Make your own free website on

Problem of contiguous segment application as a base for solving of geometrical analysis problem of following level - namally - jam-resistent positional detection of contour figures of arbitrary sapes is considered.
3.1. - Essential Statement of the Problem Of Positional Detection Via Registration of Unison of Parametrical Hypothesis.
3.2. - General Statement of the Problem.
3.3. - Comparision With Other Works.
3.4. - Main Particularities of Application of Unison-Technique.
3.5. - Procedure Particularities. Results of the  Experiments.
3.6. - The Possible and Wanted Improvements.


Let's make preciser the common sense of problem statement of positional detection. Let us, it is a triangle of the predicted form and sizes (fig.19a). It is necessary to dispose the triangle on some picture so that its elements will obtain a maximal confirmation by elements of this picture. Let us, on the tested picture it is illustrated same triangle only in other orientation (fig.19b).


We shall superimpose the sample triangle on the analyzed picture each time juxtaposing one of the sides with any contour segment of equal length. / Besides individual segment will determine two possible juxtaposings - one at each side. /

Obviously, that in center it is obtained the multiple superposition (on the number of the sides) of the reference triangle (fig.19c), that also determines, in this case, the sought solution of problem of positional detection.


This superposition can be registered without any direct matching of contours themselves. It is enough to draw on the reference triangle some unit basis vector (see fig.19a), and register the superposition of this vector (see fig.19c).

In turn, the last problem is reduced to a search as much as possible marked point of space of the parameters defining a position of a basis vector on the analyzes image. Fig.19d illustrates the given situation. On this picture the axonometric projection of space of defining parameters is shown. In this case two parameters define coordinates of the beginning of a basis vector, and one - an angle of declination of this vector. Values of last parameter are registered on a vertical axis.  Here the each marks is defined by a position of a basis vector of a reference figure, at one of possible (initial) matching its contours with an analysis picture.


It is obvious, that registration of as much as possible marked point demands essentially smaller computing expenditures, than direct examination on coincidence of the standard of search object with an analysed picture in the every possible positions generated independently by initial juxtapositions.

Now we shall return to our case, - when in the analyses picture initial elements (contour segments) are detected not completely, but in part (see fig.18a). In this case, to possible matchings of the standard of a search figure with a separate (in part detected) segment, will be corresponded to the line segment. This segment represents the track left by the basis vector in the space of parameters, during sliding of the standard concerning the separate detected segment (see fig.18b,c). The search position, i.e. a position in which the sides of the standard cover the greatest amount of the detected segments corresponding a search figure, obviously, will be determined by the point of the most powerful intersection of the generated segments in a base space.

And at last, in case of objects of the curvilinear form, to possible matchings of the search figure standard to a separate approximating (contiguous) segment, some curve of the space of determining parameters will correspond, and to a search position - intersection of such curves. Particularly, the positional detection process in this case represents the following sequence of operations. The whole undistorted image of search object (fig.20a) is registered. To this image - to the standard - it is assigned a unit - basis - vector. For a determinacy it is possible to consider, that as an attachment point of the barycentre of a contour of the standard (though it is not necessary) is chosen.


Then, along a contour of the standard some formal approximating (contiguous) segment (with continuous fine tuning branches) compulsorily moves ahead. During such movement - slidings - the position of the standard (its basis vector) in own frame (bOd) of the sliding formal segment (fig.20b) is registered. The set of the obtained collections of parameters represents some reference generated curve. The axonometric projection of the space of determining parameters for this case is shown on fig.20c. Here on the vertical axis the angle between the basis vector and the longitudinal axis of segment Ob is registered. Points of the obtained curve show possible positions of the standard of the search figure (an its basis vector) concerning a separate approximating (contiguous) segment.

In the process of the positional detection itself, the reference generated curve sequentially is juxtaposed with each segment detected on the analyses image. Such juxtaposition represents juxtaposition frames of the reference curve with a segment frame and the subsequent translation of its points in a frame of the parameter space defining a position of the basis vector on the analyses image. Thus to values of angles the declination value of a flowing segment is added. Now points of each generated curve show, what positions the standard of search object (its basis vector) concerning the given detected segment can occupy at sliding the standard with the contour on this segment.

Thus, the problem of positional detection of flat objects is reduced to the search of intersections of the similar curves corresponding to individual approximating segments. An example for two segments is shown on fig.21. Here xOy - a frame of the image. On the vertical axis the angle between the basis vector and horizontal axis Ox is registered.


The cross point of the majority of the generated curves defines such position of the standard of search object (its basis vector) at which the contour of the standard simultaneously coincides with the greatest number of the detected segments. It, obviously, also is a solution of the considered positional detection problem.

It is necessary to note, that the considered algorithm does not assume an obligatory continuous smoothness of contour boundaries of search objects. For serviceability of the algorithm it is necessary, that the number of singular points of these boundaries (angles) was finite. Objects should not remind a hedgehog or a cloud. Presence of separate smooth pieces is required only. It, as a rule, is ensured with the process engineering of operations on shaping objects: cutting, planing, grinding, stretcting, forging, etc., and also the physics of the nature: forces of inertia, a superficial tension; regularities of processes of cleaving, cracking, etc., etc. The reference curve is under construction on such smooth pieces and represents set of separate curves (space of search parameters), corresponding these pieces.

Also absence on the analyses image of separate parts of the contour boundaries corresponding to the search object is obvious, that does not render a fatal influence on outcome of a positional detetion. In that case only a potency of a search cluster accordingly will decrease. 


To compare the suggested positional detection procedure to already available ones, to show its place among them, and also to offer possible expansions of the use domain, we shall give a formal statement of the detection problem by means of registration of an unison of parametrical hypotheses.

Let there is a set of hypotheses H about some physical phenomenon and a set of facts T which can be registered at research of this phenomenon. Thus it is known, that each hypothesis h H is confirmed by the defined subset of facts T(h) T. Further each such subset we shall name a factogram (factograph) of the given hypothesis.

Usually, the problem of a hypothesis choice h_o H, corresponding to some set of facts T_o T, further named a set of the realized facts, is put and solved as a problem of searching of a hypothesis which factogram has a maximum conformity with this set:


S - the function defining a conformity between sets of the facts.

In the simplest case the conformity is defined by number of the elements simultaneously belonging to both compared sets:


E(t,T(h)) = 1, if t T(h);
0, if t T(h).

It is possible to tell, that in this case it is prospected the hypothesis CONFIRMED by a maximum quantity of the realized facts.

Now we shall remark, that the same problem of search of the hypothesis corresponding to the realized facts, it is possible to put as a problem of search of the hypothesis GENERATED simultaneously by the greatest amount of these facts:


H(t)       =  
{h|h H,t T(h)} - a subset of the hypotheses generated by the individual fact, i.e. confirmed by this fact. Further each such subset will refer to as a hypogram (hypograph) of the concrete fact. Fig.22 picturesquely illustrates the essence of introduced concepts.
E^(h,T(t)) =  1, if h H(t);
0, if h H(t).


Namely this sense is put in the concept of search by means of registration of an unison of parametrical hypotheses.

Before such kind of search named as search by a principle of accumulation of hypotheses [3]. The modified name reflects the essence of the process more precisely. The former name concerns to particularities of realization. The considered statement of the search problem naturally leads in its realization, based on use of some set of accumulators, isomorphic to a set of sorted out hypotheses. In this case each next fact is taken into account one time, by a mark of accumulators which correspond to the hypotheses which are included in a hypogram, generated by this fact. After the termination of search of all facts and marking of accumulators it is necessary to register an accumulator having a maximum number of scores. This accumulator also defines, on a new nomenclature, the unison - consent of the generated hypotheses.

Comparing the first and second kinds of search, first of all it is necessary to tell, that outcomes obtained with their help are in essence equivalent. It is caused by that an amount of confirmations of any hypothesis obviously equally to number its generations.

Rationality of use of this or that hypothesis search mode corresponding to the realized facts, depends, first of all, on a mode of definition of a set of hypotheses H subject to examination. Advantages of the technique of accumulation of hypotheses appear indisputable when hypotheses are directly generated by individual facts on which examination of these hypotheses, namely when the set of hypotheses is defined as is made also:


In this case there is no repeated return to already overlooked facts expressing in evaluation of the conformity function.

If character of a problem allows to limit somehow number of hypotheses in comparison with what is generated by all realized facts separately (that rather frequently takes place) expenditures on designing of an accumulator can appear unjustified. In this case it is possible to be limited to direct examination of confirmation of the factograms corresponding to the available hypotheses. It is necessary to take into account concrete particularities of a decision problem.

At a solution of the positional detection problem, the role of the facts is played with separate elements of analysis images. These elements - initial features, under the supposition, correspond to some elements (fragments) of search objects. Each initial feature chosen in an analysis picture, generates a set of hypotheses about parameters of the search object, i.e. a hypogram. Thus to each hypothesis the point of space of the parameters defining a position and orientation of object corresponds, and to an individual hypogram - the defined set of such points. Further for simplicity these point sets will refer too as hypograms. The search position of object is defined by a cross point of hypograms. Naturally, generally it is necessary to speak about this point as about a point of a cluster (clusterization), i.e. about a point in which neighbourhood the greatest number of hypograms hits.


The positional detection algorithms based on the cluster analysis of the space of search parameters, draw attention of experts on visual recognition of objects. The majority of researches of this direction goes back to the widely known Hough transform [7, 20 - 27]. The essential shortage of these algorithms consists that the search parameters are considered as parameters of some abstract transformations of the elements of the reference image into similar elements of the analysis image [22, 25]. Such statement of a problem hampers understanding of logic of build-up and further improvement of similar detection algorithms. In particular, it leads in enough abstract reasonings on "collinearity" of contour points [7, 28] or about "not dot transformations" [21, 22]. It is necessary to prove equivalence of the outcomes obtained with the help of these algorithms and recognition algorithms in which basis comparison with the standard [29] lays.

The substantial approach to positional detection problem as to a search problem of superposition of modelling images of the search objects generated independently by individual fragments (features) of these objects [19], directly suggests parametrization methods. Obviously, as the search parameters it is necessary to use parameters of the simplest configurations of structural elements rigidly connected to the standard of search object and permitting completely to define its position and orientation. Thus, the essence of the detection problem is reduced to search of superposition of such configurations. In the case of flat objects a basic configuration is any unit vector assigned in the beginning to the object standard. If the scale changes, respective variation of length can be assigned to this vector. In the case of 3D bodies two unit vectors starting from one point can be a basis structure. The position of such structure is determined by six parameters: three ones set coordinates of the attachment point, two ones - orientation of a principal vector of this design, and one parameter - rotation of an auxiliary vector around of principal one. It is possible to use the Eulers angles. In this case the basis strucure will represent the thriplet of mutually perpendicular vectors which are starting from one point. In all these cases an individual hypogram will be defined by set of positions of the basic structure which it can occupy concerning the given initial feature (fragment) of the search object.

The suggested ontological scheme represents generalization and attempt of more deep clearing up of the essence of similar algorithms. First of all, this scheme directly shows equivalence of the outcomes obtained with the help of this or that technique of object positional detection. A vivid example of it is work of a matrix decoder which can be considered and as parallel comparison with the standard (set on the address register) and as search of intersection (cluster cells) of hypograms assigned to address buses. It is necessary to note, that the Guzman idea about promotion of global hypotheses on the base of local data have served as initial push in the direction of idea of accumulation of hypotheses [30], and the Walts idea of about search of a consistent (compatible) mark of contour edges [31] - for the idea of superposition of models .

Probably, the proposed ontological scheme will serve further movement to understanding of the essence of various search kinds and correlation between them. The considered example, where the hypothesis choice is carried out with simple calculation of binary voices, reflects only the simplest case. A purpose of this example - to show basic equivalence of indexes of confirmation measure and measure of generationess of hypotheses, in the simplest case - the binary count of the facts. Generally the membership functions can have more complicated view: to accept a continuous spectrum of values (for example to be fuzzy), to accept negative values (individual facts can refute some hypotheses), etc., etc.


Further, the technique of positional detection based on registration of unison of parametrical hypotheses will be named also simply as unison-technique (unison-detection ets.). Use prospectivity of thise technique in comparison with the technique of direct examination of hypotheses confirmation, which in this case can be named examination on conformity with models-standards, depends on concrete particularities of the problem solved.

The technique of direct examination on conformity with models is efficient when the search of small amount of hypotheses is required, for example, when there is a possibility of efficient detection of the high informative fragments essentially bounding a set of generated hypotheses or when character of a problem allows to apply any strategy reducing search. In particular, it concerns to the case when the conformity measure on an admissible set of hypotheses is the smooth function having limited number of extremums. In this case for search of its maximums it is possible to use gradient procedures. Such search strategy, for example, is applied in the proposed representation mode of contour boundaries in terms of contiguous segments.

In those cases when on a problem there are only low informative initial features (facts), each of which separately poorly limits an amount of admissible hypothesises, advantages of the hypotheses accumulation technique can appear resolving. As here, instead of multiple and, as a rule, protracked evaluation of the conformity function, a simple coincidence of points in own space of hypotheses is registered.

As well as by search, in which base evaluation of the conformity function lays (search by principle of direct hypothesis test), the choice of initial features is a key defining boundaries of applicability of hypotheses accumulation technique. If to use the most universal initial features - contour points [20 - 22], computing resources of modern computers allow to solve practically only the problem of positional detection of two-parameter objects (in particular, infinite straight lines).

To limit number of generated hypotheses up to a comprehensible level, at the solution of more complicated problems of geometrical detection (with a big number of unknown parameters) it is necessary to use more complicated initial features (fragments) of search objects too. The basic difficulty thus will be, that the following on the complexities, the most natural initial features, - the rectilinear contour segments connecting points of inflection - sharply limit a class of recognition objects. As it was already marked,it is impossible to divide univalently the contour boundaries of the objects having smooth contours (fig. 20a) into separate segments. Approximating segments on such contours generally cannot be fixed legiblly enough. Thus, each segment generates rather extensive - sliding - set of possible positions of the search object. Here by the best way of detection by the registration of unison of parametrical hypotheses proves.

The basic difficulty at realization of such positional detection algorithm consists in necessity of steady definition of orientation and cross position of sliding approximating segments in the case of real curves, when these curves are set discretely and deformed by various interferences.

If to define these directions by short segments (to provide common universality of the algorithm, irrespective of the form of objects), in real conditions casual oscillations of these segments will lead to essential oscillations of the base vector. For example, if to use contour elements obtained directly with the help of the Hueckel's operator [5, 6] (as it was offered in [23]). In this case, oscillations of the ends of obtained elementary contours with an amplitude plus or minus one point, at diameter of the entering piece in 7 points of the raster, on the distance of 50 points (that corresponds to average distance up to the center of a figure in diameter in 100 points), lead to oscillations of the basis vector with amplitude of 15 raster points (50/(7/2)).

In this case it is necessary to hope only that the searched correct solution nevertheless will turn out due to statistical averagement. Such solution assumes the organization of the count of a great numbery of the hypograms generated by individual elementary segments. The extended machine memory for storage of a hypotheses accumulator is required. Significant expenditures of machine time for the procedure of hypotheses accumulation and the analysis of the obtained (in an accumulator) pictures are necessary.

On the other hand, the suggested in [25] sliding matching on extended pieces of contour lines basically can satisfy stability requirements. However, it does not ensure universality as it is limited only to the case of rectilinear segments. Though and for this case here it is necessary to expect essential difficulties owing to absence of an effective mode of segment approximation.

In the proposed scheme of realization of unison-procedure the sliding segments, which are simultaneously meet the requirements of universality and stability, namely - contiguous segments, are used. Virtues of these segments are defined by that the use of bell-like memberships functions included in their basis allows to tune out effectively from jamming. Bell-like function enables correctly - on a maximum - to lean on length of juxtapositioned contours.

As the result, it was possible to construct an jam-resistant, costructivelly realizable procedure of positional detection of arbitrary form objects.


The proposed positional detection algorithm has been realized in the language FORTRAN (a small part - on the autocode).

Build-up of a reference hypogram of the search object is carried out under its full undistorted image. As an attachment point of the basis vector the barycentre of contour boundaries gets out. During build-up of a reference hypogram the step of movement of a formal contiguous segment is continuously arranged with the help of feed-back so that to keep adjacent points of a reference hypogram within the limits of one zone into which the space of search parameters is supposed to divide. This space is represented as set of rectangular zones.

The size of zones on an axis defining declination of a basis vector, gets out according to a condition of approximate equivalence of shift ( L) and turn ( A) a reference figure:  A= L/R, where R - radius of object. This value is calculated as arithmetic mean lengths of the radiuses - vectors directed from a barycentre to each points of contour boundary.

Hypograms of the contiguous segments detected on the analysis image are obtained by "juxtaposition" to them of a reference hypogram (thus coordinates of its points are accordingly transformed).

Hypograms are set pointelly with a step commensurable with linear sizes of the zones. Therefore, marking in the intersection field of several hypograms can not give one zone which would be marked simultaneously with all these hypograms. To overcome similar accidents of a discrete mark, in the program realizing the algorithm the averaging of obtained pictures is made within the limits of adjacent zones. For each marked zone its weight - the arithmetical sum of contents of adjacent zones - is calculated. Contents of the zone are a number hypograms marked it. 27=3x3x3 zones are considered as adjacent, including central one. The separate cluster is registered as a zone which weight exceeds weights of the neighbours. The central point of such zone starts a cluster point. As a whole, such operation is called as smoothing.

In the program realizing the algorithm, the information only about marked zones is remembered. For this purpose some array is assigned. The entry is executed with the help of hashings [Appendix_3.5.A].

Reference hypograms of recognition subjects - insoles, an iron and a case from glasses (see fig.13) - were constructed on their separate images. The sizes of search parameter space zones on the axes defining a position of an attachment point ( L) was equal to eight (8) points of the raster. On the axis defining declination of the basis vector ( A) it was equal 0.1 radians. The linear size of the zones got out approximately equal to a minimum distance on which two parallel contours are distinctive. In this case this distance - solution - was defined by the diameter of entering area of the Hueckel's operator with which help elements of contour boundaries were selected. Diameter of this area was 7 points of the raster [2.2.]. The concrete value "8" has been chosen as the nearest power of 2, that essentially accelerates operation of definition of the zone number (instead of division - shift...) . The angle sizes of zones according to above indicated equivalence criterion of shift and turn for figures with average radius of 50 raster points (average radius of objects in this case).

At such zones sizes of reference hypograms of the search objects contained accordingly 85, 60 and 45 points.

During accumulation of hypotheses about an insole position (on fig.13), 1784 zones have been marked. From them: fourfold - 1, threefold - 3, twice - 47, one time - 1733 (time of accumulation of hypotheses 1,5 s; computer efficiency - 1 million operations from a floating point). Among these zones after operation of smoothing 18 separate clusters (smoothing time 1,0 s) have been registered. Relationship of weights of these clusters is shown on fig.23.


Histograms of maximum weight zones of the most powerful clusters are shown on fig.24.


Each histogram shows values of the most powerful zones situated on various distances from the central zone. Distances are registered on horizontal axes and measured in terms of a digitization step. Numbers of the hypograms for the first time appearing on the given distance are present on the histograms. Indexing of segments see fig.25.


On fig.25 fat primes show the position of the standard of the search object, defined by the central zone (the central point of this zone) of the most powerful cluster (fig.24a). On the same picture thin primes show positions of the standard, defined by two most powerful false clusters (see fig.24b,c).

In [Appendix_3.5.B] the problem of common expediency of smoothing operation is considered.

Experiments were carried out with various variants of disposition of contiguous segments on the contour boundaries of search objects. At attempts to fix contiguous segments on represantation contours [2.4.] the most various dispositions of these segments were obtained. In particular, for an insole it has been analysed about ten variants, which include approximately 70 (7 segments X 10 variants) different positions for a separate contiguous segment (including the most unsuccessful, corresponding to the pieces of significant curvature). In all these cases the point of parameter space corresponding to the true position of the search object on the flowing image was in the central zone of the most powerful cluster. Thus all hypograms generated by segments contiguous to a search object contour hitted (by any points) if not in this zone, in the last resort in one of adjacent zones.

The experiments carried out in this way have confirmed basic serviceability of the considered positional detection scheme of arbitrary form objects and have shown a possibility of its to constructive realization.


The accepted concept of equivalence of contiguous segments is intended for the worst situation - a case of very smooth contours, with feebly marked changes of curvature. Naturally, in general case at matching of contiguous segments it is necessary to take into account, even roughly, parameters of their branches. With all evidence it is shown with exibited examples of false situations of the search object (see fig.25). If the insole heel is approximately entered in the contiguous segments representing a glasses case contour and an its nose - in an iron contour, confirmation of the rectilinear piece of contour boundary of search object by curvilinear segment N~13 and the curvilinear piece by rectilinear segment N~2 only on the basis of their mutual tangency evidently is not justified. Organization of hierarchical count of hypograms, i.e. accumulation of hypotheses by way of their probability is a problem of further researches. And this problem has a basic value as thus the relative weight of false cluster points should decrease considerably.

Research of the problem of use of configurations of initial matching (initial fragments) built of several (first of all from pairs) adjacent contiguous segments seems to be rather prospective also.

For reduction of computing expenditures by the hypotheses accumulation principle it is possible to make search iterativelly: initially to work with big zones and rough representation of hypograms and then to analyze intersection zones using more precious representation and smaller zones.

Other mode of reliable registration of hypogram intersections (without use of smoothing) consists in marking of all tops of each zone. At increase of dimension of the space of search parameters expenditures in additional memory can appear smaller than on an evaluation of the local (neighboring) sums. In this connection it would be expedient to solve the problem of subdivision of space of parameters into zones having a minimum number of tops (for two-parameter space they are triangles, for three-parametrical one - tetrahedrons, etc.).

Naturally, realization of the suggested scheme can appear too bulky for direct positional identification of complicated objects, i.e. objects which contour boundaries demand for their description about ten and more contiguous segments. Here the suggested scheme can be applied to detection of separate fragments of such objects used further for a final geometrical detection problem solution with the help of structural methods or subsequent direct matching of models (standards) of search objects with analysed pictures. As a whole, in detection of such fragments already enough legiblly corresponding to separate parts of actual objects it is possible to see the basic applicability of the suggested scheme.

It is possible, that the count of more thin performances of contiguous segments and also improvements of realization technics of the identification scheme will enable to spread its to positional detection of 3D bodies on their images, anyway their fragments. Thus, for restriction of volume of hypograms a use of the contiguous ("sliding") surfaces describing a distance image essentially would help. This description can be obtained as a result of the analysis of half-tone pictures, methods of stereosight or direct mesurement of distance (for example, using laser technics).