 3. Semi-continuous recording of images and subsequent storage in a buffer having a storage capacity of e.g. a few hundred images with a total time of e.g. a number of se¬ conds. The buffer has a cyclic structure, for example: it always contains the most recently recorded images. When the full capacity is used, the "oldest" image is removed from the buffer and the "latest", most recent image is added to the buffer. 4. Per time interval, e.g. shorter than the buffer time, directly and/or afterwards selecting from the semi- continuous recorded images the image best suitable for use in stimuli identification. On the basis of the extent of the image information and the quality of said information for identification, for example. With other methods, e.g. one image per page is recorded in an instance which e.g. is determined by a preset measu¬ ring frequency. Such methods do not take into account the individual leafing behaviour of respondents. For example, if e.g. a page is not released, and as a result may be moving continuously, an image could be "moved", to such an extent that the image information will not be suitable for identi¬ fication purposes. However, with the method according to the invention, semi-continuous recordings of the same pages are made e.g. as long as they are lying open. From the recorded images, directly and/or e.g. per buffer time, one determines the most suitable image, by means of which the identification will be executed. The best image is selected directly and/or per buffer time. Identification takes place on all thus selected ima¬ ges. Several of these images can relate to one and the same stimulus, stimulus view and/or parts of it to be identified. In addition, the starting time at which the pages are opened is determined accurately, as well as the length of time during which said pages remain opened and the stimuli can thus be visible for respondents. Depending on whether or not a, e.g. predetermined, maximum time to be spent, e.g. per stimulus, per page, per double page or per magazine, a signal can be produced by a means of a signalling device, e.g. a loudspeaker. For exam¬ ple, for leafing further, or, generally, handling another object. 5. Determining the contours of an object per image, e.g. on the basis of density value transitions. For an opened magazine this means: determining the outer contour of e.g. the opened double page. 6. Dividing the contours into segments. 7. Determining the location, movements and transforma¬ tion of each of the segments from subsequent semi-continuous images recorded by the sensors. Here, among other things, the segments play the role of "movement detectors" and are situated with reason along the contour of the magazine. After all, on turning a page, it can already be moving at the edge, whereas the middle of the page is still. Therefore, movement detection preferably takes place at the edge of the magazine. From the shape of the segments, it can be deducted if pages are lying open; from the transformations it can be deducted if, and to what extent, pages are partly covered, and also if, and in what direction, pages are turned. 8. Determining the position, rotation, orientation of the pages and with that of the magazine, on the basis of the geometry of said segments. The method according to the invention is characterized in that before the image contents are involved in the actual identification, the stimulus position, rotation and orientation are always determined accurately. Steps for the purpose of identication of stimuli 9. Selecting the available stimuli previously stored for identification, on the basis of the geometry of the contour segments of the stimulus read-in. 10. Dividing the visible part of the pages being within the contours into one or more planes, such as rectangles, polygons or circles, including concentric circles, e.g. with identical surfaces. For example, 9 planes per page, 18 planes for the double page. Determining the monochrome density value characteristics per plane. For example, the average, minimum, maximum and/or most frequent density value, e.g. a distribution, including a histogram distribu¬ tion, of the monochrome density values present in the read- in image. For a double page divided in 18 planes, this yields 18 times a ranking of comparison values. If the rankings are spectrally divided and are individually composed, e.g. for the colours red, green and blue, this yields 3 x 18 = 54 rankings of spectral density comparison values. 12. Comparing the comparison values of the stimulus to be identified with the predetermined comparison values of the identification stimuli available for comparison. In the comparisons, individual and respective values are compared to each other and the numerical differences thereof are determined. The numerical differences are summa- ted, resulting in the "error value" of the stimulus to be identified with one of the reference stimuli. Thus, said stimulus to be identified is compared with all reference stimuli. 13. Ranking according to probability values on the basis of the ranking of "error values" determined from the above. The smalles error value occupies the highest position in the ranking. This concerns the page which according to said comparison method is the most likely to correspond to (resembles the most) the page to be identified. The greatest error value occupies the lowermost position in the ranking. This is the page which is the least likely to correspond to the page to be identified. 14. Said probability value ranking concerns the partial result according to the density value comparison algorithm. Due to the fact that the comparison method is based on density values, e.g. a white and a black page will not in the least resemble each other. However, a uniformly grey page and a text page can resemble each other in the case that e.g. the average density of the text page corresponds to that of the uniformly grey page. In order to eliminate the algorithm-connected disadvan¬ tages of comparison methods, with the method according to the invention it is possible to apply different methods and algorithms in a certain combination with each other. Thus, in addition to said method based on monochrome and/or spectral densities, one also applies methods based on monochrome and/or spectral density transitions, expressed in image frequencies, as well as methods based on monochrome and/or spectral density transition directional distributi¬ ons. Densities can be connected with irradiating the stimuli through optical irradiation sources, such as visible light sources, infrared and ultraviolet sources, etc., but also with irradiation through any other type of irradiation source, inclusing ultrasound, radar, and X-ray . According to methods based on density transition values, a uniformly grey page and a text page will not resemble each other. After all, in case of the text page, there are many abrupt black and white transitions. In case of a uniformly grey page, there are no transitions at all. 15. Determining per plane the characteristics of the image frequencies, e.g. the minimum, maximum and/or most frequently occurring density transition value, e.g. a dis¬ tribution, including a histogram distribution, of the image frequencies present in the recorded image. For a double page divided in 18 planes, this produces 18 times a ranking of comparison values for this method too. If the rankings are spectrally separated and are separately composed, e.g. for the colours red, green and blue, this produces 3 x 18 = 54 rankings of comparison values. 16. Comparing the comparison values of the stimulus to be identified to the predetermined comparison values of the identification stimuli available for comparison. With the comparisons, the respective individual values are compared to each other and the numerical difference thereof is determined. The differences are summated, which again results in an "error value" of the stimulus to be identified with one of the reference stimuli. Thus, the stimulus to be identified is compared to all reference stimuli. 17. Classify according to probability value on the basis of the ranking of "error values" determined from the above. 18. This classification of probability values concerns the partial result of the density transition value compari¬ son algorithm. 19. Comparing the respective first, second, third, etc. selections according to the various algorithms and determi¬ ning the composite error values. This is the sum of the error values of one and the same identification for each of the individual methods. 20. Classifying the composite error values. The result of the classification of the composite error values can be such that the page having the smalles composi¬ te error value, i.e. the top position in the final classifi¬ cation and with that the page which according to the combi¬ nation of each of the comparison methods applied is the most likely to correspond to the page to be identified, does not result in the smallest error value with each of the compari¬ son methods, but takes up the second, third, or another position in the classification. The reverse is also possi- ble: the result of the classification of the error values of the individual comparison methods can be such that a speci¬ fic page will hardly ever result in the smallest error value, but will always take up a second or third position in the classification, whereas that page will result in the smallest composite error value, i.e. takes up the top posi¬ tion in the final classification and with that the page which according to the combination of each of the comparison methods applied is the most likely to correspond to the page to be identified. The smallest composite error value takes up the highest position in said final classification. This concerns the page which according to the combination of each of the comparison methods applied is the most likely to correspond to the page to be identified. 21. Testing the most likely identification by logical connections. For example, is the number of the identified page in ranking with the previous and following page numbers? After all, on leafing through a magazine from beginning to end, the subse- quent pages will generally show increasing page numbers. For example, was the number of the identified page still missing in the ranking of identified pages? Or had the most likely page already been identified once or several times, i.e., turned up once or several times by the same respon- dent, etc. For example, is the page identification specific? That is, is the difference in composite error values of the page having the highest final classification big in relation to that of the page having the second highest final classifica- tion, etc.. The order of leafing, leafing further and leafing back, can be determined e.g. by the way in whicht the contour segments between consecutive images move. In doing so, one can obtain one or more logical connections from a ranking of images as well, which will have to correspond to the identification result to a certain extent. When, e.g. in the case of 3 conditions, two out of the three conditions can be confirmed positively, the most likely page identification according to the method will be confirmed positively too. However, if two out of the three conditions should be confirmed negatively, the most likely page identi¬ fication will be rejected. 22. Determining whether the most likely identification is able to confirm at least a part, e.g. half or two thirds, of the number of conditions to be tested, as being positive. 23. In the case where the most likely identification is able to confirm at at least a part of the number of conditi¬ ons to be tested, as being positive, said most likely iden- tification counts as the overall identification result. If the determined part of the conditions which should be con¬ firmed positively is not made, the conditions will be tested by the second most likely identification, which, as descri¬ bed above, can be the most likely identification of one of the individual comparison methods, and after that the third most likely identification, etc., for determining the over¬ all identification result. If the composite error values are unusually large and none of the likely identifications makes the determined part of the conditions that must be confirmed positively, the system according to the invention indicates that the identification can not be executed. The method described above not only enables very accu¬ rate identifications to be made, but also provides specifi- city values and certainty values, by which the method in¬ trinsically has a means of indicating how recognizable a stimulus is and how certain an identification is. With the method described above, an error margin of better than 1%o can be realized. It will be obvious, that in the above the invention has only been explained by way of some specific examples, as regards to the method, the equipment used with it, the applications and the possible result of measurements, and that many changes and/or additions can be made without leaving the inventive idea. - claims -

Claims: C L A I M S 1. Method of indentifying stimuli in their original manifestation, as well as stimuli positions and stimuli orientations. 2. Method according to claim 1, characterized in that it is determined and recorded from which moment on, and for how long, respondents have stimuli within their fields of vision. 3. Method according to one of the preceding claims, characterized in that it is determined in what way respon¬ dents handle and manipulate stimuli. 4. Method according to one of the preceding claims, characterized in that characteristics of human beings and animals are identified. 5. Method according to one of the preceding claims, characterized in that movements and dynamic actions are identified, such as movements of human beings and animals, including natural movements, facial expressions, and move¬ ments as produced by mechanisms, including those of appara- tus. 6. Method according to claim 1, characterized in that both one comparison method and a combination of various comparison methods are applied for the identification of stimuli and stimuli parts. Methods such as: density methods, including: density value methods and spectral density value methods, such as: density transition methods, including: density transition value methods and spectral density trans¬ ition value methods, such as: density direction distribution methods, including: density value direction distribution methods and spectral density value direction distribution methods and such as: density transition value direction distribution methods, including: density transition value direction distribution methods and spectral density trans- ition value direction distribution methods. 7. Method according to claim 1, characterized in that contours and contour segments are used for determining and recording geometric stimuli characteristics such as: positi- on, rotation and orientation. 8. Method according to claim 7, characterized in that it is determined how respondents handle and manipulate stimuli. 9. Method according to claim 7, characterized in that characteristics of human beings and animals are identified. 10. Method according to claim 7, characterized in that movements and dynamic actions are identified, such as move¬ ments of human beings and animals, including natural move¬ ments, facial expressions, and movements as produced by mechanisms, including those of apparatus. 11. Method according to claim 1 , characterized in that a flexible measuring frequency is employed, in which the measuring instances are made dependent on conditions. 12. Method according to claim 1, characterized in that semi-continuous images are recorded, the images are stored in a buffer, and that both immediately and per time inter¬ val, the images best suitable for being used for stimuli identification are selected. 13. Method according to one of the preceding claims, characterized in that the most likely identifications of a stimulus are tested by conditions relating to logical con¬ nections and that said identification, for final acceptance, is able to positively confirm at least a part of the number of conditions to be tested.

Inventors: CLAESSENS DOMINIQUE PAUL GERAR, CLAESSENS, DOMINIQUE, PAUL, GERARD
Applicants: CLAESSENS DOMINIQUE PAUL GERAR, EYELIGHT RESEARCH NV, CLAESSENS, DOMINIQUE, PAUL, GERARD, EYELIGHT RESEARCH N.V.
Application year: 1997
Main classification: A61B3
IPC codes: A61B3, A61B5