Abstract:
Systems and methods for automated pattern recognition and object detection. The method can be rapidly developed and improved using a minimal number of algorithms for the data content to fully discriminate details in the data, while reducing the need for human analysis. The system includes a data analysis system that recognizes patterns and detects objects in data without requiring adaptation of the system to a particular application, environment, or data content. The system evaluates the data in its native form independent of the form of presentation or the form of the post-processed data.

Description:
PRIORITY CLAIM 
       [0001]    This application claims priority to provisional patent application 60/743,711 filed on Mar. 23, 2006 and is incorporated herein by reference. 
     
     FIELD OF THE INVENTION 
       [0002]    The present invention, in various embodiments, relates generally to the field of data analysis, and more particularly to pattern and object recognition in digital data. 
       BACKGROUND OF THE INVENTION 
       [0003]    With the increasing use of computers and computerized technology, the amount of information represented digitally has become enormous. Analysis of these vast quantities of digital data generally involves the recognition of known patterns. 
         [0004]    In many cases, information that originates in a digital form is ultimately analyzed through manual review by a person, often requiring substantial training. For example, medical image analysis typically requires a high level of expertise. In order for people to interact with the volumes of digital data, the information is typically converted into a visual, audible, or other human-perceivable representation. However, during the process of translating digital data from its raw form into a convenient output form, some information can be lost. Data is often processed and filtered for presentation before analysis, losing significant information from the original data. For example, the data of ultrasound, seismic, and sonar signals are all initially based on sound. The data of each of these is typically processed into a graphical form for display, but the processing often sacrifices substantial meaning and detail for the sake of human readability. 
         [0005]    While humans can be trained to analyze many different types of data, manual human analysis is generally more expensive than automated systems. Additionally, errors are often introduced due to the limits of human perception and attention span. The data often contains more detail than human senses can discern, and it is well-known that repetition causes errors. 
         [0006]    To address these shortcomings of human analysis, many automated pattern recognition systems have been developed. However, most of these solutions are highly data-specific. The inputs that a pattern recognition system can handle are often fixed and limited by design. Many systems are inherently limited by design on the basis that many systems are designed by use on a specific modality. For example, medical image analysis systems perform well on X-ray or MR imagery but perform poorly on seismic data. The reverse is also true. The system by which the data is evaluated is tightly coupled with the specific data source it was designed to evaluate. Therefore, improvements across a broad range of systems are very difficult. 
         [0007]    Within each system, pattern and feature recognition is processing-intensive. For example, image analysis commonly uses complex algorithms to find shapes, requiring thousands of algorithms to be processed. The time to discover, develop, and implement each algorithm causes an incremental delay in deploying or improving the system. 
         [0008]    Thus, there still remains substantial room for improvement in the field of automated pattern recognition systems. 
       SUMMARY OF THE INVENTION 
       [0009]    This system is designed not to be limited by any specific modality or by the limited knowledge of those developing the system. The present invention provides an automated pattern recognition and object detection system that can be rapidly developed and improved using a minimal number of algorithms for the data content to fully discriminate details in the data, while reducing the need for human analysis. The present invention includes a data analysis system that recognizes patterns and detects objects in data without requiring adaptation of the system to a particular application, environment, or data content. The system evaluates the data in its native form independent of the form of presentation or the form of the post-processed data. 
         [0010]    In one aspect of the present invention, the system analyzes data from any and all modalities within all data types. Example data modalities include imagery, acoustic, scent, tactile, and as yet undiscovered modalities. Within imagery, there exists still and moving images with applications in the fields of medicine, homeland security, natural resources, agriculture, food sciences, meteorology, space, military, digital rights management, and others. Within acoustic, there exists single and multi-channel audio sound, ultrasound-continuous stream, seismic, and SONAR with applications in the fields of medicine, homeland security, military, natural resources, geology, space, digital rights management, and others. Examples of other digital data streams include radar, scent, tactile, financial market and statistical data, mechanical pressure, environmental data, taste, harmonics, chemical analysis, electrical impulses, text, and others. Some data modalities may be combinations of other modalities, such as video with sound or multiple forms of a single modality such as where multiple images of different types are taken of the same sample, for example correlated MRI and CT imaging; combined SAR, photograph and IR imagery. Improvements made in the common system benefit all modalities. 
         [0011]    In other aspects of the present invention, the system uses a relatively small number of simple algorithms that capture more fundamental relationships between data elements to identify features and objects within the data. This limited set of algorithms can be implemented quickly in each modality and in multiple modalities. 
         [0012]    In still other aspects of the present invention, the system provides an automated system that operates on the full resolution of the native data. The results are produced in a timely manner, alleviating the tedium of preliminary human analysis and alerting the operator to examine a data set that requires attention. 
     
     
       DESCRIPTION OF THE DRAWINGS 
         [0013]    The preferred and alternative embodiments of the present invention are described in detail below with reference to the following drawings. 
           [0014]      FIG. 1  shows an overview of one embodiment of the invention; 
           [0015]      FIG. 2  shows an example system for executing a data analysis and feature recognition system; 
           [0016]      FIG. 3  shows an example method for using a data analysis and feature recognition system; 
           [0017]      FIG. 4  shows an example method for creating a datastore; 
           [0018]      FIG. 5  shows an example method for creating a known feature; 
           [0019]      FIG. 6  shows an example method for modifying a synaptic web by training or untraining; 
           [0020]      FIG. 7  shows an example method for generating an algorithm value cache; 
           [0021]      FIG. 8  shows an example method for training a known feature; 
           [0022]      FIG. 9  shows an example method for creating a collection of training paths from positive and negative training value sets; 
           [0023]      FIG. 10  shows an example method for removing negative training values sets from the collection of training paths; 
           [0024]      FIG. 11  shows an example method for creating a synaptic path from a training path; 
           [0025]      FIG. 12  shows an example method for associating a synaptic leaf with a known feature; 
           [0026]      FIG. 13  shows an example method for untraining a known feature; 
           [0027]      FIG. 14  shows an example method for using a set of algorithm values to retrieve a synaptic leaf in the synaptic web; 
           [0028]      FIG. 15  shows an example method for disassociating a synaptic leaf from a known feature; 
           [0029]      FIG. 16  shows an example method for identifying known features; 
           [0030]      FIG. 17  shows an example method for determining if a known feature has been found; 
           [0031]      FIG. 18  shows an example method for evaluating cluster and threshold detection; 
           [0032]      FIG. 19  shows an example method for evaluating threshold detection; 
           [0033]      FIG. 20  shows an example method for evaluating cluster detection; 
           [0034]      FIG. 21  shows an example method for processing the known features identified for an area; 
           [0035]      FIG. 22  shows an example method for performing a known feature action; 
           [0036]      FIG. 23  shows an example 10×10 pixel array of grey scale image data; 
           [0037]      FIG. 24  shows an example 10×10 array containing the outputs of the mean algorithm; 
           [0038]      FIG. 25  shows an example 10×10 array containing the outputs of the median algorithm; 
           [0039]      FIG. 26  shows an example 10×10 array containing the outputs of the spread of values algorithm; 
           [0040]      FIG. 27  shows an example 10×10 array containing the outputs of the standard deviation algorithm; 
           [0041]      FIG. 28  shows an example synaptic web containing a single synaptic path using the values calculated in  FIGS. 24-27 ; 
           [0042]      FIG. 29  shows an example synaptic web containing two synaptic paths using the values calculated in  FIGS. 24-27 ; 
           [0043]      FIG. 30  shows an example synaptic web containing many synaptic paths using the values calculated in  FIGS. 24-27 ; 
           [0044]      FIG. 31  shows the example synaptic web from  FIG. 30  with the next synaptic path added, showing how the synaptic web can branch; 
           [0045]      FIG. 32  shows an example synaptic web containing all the synaptic paths using the values calculated in  FIGS. 24-27 ; 
           [0046]      FIG. 33  shows a synaptic path which results in a synaptic leaf having multiple known features; 
           [0047]      FIG. 34  shows a series of arrays for a 6×6 grey scale image; 
           [0048]      FIG. 35  shows a screenshot of an introduction screen when setting up a datastore; 
           [0049]      FIG. 36  shows a screenshot of entering a set of initial values; 
           [0050]      FIG. 37  shows a screenshot of the expanded submodality combo box; 
           [0051]      FIG. 38  shows a screenshot of a series of textboxes used to add optional descriptive parameters; 
           [0052]      FIG. 39  shows a screenshot of the selection of a target data area shape and a set of algorithms for the shape; 
           [0053]      FIG. 40  shows a screenshot of a review of the datastore properties previously selected; 
           [0054]      FIG. 41  shows a continuation of the summary displayed in  FIG. 40 ; 
           [0055]      FIG. 42  shows a screenshot of an example application after finishing the creation of a datastore; 
           [0056]      FIG. 43  shows a screenshot of the algorithms of the grey adjacent pixel target data area; 
           [0057]      FIG. 44  shows a screenshot of a “create or edit a known feature” wizard; 
           [0058]      FIG. 45  shows a screenshot of the selection of a name and detection method for a known feature; 
           [0059]      FIG. 46  shows a screenshot of the expanded combo box from  FIG. 45 ; 
           [0060]      FIG. 47  shows a screenshot of the training count values for a known feature; 
           [0061]      FIG. 48  shows a screenshot of the cluster range values for a known feature; 
           [0062]      FIG. 49  shows a screenshot of the action value of a known feature; 
           [0063]      FIG. 50  shows a screenshot of a review of the known feature properties previously selected; 
           [0064]      FIG. 51  shows a screenshot of an image of a forest with a selected region of interest; 
           [0065]      FIG. 52  shows a screenshot of an introduction screen for a training wizard; 
           [0066]      FIG. 53  shows a screenshot of the selection of forest as a known feature from the datastore; 
           [0067]      FIG. 54  shows a screenshot of the selection of an area training option; 
           [0068]      FIG. 55  shows a screenshot of a review of the training properties previously selected; 
           [0069]      FIG. 56  shows a screenshot of the results of training; 
           [0070]      FIG. 57  shows a screenshot of an image with an area of forest; 
           [0071]      FIG. 58  shows a screenshot of the results of training the image in  FIG. 57 ; 
           [0072]      FIG. 59  shows a screenshot of a wizard for known feature processing; 
           [0073]      FIG. 60  shows a screenshot of a list of known features a user may want to process; 
           [0074]      FIG. 61  shows a screenshot of a known feature&#39;s significance value; 
           [0075]      FIG. 62  shows a screenshot of optional overrides for the training count values for a single processing run; 
           [0076]      FIG. 63  shows a screenshot of optional overrides for the cluster values for a single processing run; 
           [0077]      FIG. 64  shows a screenshot of a review of the processing properties previously selected; 
           [0078]      FIG. 65  shows a screenshot of the results of processing; 
           [0079]      FIG. 66  shows a screenshot of an image with a green layer showing pixels the system identified as forest; 
           [0080]      FIG. 67  shows a screenshot of a composite image with a forest layer; 
           [0081]      FIG. 68  shows a screenshot of a second image processed for the forest known feature; 
           [0082]      FIG. 69  shows a screenshot of an image with a green layer showing pixels the system identified as the known feature forest; 
           [0083]      FIG. 70  shows a screenshot of a composite image with a forest layer; 
           [0084]      FIG. 71  shows a screenshot of an image with water selected; 
           [0085]      FIG. 72  shows a screenshot of the results of training using the previously selected water; 
           [0086]      FIG. 73  shows a screenshot of an image with both forest and water; 
           [0087]      FIG. 74  shows a screenshot of a review of the processing properties previously selected; 
           [0088]      FIG. 75  shows a screenshot of the results of processing; 
           [0089]      FIG. 76  shows a screenshot of a water layer; and 
           [0090]      FIG. 77  shows a screenshot of a composite image with both the forest layer and the water layer. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0091]    Although several of the following embodiments and examples of a data analysis and feature recognition system are described with reference to specific data types, such as image data and audio data, the invention is not limited to analysis of these data types. The systems and methods described herein can be used to recognize discrete features in a data set or any other collection of information that can be represented in a quantifiable datastore. 
         [0092]    The embodiments of a data analysis and feature recognition system described herein generally involve the analysis and organization of digital data streams for the purpose of learning and repeatedly recognizing patterns and objects within the data. The digital data streams may be conversions of an analog source to digital form. In some embodiments, the data organization structure used by the system involves a web (referred to herein as a “synaptic web”) of interconnected data fields used to describe the elements of a defined object. 
         [0093]    In one embodiment, illustrated for example in  FIG. 1 , a data analysis and feature recognition system is configured to accept a source data set  80  containing a known and pre-identified feature “X”  81  (e.g. a known pattern, shape or object). The system is generally configured such that a user can “train”  82  the system to recognize the known feature “X.” This training is accomplished by executing a plurality of algorithms to analyze  83  the data representing feature “X” in order to identify sets of values defining the characteristics of the feature. The sets of values defining the feature “X” are then stored  84  in an organizational structure referred to herein as a “synaptic web”  85 , which is made up of a plurality of “synaptic leaves” interconnected by a plurality of “synaptic paths.” 
         [0094]    Once the system has been trained for a known feature, a new data set  86  containing an unknown set of features  87  can be presented to the system. The system can be configured to accept a user request  88  to analyze  89  a selected portion of the new data set using the same plurality of algorithms and comparing  90  results with information stored in the synaptic web  85  in order to identify any known features (such as Feature “X,” or any other previously-trained features) contained therein. Once a known feature is found in the new data set, the system can notify  91  a user of the fact that known features have been identified and/or the system can present  92  a representation of the known feature to the user (e.g. in the form of a graphical image, an audible sound, or any other form). 
         [0095]    As used herein, the term “datastore” carries its normal meaning, and is generally used herein to refer to any software or hardware element capable of at least temporarily storing data. In several embodiments, the datastores referred to herein contain a plurality of known features represented by a plurality of synaptic webs, each synaptic web containing a plurality of synaptic leaves joined by synaptic paths as further illustrated below. 
         [0096]    As used herein, the term “target data element” (TDE) refers to a discrete portion of a larger data set in a given media being evaluated for characteristics using algorithms. A target data element can be any size appropriate for a particular type of data. For example, in a set of graphical data, a TDE may consist of a single pixel, or it may comprise a localized group of pixels or any other discrete group of pixels. In several embodiments, regardless of its size, a TDE is a “point” that is evaluated in a single discrete step before moving on to the next TDE. 
         [0097]    As used herein, a “target data area” (TDA) is a collection of data immediately surrounding a target data element. The size and shape of a TDA can vary depending on the type of data or media being evaluated. The size and shape of the TDA defines the data points available for the calculations performed by the algorithms. 
         [0098]    As used herein, the term “known feature” is used to refer to an element of data representing an item, object, pattern, or other discretely definable piece of information known to be present in a particular data set during training. At the time of processing the system searches a new data set for one or more of the previously defined known features. 
         [0099]    As used herein the term “synaptic web” refers to an organizational structure for storing information about discrete features, patterns, objects or other known data sets in an implementation of a rooted, fixed depth tree. A synaptic web advantageously allows the information about the known features to be quickly added, and an unknown data set to be quickly evaluated to identify any known features contained therein. 
         [0100]    As used herein, the term “synaptic leaf” generally refers to a terminal node in a synaptic web representing a plurality of known features identified by the set of algorithm values used to get to the leaf. 
         [0101]    As used herein, the term “synaptic path” refers to a plurality of values from all of the algorithms. The synaptic path is used to reach a synaptic leaf based on calculations for target data elements. 
         [0102]    As used herein, a “training event” is the process of associating a plurality of algorithm values to a known feature by creating or updating synaptic paths and synaptic leaves. 
         [0103]    As used herein, the term “algorithm” carries its normal meaning, and refers without limitation to any series of repeatable steps resulting in a discrete “value.” For example, an algorithm includes any mathematical calculation. In several embodiments, various algorithms are performed on target data elements in relation to a previously defined target data area to produce a single, meaningful value. 
         [0104]    As used herein, the term “hit detection” refers to a method for determining whether a known feature is present in a test data set based on matching a synaptic path encountered during processing with any path trained for the known feature. 
         [0105]    As used herein, the term “cluster detection” refers to a method of determining whether a known feature is present in a test data set based on both hit detection and the detection of a specified number of additional hits within a pre-defined “cluster distance” of a target data element. 
         [0106]    As used herein, the term “cluster distance” refers to one or more user-defined distance specifications for evaluation of a target data element. A cluster distance may refer to an actual physical distance, or may represent a mathematical relationship between discrete data elements. 
         [0107]    As used herein, the term “threshold detection” refers to a method for determining whether a known feature is present in a test data set based on both hit detection and the number of times the synaptic path used in hit detection has been trained as the known feature. 
         [0108]    As used herein, the term “positive training value sets” refers to the sets of algorithm values that were in the area of data trained as the user defined known feature. 
         [0109]    As used herein, the term “negative training value sets” refers to the sets of algorithm values that were outside the area of data trained as the user defined known feature. 
         [0110]    As used herein, the term “area training” refers to a process used in a training event where each set of algorithm values found in a positive training value set is used to generate synaptic paths for the known feature. 
         [0111]    As used herein, the term “relative adjusted training” refers to a process used in a training event where each set of algorithm values found in a negative training value set nullifies one matching set of algorithm values found inside the positive training value set. The remaining positive training value sets can then be used to generate synaptic paths for the known feature. 
         [0112]    As used herein, the term “absolute adjusted training” refers to a process used in a training event where each set of algorithm values found in a negative training value set nullifies all matching sets of algorithm values found inside the positive training value set. The remaining positive training value sets can then be used to generate synaptic paths for the known feature. 
         [0113]    As used herein, the term “modality” is used in its normal sense and generally refers to one of the various different forms or formats of digital data that can be processed. For example, image data represents one modality, while audio data represents another modality. In addition to describing data types that conform to one or more human sensory modalities, the term is also intended to encompass data types and formats that might have little or no relation to the human senses. For example, financial data, demographic data and literary data also represent modalities within the meaning of the term as used herein. 
         [0114]    As used herein, the term “submodality” refers to a sub-classification of a modality. In some embodiments, a submodality refers to one of the applications or sources for the data that can affect how the data is processed. For example, X-Ray and Satellite Photography are submodalities of imaging. Systems for producing X-Ray images from different vendors (such as GENERAL ELECTRIC or SIEMENS) can differ enough in their data formats to be described as different submodalities. 
         [0115]      FIG. 2  shows an example system  100  for executing a Data Analysis and Feature Recognition System. In one embodiment the system  100  includes a single computer  101 . In an alternate embodiment the system  100  includes a computer  101  in communication with a plurality of other computers  103 . In an alternate embodiment the computer  101  is connected with a plurality of computers  103 , a server  104 , a datastore  106 , and/or a network  108 , such as an intranet or the Internet. In yet another alternate embodiment a bank of servers, a wireless device, a cellular phone and/or another data entry device can be used in place of the computer  101 . In one embodiment, a datastore  106  stores a data analysis and feature recognition datastore. The datastore can be stored locally at the computer  101  or at any remote locations while being retrievable by the computer  101 . In one embodiment, an application program is run by the server  104  or by the computer  101 , which then creates the datastore. The computer  101  or server  104  can include an application program that trains a known feature. For example, the computer  101  or the server  104  can include an application program that identifies a previously defined known feature in a digital media. In one embodiment, the media is one or more pixels in image data or one or more samples in a sound recording. 
         [0116]      FIG. 3  shows a method formed in accordance with an embodiment of the present invention. At block  112  a datastore is created, which will be described in more detail below in  FIGS. 4 and 5 . In block  114  a known feature is trained. Training is described in more detail below with respect to  FIGS. 6-15 . At block  116  a known feature is identified, which will be described in more detail in  FIG. 16-20 . At block  118 , a known feature action is performed, which is further illustrated in  FIG. 20 . 
         [0117]      FIG. 4  shows an example method (block  112 ) for creating the datastore. The method (block  112 ) begins at block  120  by assigning a plurality of datastore properties. In one embodiment, the datastore properties include modality and submodality. Within each modality, there is a plurality of submodalities. In one embodiment, at block  122  a known feature is created, which is further illustrated in  FIG. 5 . In one embodiment, at block  124  a target data area is assigned. In one embodiment, a target data area is selected. One example target data area for an imaging modality is a pattern of near and far neighboring pixels surrounding a target pixel. In one embodiment, at block  126  target data area algorithms are selected. At block  128  the datastore  106  is saved to the computer  101  or the network  108 . Blocks  120 ,  122 , and the combination of  124  and  126  can be executed in any order. 
         [0118]      FIG. 5  shows an example method (block  122 ) for creating a known feature. At block  140  the user enters a name for a known feature. In one embodiment, at block  142  the user assigns a method for detection to the known feature. In one embodiment, the method of detection can be selected as hit detection. In one embodiment, cluster detection can be used. In one embodiment, threshold detection can be used. In one embodiment, cluster and threshold detection can be used. In one embodiment, at block  144 , a processing action can be chosen for the method of notification that the known feature was found. In one embodiment, the user may select no action, playing a system sound, or painting a plurality of pixels. Blocks  140 ,  142  and  144  can be executed in any order. 
         [0119]      FIG. 6  shows an example method (block  114 ) for modifying a synaptic web by training or untraining. In one embodiment, the method begins at block  150  with generating an algorithm value cache, which is further described in  FIG. 7 . In one embodiment, the method begins at block  152  when an area of data is selected by the user that is known to contain the feature to be trained. At block  153 , the positive training value sets are retrieved. In one embodiment, at block  154  a decision is made as to whether a user is performing adjusted training. If YES, at block  156  the negative training value sets are retrieved. In one embodiment, a decision is made at block  158  whether the user is training or untraining a known feature. If TRAINING, then at block  159 , the known feature is trained, which is further illustrated in  FIG. 8 . In one embodiment, at block  160  a report is given to the user showing the number of unique synaptic paths added and updated. If UNTRAINING, then a known feature is untrained, which is further explained in  FIG. 13 . In one embodiment, at block  162  the number of unique synaptic paths removed is reported to the user. Blocks  150  and  152  can be executed in any order. Blocks  153  and the combination of  154  and  156  can be executed in any order. 
         [0120]    In some circumstances, limitations in the ability of the user to finely tune a region of interest may cause some of the positive training value sets to actually contain parts of the data that the user knows to not be what he/she wishes to train. These cases are handled by adjusted training, which can be selected by the user. This area outside the region of interest, in a still image, is usually the background or normal area that the user does not want to train as the known feature. By identifying the negative training value sets, those sets of algorithm values from within the region of interest (the positive training value sets) that actually are not the feature the user wishes to train as the known feature can be removed. 
         [0121]      FIG. 7  shows an example method (block  150 ) for generating an algorithm value cache. In one embodiment, an algorithm value cache consists of an array storing the numerical results of the previously selected algorithms. The method (block  150 ) begins at block  170  with the method retrieving the first TDE in the data. At block  176 , algorithm values are calculated on the TDA for the TDE. At block  180  the algorithm values are stored in an algorithm value cache for the TDE. At block  174  a decision is made whether more TDEs are available in the data. If FALSE, at block  172 , the algorithm cache is completed. If TRUE, at block  178  the next TDE is retrieved and processing returns to block  176 . 
         [0122]      FIG. 8  shows an example method  159  for training a known feature. The method  159  begins at block  190  where a known feature is retrieved for training and a training synaptic path array is established. At block  192  the training synaptic path array is developed from positive and negative training value sets. At block  194  a new synaptic path is created and followed. At block  196  the synaptic path is associated with a known feature which is further explained in  FIG. 12 . At block  202 , a decision is made as to whether there are more entries in the training path array. If YES, then return to block  194 . If NO, then in one embodiment the training counts are updated. In one embodiment, at block  200  the synaptic leaves are sorted. At block  204  the method (block  159 ) is completed. Blocks  190  and  192  can be executed in any order. 
         [0123]      FIG. 9  shows an example method (block  192 ) for developing a training synaptic path array from positive and negative training value sets. At block  210 , a training type and positive and negative training value sets are retrieved. At block  212 , the positive value sets are assigned to the training array. At block  214 , a decision is made as to whether the user is performing adjusted training. If YES, then at block  216 , the negative training value sets are removed from the training array which is further explained in  FIG. 10 . At block  218 , developing the training synaptic path is complete. 
         [0124]      FIG. 10  shows an example method (block  216 ) for performing adjusted training. In one embodiment, relative and/or absolute adjusted training are available. At block  220 , a synaptic path is selected in a set of negative training value sets. At block  222 , a decision is made whether the training type is absolute adjusted training. If YES, then at block  226  all synaptic paths from the training array that match the current synaptic path are removed. If NO, then at block  228 , remove one synaptic path from the training array that matches the current synaptic path. At block  230 , a next synaptic path is selected, and if there are no further synaptic paths, then at block  218 , the method returns to  FIG. 9 , block  216 . 
         [0125]      FIG. 11  shows an example method (block  194 ) for creating and following a synaptic path. At block  240 , the process sets the current node to a root node of a synaptic web. At block  242 , an algorithm value in a synaptic path is selected. At block  244 , a decision is made as to whether the current node has a next node link for the current algorithm value. If YES, then the current node is set to the next node at block  248 . If NO, then at block  246  a new node is created; the current node is linked to the new node with the current algorithm value. At block  248  the current node is set to the next node. At block  250  the next algorithm value is selected. At block  252  a resulting synaptic leaf is returned to block  194  in  FIG. 8 . 
         [0126]      FIG. 12  shows an example method (block  196 ) for associating the synaptic path with a known feature. At block  260 , a current synaptic leaf is set to the synaptic leaf returned from  FIG. 11  to block  194  in  FIG. 7 . At block  266  a decision is made as to whether the current synaptic leaf contains the index value of the trained known feature. If YES, then at block  268  the current synaptic leaf hit count is updated. If NO, then at block  270  the decision is made as to whether the current synaptic leaf has a next synaptic leaf. If YES, then the current synaptic leaf is set to the next synaptic leaf at block  276 . If NO, then at block  272  a new synaptic leaf is created containing the index of the trained known feature, and it is linked to the current synaptic leaf. At block  280  the process returns to block  196  in  FIG. 7 . 
         [0127]      FIG. 13  shows an example method (block  161 ) for untraining a known feature. At block  320  a known feature to untrain and a plurality of positive training value sets are retrieved. At block  322  the current set of values is selected. At block  324  the synaptic path is followed for the current positive training value set. At block  326  the synaptic path is tested to see whether it exists. If YES, then the synaptic path is disassociated from a known feature at block  328 . If NO, then at block  330  go to the next set of positive training values. Once all positive training value sets have been evaluated, then at block  332  return to block  161  in  FIG. 6 . 
         [0128]      FIG. 14  shows an example method (block  324 ) for following a synaptic path to identify a leaf based on a set of algorithm values. At block  340  a current node is set to a root node of a synaptic web. At block  342  an algorithm value is selected from the synaptic path for the algorithm for the current node. At block  344  a decision is made as to whether the current node has a next node link for the current algorithm value. If YES, then at block  346  the current node is set to the next node. At block  348  a next algorithm value is selected. If there are no further algorithm values, then at block  350  the synaptic leaf is returned at the end of the synaptic path. If NO, then at block  352  the synaptic path does not exist. The process returns to block  324  in  FIG. 13 . 
         [0129]      FIG. 15  shows an example method (block  328 ) for dissociating a synaptic path from a known feature. At block  360  a current synaptic leaf is set to the leaf returned by  FIG. 14  to block  324 . A decision is made at block  362  as to whether the current leaf contains the index of the known feature. If YES, then the leaf is removed at block  364 . If NO, then at block  365  a decision is made as to whether the current leaf has a next leaf. If YES, then the current leaf is set to the next leaf and the process is repeated. If NO, then the process at block  370  returns to block  328  in  FIG. 13 . 
         [0130]      FIG. 16  shows an example method (block  116 ) for identifying known features. In one embodiment, at block  390  an algorithm value cache is generated. (See  FIG. 7 ) At block  392  an area is selected in the current data. At block  393 , the first TDE is selected. At block  394 , a decision is made whether the TDE is in the selected area. If YES, then at block  398  algorithm values for the TDE are retrieved from the algorithm value cache if available; if not, the algorithm values are calculated for the TDE. At block  400  the datastore is queried with the algorithm values. (See  FIG. 14 ) At block  404  a decision is made whether a path exists for the algorithm values. If YES, then at block  406  it is determined whether the match is a hit of a known feature, which is further explained in  FIG. 17 . If NO, then at block  402  the next TDE is retrieved. If NO from block  394 , then at block  396  the identified known features are returned. Blocks  390  and  392  can be executed in any order. 
         [0131]      FIG. 17  shows an example method (block  406 ) for determining if a known feature in a leaf hits. At block  420  for each of the known features found for the leaf, the following process is executed. At block  426 , the feature is checked to see if a user selected it for identification. If YES, at block  428 , then the known feature is checked to see if the hit method is set as hit detection. If NO, at block  428 , then at block  434  the known feature is checked to see if the hit detection method is set as thresholded. If NO, at block  434 , then at block  440 , the known feature is checked to see if the known feature hit method is set as clustered. If YES from block  428 , then at block  430  the known feature is added to the list of identified features for the current set of algorithm values. If YES from block  434 , then at block  436  the known feature is checked for a thresholded hit which is further explained in  FIG. 19 . If YES from block  400 , then at block  442  a check for a clustered hit is performed, which is further explained in  FIG. 20 . If NO from block  440 , then at block  444  the system checks for a clustered and thresholded hit, which is further explained by  FIG. 18 . At blocks  436 ,  442 , and  444  the data returned is either TRUE or FALSE for a hit. At block  438  the returned value is analyzed to determine if there is a hit at this location. If YES, then at block  430 , the known feature is added to the list of identified features for the current set of algorithm values. If NO, in one embodiment at block  424  it is determined whether the method is processing only the most significant known feature. If YES, the method is complete; if NO, at block  422  or block  426 , there is a check to see if there are additional known features associated with the current leaf. If YES, go to block  420 ; if NO, the method is now complete and returns through block  432  to block  406  in  FIG. 16 . 
         [0132]      FIG. 18  shows an example method (block  444 ) for checking for a clustered and thresholded hit. At block  450  the method performs the check for a thresholded hit. At block  452 , whether the thresholded hit was found is checked. If NO, the method proceeds to block  459 . If YES, the method proceeds to block  454 . In block  454 , the method performs the check for a clustered hit. At block  456 , whether the clustered hit was found is checked. If NO, the method proceeds to block  459 . If YES, the method proceeds to block  458 . At block  458 , a hit was detected in thresholded and clustered processing, and so TRUE is returned to block  444  in  FIG. 17 . At block  459 , a hit was not detected in one of thresholded or clustered processing, and so FALSE is returned to block  444  in  FIG. 17 . The combination of blocks  450  and  452  and the combination of blocks  454  and  456  can be executed in any order. 
         [0133]      FIG. 19  shows an example method (block  436 ) for checking for a thresholded hit. At block  460  the system checks to see if processing thresholds are set. If YES, at block  462  a decision is made whether the known features hit count on the synaptic leaf is between the processing minimum and maximum. If YES, then TRUE is returned at block  468 ; if NO, then FALSE is returned at block  466 . If NO from block  460 , then at block  464  the known feature is checked to determine whether the hit count on the synaptic leaf is between the known feature minimum and maximum. If YES, then TRUE is returned at block  468 ; if NO, then FALSE is returned at block  466 . 
         [0134]      FIG. 20  shows an example method (block  442 ) for checking for a clustered hit. At block  470  the system checks to see if a processing cluster distance is set. If NO, then at block  472  the method performs a clustered check with known feature cluster distance. If YES, then at block  474  a clustered check is performed with processing clustered distance. Then at block  476  a check is made to see whether a cluster is found. If YES, then at block  478  TRUE is returned. If NO, then at block  480  FALSE is returned. 
         [0135]      FIG. 21  shows an example method (block  118 ) for processing the known features identified for an area. At block  492  the first TDE in a selected area is retrieved. At block  496  the TDE is checked to determine whether it is within the selected area. If NO, then the processing actions are complete. If YES, then at block  500  the list of features identified for the TDE is retrieved. At block  501 , the actions for the list of features are performed. Once this is complete, then at block  502  the next TDE is retrieved. 
         [0136]      FIG. 22  shows an example method (block  501 ) in one embodiment for performing actions for a list of known features. The method (block  501 ) begins at block  503 . At block  503 , the current known feature is set to the first known feature in the list for the TDE. At block  504  the known feature action is checked to determine whether the action is a sound. Setting up a known feature action was illustrated in  FIG. 5 . If YES, then at block  506  the system determines whether the sound has been played at least once before. If NO from block  506 , then the sound is played which is specified by the known feature action data at block  508 . If NO from block  504 , then at block  510  the known feature action is checked to determine if it is paint. If YES, then the image color for the TDE is set by the known feature action data. At block  511 , a check is made to see if more known features are present in the list for the TDE. If YES, the current known feature is set to the next known feature, block  515 , and the method continues at block  504 . If NO, the method returns at block  513 . Additional actions or combinations of actions are possible as needed by other embodiments. The actions may be checked and executed in any order. 
         [0137]      FIG. 23  is an example array  600  for a 10×10 pixel image. The X coordinate for the pixel is represented by the number in the rows  604 . The Y coordinate for the pixel is represented by the number in the columns  602 . In one embodiment, the numbers shown within the array  600  are the original grey scale values of the 10×10 pixel image. The numbers shown are the numbers that will be manipulated using the pre-selected algorithms using the adjacent pixels TDA that includes the eight pixels surrounding the target pixel. In this example, the algorithms chosen are mean, median, spread of values, and standard deviation. Further,  FIGS. 24-34  show an example of training a known feature described in  FIG. 3 . 
         [0138]      FIG. 24  shows an example array  605  for the 10×10 pixel image using the mean algorithm for the adjacent pixels TDA. As shown in the array  605 , the first and last rows  609  are shaded and the first and last columns  607  are shaded. These areas are shaded because they do not contain the requisite bordering pixels. The first valid pixel, which is the first pixel that is bordered on all sides by another pixel, is (2, 2), and the algorithm result is 153. The result 153 will be used further starting at  FIG. 28 . 
         [0139]      FIG. 25  shows an example array  610  for the 10×10 pixel image using the median algorithm for the adjacent pixels TDA. The algorithm result for the first valid pixel is 159. The result 159 will be used further starting at  FIG. 28 . 
         [0140]      FIG. 26  shows an example array  620  for the 10×10 pixel image using the spread of values algorithm for the adjacent pixels TDA. The algorithm result for the first valid pixel is 217. The result 217 will be used further starting at  FIG. 28 . 
         [0141]      FIG. 27  shows an example array  630  for the 10×10 pixel image using the standard deviation algorithm. The algorithm result for the first valid pixel is 64. The result 64 will be used further starting at  FIG. 28 . 
         [0142]      FIG. 28  shows a synaptic web  640 , in one embodiment, containing a single synaptic path formed from the first valid pixel values calculated in  FIGS. 24-27 . The first value ( 642 ) comes from the first algorithm (abbreviated ALG) ( FIG. 24  at pixel 2, 2) which is 153. Therefore,  642  shows 153, count 1. Count 1 signifies the number of times during training the first algorithm had a result of 153. A second node  644  shows the result of the second algorithm ( FIG. 25  at 2, 2) which is 159. Therefore,  644  shows 159, count 1. A third node  646  shows the result of the third algorithm ( FIG. 26  at 2, 2) which is 217. Therefore,  646  shows 217, count 1. A fourth node  648  shows the result of the fourth algorithm ( FIG. 27  at 2, 2) which is 64. Therefore,  648  shows 64, count 1. Following this synaptic path leads to a synaptic leaf containing a known feature (abbreviated KF)  1 . This is the first time this synaptic path has been created, and therefore, the count is also 1, see block  650 . In this example, the synaptic leaf  640  is a first synaptic leaf in the synaptic web. 
         [0143]      FIG. 29  shows an example synaptic web  660 , in one embodiment, containing two synaptic paths using values calculated in  FIGS. 24-27 . A synaptic leaf  664  was shown and described in  FIG. 28 . A synaptic leaf  666  represents the algorithm values for the pixel (2, 3) from each table shown in  FIGS. 24-27 . Therefore, after analyzing two pixels, there are two different synaptic paths that identify the same known feature. 
         [0144]      FIG. 30  shows an example synaptic web  670 , in one embodiment, using values calculated in  FIGS. 24-27 . The values calculated from the tables shown in  FIGS. 24-27  represent pixels (2, 2) through (3, 4). The values were taken from left to right within the rows. At this time in the calculation, there has not been a repeat in the values from the first algorithm; therefore, for every pixel evaluated, a completely new synaptic path and a new synaptic leaf were added to the synaptic web. 
         [0145]      FIG. 31  shows an example synaptic web  720 , in one embodiment, using values calculated in  FIGS. 24-27 . In the synaptic web  720 , there is a repeat value shown at  722 . The first algorithm value 151 was found both at (2, 8) and (3, 5) therefore increasing the count at that position to equal 2. At  722 , the synaptic path splits because of different values retrieved from the second algorithm. A portion of a new synaptic path and a new synaptic leaf are generated for the set of values. 
         [0146]      FIG. 32  shows an example synaptic web  730 , in one embodiment, using values calculated in  FIGS. 24-27 . This example shows a more populated synaptic web  730  with repeats in the first algorithm value at  732 ,  734 , and  736 . The repeats show that at any node in the synaptic web a new branch can be formed and a new synaptic path will be formed. As shown in node  732 , there are three diverging results that still result in the same known feature.  FIG. 32  further demonstrates a graphical representation of what fully populated synaptic web may look like after training a known feature. 
         [0147]      FIG. 33  shows a synaptic path  740  that results in a synaptic leaf having multiple known features  742 . When multiple known features are associated with a synaptic path, the features are stored in a sorted list ordered by the feature&#39;s hit count. The known feature that has most often been associated with the synaptic pattern appears first in the list, followed by other known features, in decreasing hit count order. In case of a tie, the first known feature associated with the synaptic path will appear first. 
         [0148]      FIG. 34  shows a series of arrays for a 6×6 black and white image. The array at the top of the page shows the brightness value for all the pixels in the image. The next array  680  shows the results of the mean algorithm applying the adjacent pixels TDA to top array. Array  690  shows the results of the median algorithm after applying the adjacent pixels TDA to top array. Array  700  shows the results of the spread of values algorithm after applying the adjacent pixels TDA to top array. Array  710  shows the results of the standard deviation algorithm after applying the adjacent pixels TDA to top array. As an example, the results of arrays  680 - 710  are applied to the synaptic web in  FIG. 32 . The resultant value shown in (2, 2) from array  680  is 164. Now referring to  FIG. 32 , the value 164 is found in the first node of the synaptic web at  732  in  FIG. 32 . Next, using the value 152, which is the value found at (2, 2), it is shown in  FIG. 32  that the next node following  164  is 152. Therefore, these first two values follow a known synaptic path. Following this synaptic path and the values in (2, 2) in arrays  700  and  710  show that at pixel (2, 2); there is a match of the known feature trained in the synaptic web. 
         [0149]    In  FIGS. 35-77 , the screenshots represent one example of an interface; infinite alternatives exist. 
         [0150]      FIG. 35  is a screenshot  800  of an introduction screen when setting up a datastore. This shows the introduction for a wizard  802  that will guide the user through the steps in this application to create and/or edit a datastore. Also shown in this  FIG. 35  is a series of tabs  804 . These tabs show the user&#39;s position within the wizard. In the top right corner is a button providing the ability to close and exit the wizard  802 . At the bottom of the screenshot is an option button  808  to cancel, the option button  810  to go back, the option button  812  go to the next step, and the option button  814  to finish. The general layout described above is prevalent throughout most screenshots. 
         [0151]      FIG. 36  is a screenshot showing the entering of the initial values defining the datastore. The tab “Required”  804  is selected showing a set of values necessary in this application. At this stage a user is identifying the type of digital data to be processed. A modality combo box  820  contains a series of modalities which specifies the format of the digital data stream. A submodality combo box  822  contains a series of submodalities which specifies the use of the information or specific application of the modality. Logging is represented by a checkbox  824 . 
         [0152]      FIG. 37  shows a screenshot showing the submodality combo box  822  expanded. The submodality combo box  822  has been expanded to show, in one embodiment, a configurable list of submodalities that have currently been set up for a two-dimensional image modality. This combo box  822  shows a user the number of sub classifications within the previously selected form of digital data to enable a user to address differences in digital data within a modality. 
         [0153]      FIG. 38  is a screenshot showing a series of textboxes to add optional descriptive parameters in this application. The “Optional” tab has been selected. The information from this screenshot can be used to categorize datastores received and stored by a network. At textbox  830 , a vendor&#39;s name is entered. At textbox  832 , a machine type is entered. At textbox  834 , a model for the machine type is entered. At textbox  836 , the name of the trainer is entered. At textbox  838 , the use of the datastore is described. 
         [0154]      FIG. 39  is a screenshot allowing for the selection of a TDA shape and a set of algorithms for the shape. The “Target Data Shape” tab  804  is selected. A combo box  840  allows a user to select a target data shape in order to determine how data is collected immediately surrounding the TDE. In one embodiment, a “Grey Adjacent Pixels” TDA is selected. In one embodiment the process of selecting algorithms begins by choosing a TDA shape. In the case of  FIG. 39 , the TDA shape chosen is a square of 9 pixels with the center pixel being the TDE (known here as “Grey Adjacent Pixels” because all of the remaining data elements touch the TDE). Next, a group of three algorithms are chosen. In this example, Algorithm 2, Algorithm 3 and Algorithm 4 (algorithms may be simple or complex) are used to extract the data to be used in training within the Synaptic Web. Note that in this example, it is a combination of the results of the three algorithms that are used by the Synaptic Web for training and processing, not just a single algorithm. 
         [0155]    At this point an area of the image is selected that contains the part of the image whose contents will be used in the training (shown in  FIG. 51 ). This area is called the Selection Area. With the Selection Area chosen, the system steps the TDA onto the Selection Area with the TDE at the first pixel in the Selection Area. At this location, the group of three algorithms chosen for the training is run on the TDA. Algorithm 2 (Mean of the TDA values) sums the values of all of the pixels in the TDA and divides that sum by the number of the pixels, 9, resulting in the mean of the TDA. This mean value is put in the Synaptic Web for its use in the training session as described within the section on the Synaptic Web. Algorithm 3 (Median of the TDA values) determines the median value of all of the 9 pixels in the TDA. This median value is put in the Synaptic Web for its use in the training session as described within the section on the Synaptic Web. Algorithm 4 (Spread of the TDA values) determines the lowest pixel value and highest pixel value of all of the 9 pixels in the TDA. It then subtracts the lowest value from the highest value resulting in the spread of the values of the TDA. This spread is put in the Synaptic Web for its use in the training session as described within the section on the Synaptic Web. At this point, the system steps the TDA shape by one position where the TDE is now the next pixel with 8 adjacent pixels. The same group of 3 algorithms is run on this new TDA and the results put in the Synaptic Web for its use. The system will step the TDA and run the group of algorithms one position at a time until all of the pixels in the Selection Area have been a TDE. The above process for training is similar to the identification process. The same TDA Shape and Algorithms are used for identification as training. A Selection Area is chosen and the TDA is shifted across the Selection Area and at each new point runs the group of algorithms. At this point the results of the algorithms are not used by the Synaptic Web for training, but compared to known features for identification. 
         [0156]    The algorithms available to the user are designed to analyze possible characteristics of the area surrounding the target pixel. Some examples are arithmetic algorithms, such as sums or spread of values, or statistical algorithms such as standard deviation. For certain TDA shapes, additional algorithms can be developed that consider the geometry of the shape. For example, an algorithm for 2D imaging can be implemented that sets bit values to 1 when particular pixels surrounding the target pixel are above a known value, thus creating a number from 0 to 255 reflecting the neighboring pixel surrounding the target pixel. The type of algorithm and the range of values returned for a given range of input values are factors for the user to consider when choosing which algorithms to select for a given process. For example, the spread and sum of values are useful in almost any application, while the neighboring pixels algorithm might only be useful in image processing where high contrast is expected and the specific orientation of the pixels is known or expected. In most embodiments, a single algorithm is generally insufficient to identify features; a combination of algorithm values is used to learn and/or identify features. 
         [0157]      FIG. 40  is a screenshot showing a review of the datastore properties previously selected. The summary tab  804  has been selected denoting that this screen shows a user the summary of all his/her settings. The screen allows for a user to confirm all his/her selections by pushing the “finish” button or by editing his/her features by selecting the “back” button. Shown in this table is that modality is set as Imaging 2D  851 . The submodality is set as X-Ray  852 . The logging is selected as True  854 .  FIG. 41  shows the screenshot showing the table  850  in  FIG. 40  scrolled down. Further shown in  FIG. 41  is the target data shape selected with a “Grey Adjacent Pixels” TDA  860  and the number of algorithms selected with seven  862 . 
         [0158]      FIG. 42  shows a screenshot of an application after finishing the creation of the datastore. At the conclusion of the wizard ( FIGS. 35-41 ), the screen  900  is shown to the user. Screen  900  contains a menu bar  910 , which is known in the art, a set of icons  914  and an area to review multiple datastores  912 . A shaded area  926  can display a set of pictures that a user can use to train the datastores and identify different features. In the area  916 , a list is displayed of the selections made by the user at this point. In one embodiment, there is one datastore for 2D imaging  918 . A set of known features, when defined, are stored in the known features folder  920 . The “Grey Adjacent Pixels” TDA is displayed at  924 . 
         [0159]      FIG. 43  is a screenshot showing an expansion of the TDA  924 . The TDA  924 , as shown in  FIG. 43 , is now expanded to show possible algorithms that could be used in conjunction with the TDA. In this application, the selected algorithms have a filled-in box denoting that they have been selected. 
         [0160]      FIG. 44  is a screenshot showing a “create or edit a known feature” wizard  950 . In the wizard  950  is a set of tabs  952 . The “Start” tab is selected denoting that this is the introduction to the wizard. This wizard will guide a user through the steps in this application to create and edit a known feature, see area  954 . 
         [0161]      FIG. 45  is a screenshot showing the “Identification” tab  952  of the “create or edit a known feature” wizard. The textbox  960  contains the name of the known feature. In one embodiment, the user enters a name that describes the known feature; in this example “forest” was entered. The combo box  962  shows the method of hit detection selected by the user. The check box  964  allows the user to determine whether the process should stop after the first occurrence of that particular feature has been found. A user may select check box  964 , if only looking for an instance of the known feature, such as foreign matter in a food sample in a food safety application.  FIG. 46  is a screenshot showing the expansion of the combo box  962  from  FIG. 45 . The identification method combo box  962  contains the method used to determine how a feature will be identified. 
         [0162]      FIG. 47  is a screenshot showing the “Training Counts” tab  952  of the “create or edit a known feature” wizard. A user may select a threshold value representing the minimum number of times a known feature must be associated with a synaptic path during training to meet the user&#39;s needs. By increasing the threshold value, a user guarantees that only recurring paths that have higher number of instances than the threshold value are used in processing, thus giving a higher level of confidence to the eventual identification of the feature. A limit value may also be selected and contains a value that represents the maximum number of times a known feature may have been associated with the synaptic path during training. A sliding scale  970  is used to represent the threshold number, and a sliding scale  974  is used to represent the limit number. 
         [0163]      FIG. 48  is a screenshot showing the “Cluster Range” tab  952  of the “create or edit a known feature” wizard. The tab allows the user to select how far in each dimension, from a TDE where a known feature is identified, the system looks to find other occurrences of the same known feature. In one embodiment, the dimension combo box  980  contains a two-dimensional X and Y selection. The sliding scale  982  represents the dimension value, and the sliding scale  984  represents a cluster count. Specifying different cluster ranges for each dimension allows the user to account for peculiarities of the data. For example, if the vertical scale of an image is not the same as the horizontal scale, then a user could enter adjusted values to the range to attempt to get the desired cluster area. 
         [0164]      FIG. 49  is a screenshot showing the “Actions” tab  952  of the “create or edit a known feature” wizard. The user can select the action to be performed when a known feature is identified. A combo box  990  contains a list of actions; in this application, the possible actions are playing a system sound, painting a pixel and no action. In one embodiment a user may select sound in order to alert the user when an instance of the known feature is found in the digital data. A user may select paint in order to identify those areas, in a selection of digital data, that a known feature has been identified. 
         [0165]      FIG. 50  is a screenshot showing the “Summary” tab  952  of the “create or edit a known feature” wizard. In the table, the name of the known feature forest is selected, shown in row  1000 . The method of detection is hit detection, shown in row  1002 . The threshold is set to 1 at row  1004 . The limit is set to 2,147,483,647, shown in row  1006 . The cluster range is set at X: 0, Y: 0, cluster count: 1, shown in row  1008 . The action on detection is set as paint, shown in row  1010 . The data is set as forest green, shown in row  1012 . 
         [0166]      FIG. 51  is a screenshot showing an image  1020  of a forest with a selected area  1028 . The layout of this screen was described in  FIG. 42 . The screen  900  also contains smaller “thumbnails” of other pictures loaded into a system  1030 . Mouse position and color values  1022  are shown based on the cursor location, as is common in the art. Layers  1026  of the picture  1020  are listed. The selected area  1028  is what a user has set as a region of interest, and what will be trained as the known feature forest in  FIGS. 52-56 . 
         [0167]      FIG. 52  is a screenshot showing the “Start” tab  1110  of the “known feature training” wizard. The training wizard will guide a user through the steps to train selected known features. At this point a user will call on a previously setup known feature and identify that known feature on a section of digital data in order to train the system. 
         [0168]      FIG. 53  is a screenshot showing the “Known Features” tab  1110  of the “known feature training” wizard. There is a list  1120  showing the first datastore. The list contains a known feature water  1124  and a known feature forest  1122 . Both water and forest were setup in the “create or edit a known feature” wizard. In this example, forest  1122  is selected. If multiple datastores are open, the user can choose to train known features in multiple datastores. 
         [0169]      FIG. 54  is a screenshot showing the “Method” tab  1110  of the “known feature training” wizard. There is a series of radio buttons next to four choices of training methods: area training  1130 , untraining  1132 , absolute adjusted training  1134  or relative adjusted training  1136 . At this point a user selects the method of training that is optimal for the selected modality, submodality and sample quality. 
         [0170]      FIG. 55  is a screenshot showing the “Summary” tab  1110  of the “known feature training” wizard. The table contains the number of known features  1140 , which is one in this example. In this example, the method of training is area training, see row  1142 . 
         [0171]      FIG. 56  is a screenshot showing the results of training. After a user selects the finish button in  FIG. 55 , the datastore is trained according to the user&#39;s selections. The table  1210  shows the results. The datastore selected was “SyntelliBase1” (the default name assigned to the datastore by the application and can be changed by the user), the known feature trained was forest, and the number of new data patterns found was 30,150. The number of new data paths found was 0. The number of updated data patterns found was 0. A user may elect not to see the summary of the results. 
         [0172]    The new and updated patterns were generated as a result of executing the algorithms selected above in  FIG. 39  on the pixel values in the selected area of the image in  FIG. 51  using the process illustrated above in  FIGS. 23-33 . The algorithm values for each pixel were calculated and taken as a set; those values generated a data pattern associated with the known feature in the web. In the selected area of the image, the actual area probably contained an assortment of trees, shrubs, and other vegetation. The 30,150 patterns that were found reflected the algorithm values from these different materials, and all of those patterns were associated with the known feature “forest”. 
         [0173]      FIG. 57  is a screenshot showing an image with an area of forest and an area of water. The forest is represented by the lighter shaded area, and the water by the darker shaded area.  FIG. 57  relates to  FIG. 51  in that the same pictures are loaded. However, a different picture  1252  is now selected. The picture  1252  shows an area of forest selected, the selected area is shown with black lines. This is the area a user has defined, in this example, as an area known to be the known feature “forest.” 
         [0174]      FIG. 58  is a screenshot showing the results of training the area selected in  FIG. 57 . The training event added 8,273 new data patterns and updated 2,301 data paths. 
         [0175]    The training process on this image generated patterns using the process illustrated in  FIGS. 23-33  on the selected area of the image in  FIG. 57 . 2,301 patterns were previously associated with the known feature, and those associations were updated. 8,273 data patterns were not previously associated with the known feature, and those associations were created. 
         [0176]      FIG. 59  is a screenshot showing the “Start” tab  1310  of the “known feature processing” wizard, which guides a user through the steps in this application to process selected known features. This wizard allows a user to process a new section of digital data using the previously trained known features in order to determine if the known feature is present. 
         [0177]      FIG. 60  is a screenshot showing the “Known Features” tab  1310  of the “known feature processing” wizard. Table  1320  shows all of the datastores that contain training data. In this example, SyntelliBase1, shown in row  1322 , is available. A user can check or uncheck any or all listed known features within the particular datastore that the user wants to identify. In this example, forest is selected. 
         [0178]      FIG. 61  is a screenshot showing the “Significance” tab  1310  of the “known feature processing” wizard. The user can optionally override significance processing options. The option button  1330  allows for identification for any known feature trained for a specific data point, and option button  1332  identifies the known feature trained most often. In some cases, multiple known features can be identified at any given data point. The first option allows all of those known features to be identified. The second option allows only the feature that was most often associated with the given data pattern to be identified. 
         [0179]      FIG. 62  is a screenshot showing the “Training Counts” tab  1310  of the “known feature processing” wizard. The user can optionally override the training count values for processing. The threshold values, shown as a sliding scale  1340 , are the minimum number of times a known feature must have been associated with the synaptic path during training to be identified. A limit value, shown as a sliding scale  1342 , is the maximum number of times a known feature could have been associated with the synaptic path during training to be identified. 
         [0180]      FIG. 63  is a screenshot showing the “Cluster Range” tab  1310  of the “known feature processing” wizard. A user can optionally override cluster range values. The combo box  1350  allows the user to select a particular dimension. In a two-dimensional image, the combo box  1350  can contain the X-dimension and the Y-dimension. The dimension value is selected on the sliding scale  1352 . The cluster count is selected on a sliding scale  1354 . 
         [0181]      FIG. 64  is a screenshot showing the “Summary” tab  1310  of the “known feature processing” wizard. The values include the number of known features  1360 , the threshold override  1362 , the limit override  1364 , the significance override  1366  and cluster range override  1368 . 
         [0182]      FIG. 65  is a screenshot showing a processing result summary. The processing result summary shows that out of the 31,556 patterns encountered for the known feature forest, one or more of those occurred 131,656 times, and that the known feature action to paint one or more pixels forest green was performed. The data patterns were generated using the process discussed above for  FIG. 34  using the algorithms the user selected in  FIG. 39 . These algorithms are, and must be, the same algorithms that are used in training above in  FIGS. 56 and 58 . When the same algorithm set is executed and returns the same set of values, the same data pattern is developed as was developed in training, and the known feature associated with the data pattern is identified. In the processing in  FIG. 65 , there were 131,656 pixels identified as the known feature “forest” because 31,556 of the data patterns developed matched data patterns associated with that known feature. A layer for the identified known feature forest was added to the image. This is further shown in  FIG. 66 . 
         [0183]      FIG. 67  is a screenshot showing the result of processing. The image  1420  contains 131,656 pixels that should be painted forest green because they were identified as forest in processing. 
         [0184]      FIG. 68  is a screenshot showing the processing of a second image, again looking for the known feature forest. The datastore  1402  used in the processing was SyntelliBase1. The known feature forest  1404  was found 89,818 times using 17,999 total data patterns. The known feature action  1406  was to paint the forest “forest green.” Because these images are black and white, the pixels that would be painted forest green are printed black. 
         [0185]      FIG. 69  is a screenshot showing an image  1430  with a layer for the known feature forest showing pixels that the application identified as forest. The solid block of forest green in the image shows the area where training occurred on the area selected in  FIG. 57 . This area is completely identified as forest because the user selected that area and instructed the application that the area is forest. 
         [0186]      FIG. 70  is a screenshot showing a composite image containing the original picture  FIG. 57  and the layer where the application identified forest shown in  FIG. 69 . 
         [0187]      FIG. 71  is a screenshot showing an image  1450  with an area of water selected. 
         [0188]      FIG. 72  is a screenshot showing the results of training the selection in  FIG. 71  as the known feature water. The training of the selection added 1 data pattern. In  FIG. 71 , the pixels in the selected area are uniform. When the algorithms selected in  FIG. 34  above are executed on the pixels in the selected area, a single data pattern is the result. 
         [0189]      FIG. 73  is a screenshot showing the processing of both the forest and the water known features for an image. By selecting both forest and water  1512 , the user is asking the system to identify both of those features during processing. 
         [0190]      FIG. 74  is a screenshot showing a summary of the values that a user has supplied or has selected for processing the image in  FIG. 71 . In this example, the number of known features selected, shown in row  1522 , was 2. The threshold override, shown in row  1524 , was 0. The limit override, shown in row  1526 , was 100,000. The significance override, shown in row  1528 , was to use any known feature trained for a TDE. The cluster range override, shown in row  1530 , was set to X: 0, Y: 0, cluster count: 0. 
         [0191]      FIG. 75  is a screenshot showing the summary of the processing set up in  FIG. 74 . In this image, the datastore used, shown in row  1542 , was SyntelliBase1. A known feature forest, shown in row  1544 , was found 89,818 times using 17,999 data patterns trained as forest. The known feature action, shown in row  1546 , was to paint the identified pixels forest green. The known feature water, shown in row  1548 , was found 45,467 times using one data pattern trained as water. The known feature action, shown in row  1550 , was to paint the identified pixels blue. In one embodiment, the system does not remove all previous designated data, but actually processes “all” the data each time it processes. 
         [0192]      FIG. 76  is a screenshot showing the layer of water found in the image. Image  1570  shows the pixels found to be water and painted blue; however in these images, water is represented as striped black lines. 
         [0193]      FIG. 77  is a screenshot showing the composite image showing the original image, water and forest. Image  1580  shows the areas where water is identified in blue and the areas where forest is identified in forest green. In this image, the contrast is shown between water, the dark forest area and the white spots, which are unidentified. Note the area  1590  that is not marked as water. In the original image  76 , that area appeared to be water, but the processing system has detected characteristics that indicate it is not water like the rest of the image. It is likely to be an area of shallow water or shoreline. 
         [0194]    In an embodiment not shown, any displayed anomalies that are not identified (previously trained features) are painted to distinguish them from trained features. 
         [0195]    In still another embodiment, a visual or audible alarm may be a function that is associated with a known feature. Thus, during an analysis of a data set, an alarm would be triggered if a previously known feature was found. 
         [0196]    While the preferred embodiment of the invention has been illustrated and described, as noted above, many changes can be made without departing from the spirit and scope of the invention. Accordingly, the scope of the invention is not limited by the disclosure of the preferred embodiment. Instead, the invention should be determined entirely by reference to the claims that follow.