Patent Application
20240283879
During scanning operations, automatic document feeders (ADFs) may feed media past imaging sensors. As the media is moved across the imaging sensors, the imaging sensors may capture images of content on the media.
Features of the present disclosure are illustrated by way of example and not limited in the following figure(s), in which like numerals indicate like elements, in which:
For simplicity and illustrative purposes, the present disclosure is described by referring mainly to examples. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be readily apparent however, that the present disclosure may be practiced without limitation to these specific details. In other instances, some methods and structures have not been described in detail so as not to unnecessarily obscure the present disclosure.
Throughout the present disclosure, the terms “a” and “an” are intended to denote at least one of a particular element. As used herein, the term “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on.
Generally, scanners may have a mechanism, such as automatic document feeders (ADFs), to feed media through the scanners. The scanners may be implemented to detect positions of the media as the media is being fed through the scanners. In some scanners, dedicated hardware components may be implemented to detect the positions of media. For instance, hardware components such as optical sensors, PCBs, connectors, harnesses, ferrite cores, and the like may be used in some scanners. However, concerns associated with such hardware-based systems may be relatively high costs associated with the hardware components, including maintenance and replacement costs.
In other examples, the imaging sensors of the scanner may be used to detect the positions of the media, in lieu of the hardware components. However, such implementations may be based on capturing images of components of the scanner, rather than a dedicated pattern for use in media detection, and analyzing properties of the corresponding signal. For instance, such implementations may capture an image of a surface of an automatic document feeder (ADF), and may analyze properties found in the resulting image to make media presence determinations. Concerns associated with such implementations may be that, in some instances, they may be limited to color scans, and as such, detection may be limited to analysis of color parameters such as chroma. These implementations may also have reduced edge detection reliability because accuracy of media detection may be sensitive to the scanner module and the media type. In some instances, these implementations may be limited to certain types of media, such as plain, non-glossy paper, may be sensitive to content of the page being scanned, may require manual tuning of firmware for different media types and scanner modules, and/or the like.
Disclosed herein are apparatuses, systems, methods, and computer-readable media in which an image of a pattern captured by an imaging sensor may be used to determine a position of media in a scanner. By way of particular example and for purposes of illustration, a pattern may be specific arrangement of printed features having specific CIE L*a*b* values, which may be implemented as a sticker. The pattern may be applied to a surface of the scanner, for instance on a white background surface of an ADF that faces an imaging sensor, so that the imaging sensor of the scanner may capture an image of the pattern. The processor may analyze incoming images from the imaging sensor to determine presence of the features in the received images. When the media reaches the imaging sensor, the features of the pattern may be blocked, in which case the processor may make a determination on a position of the media, for instance, that a beginning of the media may be positioned to be scanned by the imaging sensor.
In some examples, the processor may receive a first signal corresponding to an image of a pattern captured by an imaging sensor. The pattern may be positioned at a spaced relation with respect to the imaging sensor. The processor may identify first property values from the first signal along the image of the pattern. In some examples, the processor may identify peak values of the first property values, such as peak grey levels which may correlate to presence of features of the pattern in the captured image. The processor may receive a second signal corresponding to a second image captured by the imaging sensor and may identify, from the second signal, second property values in the second signal, along the second image. The identified second property values may be peak values of the second property values. The processor may determine whether the second property values differ from the first property values, which may indicate that the media is covering the image. In some examples, the processor may determine that the media or a portion of the media, such as a beginning portion or an end portion of the media, is positioned to be imaged by the imaging sensor.
By enabling detection of media using signals corresponding to an image of a pattern captured by an imaging sensor, the disclosed apparatuses, systems, methods, and computer-readable media may reduce costs associated with hardware components that may be dedicated to detect media presence. In some examples, the apparatus may prolong the life of the system by allowing removal of hardware components which may potentially fail, and at no additional costs for replacement of the hardware components that may be removed.
Additionally, when compared to implementations that use an imaging sensor to detect page edges, the apparatuses of the present disclosure may have several benefits including having relatively less limitations on supported scan modes, improved accuracy of page detection, relatively greater number of supported media types, relatively less dependency on document content, a relatively shorter firmware development time, improved power-up paper jam clearing, and/or the like. By improving the performance of page detection as described herein, the apparatuses of the present disclosure may reduce print media and energy consumption by reducing a number of defective scan/print jobs that may be caused by inaccurate detection of media.
Reference is made to
In some examples, the apparatus 100 may be implemented in a scanner, an ADF of a scanner, a printer (such as an inkjet printer, a laser printer, a photo printer, or the like), a computing device, and/or the like. As shown, the apparatus 100 may include a processor 102 and a non-transitory computer-readable medium, e.g., a memory 110. The processor 102 may be a semiconductor-based microprocessor, a central processing unit (CPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or other hardware device. Although the apparatus 100 is depicted as having a single processor 102, it should be understood that the apparatus 100 may include additional processors and/or cores without departing from a scope of the apparatus 100 and/or system 200. In this regard, references to a single processor 102 as well as to a single memory 110 may be understood to additionally or alternatively pertain to multiple processors 102 and/or multiple memories 110. As depicted in
The memory 110 may be an electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. The memory 110 may be, for example, Read Only Memory (ROM), flash memory, solid state drive, Random Access memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a storage device, an optical disc, or the like. The memory 110 may be a non-transitory computer-readable medium. The term “non-transitory” does not encompass transitory propagating signals.
As shown in
In some examples, the processor 102 may calibrate for a pattern 208 based on a first signal 206 corresponding to an image of the pattern 208. The processor 102 may receive the first signal 206 corresponding to the image of the pattern 208 captured by an imaging sensor 210. In some examples, the pattern 208 may be a printed pattern that may be applied to a surface 302 of a scanner 300, a molded pattern that may be formed integrally on the surface 302, and/or the like. As depicted in
By way of particular example and for purposes of illustration, the pattern 208 may include a pattern of features 214. In some examples, the features 214 may extend along a scan direction in which the media 308 may be fed, as depicted by the arrow 306 in
The pattern 208 may include a plurality of features 214a to 214n as depicted in
The features 214 may have a predetermined color. By way of particular example and for purposes of illustration, the features 214 may be CIE L*a*b* controlled grey line pairs. In these instances, the features 214 may have a predetermined level of gray. The predetermined color of the features 214 may be determined to prevent bleed-through, for instance, in cases where the media 308 is positioned over the features 214 to block the features 214 from view of the imaging sensor 210. In some examples, the surface 302 of the scanner 300 to which the pattern 208 is applied may have a predetermined color that may provide sufficient contrast to the color of the features 214. In some examples, the surface 302 of the scanner 300 may be white and may provide a white background for the pattern 208. It should be understood that, while the pattern 208 is described in the present disclosure as being a pattern of grey line pairs, the pattern 208 may include various types of patterns, which may be formed using different shapes, colors, characteristics, and/or the like.
The processor 102 may fetch, decode, and execute the instructions 112 to identify first property values 212 along the image of the pattern 208. The first property values 212 may be based on a first signal 206 corresponding to the image of the pattern 208. In some examples, the processor 102 may identify the first property values 212 based on the first signal 206 received from the imaging sensor 210. Alternatively or additionally, the processor 102 may identify the first property values 212 based on information stored on the memory 110, the server 204, and/or the like, corresponding to the image of the pattern 208 captured by the imaging sensor 210. The first property values 212 may be values of certain properties associated with the features 214 in the pattern 208 of the captured image. As such, the first property values 212 may be used to generate learned data, which may form reference values for a property of the features 214 for later comparison with other received signals. In some examples, the first property values 212 may be grey levels along the image of the pattern 208, which may be identified from the first signal 206. It should be understood that, while grey levels are used herein as an example for purposes of description, other types of properties of the captured image may be used.
In some examples, in order to identify the first property values 212, the processor 102 may identify scan windows in the first signal 206, such as scan windows 400 depicted in
The scan windows 400 may include a plurality of scan windows 400a to 400m, in which the variable may represent a value greater than one. Each of the scan windows 400a to 400m may correlate to respective positions of the plurality of features 214a to 214n included in the pattern 208. For instance, each of the scan windows 400a to 400m may be sections of the first signal 206 correlating to respective positions of the plurality of features 214a to 214n included in the pattern 208. The processor 102 may extract a section of the first signal 206 as a corresponding scan window 400a to 400m for further processing. The first property values 212 may include peak values within respective scan windows 400a to 400m corresponding to areas around the plurality of features 214a to 214n in the pattern 208.
Each scan window 400a to 400m may have a predetermined width 402. In some examples, the imaging sensor 210 may be made up of a series of sensors, each of which may correlate with a pixel in the captured image. The processor 102 may identify groups of the sensors, correlated to areas around the features 214a to 214n, according to the predetermined width 402 of the scan windows 400a to 400m. By way of particular example and for purposes of illustration, the predetermined width 402 of the scan windows 400a to 400m may be 40 pixels wide, and the processor 102 may identify 40 pixels centered around respective features 214a to 214n in the respective scan windows 400a to 400m. The widths of the scan windows 400 may be user-defined, or may be based on testing, modeling, simulations, and/or the like.
In some examples, the processor 102 may adjust positions of the identified scan windows 400a to 400m to center the identified scan windows 400a to 400m on peak values of the first property values 212a to 212p correlated to respective features 214a to 214n of the plurality of features 214. The processor 102 may adjust the positions of the identified scan windows 400a to 400m based on the relative positions of each of the identified scan windows 400a to 400m to each other.
In this regard, the position of the pattern 208 may shift or change in the scanner 300. That is, the position of the pattern 208 relative to a position of the imaging sensor 210 may not be fixed and may shift from scan job to scan job, for instance, due to lateral mechanical movements of the scanner components. As such, the processor 102 may calibrate the positions of the scan windows 400a to 400m to correlate with the positions of the features 214a to 214n. In some examples, the absolute positions of the peaks of the first property values 212 in each of the scan windows 400a to 400m may be individually calibrated throughout the scan job. However, such methods to individually calibrate positions of each scan window 400a to 400m may be processing resource intensive and time consuming.
As such, in some examples, the processor 102 may adjust the positions of the identified scan windows 400a to 400m based on relative positions of each of the identified scan windows 400a to 400m to each other. For instance, since the distances between each of the features 214a to 214n in the pattern 208 may be predetermined and known, the processor 102 may determine a position error for one scan window 400a and may apply the determined position error to the remaining scan windows 400b to 400m based on the known relative positions of each of the scan windows 400a to 400m. By way of particular example, based on a determination that the distance 406 of the peak of the first property value 212a should be increased by 10 pixels, the processor 102 may shift each of the scan windows 400a to 400m by 10 pixels from their respective relative positions to each other.
In some examples, the processor 102 may process the first signal 206 to improve a quality of a waveform 500 of the first signal 206 prior to using the first signal 206 to identify the first property values 212. The processor 102 may apply a photo response non-uniformity (PRNU) algorithm, a horizontal moving window average (MWA) algorithm, a vertical MWA algorithm, and/or the like. PRNU may describe a gain or ratio between optical power on a pixel versus an electrical signal output, and may address differences in sensitivity of individual sensors that make up the imaging sensor 210. By way of particular example, in some instances, PRNU compensation may be applied to raw scan data in hardware within an ASIC. However, in cases where the data for media detection is available before the hardware component in which PRNU compensation may be performed, PRNU may be applied in firmware. In some examples, the processor 102 may apply the PRNU algorithm to process the first signal 206 to compensate for the differences in sensitivity of the individual sensors in the imaging sensor 210. As depicted in
The processor 102 may apply horizontal MWA and vertical MWA to the PRNU applied waveform 504 to further compensate the first signal 206 to generate the MWA applied waveform 506, as depicted in
In the present disclosure, the initial processes to generate the learned data, which may define baseline values of the first property values 212 used for comparison at later phases, may be referred to as the learning phase. Once the peak values of the first property values 212 in the scan windows 400 are determined, the processor 102 may apply additional algorithms in the learning phase to improve the accuracy and the reliability of the first property values 212. These algorithms may include identification of skip windows, average of N samples, and generation of learned data for use as a reference set of values for the first property values 212.
As to identification of skip windows, in some examples, dust may accumulate in any of the scan windows 400, which may result in inaccurate detection of peaks in the first property values 212. To protect against such errors, the processor 102 may identify as a skip window any scan window that has a relatively high difference in the position of the peak as compared to other scan windows 400. The processor 102 may skip over such identified scan windows during processing in the learning phase.
As to average of N samples, the processor 102 may generate the learned data for the first property values 212 based on an averaged output of a predetermined number of scan lines from the imaging sensor 210. As such, the processor 102 may avoid potential errors associated with using only one scan line during the learning phase.
As to generation of learn data, the processor 102 may generate the learned data for the first property values 212 based on the positions of peaks and peak values of the first property values 212, such as the grey levels at the peaks. The processor 102 may store the learned data for the first property values 212 for comparison with data identified during a scanning phase. In the present disclosure, the period that occurs after the learning phase, during which the media 308 is scanned, may be referred to as the scanning phase.
The processor 102 may identify the first property values 212 during the learning phase, prior to initiation of a scan job to scan the media 308. In some examples, the processor 102 may identify the first property values 212 at predetermined times, for instance, prior to each scan job, prior to each instance that the media 308 reaches the imaging sensor 210, and/or at predetermined intervals prior to scan jobs, such as every second, minute, day, and/or the like. In instances in which the processor 102 identifies the first property values 212 at predetermined intervals prior to scan jobs, the first property values 212 may be stored in the memory 110 at the apparatus 100, on the server 204, and/or the like, and may be retrieved during the scanning phase for media detection.
The processor 102 may fetch, decode, and execute the instructions 114 to receive a second signal 216 corresponding to a second image captured by the imaging sensor 210. The second signal 216 may be a signal received during the scanning phase. The processor 102 may begin receiving the second signal 216 before the media 308 reaches the imaging sensor 210. In some examples, the processor 102 may continuously receive the second signal 216 beginning prior to the media 308 reaching the imaging sensor 210, and continuing until after the media 308 is determined to have passed the imaging sensor 210.
The processor 102 may fetch, decode, and execute the instructions 116 to identify, from the second signal 216, second property values 218 along the second image. The second image may be an image captured by the imaging sensor 210 at the same location as the first image for the first signal 206. The processor 102 may determine whether the second signal 216 includes the second property values 218 along the second image. The processor 102 may determine peak values of the second property values 218. The process to detect the peak values of the second property values 218 may be the same as to detect the peak values in the first property values 212, as previously described. For instance, to process the second signal 216, the processor 102 may identify scan windows, apply a PRNU algorithm, and/or apply a horizontal MWA and a vertical MWA, as previously described with respect to the first signal 206.
The processor 102 may fetch, decode, and execute the instructions 118 to determine whether the second property values 218 differ from the first property values 212. In this regard, the processor 102 may compare the relative positions and/or peak values of the second property values 218 with the relative positions and/or peak values of the first property values 212 determined during the learning phase. In some examples, the processor 102 may determine whether the second property value 218 is within predetermined thresholds for the relative positions and/or the peak values compared to those of the first property values 212. Based on the comparison of the second property values 218 and the first property values 212, the processor 102 may determine whether the media 308 is positioned to be scanned by the imaging sensor 210, for instance, in a “media present” state, or whether the media 308 is not positioned to be scanned by the imaging sensor 210, for instance, in a “media not present” state.
In this regard, based on a determination that the second property values 218 include second positions and/or peak values that match the positions and/or peak values of the first property values 212, the processor 102 may determine that the media 308 is not positioned to be imaged by the imaging sensor 210, as media 308 may not be blocking the pattern 208 applied to the surface 302 of the scanner 300. Alternatively, based on a determination that the second positions and/or peak values of the second property values 218 do not match positions and/or peak values of the first property values 212, the processor 102 may determine that the media 308 is present, as the media 308 may be blocking the pattern 208 from the imaging sensor 210.
The processor 102 may fetch, decode, and execute the instructions 120 to determine that a beginning 310 (e.g., top) of a media 308 may be positioned to be imaged by the imaging sensor 210 based on a determination that the second property values 218 differ from the first property values 212. In some examples, the processor 102 may determine that the beginning 310 of the media 308 has reached a position to be scanned by the imaging sensor 210 based on a transition from the “media present” state to the “media not present” state. In some examples, based on a determination that the media 308 is positioned to be imaged by the imaging sensor 210, the processor 102 may output an instruction, for instance, to the imaging sensor 210, an imaging subsystem (not shown), and/or the like, to begin scanning the media 308.
The processor 102 may apply filters to reduce a number of false positive triggers for media detection. In some examples, dust accumulation in the scanner 300 may trigger false detection of the beginning 310 of the media 308, in which case the processor 102 may incorrectly detect a non-match state between the second property value 218 and the first property value 212. In these instances, the processor 102 may apply a dust filter, in which the processor 102 may identify a predetermined number of scan windows 400 that fail the matching condition before the processor 102 makes a determination that a transition has occurred from a “media not present” state to a “media present” state. As such, the processor 102 may reduce a number of occurrences of false detections of the beginning 310 of the media, which may be caused by dust accumulation. In some examples, the processor 102 may apply a skip window filter, in which the scan windows 400 identified as skip windows, for instance in the learning phase as previously described, may be skipped over in subsequent scanning phases.
In some examples, the processor 102 may detect an end 312 (e.g., bottom) of the media 308. The processor 102 may determine third property values (not shown) corresponding to a third image captured by the imaging sensor 210. The processor 102 may determine whether the third property values match the first property values 212, identified during the learning phase. In some examples, the processor 102 may determine whether a position and/or a peak value of the third property values correlating to the scan windows 400 match the position and/or peak values of the first property values 212. In this regard, when the third property values match the first property values, the processor 102 may determine that a transition has occurred from the “media present” state to the “media not present” state. Based on the third property values matching the first property values 212, the processor 102 may determine that an end 312 of the media 308 has passed the imaging sensor 210. In some examples, based on a determination that the end 312 of the media 308 has passed the imaging sensor 210, the processor 102 may output an instruction, for instance, to the imaging sensor 210, an imaging subsystem, and/or the like, to stop scanning the media 308.
In some examples, the processor 102 may apply a decision glitch filter, which may filter false determinations that the end 312 of the media 308 has passed the imaging sensor 210. For instance, determinations based on samples from a relatively small number of scan lines may increase changes for false positives. As such, the processor 102 may make the determination that the end 312 of the media 308 has passed the imaging sensor 210 based on data from a predetermined number of scan lines.
Various manners in which the processor 102 may operate are discussed in greater detail with respect to the method 600 depicted in
At block 602, the processor 102 may receive a first signal 206 corresponding to an image of a pattern 208 of features 214, for instance, as depicted in
At block 606, the processor 102 may receive a second signal corresponding to a second image captured by the imaging sensor. At block 608, the processor 102 may identify, from the second signal 216, second property values 218 along the second image.
At block 610, the processor 102 may determine whether the second property values 218 differ from the first property values 212. At block 612, based on a determination that the second property values 218 differ from the first property values 212, the processor 102 may determine that a beginning 310 of a media 308 may be positioned to be imaged by the imaging sensor 210.
In some examples, the processor 102 may identify scan windows 400 in the first signal 206. The identified scan windows 400 may be sections of the first signal 206 correlating to respective positions of a plurality of features included in the pattern 208 of features 214. The processor 102 may adjust positions of the identified scan windows 400 to center the identified scan windows 400 on peak values of the first property values 212 correlated to the plurality of features in the pattern 208 of features 214. The positions of the identified scan windows 400 may be adjusted based on relative positions of each of the identified scan windows 400 to each other.
The processor 102 may determine a presence of the media 308 relative to the imaging sensor 210 based on comparison of the first property values 212 and the second property values 218. Based on a determination that the media 308 is positioned to be imaged by the imaging sensor 210, the processor 102 may output an instruction, for instance, to the imaging sensor 210, an imaging subsystem, and/or the like, to begin scanning the media 308.
The processor 102 may determine third property values (not shown) corresponding to a third image captured by the imaging sensor 210. The processor 102 may receive a third signal for the third image after detection of the beginning 310 of the media 308. The processor 102 may determine whether the third property values match the first property values 212.
Based on the third property values matching the first property values 212, the processor 102 may determine that an end 312 of the media 308 has passed the imaging sensor 210. In some examples, based on a determination that the end 312 of the media 308 has passed the imaging sensor 210, the processor 102 may output an instruction, for instance, to the imaging sensor 210, an imaging subsystem, and/or the like, to stop scanning the media 308.
Some or all of the operations set forth in the method 600 may be included as utilities, programs, or subprograms, in any desired computer accessible medium. In addition, the method 600 may be embodied by computer programs, which may exist in a variety of forms both active and inactive. For example, they may exist as computer-readable instructions, including source code, object code, executable code or other formats. Any of the above may be embodied on a non-transitory computer-readable storage medium.
Examples of non-transitory computer-readable storage media include computer system RAM, ROM, EPROM, EEPROM, and magnetic or optical disks or tapes. It is therefore to be understood that any electronic device capable of executing the above-described functions may perform those functions enumerated above.
Turning now to
The computer-readable medium 700 may have stored thereon computer-readable instructions 702-716 that a processor, such as the processor 102 depicted in
The processor may fetch, decode, and execute the instructions 702 to receive a first signal 206 corresponding to an image of a pattern 208 captured by an imaging sensor 210. The pattern 208 may be positioned at a spaced relation with respect to the imaging sensor 210 as discussed herein.
The processor may fetch, decode, and execute the instructions 704 to identify first scan windows, such as the scan windows 400 depicted in
The processor may fetch, decode, and execute the instructions 706 may identify, from the first signal 206 in the first scan windows, first property values 212 along the image of the pattern 208.
The processor may fetch, decode, and execute the instructions 708 to receive a second signal 216 corresponding to a second image captured by the imaging sensor 210. The processor may fetch, decode, and execute the instructions 710 to identify second scan windows, such as the scan windows 400 depicted in
The processor may fetch, decode, and execute the instructions 712 to identify, from the second signal 216 in the second scan windows, second property values 218 along the second image. In some examples, the first signal 206 may be received during a learning phase prior to scanning, and the second signal 216 may be received during a scanning phase, which may occur after the learning phase. In some examples, the scanning phase may include periods before and after the imaging sensor 210 is controlled to scan the media 308.
The processor may fetch, decode, and execute the instructions 714 to determine whether the second property values 218 differ from the first property values 212. Based on a determination that the second property values 218 differ from the first property values 212, the processor may determine that a beginning 310 of the media 308 may be positioned to be imaged by the imaging sensor 210.
Although described specifically throughout the entirety of the instant disclosure, representative examples of the present disclosure have utility over a wide range of applications, and the above discussion is not intended and should not be construed to be limiting, but is offered as an illustrative discussion of aspects of the disclosure.
What has been described and illustrated herein is an example of the disclosure along with some of its variations. The terms, descriptions and figures used herein are set forth by way of illustration and are not meant as limitations. Many variations are possible within the scope of the disclosure, which is intended to be defined by the following claims—and their equivalents—in which all terms are meant in their broadest reasonable sense unless otherwise indicated.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/034321 | 5/26/2021 | WO |
