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PDF Expert 7 Receives Powerful New Features for iOS 13 and iPad OS.

 
 

The probability of each of an exact subgraph isomorphism for the conceptual graph in these elements is reevaluated using information from a domain- Fig. Hand-gesture elements that can be trated in Fig. Unfortunately, exact graph-matching methods used to form concepts that make sense when considering the are not practical since perfect recognition of hand-gesture complete set of hand gestures are more likely to be selected over elements seldom occurs in practice. Since multiple exact matches are possible by applying different modifications, criteria and constraints are required to select the most appropriate set of modifications.

Conceptual Graph Manipulation Ideally, modifications should minimize both the semantic distance between the modified conceptual graph representing the set of hand-gesture elements and a subset of a conceptual graph representing domain knowledge, and the difference between the modified and unmodified hand-gesture conceptual graph. The semantic difference between sets of hand gestures and their identified meaning in the knowledge base is quanti- fied by assigning penalties for each type of conceptual graph modification, resulting in the objective function in 1 Fig.

Excerpts from the domain-specific knowledge base. Relevant concepts where is the set of conceptual graph elements that must be and their relationships are used to determine the meaning of sequences of hand added to the existing conceptual graph to obtain an exact sub- gestures. To relate the objective function to hand-gesture under- standing, costs of each graph modification are defined based on the semantic distance or logical difference between the original and modified graphs.

To relate the costs in the objec- tive function to these semantic distances, costs are specified in terms of fundamental conceptual graph operations [19]. These operations include restricting and unrestricting a concept, or changing its scope, and joining and detaching concepts and relations. Substitution of one node in the hand-gesture graph for an- other is accomplished using the restrict and unrestrict opera- tions. These operations change the meaning of a node by mod- ifying its referent or its type.

If the knowledge base contains information about rigid objects and the hand-gesture graph ref- Fig. Noisy hand-gesture elements. Several candidate concepts are possible erences a solid box, the solid box concept can be unrestricted when elements are ambiguous or not recognized accurately. Similarly, if the hand-gesture graph contains a specific solid box, it can be unrestricted or generalized into IV.

Since relation nodes do not have referents, the restrict and unrestrict operations only modify the type of a The objective in understanding hand gestures using these con- relation node. A portion of the domain-spe- the concept or relation hierarchy. Costs are assigned in the con- cific knowledge base is illustrated in Fig. As is standard with conceptual graphs, the rectangles minimum cost path between the two types in the relevant hier- represent concepts, and circles represent relationships.

The con- archy. The resulting cost of the restrict and unrestrict operations cepts formed from hand-gesture elements in Fig. A more realistic, ambiguous 2 scenario is illustrated with the conceptual graph shown in Fig. When an exact subgraph isomorphism does not exist, modi- where , if node has no referent or the referents of node fications can be applied to the conceptual graph to find exact and are identical, otherwise.

The cost of restricting a referent, and the lution landscape of the problem. Adaptation to the solution land- cost of unrestricting a referent, are defined in terms of the scape is influenced by several parameters including the Tabu minimum cost path between the types of nodes and using list length, type, and aspiration criteria. Other viable approxi-. For sim- Since the Tabu search is a deterministic optimization algo- plicity, all edges are assigned a fixed cost, except for edges con- rithm, the initial solution has a significant impact on its perfor- nected to the absurdity and entity types, which are assigned in- mance.

A poorly selected initial solution may prevent the search finity and a large value, respectively. Adjustment of these edge from finding the global optimum. In this paper, the initial solu- costs to more accurately reflect relative similarities may im- tion is selected by mapping each node on the gesture concep- prove the accuracy, but these costs are application-specific pa- tual graph to one of the closest node matches on the knowledge rameters.

Al- The join operation allows additional conceptual graphs to be ternative initial solutions include random assignment, or use of attached to the gesture conceptual graph. Similarly, the detach a stochastic approximation method to generate initial solutions. These two operations allow adjacent nodes from the search include definition of a local neighborhood, and man- original gesture conceptual graph to be mapped on to nonadja- agement of a Tabu list.

In this graph-based hand-gesture un- cent nodes in the knowledge conceptual graph, and nonadjacent derstanding application, neighborhoods are defined based on nodes from the original gesture conceptual graph to be mapped changing the mapping between input nodes and a model nodes. Per- The neighborhood for each gesture node mapping includes any forming join and detach operations on the hand-gesture graph adjacent node on the knowledge graph of the same type.

This allows additional information to be added or existing informa- constraint to map only nodes of the same type ensures the re- tion removed to improve the match in existing knowledge. The neighborhood also includes mapping a ges- isting nodes. The cost of joining concepts and relation links ture node to empty, resulting in deletion of the node. The change between node and node is shown in 3. In this equation, in cost to each neighboring solution is determined by subtracting is the cost of joining an additional concept, and the cost of the contribution of the new mapping from the contribution of the joining an additional relation link current mapping using the previously defined objective func- tion.

This approach ensures Similarly, the cost of deleting concepts and relation links recent solutions are not repeated, helping to balance exploration between node and node is shown in 4. In this equation, of the solution space against exploitation of the local solution is the cost of joining an additional concept, and the cost of landscape. Separate lists are maintained for concepts and rela- joining an additional relation link.

A recently used ges- ture concept is not used in another move until it is no longer in the list or the aspiration criteria is met. These Tabu lists are 4 overridden if a neighboring solution is better than any solution found so far. To ensure the conceptual graph remains consistent, only valid conceptual graphs are detached or joined to an existing concep- tual graph.

To enforce this constraint, each relation must have V. To facilitate experiments with an emphasis on under- links are attached to concepts. Tabu Search is measured using a glove-based input device. One sensor Due to the NP-completeness of subgraph isomorphism, and CyberGlove with an attached flock of birds is used to obtain the increased complexity of error-correcting subgraph isomor- accurate three-dimensional 3-D representations of the hand.

The Tabu search technique freedom for the proximal interphalangeal joint angles and two [20] is used to find an approximate subgraph isomorphism, since degrees of freedom for the metacarpophalangeal MCP joint this deterministic algorithm can take advantage of the structure angles for each finger. The thumb is measured using one de- of conceptual graphs during the search. Distal finger-joint measurements were not obtained, as the distal joint angles for each finger depend heavily on the two joint measured angles.

Wrist flexion and abduction is measured in addition to palm curvature to calculate the position of each joint with respect to the forearm. The position and ori- entation of the forearm is measured to complete an internal 3-D spatiotemporal model of the hand.

The data acquisition hard- ware used to obtain these measurements is illustrated in Fig. It is important to note that the data obtained with these tools can be directly replaced less intrusive hardware using 3-D reconstruction techniques as they mature [21]—[23]. Data acquisition is performed at Hz and synchronized to avoid the vertical refresh of the closest monitor and reduce the effects of electromagnetic interference.

Data is then down-sam- pled to between 10 and 30 Hz for use in training and recog- nition. Although the presented approach is applicable in many domains, the knowledge domain for these experiments contains food consumption and meal-related concepts.

These concepts may be relevant to an interactive service robot for assisting the elderly or disabled with meal preparation and cleanup. A soft- ware-visualization component used for immediate feedback can be observed in Figs.

The language selected for these hand-gesture understanding experiments consists of single-handed signs based on na- tive signs in the American Sign Language ASL. Natural signs are used due to their efficiency in conveying concepts. Finger-spelling approaches are extremely inefficient in terms of time required to communicate concepts, and do not take advantage of the full capabilities of hand-gesture languages.

Al- though occasionally necessary for names and specific objects, finger-spelling only emulates speech and text-based languages. In contrast, natural signs in ASL convey concepts without each hand gesture requiring an equivalent English word or letter. The maturity and widespread North American use of ASL also plays a role in its selection for use in these hand-gesture understanding experiments.

Refined training resources already exist to simplify the task of human learning, where necessary, and those that are already familiar with ASL can immediately apply their knowledge to simplify communication with ubiqui- tous devices. Recognition Only Initial experiments were performed to determine the recogni- tion performance of hand-gesture elements in isolation.

These results provide a base for a relative comparison against under- standing using the approximate graph matching approach. His fingers are controlling and equalizing the bowl thickness and at the end the potter passes a very fine wire between the bowl and the wheel in order to take the bowl.

It provides an automatic filtering for the correction of magnetic disruption. It has been selected for the gesture capturing and the implementation of the methodology described below. It is occlusion-independent and it provides a high precision rotational representation of body segments. The 11 sensors are integrated in the suit and after the calibration they provide and capture information related to the XYZ axis rotations with the use of integrated gyroscopes, accelerometers and magnetometers.

Since magnetometers are used among other sensors in the suit with the inertial sensors, the quality of data captured can be influenced by magnetic disturbances. During the first day of data acquisition with the potter B these disturbances were very strong since he was using an old model of wheel, containing many metallic devices.

Despite the fact that data are online corrected by the system if weak magnetic disturbances are identified, the data acquired at the first day were of a very bad quality. For this reason another data acquisition session has been realized with the use of a more modern wheel with less magnetic disturbances. Some of them may play a more important role in the technical gesture depending on the type of handicraft, but all the following body articulations are involved in the performance of the gesture of the craftsman.

We are also aware about the important role of fingers in wheel-throwing process. Table I. A prerequisite for the creation and the application of our methodology was to design the ArtOrasis system and interface Figure 5. This gesture recognition system is entirely implemented in MaxMSP, an interactive programming environment that uses the Jitter toolbox and it aims at the recognition of technical gestures. ArtOrasis can also be used for capturing, modeling, and recognition. It also provides functionalities for the visualization of the skeleton of the craftsman.

Moutarde Fig 5. Screenshot of the interface of the ArtOrasis system presenting the learning and recognition phases In case of wheel throwing pottery 11 segments of the human skeleton listed in the table below have been selected and used for the training of ArtOrasis system.

Concerning the different gestures separation, the training has been based on the 4 effective gestures identified during the second stage. According to the model defined previously the user researcher, potter, learner of ArtOrasis can define and choose which are the most important parts of the body that participate in the execution of a technical gesture and train the system based on ones.

This stage corresponds to the machine learning phase of the methodology. The training of the gesture recognition system is also based on the effective gesture separation defined in second methodological step. After the training of the system the last step is the gesture recognition that is evaluated below. To attain this goal, we need to compare gesture realization by an apprentice with the recorded and modeled gestures by experts.

A pre-requisite before estimating such similarity, is the automated recognition by the system of what particular step the apprentice is trying to perform. For this reason we are convinced that online technical gesture recognition is essential for the comparison of handicraft skills between apprentices and expert.

Furthermore, the segmentation of the data captured into a set of specific gestures, and the training of models, provides the data with a semantic dimension. In order to validate our approach, and evaluate the recognition accuracy of the system for all the 1 , it has been asked to each of the expert potters to create five bowls. It has to be mentioned that very often, expert craftsmen are not available to create many copies of exactly the same object since this procedure is considered as a creative art process or because of ageing.

Nevertheless, in case of the potter A the repeatability of his gestures can be considered as being of a high level, since he was very concentrated and careful in the way he performed the gestures. In case of the potter B the repeatability is of a medium level since he is easily disturbed in his everyday work by external elements neighbors visiting his atelier, etc. The gesture recognition rates have been evaluated based on the « jackknife » method [Abdi, Williams ]. In our case, jackknifing means estimation of the recognition accuracies for manually segmented gestures isolated gestures by using subsets of the available gestural data.

The basic idea behind the jackknife variance estimator lies in systematically recomputing the statistic estimate leaving out one or more observations at a time from the sample set. In total, five observations for each gesture have been recorded and distinct databases for learning and test have been used in five iterations. For each iteration, one dataset is left out to be used as the learning database and train one model 6 per gesture until all the data sets are used once and the four remaining datasets are used as a database for testing.

For each of the eleven body segments, one Euler angle per axis has been computed. The table II shows the results of the five iterations of the jackknifing as well as the Precision and Recall per gesture of the potter A. Moutarde Table II. Precision and Recall per gesture from the potter A based on five iterations of jackknifing using Euler angles.

In table III we can see that all three ways of the motion representation give excellent results for the recognition of all the effective gestures. Table III. Additionally, DCM are widely used on animation but not for analysis, recognition or modeling of rotations.

With regards to the potter B, we have also evaluated the recognition accuracy based on jackknife method. In the table IV, the precision and recall for his. During this test we use Euler angles since they have been previously identified as the most relevant descriptor.

Like in the previous example 20 queries for recognition per GK have been asked to the 6. The precision and recall are perfect for. The difference between -. Gesture G0 has the lowest recognition rate because the potter was very disturbed. The repeatability of this gesture is low and it has a direct impact on its recognition rate.

Even if the number of. Table IV. Precision and Recall per gesture based on 5 iterations of jackknifing using Euler angles-Potter B. Maximum likelihoods LG 6. Get project updates , sponsored content from our select partners, and more. Full Name. Phone Number. Job Title. Company Size Company Size: 1 – 25 26 – 99 – – 1, – 4, 5, – 9, 10, – 19, 20, or More. Get notifications on updates for this project. Get the SourceForge newsletter. JavaScript is required for this form. No, thanks. Save relevant discoveries into search history to make them more convenient to recall.

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