Patent Application: US-77433310-A

Abstract:
the invention relates to a method for processing video signals from a video sensor , in order to extract 3d shape information about objects represented in the video signals , the method comprising the following steps : providing a memory in which objects are stored in a 3d shape space , the shape space being an abstract feature space encoding the objects &# 39 ; 3d shape properties , and mapping a 2d video signal representation of an object in the shape space , the coordinates of the object in the shape space indicating the object &# 39 ; s 3d shape .

Description:
the invention presents a mechanism for extracting geometric shape properties of objects from 2d images and image sequences . additionally , the system can use visual or non - visual information for implementing a more general similarity measure . this extraction process is based on two - dimensional views but the system is largely independent of the specific view of the three - dimensional object during its operation phase . in a first phase , the training phase , the system extracts the relevant image statistics from unlabeled image sequences . the training phase uses an established algorithm based on the “ slowness objective ” for finding features that typically vary slowly or seldom in the training data set , e . g ., slow feature analysis ( wissej 2002 ), the trace rule ( foeldiak 1991 ) or temporal stability ( kayser 2001 ). additional constraints guarantee that multiple non - trivial solutions ( like the constant solution ) are computed . the proposed system can use a single or multiple steps of optimizing the slowness objective , typically multiple steps are performed in a converging hierarchical layout as in ( franzius 2007a ). in contrast to existing systems , supervised information can be used to adapt the features generated by the unsupervised optimization of the slowness objective ( see later on ) and information of physical properties of an object can be autonomously incorporated . in the operation phase after the training phase , the system &# 39 ; s output is a mapping m from 2d input views to a “ shape feature space ”. this mapping is highly efficient because it can be instantaneously computed ( in the mathematical sense ), i . e ., a single 2d input view will generate a single shape feature representation . the extracted features can form clusters in the shape space , so that different views of the same object produce similar features and similar views of distinct 3d objects are separated . while this clustering property has been published earlier , a new quality is introduced here by identifying and using the interrelations of the cluster centers . moreover , a mechanism for directly controlling these cluster distances is introduced . the extracted shape space can implement a similarity measure in 3d shape space rather than in 2d appearance space , as well as other similarity measures . specifically , the system generalizes ( i . e ., produces meaningful results ) to views of objects with shapes that have not been presented during the training phase . for example , if the system was trained with image sequences of two shapes ( e . g ., cube and sphere ) and is applied to views of an intermediate shape ( e . g ., a morphed sphere - cube ), the extracted features will have intermediate values between “ cube ” and “ sphere ”. thus , the extracted shape features of a view of an arbitrary shape characterize its shape meaningfully as a distance to the known shape cluster centers ( e . g ., 30 % similarity to cube , 70 % similarity to sphere ). the system is not limited to processing 2d views , alternatively or additionally 3d depth data ( e . g ., from laser scanners ), multiple 2d views ( e . g ., from spatially separated cameras ), and combinations of these are possible . previous models like ( franzius 2007a ; franzius 2009 ) were only applied to artificial rendered data , whereas the proposed system has to be applied to real - world data as generated by a camera in possibly cluttered scenes . therefore , typically a segmentation step will be performed for preprocessing of the raw image data . optionally , non - rigid ( deformable ) objects can be recognized by the system . if , during the training phase , view sequences of an object undergoing non - rigid deformation are presented , the system can learn to associate the different configurations of the non - rigid object as a single shape . such sequences could also be generated artificially , e . g ., as morphs between shape a and b , in order to force the system into making shape representations of a and b more similar . as the training data sequence determines the invariance properties of the model output ( franzius 2007a ), the training data sequence needs to have certain properties in order for the model to produce view - invariant and shape - specific properties , e . g ., objects should be visible for extended periods of time while they undergo rotation in depth , for example . if the movement properties of the training data set cannot be directly controlled during the training phase and the movement properties are not favorable for extracting view - invariant shape features ( e . g ., rapid switching between views of different objects ), it is possible to control the learning rate such that learning only takes place during episodes of favorable movement ( franzius 2007a ). this principle has been proposed before in the context of learning egocentric spatial codes . a possible extension to invariant shape recognition is pose and position extraction . pose and position extraction is not new by itself ( franzius 2009 ), but gains a new quality for the generalized shape recognition introduced here . previous approaches considered some aspects of the movement statistics of objects for the invariance properties of a system based on optimizing the slowness objective ( franzius 2007a ). however , no prior work has considered the movement statistics induced by the physical properties of objects in this context before . as an example , if one considers an object &# 39 ; s movement after stimulation , e . g ., by applying an impulse in one spatial direction , which causes a movement trajectory depending on the shape properties of the object . some objects , like spherical objects , will move for extended time with relatively high speed , whereas other objects , e . g ., of cube - like shape , will quickly cease to move . given these movement trajectories , optimizing the slowness principle leads to different invariance and specificity properties for objects with different trajectories . specifically , if the movement trajectories have the same maximum amplitude ( e . g ., if the object movement is constrained by a box ) but different frequency ( e . g ., high for a sphere - like object and low for a cube - like object ), the object - specific clusters will have diameters depending on this frequency . the higher the typical movement frequency of an object , the more compact the object - specific cluster will be in order to optimize the slowness objective while fulfilling a minimum variance constraint . cluster diameters can afterwards be computed and interpreted in an unsupervised manner . thus , the proposed system can autonomously and in an unsupervised manner build up a representation of objects based on their physical properties , e . g ., shape . with such an ability , a system can autonomously learn about those relevant physical properties of previously unknown objects needed for interaction with the objects , e . g ., for grasping or positioning objects at a desired position . it is possible to extend the unsupervised training phase and the operation phase by an intermediate supervised learning phase . the beginning of the training phase during which sequences of views are presented remains unchanged . at the end of the training phase , a new supervised training phase is inserted . in the case of implementing the slowness optimization with slow feature analysis , this step is inserted after computing the covariance matrices of the data and the derivatives of the data but before diagonalizing the matrices for finding the optimally slow solutions . the new supervised training phase serves for adapting the computed features to become more similar to a desired similarity measure . given a similarity matrix s for some views ( e . g ., based on affordances , see below ), the system adapts the feature representation computed by the slowness learning rule such that it becomes more similar to s . this is implemented by presenting view pairs ( vi , vj ) as “ micro - sequences ” to the system additionally to those from the unsupervised learning phase . specifically , view pairs ( vi , vj ) with high similarity entries si , j in s are shown to the system . alternatively , all pairs ( vi , vj ) can be presented to the system , which adapts its learning speed proportionally to si , j . in the case of sfa , such a learning speed adaptation consists of weighting the updates to the covariance matrices of the data and the data derivatives by a factor proportionally to si , j , whereas gradient - descent based implementations can adapt their inherently present learning rate by multiplying it with a factor proportionally to si , j . the similarity matrix is considered as a requirement to the system . as a result , distances of a pair of inputs a , b in the shape feature space should approximate : norm ( s ( a ), s ( b ))˜ 1 / m ( a , b ). this result is achieved by means of changing the temporal presentation order in a supervised way for some stimuli as explained herein . thus , distances in shape space are to be measured and these distances approximate a desired and given similarity matrix s . a main difference to other previously described methods is that the information from object trajectories is used to gather information on physical properties like object shape , whereas in previous work , movement trajectories of different objects were carefully chosen to be as similar as possible . in known methods , no ( or no systematic ) temporal context between different objects exists in the training data . this is generally the case because switching objects before a camera takes time and in the meantime only background is present in a view . in theory , the relative positions of the cluster centers are arbitrary in this case , i . e ., any permutation of cluster centers is an equally good solution of the slowness optimization . these possible permutations are effectively free and uncontrolled parameters of the system . in practical implementations , especially in hierarchical ones , these free parameters are set based on the uncontrolled and poorly understood properties of the intermediate representations . the supervised training phase proposed here fixes these previously undetermined free parameters of the representation in a meaningful way , i . e ., to represent the desired similarity measure . the integration of s at this point is much more efficient than a later supervised classification because the setting of relative cluster distances requires only little additional information at this point of the learning process . as an effect , the resulting feature representation can directly implement both view invariance , as learned from the temporal proximity of different views of identical objects during the training phase , as well as the desired similarity measure between objects . one example of such a desired similarity measure is a texture - and view - invariant shape space . in the operation phase of the model presented by [ franzius , 2007 , 2009 ], a supervised classification or regression can be performed , e . g ., for view - invariant object classification . such a step is still possible with the method proposed here . nevertheless , the classifier can be simpler ( e . g ., linear ) and needs less training data , which makes it more efficient , because the underlying feature space is better adapted for the classification task . thus , only very few pairs of views ( vi , vj ) are needed to implement the desired similarity in the resulting feature space ( and thus , for example , perform object classification ). if no pairs ( vi , vj ) are used for supervised learning , the system behaves as in [ franzius 2007 , 2009 ]. if no slowness optimization is performed and all information is provided by the system in the form of distances of view pairs ( vi , vj ), the system effectively performs fisher discriminant analysis ( fda ). thus , the proposed approach here implements a synthesis of both and combines the controllability of fda with the power of the slowness objective to generate view - invariance . in principle , the similarity matrix s can have arbitrary meaning , visually related or not . a class of specifically relevant similarity matrices cs is related to physical function or affordances of an object or object class . this information can usually not be derived from purely visual data , i . e ., the object appearance . we propose the following similarity measures implemented by s : physical properties of an object , including friction and movement type on a flat surface . round objects can move extendedly on flat surfaces , whereas objects with sharp edges typically come to rest earlier . by measuring the average time of movement until an object comes to rest after pushing it , a similarity matrix s can be used to characterize physical shape and friction properties , which are highly important characteristics of objects for an autonomous system that needs to manipulate these objects . the measurement of such properties can be performed manually or by the autonomous system itself . in contrast to the completely autonomous approach of the previous section , this approach requires some additional programming for identifying the similarity matrix s . object categorization : objects with distinct visual appearance but similar category membership can be clustered by the system if the similarity matrix s is based on category membership . for example , views of objects of similar color could have a high pair - wise similarity , as well as views of objects of similar size . however , learning is restricted to cases where the objects &# 39 ; trajectories are influenced by other objects , which excludes cases of free rotation and extremely low friction . known methods choose features for object recognition either by hand - crafting a problem - specific feature set , or by choosing an appropriate problem - specific feature set from a pool of predefined features with a machine learning approach . these techniques are also applicable in a hierarchical fashion . the invention is based on the existing approach of generating features automatically by computing weighted nonlinear combinations of the input channels such that the slowness objective is maximized . while this approach works well in many cases , there is no direct way of controlling the feature generation process . the invention proposes an optional mechanism to add a supervised influence on the feature generation process . if classes of specific properties are known to be relevant for the generation of the shape space , e . g ., partial views showing corners or edges , these partial views can be used to train lower layers in the hierarchical model instead of ( or additionally to ) the full training views . in this way , the system can be trained to compute similar representations for each class of these partial views in lower layers , e . g ., independently of viewing angle , lighting or texture . automated shape recognition is a very basic tool that could be part of any apparatus with some degree of autonomy and sensing device ( e . g ., a camera ). automatic sorting of objects by shape or other physical properties with invariance to lighting conditions , object pose and object surface ( texture ), based on visual data . such a sorting could sort by shape similarity instead of a rigid pattern matching ( e . g ., round potatoes vs . elongated ones ). recognition of shapes for robotics : an object &# 39 ; s shape and pose determine how a robotic device can best grasp the object or perform arbitrary manipulations of the object . the slowness goal function can be optimized directly on a sequence of raw 2d images . in most cases , however , the dimensionality of the input views is too high for computationally efficient optimization . in this case , a hierarchical model is applied , such that on the lowest layer small patches of the input views are used for the optimization . the outputs of some neighboring areas are then fed into the next layer , which again optimizes the same goal function , until the hierarchy converges to a certain resolution , or in the extreme case , to a single position . the model was trained with image sequences containing colored views of five different convex objects : sphere , cone , cube , pyramid , and cylinder . two different input sets were used : “ rendered ” and “ video ”. for the rendered data , the visualization toolkit ( vtk ) was used to render the views of the shaded objects in front of a homogeneous background either with or without textures . additionally , test data was generated from “ morphed ” figures whose shape can be set as a gradual interpolation between any of the five training shapes . the object poses ( configurations ) for the training sequences were generated by a random walk procedure . to generate a configuration in the sequence we add a random term to the current spatial , angular , and scaling velocities of the object . the random term is drawn from an interval with a homogeneous probability density . the velocities are cut off at certain limits and by adjusting these limits one can effectively determine the transformation timescales . the position , angles , and scale are then updated according to these velocities . if an object reaches the position boundary , it is bounced back . the whole procedure produces flat configuration histograms ( given enough time points ) and the velocity profiles are independent of the configuration values . in each step the object identity was changed with low probability ( p = 0 . 02 ). a blank frame was inserted if a switch took place to avoid linking together different objects in identical poses in the stimulus , which would introduce an element of supervised training . for the video set , three variants of objects from paper and polystyrene foam were used . one variant was left white , whereas the others were textured . additionally , a subset of the hri50 database of everyday objects consisting of six soda cans , six rubber ducks , six cardboard boxes ( e . g ., for tea bags ) and six sphere - like objects ( e . g ., a ball and an orange ) has been used . the objects were held in hand in front of a camera in a cluttered office environment . based on stereo camera depth cues and skin color detection , object views were segmented from the background before further processing as in ( wersingkirsteinetal 2007 ). due to fluctuations in the segmentation step , the object was not always perfectly segmented , centered , or scaled . optimization problem : given a function space f and an i - dimensional input signal x ( t ) find a set of j real - valued input - output functions g j ( x ) εf such that the output signal y j ( t ): = g j ( x ( t )) minimize δy j ( t ):=& lt ; y ′ j 2 & gt ; t under the constraints ( 1 ) & lt ; y j & gt ; t = 0 ( zero mean ), ( 2 ) & lt ; y j 2 & gt ; t = 1 ( unit variance ), ( 3 ) ∀ i & lt ; j :& lt ; y i y j & gt ; t = 0 ( decorrelation and order ), with & lt ; & gt ; and y ′ indicating temporal averaging and the derivative of y , respectively . the δ - value introduced above is a measure of the temporal slowness ( or rather fastness ) of the signal y ( t ). it is given by the mean square of the signal &# 39 ; s temporal derivative , so that small δ - values indicate slowly varying signals . the constraints ( 1 ) and ( 2 ) avoid the trivial constant solution and constraint ( 3 ) ensures that different functions g j code for different aspects of the input . because of constraint ( 3 ) the g j are also ordered according to their slowness , with g 1 having the smallest δ . in practical applications one typically uses only the first n solutions and discards the faster g j , to control the dimensionality of the resulting data . it is important to note that although the objective is slowness , the functions g j are instantaneous functions of the input , so that slowness cannot be achieved by low - pass filtering . slow output signals can only be obtained if the input signal contains slowly varying features that can be extracted instantaneously by the functions g j . note also that for the same reason , once trained , the system works fast , not slowly . the term “ slowly varying feature ” refers to features generated by the optimization of a slowness learning rule , e . g ., the delta value for slow feature analysis . thus , “ slowly varying features ” are a mathematically well - defined concept . in the computationally relevant case where f is finite - dimensional the solution to the optimization problem can be found by means of slow feature analysis ( sfa ) [ wissej 2002 ] and in a slightly different formulation in [ berkwisk 2005c ]. this algorithm , which is based on an eigenvector approach , is guaranteed to find the global optimum . we use the sfa implementation in the open source mdp library ( modular toolkit for data processing ) [ mdp ]. the computational model consists of a converging hierarchy of layers of sfa nodes . each sfa node finds the slowest features from its input according to the sfa algorithm and performs the following sequence of operations : additive gaussian white noise ( with a variance of 10 − 6 ), linear sfa for dimensionality reduction , quadratic expansion , another linear sfa step for slow - feature extraction , and clipping of extreme values at ± 4 . the network is implemented in python and all required elements ( including the parallelization ) are available in the mdp library ( mdp ). 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