Patent Publication Number: US-9418467-B2

Title: 3D human models applied to pedestrian pose classification

Description:
RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Application No. 61/745,235, filed Dec. 21, 2012, which is incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     1. Technical Field 
     This application generally relates to the field of object classification and, in particular, to the use of synthetic data in the classification of pedestrian pose. 
     2. Background Information 
     A vehicle (e.g., an automobile) outfitted with a pedestrian detection system can warn its driver that a pedestrian is nearby. However, pedestrian detection alone is not sufficient. The danger of the situation should be assessed also. Only when there is the risk of an accident should a warning be produced. Otherwise, the driver will be unnecessarily distracted. The danger of the situation is related to, for example, whether the pedestrian is likely to step in the path of the vehicle. 
     “Object classification” refers to the task of automatically classifying an object in a video image or a still image. For example, a classification system may determine whether a person (e.g., a pedestrian) in a still image is facing left, facing right, facing front, or facing back. Pedestrian pose classification may be used, for example, in a vehicle to increase the safety of the driver of the vehicle, pedestrians, bicyclists, and any other person sharing the road with the vehicle. 
     Many problems exist with current object classification systems. One problem is the lack of an extensive training set for training the object classification model. A training set, which includes positive samples (images including an object of a particular class) and negative samples (images not including an object of the particular class, such as images including an object of another class), is provided to a machine learning algorithm to produce an object classification model. 
     Furthermore, when generating a new training set for a certain type of object, each image is manually annotated with certain pieces of information. For example, the classification of the object present in the image and/or certain parameters of the object present in the image (e.g., color of the object and location of the object within the image) may be added to the image. The machine learning algorithm uses those annotations and images to generate a model for classifying the object. The annotation process can be tedious and time consuming. 
     APPLICATION SUMMARY 
     The above and other issues are addressed by a method, non-transitory computer-readable storage medium, and system for training a pedestrian pose classification model. An embodiment of the method comprises receiving a three-dimensional (3D) model of a pedestrian. The method further comprises receiving a set of image parameters indicating how to generate an image of a pedestrian. The method further comprises generating a two-dimensional (2D) synthetic image based on the received 3D model and the received set of image parameters. The method further comprises annotating the generated synthetic image with the set of image parameters. The method further comprises training a plurality of pedestrian pose classifiers through the annotated synthetic image. 
     An embodiment of the medium stores executable instructions for training a pedestrian pose classification model. The instructions receive a three-dimensional (3D) model of a pedestrian. The instructions further receive a set of image parameters indicating how to generate an image of a pedestrian. The instructions further generate a two-dimensional (2D) synthetic image based on the received 3D model and the received set of image parameters. The instructions further annotate the generated synthetic image with the set of image parameters. The instructions further train a plurality of pedestrian pose classifiers through the annotated synthetic image. 
     An embodiment of the system comprises a non-transitory computer-readable storage medium storing executable instructions. The instructions receive a three-dimensional (3D) model of a pedestrian. The instructions further receive a set of image parameters indicating how to generate an image of a pedestrian. The instructions further generate a two-dimensional (2D) synthetic image based on the received 3D model and the received set of image parameters. The instructions further annotate the generated synthetic image with the set of image parameters. The instructions further train a plurality of pedestrian pose classifiers through the annotated synthetic image. 
     The features and advantages described in the specification are not all inclusive and, in particular, many additional features and advantages will be apparent to one of ordinary skill in the art in view of the drawings, specification, and claims. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a high-level block diagram illustrating a pedestrian pose classification system, in accordance with an embodiment. 
         FIG. 2  is a high-level block diagram illustrating an example of a computer for use as the pedestrian pose classification system illustrated in  FIG. 1 , in accordance with an embodiment. 
         FIG. 3A  is a high-level block diagram illustrating a detailed view of the image generation module illustrated in  FIG. 1 , in accordance with an embodiment. 
         FIG. 3B  is a high-level block diagram illustrating a detailed view of the overall classification module illustrated in  FIG. 1 , in accordance with an embodiment. 
         FIG. 4A  is a flowchart illustrating a method for generating synthetic pedestrian data, in accordance with an embodiment. 
         FIG. 4B  is a flowchart illustrating a method for training multiple binary pedestrian pose classifiers for use in the overall classification module illustrated in  FIG. 3B , in accordance with an embodiment. 
         FIG. 4C  is a flowchart illustrating a method for classifying the pose of a pedestrian in a still image, in accordance with an embodiment. 
     
    
    
     The figures depict various embodiments of the embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the embodiments described herein. 
     DETAILED DESCRIPTION 
     Embodiments are now described with reference to the figures where like reference numbers indicate identical or functionally similar elements. Also in the figures, the left most digits of each reference number corresponds to the figure in which the reference number is first used. 
       FIG. 1  is a high-level block diagram illustrating a pedestrian pose classification system  100 , in accordance with an embodiment. The pedestrian pose classification system  100  may include an image generation module  105 , a training module  110  and an overall classification module  120 . Given a still image of a pedestrian, the pedestrian pose classification system  100  may classify the pedestrian&#39;s pose. In one embodiment, the pose is classified as “facing left”, “facing right”, or “facing front or back”. The pedestrian pose classification system  100  may be used in a vehicle to classify the pose of a nearby pedestrian outside the vehicle. The pose classification can then be used to determine whether the pedestrian may step into the path of the vehicle. 
     Knowledge of a pedestrian&#39;s pose may be used, for example, in a vehicle accident avoidance system to increase the safety of the people inside the vehicle and the safety of pedestrians sharing the road with the vehicle. Drivers, while driving a vehicle, may need to pay attention to multiple objects and events happening in their surroundings. For instance, a driver may need to pay attention to traffic signs (e.g., traffic lights, speed signs, and warning signs), vehicle parameters (e.g., vehicle speed, engine speed, oil temperature, and gas level), other vehicles sharing the road, pedestrians trying to cross the street, etc. Sometimes, pedestrians may be overlooked and may be involved in an accident. 
     If the presence of a pedestrian (who may step into the path of a vehicle) is detected, then the driver can be alerted of the presence of the pedestrian. For instance, consider a pedestrian located to the right of the vehicle. If the pedestrian is facing left, then the pedestrian is more likely to step into the path of the vehicle. If the pedestrian is facing right, then the pedestrian is less likely to step into the path of the vehicle. 
     The image generation module  105  receives as an input a three-dimensional (3D) virtual model of a pedestrian and a background image, generates a two-dimensional (2D) image of the pedestrian, annotates the generated 2D image, and outputs the annotated 2D image (“synthetic pedestrian data”). The image generation module  105  may also receive a set of parameters to use when generating the 2D image of the pedestrian (not shown). 
     The training module  110  receives as an input the annotated 2D image generated by the image generation module  105  (synthetic pedestrian data). The training module  110  then uses the synthetic pedestrian data to train a pedestrian pose classifier for classifying the pose of a pedestrian in an image and outputs the trained pedestrian pose classifier. The synthetic pedestrian data are further described below with reference to  FIG. 3A . 
     The overall classification module  120  receives a still image of a pedestrian and the pedestrian pose classifiers trained by the training module  110 , determines a classification of the pose of the pedestrian, and outputs the classification. In some embodiments, the still image is captured by a camera mounted on a vehicle. For instance, a still image may be captured with a charged coupled device (CCD) camera with a 1/1.8 inch sensor. To increase the shutter speed of the camera and reduce image blur, a camera with a larger sensor may also be used. In some embodiments, a still image is obtained by extracting a frame from a video. The pedestrian pose classification may be a ternary result (e.g., facing left, facing right, or facing front or back). 
       FIG. 2  is a high-level block diagram illustrating an example of a computer  200  for use as the pedestrian pose classification system  100  illustrated in  FIG. 1 , in accordance with an embodiment. Illustrated are at least one processor  202  coupled to a chipset  204 . The chipset  204  includes a memory controller hub  250  and an input/output (I/O) controller hub  255 . A memory  206  and a graphics adapter  213  are coupled to the memory controller hub  250 , and a display device  218  is coupled to the graphics adapter  213 . A storage device  208 , keyboard  210 , pointing device  214 , and network adapter  216  are coupled to the I/O controller hub  255 . Other embodiments of the computer  200  have different architectures. For example, the memory  206  is directly coupled to the processor  202  in some embodiments. 
     The storage device  208  includes one or more non-transitory computer-readable storage media such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory  206  holds instructions and data used by the processor  202 . The pointing device  214  is used in combination with the keyboard  210  to input data into the computer system  200 . The graphics adapter  213  displays images and other information on the display device  218 . In some embodiments, the display device  218  includes a touch screen capability for receiving user input and selections. The network adapter  216  couples the computer system  200  to a communications network or other computer system (not shown). 
     Some embodiments of the computer  200  have different and/or other components than those shown in  FIG. 2 . For example, the computer  200  can be an embedded system and lack a graphics adapter  213 , display device  218 , keyboard  210 , pointing device  214 , and other components. 
     The computer  200  is adapted to execute computer program modules for providing functionality described herein. As used herein, the term “module” refers to computer program instructions and/or other logic used to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules formed of executable computer program instructions are stored on the storage device  208 , loaded into the memory  206 , and executed by the processor  202 . 
       FIG. 3A  is a high-level block diagram illustrating a detailed view of the image generation module  105  illustrated in  FIG. 1 , in accordance with an embodiment. The image generation module  105  includes a pedestrian rendering module  301 , a background incorporation module  303 , an image post-processing module  305 , and an image annotation module  307 . 
     The pedestrian rendering module  301  receives as an input a three-dimensional (3D) virtual model of a pedestrian and a set of parameters, renders a two-dimensional (2D) image of the pedestrian based on the received parameters, and outputs the rendered 2D image. The set of parameters may include, for example, gender of the pedestrian (e.g., male or female), height of the pedestrian, body type of the pedestrian (ectomorph, endomorph, or mesomorph), hair color of the pedestrian (black, brown, blond, etc.), clothing of the pedestrian (shirt, pants, shoes, etc.), accessories used by the pedestrian (hat, backpack, umbrella, etc.), and/or pose classification of the pedestrian (facing left, facing right, or facing front or back). 
     Additionally, the pedestrian rendering module  301  may also receive lighting parameters (e.g., lighting source azimuth, lighting source elevation, lighting source intensity, and ambient light energy), camera parameters (e.g., camera azimuth, camera elevation, and camera rotation), and rendering parameters (image size, border size, etc.). 
     The background incorporation module  303  receives as input the 2D pedestrian image generated by the pedestrian rendering module  301  and a 2D background image, combines the pedestrian image and the background image, and outputs the combined 2D image. In some embodiments, the background image is chosen from a library of background images. The background incorporation module  303  may also receive as a parameter a location that indicates where, within the background image, the pedestrian image should be placed and places the pedestrian image in the received location. For example, the background incorporation module  303  may receive as a parameter a coordinate point indicating where to place the pedestrian image within the background image. Alternatively, the background incorporation module  303  may receive as a parameter two points defining a square in which the pedestrian image should be placed. 
     The image post-processing module  305  receives the 2D image of the pedestrian with the background generated by the background incorporation module  303 , edits the received image so that it can be used by the training module  110 , and outputs the edited image. For example, the image post-processing module  305  may smooth the image, down sample the image, crop the image, etc. 
     The image annotation module  307  receives as input the image output by the image post-processing module  305 , annotates the received image with the ground truth of the received image, and outputs the annotated image. In some embodiments, the ground truth indicates the pose classification of the pedestrian (e.g., facing left, facing right, or facing front or back). In other embodiments, the ground truth also includes other parameters used to render the image. The ground truth may also include the position of the pedestrian in the image. For example, the image annotation module  307  may annotate the image with a coordinate point (or two points defining a square) indicating where the pedestrian is located in the image. 
       FIG. 3B  is a high-level block diagram illustrating a detailed view of the overall classification module  120  illustrated in  FIG. 1 , in accordance with an embodiment. The overall classification module  120  includes a histogram oriented gradients (HOG) extraction module  311 , multiple binary classification modules  313 , and a decision module  315 . 
     The histogram oriented gradients (HOG) extraction module  311  receives a still image, extracts HOG features from the received still image, and outputs the extracted features. As used herein, histogram oriented gradients (HOG) are feature descriptors used in computer vision and image processing for the purpose of object classification. A HOG feature indicates the number of occurrences of gradient orientation in a localized portion of an image. 
     The HOG extraction module  311  extracts HOG features by dividing the received image into multiple cells. For example, the HOG extraction module  311  may calculate HOG features using a cell size of 8×8 pixels. For each cell, the HOG extraction module  311  calculates a one dimensional (1D) histogram of gradient directions over the pixels of the cell. In some embodiments, the HOG extraction module  311  normalizes the image for variation of illumination throughout the received image by dividing the image into blocks, calculating a local histogram energy of the block, and normalizing the cells within the block based on the calculated local histogram energy. For example, the HOG extraction module  311  may calculate local histogram energies with a block size of 2×2 cells. 
     In one embodiment, the HOG extraction module  311  extracts HOG features from an image with a predefined size. For instance, the HOG extraction module  311  may extract HOG features from a 32×64 pixel image. If the received image is larger or smaller in size, the HOG extraction module downscales or upscales the image until the image size is equal to the predefined image size. 
     A binary classification module  313  receives as input a set of HOG features from an image, uses a classifier (e.g., support vector machine or “SVM”) and the HOG features to determine whether the pose of a pedestrian present in the image belongs to a particular class, and outputs a binary result (e.g., yes/no) and a confidence value. In some embodiments, a binary classification module  313  uses a linear classifier, such as a linear SVM. In other embodiments, a binary classification module  313  uses a non-linear classifier, such as a radial basis function (RBF) SVM. The confidence value outputted by a binary classification module  313  indicates a probability that the binary result is correct. 
     As used herein, a linear classifier identifies whether an object (e.g., a still image) belongs to a particular class (e.g., pedestrian facing left, pedestrian facing right, pedestrian facing front or back) based on a linear combination (or function) of the characteristics or features of the object. In one embodiment, the output of the linear classifier is given by
 
 y=f (ω· x )
 
     where y is the output of the linear classification module, ω is a weight vector determined by the training module  110 , and x is a feature vector containing the values of the features of the object being classified. 
     As used herein, a non-linear classifier identifies whether an object (e.g., an image) belongs to a particular class (e.g., pedestrian facing left, pedestrian facing right, pedestrian facing front or back) based on a non-linear combination (or function) of the features of the object. 
     Each of the binary classification modules  313  may classify the pedestrian still image with respect to one pose. For instance, binary classification module  313 A may classify a pedestrian image to determine whether the image contains a pedestrian facing left, binary classification module  313 B may classify a pedestrian image to determine whether the image contains a pedestrian facing right, and binary classification module  313 C may classify a pedestrian image to determine whether the image contains a pedestrian facing front or back. In some embodiments, binary classification module  313 A generates a score (e.g., a confidence value) based on the probability that the pedestrian still image contains a pedestrian facing left, binary classification module  313 B generates a score (confidence value) based on the probability that the pedestrian still image contains a pedestrian facing right, and binary classification module  313 C generates a score (confidence value) based on the probability that the pedestrian still image contains a pedestrian facing front or back. 
     The decision module  315  receives the output from each of the binary classification modules  313  and determines the pose classification of a pedestrian in a still image. In one embodiment, the decision module determines the pose classification as: 
     
       
         
           
             
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     where i is 0, 1, or 2 and p 0  is the probability that a pedestrian in a still image is facing to the left (as determined by binary classification module  313 A), p 1  is the probability that a pedestrian in a still image is facing to the right (as determined by binary classification module  313 B), and p 2  is the probability that a pedestrian in a still image is either facing front or facing back (as determined by binary classification module  313 C). Thus, p max  is the largest value of the scores (confidence values) determined by the binary classification modules  313 . Additionally, θ is a threshold probability value, and c is the pose classification output by the decision module  315 . Thus, the output of the decision module  315  is the pose classification with the highest score (if the score is above a threshold θ) or −1 (if the largest score is equal to or below the threshold). As used herein, an output of −1 by the decision module  315  indicates that the decision module was not able to classify a pose of a pedestrian in the still image. 
       FIG. 4A  is a flowchart illustrating a method for generating synthetic pedestrian data, in accordance with an embodiment. The synthetic pedestrian data can be used with a pedestrian pose classifier (e.g., to train the classifier or to test the accuracy of the classifier). The image generation module  105  receives  401  a three-dimensional (3D) pedestrian model and a set of image parameters. 
     The pedestrian rendering module  301  renders  403  a two-dimensional (2D) image of a pedestrian based on the received pedestrian model and the received image parameters. 
     The background incorporation module  303  adds  405  a background to the rendered pedestrian image. 
     In some embodiments (not shown), the image post-processing module  305  may apply image post-processing techniques (e.g., smoothing, down sampling, cropping) to the image of a pedestrian with a background. 
     The image annotation module  307  annotates  407  the combined image (pedestrian plus background) with the ground truth. For instance, the image annotation module  307  may annotate the image with a value indicating the pose classification of a pedestrian in the image. In other embodiments, the image annotation module  307  further annotates the image with one or more of the received image parameters, such as the accessories used by the pedestrian. 
     The steps illustrated in  FIG. 4A  may be repeated multiple times (using different pedestrian models, image parameters, and/or backgrounds) to generate multiple annotated synthetic pedestrian images. For instance, the steps of  FIG. 4A  may be repeated thousands of times to produce thousands of annotated synthetic pedestrian images. 
       FIG. 4B  is a flowchart illustrating a method for training multiple binary pedestrian pose classifiers for use in the overall classification module  120  illustrated in  FIG. 3B , in accordance with an embodiment. The training module  110  receives  431  an annotated synthetic pedestrian image generated by the image generation module  105  and uses the annotated image to train multiple binary pedestrian pose classifiers using a “one-against-all” approach. 
     The training module  110  determines  433  whether a pedestrian in the received image is in a first pose classification (e.g., facing left). This determination is performed, for example, by accessing the image&#39;s annotation. If the pedestrian is in the first pose classification, the received image is used as a positive sample to train  437  a first binary pedestrian pose classifier, used as a negative sample to train  443  a second binary pedestrian pose classifier, and used as a negative sample to train  447  a third binary pedestrian pose classifier. 
     If the pedestrian is not in the first pose classification, the training module  110  determines  435  whether the pedestrian in the received image is in a second pose classification (e.g., facing right). This determination is performed, for example, by accessing the image&#39;s annotation. If the pedestrian is in the second pose classification, the received image is used as a positive sample to train  441  the second binary pedestrian pose classifier, used as a negative sample to train  439  the first binary pedestrian pose classifier, and used as a negative sample to train  447  the third binary pedestrian pose classifier. 
     If the pedestrian is not in the second pose classification, the received image is used as a positive sample to train  445  the third binary pedestrian pose classifier, used as a negative sample to train  439  the first binary pedestrian pose classifier, and used as a negative sample to train  443  the second binary pedestrian pose classifier. 
       FIG. 4C  is a flowchart illustrating a method for classifying the pose of a pedestrian in a still image, in accordance with an embodiment. The overall classification module  120  receives  411  a still image to be classified. In some embodiments, the image may be captured with a camera mounted in a vehicle. 
     The HOG extraction module  311  analyzes the received still image and extracts  413  the HOG features from the received still image. 
     The first binary classification module  313 A classifies  415 A the image using the first pedestrian pose classifier trained by the training module  110  and the HOG features extracted by the HOG extraction module. The second binary classification module  313 B classifies  415 B the image using the second pedestrian pose classifier trained by the training module  110  and the HOG features extracted by the HOG extraction module. The third binary classification module  313 C classifies  415 C the image using the third pedestrian pose classifier trained by the training module  110  and the HOG features extracted by the HOG extraction module. As part of the classification, each binary pedestrian pose classifier  313  may generate a classification score (e.g., confidence value). 
     The decision module  315  selects  417  the classification with the highest score and determines  419  whether the selected classification score is greater than a threshold. If the selected classification is greater than the threshold, the selected classification is outputted  421 . Otherwise, if the selected classification score is equal to or lower than the threshold, an error may be outputted  423 . 
     The synthetic pedestrian data generated by the image generation module  105  may also be used to benchmark a trained pedestrian pose classifier. For instance, the steps of  FIG. 4C  can be performed using an annotated synthetic pedestrian image. The pose classification output in step  421  is then compared to the synthetic pedestrian image&#39;s annotation. If the output pose classification matches the synthetic pedestrian image&#39;s ground truth (e.g., its pose classification), then it can be determined that the trained pedestrian pose classifier is classifying pedestrian images correctly. In one embodiment, multiple annotated synthetic pedestrian images are used to benchmark the trained pedestrian pose classifier, and a percentage of incorrect classifications is determined. 
     Reference in the specification to “one embodiment” or to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment. The appearances of the phrase “in one embodiment” or “an embodiment” in various places in the specification are not necessarily all referring to the same embodiment. 
     Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps (instructions) leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic or optical signals capable of being stored, transferred, combined, compared and otherwise manipulated. It is convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. Furthermore, it is also convenient at times, to refer to certain arrangements of steps requiring physical manipulations or transformation of physical quantities or representations of physical quantities as modules or code devices, without loss of generality. 
     However, all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device (such as a specific computing machine), that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Certain aspects of the embodiments include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the embodiments can be embodied in software, firmware or hardware, and when embodied in software, could be downloaded to reside on and be operated from different platforms used by a variety of operating systems. The embodiments can also be in a computer program product which can be executed on a computing system. 
     The embodiments also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the purposes, e.g., a specific computer, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Memory can include any of the above and/or other devices that can store information/data/programs and can be transient or non-transient medium, where a non-transient or non-transitory medium can include memory/storage that stores information for more than a minimal duration. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. 
     The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the method steps. The structure for a variety of these systems will appear from the description herein. In addition, the embodiments are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the embodiments as described herein, and any references herein to specific languages are provided for disclosure of enablement and best mode. 
     In addition, the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, the disclosure of the embodiments is intended to be illustrative, but not limiting, of the scope of the embodiments, which is set forth in the claims. 
     While particular embodiments and applications have been illustrated and described herein, it is to be understood that the embodiments are not limited to the precise construction and components disclosed herein and that various modifications, changes, and variations may be made in the arrangement, operation, and details of the methods and apparatuses of the embodiments without departing from the spirit and scope of the embodiments as defined in the appended claims.