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    专

    无偏机器学习方法

    PL2016000391B1
    发明人
    CHEN JIAWEI, DONG HANDE, HE XIANGNAN, QIU YANG, XIN XIN, LIN GULI, YANG KEPING
    受让人
    UNIV SCIENCE & TECHNOLOGY CHINA
    申请人
    WALDA FRANZ, LEDERER FRANZ
    申请号
    4457969
    申请日
    2003-03-24
    公开(公告)号
    PL2016000391B1
    公开(公告)日
    2024-04-15
    IPC分类号
    B26D001/18B26D007/02
    CPC分类号
    -
    优先权号
    000103
    146754P
    070121
    244993P
    优先权日
    2009-01-22
    2009-01-22
    2009-09-21
    2009-09-22
    摘要

    A trunks-type disposable diaper is configured to prevent shrinkage in the width direction of an inner thigh contact portion and to prevent the wearing feeling and occurrence of a gap. A crossing position of a front around-leg elastic member and a back around-leg elastic member positioned closest to an edge of a leg opening is positioned at the same position in a width direction as a side edge of the narrowest portion of a narrower portion of an absorber or on the center side in the width direction, and a crotch portion does not have elastic members other than the front around-leg elastic member and the back around-leg elastic member.

    权利要求
    1 - 18 . (canceled)
    19 . A processing apparatus for generating route navigation data, comprising a processor and a memory, the apparatus being configured, under control of the processor to execute instructions in the memory to: generate training data based on road network data corresponding to a network of roads in a defined geographical area, journey data sets, each journey data set comprising data indicative of a journey made by a road user through the network of roads and being derived using geolocation transmissions from a communications device of the road user, road data sets, the road data sets generated from the road network data and the journey data sets, for each road data set, the road data is associated with a specific road in the network of roads in the road data set, and data indicative of traffic flow on the respective road, wherein the data indicative of traffic flow is derived from the journey data sets; train a classifier model based on the training data; apply the trained classifier model on road data corresponding to a road in the defined geographical area, for the trained classifier model to predict a direction of traffic flow on the road; and generate the route navigation data indicative of the predicted direction of the traffic flow on the road.
    20 . The processing apparatus as claimed in claim 19 , wherein the data indicative of traffic flow is entropy data indicative of an entropy in a direction of traffic flow on the respective road.
    21 . The processing apparatus as claimed in claim 20 , wherein, for each respective road, the road data set further comprises: count data indicative of a count of the geolocation transmissions associated with the respective road.
    22 . The processing apparatus as claimed in claim 20 wherein, for each respective road, the road data set comprises: length data indicative of a length of the respective road; first road count data indicative of a first road count of incoming roads associated with nodes constituting the respective road; second road count data indicative of a second road count of outgoing roads associated with the nodes; percentage data indicative of a percentage of the incoming roads and the outgoing roads being roads for uni-directional traffic flow; and probability data indicative of a probability of the respective road being a road for uni-directional traffic flow according to a road type of the respective road, wherein the length, the first road count, the second road count, the percentage, and the probability are derived from the road network data.
    23 . The processing apparatus as claimed in claim 19 , wherein, for applying the trained classifier model on the road data, the processing apparatus is configured to apply the trained classifier model on the road data to generate prediction data indicative of a predicted probability of the road being for uni-directional traffic flow or bi-directional traffic flow for predicting the direction of traffic flow on the road.
    24 . The processing apparatus as claimed in claim 19 , further configured to, in response to a request from a user to access data associated with the road in the defined geographical area, communicate the route navigation data to a device of the user for communicating the predicted direction of the traffic flow on the road to the user.
    25 . The processing apparatus as claimed in claim 19 , further configured to process the route navigation data to generate at least one of visual information or audio information for communicating the predicted direction of the traffic flow on the road to the user.
    26 . The processing apparatus as claimed in claim 19 , further configured to process the route navigation data and data indicative of a digital map representative of the defined geographical area for displaying the digital map with information corresponding to the predicted direction of the traffic flow on the road.
    27 . A method for generating route navigation data, the method comprising: generating training data based on road network data corresponding to a network of roads in a defined geographical area, journey data sets, each journey data set comprising data indicative of a journey made by a road user through the network of roads and being derived using geolocation transmissions from a communications device of the road user, road data sets, the road data sets generated from the road network data and the journey data sets, for each road data set, the road data is associated with a specific road in the network of roads in the road data set, and data indicative of traffic flow on the respective road, wherein the data indicative of traffic flow is derived from the journey data sets; training a classifier model based on the training data; applying the trained classifier model on road data corresponding to a road in the defined geographical area, for the trained classifier model to predict a direction of traffic flow on the road; and generating the route navigation data indicative of the predicted direction of the traffic flow on the road.
    28 . The method as claimed in claim 27 , wherein the data indicative of traffic flow is entropy data indicative of an entropy in a direction of traffic flow on the respective road.
    29 . The method as claimed in claim 28 , wherein, for each respective road, the road data set further comprises: count data indicative of a count of the geolocation transmissions associated with the respective road.
    30 . The method as claimed in claim 28 , wherein, for each respective road, the road data set comprises: length data indicative of a length of the respective road; first road count data indicative of a first road count of incoming roads associated with nodes constituting the respective road; second road count data indicative of a second road count of outgoing roads associated with the nodes; percentage data indicative of a percentage of the incoming roads and the outgoing roads being roads for uni-directional traffic flow; and probability data indicative of a probability of the respective road being a road for uni-directional traffic flow according to a road type of the respective road, wherein the length, the first road count, the second road count, the percentage, and the probability are derived from the road network data.
    31 . The method as claimed in claim 27 , wherein, for applying the trained classifier model on the road data, the processing apparatus is configured to apply the trained classifier model on the road data to generate prediction data indicative of a predicted probability of the road being for uni-directional traffic flow or bi-directional traffic flow for predicting the direction of traffic flow on the road.
    32 . The method as claimed in claim 27 , further configured to, in response to a request from a user to access data associated with the road in the defined geographical area, communicate the route navigation data to a device of the user for communicating the predicted direction of the traffic flow on the road to the user.
    33 . The method as claimed in claim 27 , further configured to process the route navigation data to generate at least one of visual information or audio information for communicating the predicted direction of the traffic flow on the road to the user.
    34 . The method as claimed in claim 27 , further configured to process the route navigation data and data indicative of a digital map representative of the defined geographical area for displaying the digital map with information corresponding to the predicted direction of the traffic flow on the road.
    35 . A computer program or a computer program product comprising instructions for implementing the method as claimed in claim 27 .
    36 . A non-transitory storage medium storing instructions, which when executed by a processor cause the processor to perform a method for generating route navigation data, the method comprising: generating training data based on road network data corresponding to a network of roads in a defined geographical area, journey data sets, each journey data set comprising data indicative of a journey made by a road user through the network of roads and being derived using geolocation transmissions from a communications device of the road user, road data sets, the road data sets generated from the road network data and the journey data sets, for each road data set, the road data is associated with a specific road in the network of roads in the road data set, and data indicative of traffic flow on the respective road, wherein the data indicative of traffic flow is derived from the journey data sets; training a classifier model based on the training data; applying the trained classifier model on road data corresponding to a road in the defined geographical area, for the trained classifier model to predict a direction of traffic flow on the road; and generating the route navigation data indicative of the predicted direction of the traffic flow on the road.
    说明书
    [0001]BACKGROUND OF THE INVENTION
    [0002]1. Field of the Invention
    [0003]The invention relates to a transforming apparatus, and more particularly to a transforming apparatus used for transforming a first image into a second image.
    [0004]2. Description of the Prior Art
    [0005]Gamma correction (GC) is served as CRT response calibration initially to adjust the overall brightness of an image displayed on screen. Images that are not properly corrected may look either pale or too dark. On the other hand, gamma correction models human perceptual response quite well. In modern applications, people implement GC to enhance contrast experience for the latter purpose.
    [0006]In the past, people implemented GC in hardware by look-up tables (LUTs). While accuracy can be obtained, the needed memory space is an issue. For n-bit image data, it costs n*2 n+1 bits of memory for the transformation of single gamma coefficient, which may occupy a large area in a chip. The situation can be worse if the transformation of multiple gamma coefficients is to be supported. U.S. Pat. No. 5,706,058 tried to divide the whole LUT into two smaller sub-LUTs. Though the needed memory space was lowered down, this method was only suitable for the transformation of single gamma coefficient. U.S. Pat. No. 5,990,949 tried to match LUTs for preview and snapshot modes, which in turn needed one more LUT to accomplish their goals. Similarly, the fundamental architecture of U.S. Pat. No. 5,990,949 was designed for the transformation of single gamma coefficient.
    [0007]If the transformation of multiple gamma coefficients needs to be supported, the memory space increases monotonically. Therefore, it is desirable to implement gamma correction in pure combinational circuits. The representative patent that implemented GC in combinational circuits is U.S. Pat. No. 5,818,521, which divided the whole gamma curve into 2 to 5 sections. Each section was then approximated by 2nd-order power functions. Furthermore, the number of multipliers and adders can be optimized through careful architecture design.
    [0008]However, multipliers are inevitable in U.S. Pat. No. 5,818,521, since it is essential for 2nd-order power functions. Since multipliers are still too large for compact design, it is necessary to use shift-and-add combinational circuits to implement the computational GC.
    [0009]Accordingly, the invention proposes a transforming apparatus that the gamma curve is divided into several sections, which are approximated by 1st-order power function. Meanwhile, multipliers are replaced with shifting operations to compute the power function, simplifying the whole architecture design and resulting in less hardware cost.
    [0010]SUMMARY OF THE INVENTION
    [0011]The scope of the invention is to provide a transforming apparatus for transforming a first image into a second image according to a transforming curve.
    [0012]According to a transforming curve, the transforming apparatus of the invention is used for transforming a first image into a second image, wherein the first image includes a plurality of pixels, and the transforming apparatus includes a differentiating module, a first transforming module, an adjusting module and a second transforming module. The differentiating module, according to a predetermined value, is used for transforming the transforming curve into a plurality of sub-curves. The first transforming module is used for generating a plurality of straight lines corresponding to the sub-curves. According to the sub-curves, the straight lines and a predetermined threshold, the adjusting module is used for adjusting the straight lines to generate a plurality of approximate lines. According to the approximate lines and the pixels of the first image, the second transforming module is used for generating the second image.
    [0013]According to the aforementioned apparatus and method thereof, the invention can adjust 2 nd -order curve into 1 st -order line and further utilize the lines to transform one image into another. The 2 nd -order calculation circuit has to be performed by multipliers, but the invention just only needs simple shifters and adders to perform 1 st -order calculation. Therefore, the calculation time is saved, the circuit architecture is simpler, and the cost is reduced.
    [0014]The advantage and spirit of the invention may be understood by the following recitations together with the appended drawings.
    [0015]BRIEF DESCRIPTION OF THE APPENDED DRAWINGS FIG. 1 is a functional block diagram illustrating the transforming apparatus of the invention. FIG. 2 is a schematic diagram illustrating the transforming curve and a plurality of approximate lines of the invention. FIG. 3 is a schematic diagram illustrating how the determining module of the transforming apparatus determines a maximum range. FIG. 4 is a flowchart showing the transforming method of the invention. FIG. 5 is a flowchart showing how the transforming method of the invention generates a plurality of approximate lines. FIG. 6 is a flowchart showing how the transforming method of the invention generates the second image.
    [0016]DETAILED DESCRIPTION OF THE INVENTION
    [0017]Referring to FIG. 1 and FIG. 2 , FIG. 1 is a functional block diagram illustrating the transforming apparatus 10 of the invention. FIG. 2 is a schematic diagram illustrating the transforming curve 30 and a plurality of approximate lines 32 of the invention. As shown in FIG. 1 and FIG. 2 , according to the transforming curve 30 , the transforming apparatus 10 of the invention is used for transforming a first image 11 into a second image 13 , wherein the first image 11 includes a plurality of pixels, and the transforming apparatus 10 includes a differentiating module 12 , a first transforming module 14 , an adjusting module 16 and a second transforming module 18 .
    [0018]The differentiating module 12 , according to a predetermined value, is used for transforming the transforming curve 30 into a plurality of sub-curves. For example, the predetermined value is the number of the sub-curves. The first transforming module 14 is used for generating a plurality of straight lines corresponding to the sub-curves. According to the sub-curves, the straight lines and a predetermined threshold, the adjusting module 16 is used for adjusting the straight lines to generate a plurality of approximate lines 32 .
    [0019]Each of the approximate lines is represented by:
    [0020]y=A i *x i +B i , wherein x i represents the brightness of a first pixel of the pixels, B i represents an offset of the i-th approximate line corresponding to a y-axis. A i includes at least one summation of 2 j , i is a natural number, and j is an integer. In this embodiment, since A i includes at least one summation of 2 j , the circuitry of y=A i *x i +B i ,can be achieved by simple shifters and adders.
    [0021]Referring to FIGS. 1 through 3 , FIG. 3 is a schematic diagram illustrating how the determining module 20 of the transforming apparatus 10 determines a maximum range. The adjusting module 16 includes a determining module 20 and an approximating module 22 . As shown in FIG. 3 , the determining module 20 compares the predetermined threshold with a first sub-curve 34 of the sub-curves and a first straight line 40 of the straight lines to determine a maximum range 42 , so as to generate a transforming parameter. The predetermined threshold can be the threshold of the error between the images transformed by sub-curves and the corresponding straight lines, or the threshold of the error between the sub-curves and the corresponding straight lines. According to the transforming parameter, the approximating module 22 transforms the first straight line into a first approximate line of the approximate lines 32 , correspondingly. The approximating module 22 can adjust a set of nodes 44 relative to the first straight line 40 to generate the first approximate line.
    [0022]In this embodiment, the approximating module 22 readjusts a set of nodes 44 relative to the first straight line 40 to generate the first approximate line, such that the first approximate line will approximate the first sub-curve 34 . Then, when the first sub-curve 34 is replaced by the first approximate line to perform image transformation, the error will be reduced. In another embodiment, the approximating module 22 can re-select a set of nodes 44 relative to the first straight line 40 to generate one new first approximate line. The objective of the aforementioned embodiments is to adjust the first approximate line to approximate the first sub-curve 34 , so as to increase the accuracy of image transformation.
    [0023]According to the approximate lines and the pixels of the first image 11 , the second transforming module 18 is used for generating the second image 13 . The second transforming module 18 includes a storage module 24 and a calculating module 26 . The storage module 24 is used for storing each A i and B i of the approximate lines. According to each A i and B i of the approximate lines and the pixels, the calculating module 26 is used for generating the second image 13 .
    [0024]Referring to FIG. 4 , FIG. 4 is a flowchart showing the transforming method of the invention. As shown in FIG. 4 , the transforming method, according to a transforming curve, is used for transforming a first image into a second image, wherein the first image includes a plurality of pixels. The transforming method includes the following steps. At start, step S 50 is performed to transform the transforming curve into a plurality of sub-curves according to a predetermined value. The predetermined value can be the number of the sub-curves. Afterward, step S 52 is then performed to generate a plurality of straight lines corresponding to the sub-curves. Step S 54 is then performed to adjust the straight lines to generate a plurality of approximate lines according to the sub-curves, the straight lines and a predetermined threshold. Step S 56 is then performed to generate the second image according to the approximate lines and the pixels of the first image.
    [0025]Each of the approximate lines is represented by:
    [0026]y=A i *x i +B i , wherein x i represents the brightness of a first pixel of the pixels, B i represents an offset of the i-th approximate line corresponding to a y-axis. A i comprises at least one summation of 2 j , i is a natural number, and j is an integer.
    [0027]Referring to FIG. 5 , FIG. 5 is a flowchart showing how the transforming method of the invention generates a plurality of approximate lines. As shown in FIG. 5 , step S 54 further includes the following steps. Step S 542 is performed to compare the predetermined threshold with a first sub-curve of the sub-curves and a first straight line of the straight lines to determine a maximum range, so as to generate a transforming parameter. The predetermined threshold can be the threshold of the error between the images transformed by sub-curves and the corresponding straight lines, or the threshold of the error between the sub-curves and the corresponding straight lines. Step S 544 is then performed to transform the first straight line into a first approximate line of the approximate lines, correspondingly, according to the transforming parameter. In the step S 544 , a set of nodes relative to the first straight line is adjusted to generate the first approximate line.
    [0028]Referring to FIG. 6 , FIG. 6 is a flowchart showing how the transforming method of the invention generates the second image. As shown in FIG. 6 , the step S 56 further includes the following steps. Step S 562 is performed to store each A i and B i of the approximate lines. Step S 564 is then performed to generate the second image according to each A i and B i of the approximate lines and the pixels.
    [0029]According to the aforementioned apparatus and method thereof, the invention can adjust 2 nd -order curve into 1 st -order line and further utilize the lines to transform one image into another. The 2 nd -order calculation circuit has to be performed by multipliers, but the invention just only needs simple shifters and adders to perform 1 st -order calculation. Therefore, the calculation time is saved, the circuit architecture is simpler, and the cost is reduced.
    [0030]With the example and explanations above, the features and spirits of the invention will be hopefully well described. Those skilled in the art will readily observe that numerous modifications and alterations of the device may be made while retaining the teaching of the invention. Accordingly, the above disclosure should be construed as limited only by the metes and bounds of the appended claims.
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