The Consistent Labelling of Image Featuresusing an ATMS

R. Bodington, G.D. Sullivan & K.D. Baker

Intelligent Systems Group, Department of Computer Science,University of Reading, RG6 2AX, U.K.

R.Bodington@ ieading.ac.uk

Labelling sets of 2-D image features as model features isa constraint satisfaction problem that occurs in model-based vision. The labelling must be consistent withconstraints that describe how image features originatingfrom the modelled object would appear in the image.This paper discusses how an assumption-based truthmaintenance system, ATMS, can be used to solve sucha constraint satisfaction problem. The ATMS is used tolimit the number of constraints applied, and torepresent the multiple sets of consistent labels that arepossible. The effectiveness of the ATMS in limiting theconstraints is analysed.

This paper concerns the use of model-based visiontechniques for the identification of vehicles in complexoutdoor scenes1. Figure 1 shows a typical scene. Thereare multiple classes of vehicle and several instances ofvehicles in the scene.

Figure 1. Typical scene

Image features extracted from such scenes lead tomultiple, ambiguous interpretations. This is due to dieincompleteness of the image features generated by avehicle in the scene, and the generation of false positives.The incompleteness of image features arises from theinability of the image processing techniques to identifyall features, and because a vehicle may be self-occludingor occluded. False positives are image features generatedby other objects in the scene, such as buildings,vegetation, and other vehicle types.

In this paper, we describe how the assumption-basedtruth maintenance system (ATMS) of DeKleer2 has beenused to represent multiple interpretations, and how theATMS can support constraint based reasoning to identify

the correct interpretation. Some 3-D constraints involvea vehicle model being instantiated and projected into theimage. Typically, these are computationally expensive.The paper will show how the ATMS is used to controlthe invocation of such constraints and to record andreflect the results throughout the search space. Weanalyse how effective the use of the ATMS has been.

AN APPROACH TO MODEL-BASED VISION

Our approach to model-based vision, relies on the abilityto verify a hypothesis for the existence of an object inthe scene by matching an instantiation of a model in the2-D image. Model-based verification proceeds byinverting the view perspective3, and then performing aniconic evaluation of the model4. View perspectiveinversion requires that a set of lines in the image arelabelled as model features, and an indication of tbeviewpoint and position of the object in the image. Viewperspective inversion produces an instantiation of themodel that can be verified by the iconic evaluator.

Conceivably, model-based verification can beexhaustively applied to all sets of lines extracted fromthe image, however, the combinatorics areoverwhelming. Consequently the use of heuristicknowledge to limit the search is appropriate.

Experience has shown that the existence of a vehicle inan image may lead to the generation of certain distinctiveimage features. These features are not solely, nor arethey always generated by a vehicle. However, they doprovide independent evidence that allows a hypothesisfor the existence of a vehicle to be made with a degree ofconfidence. These image features can be considered cuesto subsequent vehicle recognition. In this paper, cues aregroups of edges in the image that can be labelled asfeatures on a model of a vehicle. An example of suchcues are closed polygons. They may have been caused bywindows on a vehicle and so should be labelled aswindows on the model. At the outset, all such cues areidentified. A search then takes place for a group of cuesthat can be labelled as the features of a single model.The search is restricted by ensuring that the groupsidentified are consistent with knowledge of how avehicle that generates such cues, would appear in the 2-Dimage. This knowledge can be expressed as constraints.The problem of identifying sets of labelled cues that areconsistent with the constraints is a consistent labelling,or constraint satisfaction problem.

The identification of a consistently labelled group ofcues will enable bounds on the viewing angle to be

AVC 1988 doi:10.5244/C.2.2

constructed using viewpatch reasoning as described byRydz5. The inversion of the view perspective and aniconic match can then be performed in an attempt toverify the hypothesis for the existence of the associatedvehicle. Verification will return a numerical scoreindicating the degree of success. The scores fromprocessing different groups can be compared to determinethose most likely to result from a vehicle. The completeprocess can be summarized in figure 2.

Image

±Edge extraction \

edgesX

Cue Extraction

Model

cues,

Labellergroups c f consistentlylabelled cues

I Viewpatchangle, position^

^—*~\Model verification^

knowledgeof the model

Figure 2. The labelling process

The result of using constraints in this manner is toreduce the amount of model-based verification.

CONSTRAINT SATISFACTION

The central problem addressed in this paper is theconstraint satisfaction problem associated with theidentification of sets of consistently labelled cues thatprovide a starting point for model-based verification.

The structure of a constraint satisfaction problem can bedefined by:

• a set of variables.• a set of values that can be assigned to a variable. This

is referred to as the domain of a variable.• a set of constraints on the values assigned to the set

of variables.A set of variables, each assigned a single value fromtheir respective domains, is a CSP-labelling. If the set ofvariables in the CSP-labelling is a subset of the variablesin the structure of the constraint satisfaction problem,then the labelling is partial, otherwise, it is complete.A constraint is applicable to a CSP-labelling if the setof variables involved in the definition of the constraintare a subset of variables of the CSP-labelling. A CSP-labelling is consistent, if all applicable constraints aresatisfied. A CSP-labelling is maximal if no consistentCSP-labelling exists that is it's superset. A solution to aconstraint satisfaction problem, is a complete andconsistent CSP-labelling.

In the model-based vision system, the model features arethe variables, the domains of which are the cues thatcould possibly be labelled as that model feature. Anassignment is then the labelling of a cue as a modelfeature. The identification of a group of labelled cues isthe identification of a CSP-labelling.

There are three basic forms of constraints used.Constraints based on the geometry of the 3-D vehicle

model and the 2-D geometry of image features. These arenot strong discriminators as foreshortening due todifferent viewpoint and the affect of scale must beaccounted for. Constraints based on the topology of themodel and cues. Constraints that determine whetherthere is a viewpoint from which the group of labelledcues are all visible5. The topological and viewconsistency constraints provide more discriminatorypower than the geometrical constraints but the overallproblem is weakly constrained leading to many partialCSP-labellings.

It is very unlikely that a complete CSP-labelling willbe found. This is due to several reasons. The cues aregenerated by vehicles (unreliably) as well as by thebackground scene. The vehicle may be self-occluding oroccluded. Consequently the variant of the constraintsatisfaction problem addressed is the identification ofthe maximal, consistent, partial CSP-labellings. Each ofthese CSP-labellings is a hypothesis for a vehiclesexistence that must be verified by model-basedtechniques. However, as the 2-D to 3-D constraints areweak, and because of the nature of the cues being used,there may a number of maximal, consistent, partial CSP-labellings. In order to reduce the model-basedverification further, more heuristic knowledge must beused.

RATIONALE FOR USING AN ATMS

While the maximal CSP-labellings are consistentaccording to the constraints, they are in fact ambiguousscene interpretations. The reasoning system employed toreduce such interpretations must be able to represent theambiguity to allow the pursuit of the solution pathdeemed most opportune at any state.

In the identification of maximal, consistent, partial CSP-labellings, there are situations where constraints areapplied to the same data in different parts of the searchspace. If the constraints employed are computationallyexpensive, such as model-based verification, then thisreplication of effort is undesirable. For example,consider the extracted cues shown in figure 3a.

Figure 3.

For simplicity, assume that the subset of model featuresbeing used as variables are the nearside front side-window, nfw, the nearside rear side-window, nrw, andthe nearside front wheel, nfwh. These are shown infigure 3b. The cues available are: cl, c2, c3, c4. Theseare the values of the constraint satisfaction problem, thestructure of which is shown in table 1. If a tree searchmethod is used to deduce the possible CSP-labellings,then the search space explored is as shown in figure 4.An assignment of a value to a variable is represented as-»-. For example: c3-^nfivh. As indicated in figure 4, thereare four possible CSP-labellings.

Table 1. Constraint Satisfaction Problem

Variablenfwhnfwnrw

Domain{c3, c4}{cl, c2}{cl, c2}

c2-*nw cl-*trw

CSP-Iabelling \:{c3~

CSP-labelling 2:{c3~

CSP-labelling 3:{c4~

CSP-labelling4:/c4-

ynfwh, cl--nfwh, c2-*nfwh, cl-*nfwh, c2-

>nfw, c2*nfw, cl->nrw}

ynfw, cl-*nrw}

Figure 4. Tree searched

If there is a constraint between the two side-windowswhich is evaluated as the search takes place, it can beseen from the diagram that the constraint will beevaluated on the partial CSP-labelling {cl-+njiv,c2-+nrw}twice. Once to check the consistency of the CSP-labelling 1 and once to check the consistency of the CSP-labelling 3. Having established that constraints definingthe relationship between two side windows are satisfiedfor the first labelling, it should not be necessary torepeat the computation for the second.

As has been said, this duplication of effort isparticularly undesirable when expensive constraints areused as they are in this application. This should becontrasted to labelling approach adopted by Grimson andLozano-Pe'rez9. In their application, it was possible toprecompile the geometrical constraints used and storethem in a lookup table. This allowed very rapidevaluation of constraints. A depth-first search wasemployed to identify the consistent labels. This searchstrategy re-evaluates constraints on the same data,however, as the evaluation of constraints iscomputationally cheap, there is little overhead.

The reasoning system has two requirements. It must becapable of representing multiple conflicting states, andresults obtained in one part of the search space must becarried across to other parts. Part of the motivation forthe development of the assumption-based truthmaintenance system, ATMS2, was to provide a generalsolution to these two requirements. Thus the ATMSwould seem an appropriate mechanism to support thereasoning required for model-based vision systems.

THE ATMS

The ATMS is a general purpose mechanism for recordingstatements of belief and reasons for belief. The ATMSwill maintain multiple sets of consistent beliefsreferred to as contexts. Each context is consistent, butany two contexts may be inconsistent. The ATMS uses adependency network of nodes and justifications to recordstatements of belief. A node represents a potentialproblem solver belief. There are two special types of

nodes. A premise node represents a problem solverpremise. An assumed node represents a problem solverassumption. The reason for belief in, or the support for anode is described by a justification. The node beingjustified is the consequent of the justification, and thenodes providing the support for the consequent are theantecedents of the justification. If all the antecedents ofthe justification are believed, then there is a valid reasonfor the belief of the consequent node.

Every node has an associated ATMS-label. An ATMS-label is a set of environments, where an environment is aset of assumptions providing support for the node. Theenvironments of an ATMS-label are minimal withrespect to each other. That is no environment in anATMS-label is subsumed by any other environment inthat ATMS-label. When a node is justified, a new reasonfor belief in a node is given. The ATMS-label of the nodeis updated to reflect this and the new belief ispropagated to all nodes justified by this node. A node isbelieved in all contexts that can be derived from theenvironments of its ATMS-label. The context of anenvironment is the set of beliefs that are supportedsolely by a subset of the set of assumptions in theenvironment.

The ATMS records inconsistencies by the justification ofa special node, the contradiction node. The set ofenvironments forming ATMS-label of the contradictionnode are referred to as nogood environments. All thecontexts represented by the ATMS must be consistent.Therefore no ATMS-label of any node, apart from thecontradiction node itself, can contain an environmentthat is subsumed by a nogood environment.

A further function performed by the ATMS is that ofinterpretation construction, where the ATMS is used toidentify the set of all consistent maximal environmentsthat can be formed from a set of assumptions. Anenvironment is maximal if it subsumes no environment.Interpretation construction proceeds as follows. First,all the environments of size two formed from the set ofassumptions and not subsumed by a nogood environmentare identified. This set of environments is then used toconstruct environments of size three, again avoidingenvironments subsumed by nogoods. This processcontinues until the set of maximal and consistentenvironments has been identified.

CONSTRAINT SATISFACTION AND THE ATMS

Each assignment in constraint satisfaction will berepresented as an assumption in the ATMS.Interpretation construction will generate all consistentenvironments formed by these assumptions, and soidentify all the possible CSP-labels.

During interpretation construction, the consistency ofeach environment explored is determined by whether itis subsumed by a nogood environment. In the constraintsatisfaction problem, these nogood environments areformed from a set of assumptions whose correspondingassignments fail to satisfy a constraint. The recording ofthe nogood ensures that the exploration of any CSP-labelling, environment, that would not satisfy the

constraint is avoided. The ATMS must be extended toprovide a mechanism that will enable the evaluation ofconstraints during interpretation construction. Such amechanism must prevent the re-evaluation of aconstraint on the same set of assignments. The consumerarchitecture of DeKleer6 provides such a mechanism. Thesubsequent paragraphs describe an implementationderived from the consumer architecture. Thisimplementation is described in more detail by Bodingtonand Elleby7.

A consumer is a special ATMS node representing aproblem solver procedure, hi the context of this paper, aconsumer contains the intended procedure fordetermining the consistency of a constraint. The ATMS-label of a consumer node consists of sets of assignmentsfor which the associated constraint can be evaluated. ThisATMS-label is constructed by creating a node for eachvariable and justifying it by every assumptionrepresenting the assignment of a value to that variable.A consumer node is justified by the set of variablesinvolved in the corresponding constraint. An example isshown in figure 5. The consumer represents theconstraint, a+c>4, between the variables a and c. Thepossible values assigned to a are 1 and 3, represented asassumptions al and a3. c can be assigned the value 5. Theconsumer can be evaluated in the environments in thelabel of the consumer, {a3,c5} and {a5,c5}.

{a3},{a5}}

{{a3tc5},{aS,c5}}

Xassumptionenvironment: {..}

Xnode

\^ "consumer nodejustification

ATMS-label: {{..}, {..}}

lo Figure 5. ATMS network for a consumer

When a consumer is evaluated in an environment, theenvironment is removed from the label of the consumer.This ensures that a constraint is only ever evaluated onceon any set of assignments. The constraint represented bythe consumer in figure 5 has not been evaluated so theATMS-label is complete.

When consumers are used, the consistency of anenvironment explored by interpretation construction isdetermined first by checking for the subsumption bynogood environments, and then by the application of anyconsumers that have the environment in their label.Interpretation construction, explores the smallestenvironments first, so constraints that generate nogoodsof low cardinality are evaluated first This avoids theredundant constraint evaluation that can occur when aconstraint is evaluated in an environment that issubsequently subsumed by a nogood environment.

The use of interpretation construction in this fashionensures that the results discovered in one part of thesearch space, the application of constraints, are availablethroughout the rest of the search space. The minimum

amount of constraint evaluation will be used todetermine the set of maximal CSP-labellings7.

PRACTICAL INTERPRETATION CONSTRUCTION

Identification of consistent groups of cues

In this application, it is plausible to identify all modelfeatures, and assign them cues that could be labelled assuch. Interpretation construction and constraints canthen be used to identify all the partially labelled cars.However, many cues may well be extracted from theimage, of which only a few are generated by the vehicles.Consequently, a large number of environments will beexplored, the majority of which will be inconsistent. Asindicated by Provan8, the number of environmentsexplored and the number of nogood environments willrise exponentially. Viewing nogood environments as setsof assumptions, this combinatorial explosion can bereduced by constructing constraints that generatenogoods of low cardinality. The smaller the nogoodsize, the more environments subsumed by it, and so themore environments pruned.

A further method of controlling the size of the searchspace is to formulate the problem using domainknowledge so that the areas of the search space explored,are those likely to contain solutions. Environments thatare known to be nogood prior to the search commencingshould be avoided. One way to achieve this is to use theconcept of a seed cue. A seed cue is used to estimate anarea of interest in the image. The area defines the boundsof the possible projections by the vehicle onto the 2-Dimage as predicted by the assignment of a model featureto the seed cue. Only cues that lie within this area couldbe caused by the same vehicle as the seed cue. Avoidingthe consideration of cues that lie outside the area ofinterest prevents the exploration of environments knownto be nogood. .

All cues provide some evidence for a vehicle in the imageand so are potential seed cues. However, certain cuesenable the invocation of more restrictive 2-D constraintsand are consequently more useful in limiting the search.For example, an S shape cue, caused by the roof,windscreen and bonnet, will give a rough indication ofthe orientation of the vehicle in the image so enablingthe exclusion of certain model features due selfocclusion. Other cues are more likely to be falsepositives. For example, closed polygons are regularlygenerated by windows on buildings. The usefulness ofcues as seed cues can be ranked according to these criteria.Search then proceeds by selecting the seed cue that willbe the most effective. All cues within the area ofinterest generated by the seed cue are identified and allpossible assignments are made. Interpretationconstruction and constraint satisfaction then takes place.The interpretation construction is focussed so that theonly environments that are explored are those thatinclude an assignment for the seed cue. This leads to theidentification of the set, MS, of all maximal, consistentCSP-labellings, such that:

MS = { mS | mS is a maximal, consistent CSP-labelling/

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Each mS is equivalent to an environment in the ATMS,and refers to a set of model features that have beenassigned a cue and includes an assignment to the seed cue.Each mS provides a hypothesis for the existence of thevehicle in the image as suggested by the seed cue.

Verification of consistent groups of cues

Once the set, MS, has been identified, a position andorientation of a vehicle for each element, mS, of the setMS, can be predicted in the image5. Model-basedverification can then be performed on each mS in order ofthe likelihood of success. Currently, the criterion onwhich this ordering is based, is the number of cuesassigned to model features forming the set mS.Additional criteria are being considered. For example,the quality of the data forming the cues, and the numberand types of constraints that the group satisfies, can beanalysed.

Model-based verification returns a numerical score. Ifthe score is of a certain level, then the assignments inthe set mS are assumed to be correct, a vehicle has beenidentified. All the assignment assumptions in the set mSare then believed, the cues are not considered again andall other environments containing the believedassumptions are ignored. If the score is extremely low,then the assignment assumptions forming the set, mS,are considered to be incorrect and form a nogoodenvironment. The possibility that the set, mS, isincorrect due to the inclusion of an assignment to a falsepositive as well as the correct partial CSP-labellingmust not be excluded. So the set of maximal consistentenvironments that subsume this nogood environmentmust then be identified. If these are maximal, relative tothe set MS, then they should be included in the set MS.

Consider the case (figures 3 and 4) where the CSP-labelling, {c3^njwh,cl^njw,c2^-nrw} has beenidentified as consistent according to 2-D constraints. Thecue c3 is in fact a false positive, so model-basedverification techniques will indicate that the CSP-labelling is a nogood environment. The maximalenvironments that subsume this nogood environment are:

{{c3-+4wh,cl^rfiv}, {c3-^nfwh,c2->nv}, {cl-*rfv,c2~*rvw}}.

Model-based verification on this set will indicate that{cl-+/fiv£2->nw} is the correct CSP-labelling.

Once all the mS sets within the set MS, have either beenverified by model-based verification techniques orsubsumed by assignments comprising of cues that havealready been correctly assigned to model features,another seed cue is selected and the process is repeated. Itis probable that the area of interest predicted by a seedcue will overlap previously explored areas, so the samecues will be re-assigned to the same model features andbe reconsidered. As the ATMS has been used, theconsistency of partial CSP-labellings formed from thesesets will not be determined by the re-evaluation ofconstraints.

When all cues have been used as the seed cue, all sets ofpossible consistent CSP-labellings will have beenexplored. The scores of the maximal CSP-labellings thatwere not high enough to be believed are now compared in

order that further vehicles may be identified. If amaximal set mS\ has a null intersection with any othermSj, then the assignments comprising mS\ are anindication of a vehicle. If the intersection set is notempty then the assignments in the mS with the highestscore are the labels for the vehicle.

RESULTS

The work described in the paper is still underdevelopment. The cue extraction process is not complete.At present, only closed polygons, wheel arches, andinverted bucket shapes can be extracted. A subset of the2-D to 3-D constraints have been implemented. Thelabelling has not been linked to the model-basedverification techniques. However, preliminary resultsgive some indication of the effectiveness of the ATMSconstraint satisfaction technique in limiting the numberof constraints evaluated. Consider the image shown infigure 1. The connected edge map that is extracted usinga single resolution Canny edge detector is shown infigure 6. Figures 7a and 7b show the symbolicrepresentations of the cues that have been extracted fromthe Canny output using different thresholds todetermine whether a group of image features areacceptable as a cue. These cues required some interactiveediting of the existing cue extraction processes.

Figure 6. Output from Canny edge detector.

The performance of the ATMS labelling is summarizedin table 2. The image in figure 1 has been used with threedifferent levels of threshold. Entry a) is a measure ofhow many of the environments explored are members ofan ATMS-label of a consumer that has already run. Suchenvironments represent a partial CSP-labelling that hasalready satisfied the constraint encoded by the consumer.Entry b) is the number of environments explored thatare immediately subsumed by a nogood environment, andhave one or more consumers pending execution. Suchenvironments represent partial CSP-labellings whoseinconsistency has been determined elsewhere in the searchspace. An indication of the savings in constraintevaluation by the ATMS can be gained by comparingthese two entries to the total number of constraintsevaluated as shown in entry c).

The results show that the more cues there are, due tolower thresholds, the more ambiguously labelled cuesare possible, so the greater the savings in constraintevaluation. This saving must obviously be countered by

l i

the increase in number of environments explored andnumber of nogood environments generated.

' • !

1

r

•i

r

" • •

LI^-

nrJ-

',nfJ, . •

i C 'Mi1,

:l Liu

u n

v-

" I .-, ..

Figure 7a. High threshold Figure 7b. Low threshold

Figure 7. Cue extracted from edges in figure 6

Table 2. Performance of ATMS labellingLow

Cues: 16Environments explored: 195Nogood environments: 89Constraints evaluated: 69Constraints satisfied: 38

Constraints failed: 31Reduction in constraints by nogoods: 0

Reduction in constraints by goods: 8

Percentage reduction inconstraint evaluation: 12

Medium2640213111347661843

54

High38758244227701574379

54

a)b)

c)

The constraints and cue extraction processes are beingcompleted, and the model-based verification techniquesincorporated. The technique will then be applied to arange of images enabling a more concrete measure of theeffectiveness of the ATMS in limiting the amount ofconstraints evaluated.

DISCUSSION AND FUTURE WORK

Currently model-based verification is only used once theset of maximal CSP-labellings consistent with the 2-Dconstraints have been identified. The reason being, that ifa maximal set satisfies all the 2-D constraints, then it islikely to be a correct labelling. However, as discussed,these maximal sets may include false positives resultingin further model-based verificatioa It may prove abetter policy to invoke the model-based verificationtechniques as soon as possible. These constrain the searchmore effectively than the 2-D constraints and soinconsistent partial CSP-labellings will be discoveredearly in the search, thus pruning large sections. Aconsequence of this approach, is that computationallyexpensive model-based verification techniques are morelikely to be repeated throughout the search space on thesame data. This strengthens the case for using an ATMSin the manner described. This is being investigated.

The approach described in the paper is totally dependenton the extraction of adequate cues. Currently the cuesused are edge-based and can be assigned a limited numberof model features. It may prove useful to initiate thesearch using cues of this nature and then once model-based verification has been used, predict the location ofmodel features in the image that can be assigned moreambiguous image features such as vertices or angles. Asearch for these features in the image can then take place.This is similar to the approach adopted by Bolles and

Horaud10. A further consideration is to use region orcolour-based cues to start the search, to predict areas ofinterest, and to rank the seed cues.

The ATMS has performed two roles in this application.It has been used to represent multiple, ambiguous labelsallowing best first or opportunistic reasoning to takeplace. The ATMS has also been used to improve theefficiency of the search by reducing the number ofconstraints applied. If this is to result in real savings,then the improvement in search efficiency must outweighthe inherent cost of using the ATMS, which as Provan8

showed may generate an unmanageable number ofenvironments and nogoods. The problem has beencontrolled here by using cues and seed cues. Whilst earlyresults are encouraging, it remains to be seen howrealistic the savings offered by the ATMS are in generalpractice.

ACKNOWLEDGMENTSThe authors have received significant technical help, and ideas from PeterElleby regarding the use of the ATMS in constraint satisfaction problems.The work on the cue extraction has been done by Andrew Rydz and TinaAngelikaki. Steve Rake of the IBM Scientific Centre Winchester andmembers of the Intelligent Systems Group at Reading have providedmany valuable discussions.

REFERENCES

1. Baker, K.D. & G.D.Sullivan "Alvey MMW07Vehicle Exemplar: The Knowledge-Based Approach,"Proc. Alvey Vision Conference, AVC87,15 Sept.1987.

2. DeKleer, J. "An Assumption-Based TMS,"Artificial Intelligence, vol. 28, no. 2, March 1986.

3. Worrall, A., G.D.Sullivan & K.D.Baker, "Model-based Perspective Inversion," Proc. Alvey VisionConference, AVC88, Sept. 1988.

4. Brisdon, K., G.D.Sullivan & K.D.Baker, "FeatureAggregation in Iconic Model Evaluation," Proc.Alvey Vision Conference, AVC88, Sept. 1988.

5. Rydz, A., G.D.Sullivan & K.D.Baker, "Model-based Vision Planar Representation of theViewsphere," Proc. Alvey Vision Conference,AVC88, Sept. 1988.

6. DeKleer, J. "Problem solving with the ATMS,"Artificial Intelligence, vol. 28, no. 2, March 1986.

7. Bodington, R. & P.EUeby "Justification andAssumption-based Truth Maintenance Systems: Whenand How to use them for Constraint Satisfaction,"AISB workshop on Reason Maintenance Systems,University of Leeds, 14-15 April 1988

8. Provan, G. M. "Efficiency Analysis of Multiple-Context TMSs in Scene Representation." ProceedingsAAAI-87,1987

9. Grimson, W.E.L. and T.Lozano-Perez, "Model-Based Recognition and Localization from SparseRange or Tactile Data." MIT internal report, A.I.Memo 738, August 1983.

10. Bolles, R.C. and P.Horaud, "3DPO: A Three-Dimensional Part Orientation System," The Intl. Jnl.of Robotics Research , vol. 5, no. 3, Fall 1986.

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