Source: http://www.google.com/patents/US20030099407?dq=3798359
Timestamp: 2013-12-06 09:01:45
Document Index: 437043772

Matched Legal Cases: ['art 202', 'art 203', 'art 204', 'art 202', 'art 203', 'art 202', 'art 204', 'art 202', 'art 203', 'art 204', 'art 204', 'art 202', 'art 202', 'art 203', 'art 203', 'art 203', 'art 203', 'art 203', 'art 202', 'art 203', 'art 202', 'art 203', 'art 202', 'art 203', 'art 204', 'art 233', 'art 234', 'art 235', 'art 234', 'art 233', 'art 250', 'art 251', 'art 252', 'art 250', 'art 202', 'art 203', 'art 204', 'art 250', 'art 233', 'art 202', 'art 203', 'art 204', 'art 250', 'art 250', 'art 251', 'art 250', 'art 202', 'art 203', 'art 204', 'art 233', 'art 252', 'art 251', 'art 251', 'art 234', 'art 251', 'art 234', 'art 234', 'art 235', 'art 251', 'application No. 2001']

Patent US20030099407 - Image processing apparatus, image processing method, computer program and ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Advanced Patent Search | Sign inAdvanced Patent SearchPatentsAn image processing apparatus includes a determination part, an information obtaining part, a dynamic range setting part and a processing part. The determination part determines whether or not an image includes an area where destruction of gradation due to contrast correction tends to be conspicuous....http://www.google.com/patents/US20030099407?utm_source=gb-gplus-sharePatent US20030099407 - Image processing apparatus, image processing method, computer program and storage mediumPublication numberUS20030099407 A1Publication typeApplicationApplication numberUS 10/270,065Publication dateMay 29, 2003Filing dateOct 15, 2002Priority dateNov 29, 2001Also published asUS7167597, US7315657, US20070041637Publication number10270065, 270065, US 2003/0099407 A1, US 2003/099407 A1, US 20030099407 A1, US 20030099407A1, US 2003099407 A1, US 2003099407A1, US-A1-20030099407, US-A1-2003099407, US2003/0099407A1, US2003/099407A1, US20030099407 A1, US20030099407A1, US2003099407 A1, US2003099407A1InventorsYuki MatsushimaOriginal AssigneeYuki MatsushimaReferenced by (30), Classifications (20), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetImage processing apparatus, image processing method, computer program and storage mediumUS 20030099407 A1Abstract An image processing apparatus includes a determination part, an information obtaining part, a dynamic range setting part and a processing part. The determination part determines whether or not an image includes an area where destruction of gradation due to contrast correction tends to be conspicuous. The information obtaining part obtains information of the area when the determination part determines that the image includes such an area. The dynamic range setting part sets a dynamic range using the information obtained by the information obtaining part. The processing part performs the contrast correction on the image using the dynamic range set by the dynamic range setting part. Images(23) Claims(21)
[0133]FIG. 21 is a block diagram showing the structure of a third embodiment of the present invention. FIG. 22 is a flow chart for explaining the procedure in this embodiment. [0134] An image processing apparatus 201 shown in FIG. 21 performs the gradation correction of an image and includes a first determination part 202, a second determination part 203, and a processing part 204. The first determination part 202 performs the process of step S201 in FIG. 22. The process of step S201 determines whether or not an input image is a type D, according to a color signal of a background area of input color image data. The second determination part 203 performs the process of step S203 in FIG. 22 in a case where the first determination part 202 determines that the image is not the type D (NO in step S201). The process of step S203 determines whether the image is a type E or a type F by checking the relationship of the relative brightness between the background area and an object area. The processing part 204 performs the appropriate gradation correction process corresponding to the type of the image determined by the first determination part 202 and the second determination part 203. More specifically, the processing part 204 creates a gradation correction table corresponding to the type in step S202 if YES in step S201, in step S204 if YES in step S203, and in step S205 if NO in step S203 in FIG. 22. Further, by using the gradation correction table, the processing part 204 performs a gradation correction process of step S206 in FIG. 22 on image data. [0135] In the following, a detailed description will be given of each of the parts of the image processing apparatus 201. [0136] First, a description will be given of the first determination part 202 that performs step S201 in FIG. 22. The first determination part 202 determines whether or not an input image is an image photographed against backlight (type D). The image determined as the type D is an image having a background area mainly comprising high-luminance white light, that is, a complete backlit image having a whitish background. Various methods may be employed for this determination. However, among the various methods, two examples will be explained below. [0137] In a first method, as shown in a flow chart in FIG. 23, image division is performed on an input image in step S210 so as to divide the input image into a background area and an object area. In the case of FIG. 3, the image is divided as shown in FIG. 24. Then, in the background area, high-luminance white light pixels are counted in step S211. Step S212 determines whether or not no less than 70%, for example, of pixels of the background area are the high-luminance white light pixels. If no less than 70% of pixels of the background area are the high-luminance white light pixels (YES in step S212), the image is determined as the type D, and the process proceeds to step S202 in FIG. 22). If the decision result in step S212 is NO, the process proceeds to step S203 in FIG. 22. The high-luminance white light pixels refer to pixels of which color components (R, G, and B) satisfy the above-mentioned equation (5). Further, it is also possible to simultaneously perform the image division process and the counting process of the high-luminance white light pixels. [0138] In a second method, as shown in a flow chart of FIG. 25, the high-luminance white light pixels are counted with respect to the whole area of an input image in step S220. At this moment, step S220 also identifies whether or not each pixel belongs to an end part of the image. The end part of the image refers to a peripheral area of an image as shown by a hatched part in FIG. 26. Then, step S221 determines whether or not the number of the high-luminance white light pixels is no less than 7%, for example, of the total pixel number, and at the same time, no less than 70% of the high-luminance white light pixels are in the end part of the image. If the decision result in step S221 is YES, the image is determined as the type D, and the process proceeds to step S202 in FIG. 22. If the decision result in step S221 is NO, the process proceeds to step S203 in FIG. 22. [0139] Next, a description will be given of the second determination part 203 that performs step S203 in FIG. 22. The second determination part 203 determines the image as the type E when, in the image that is not determined as the type D by the first determination part, the background area is relatively darker than the object area. When the background area is not relatively darker than the object area, the second determination part 203 determines the image as the type F. FIG. 4 is the portrait image photographed at night showing an example of the image that is determined as the type E. [0140] A description will be given of the process of each step performed by the second determination part 203, with reference to FIG. 27 showing the process flow. [0141] Step S250 creates a luminance histogram of the whole area of an input image. A luminance Y is obtained by the following equation using color components (R, G, B). In each of the above-mentioned embodiments, the luminance Y is obtained in a similar manner. Y=0.299�R+0.587�G+0.114�B (10) [0142] Step S251 obtains dynamic ranges (Scene_Min, Scene_Max) for the type determination from the luminance histogram created in step S250. More specifically, in the luminance histogram, a cumulative frequency is obtained from the minimum level, and a level at which the value of the cumulative frequency reaches 1%, for example, of the total frequency is set to the shadow point Scene Min. In addition, the highlight point Scene_Max is set to a level at which the cumulative frequency obtained from the maximum level reaches 1%, for example, of the total frequency. Further, in an image photographed by a digital camera, noise tends to occur at level 0 and level 255. Thus, it is preferable to set the minimum level to 1 and the maximum level to 254. [0143] In order to determine a relatively dark part in the image from an area of distribution of the luminance histogram, step S252 quantizes an area between the shadow point Scene_Min and the highlight point Scene_Max that are obtained in step S251 into four sections 0, 1, 2 and 3 as shown in FIG. 14, by dividing the area into four parts. The section 0 is the relatively dark area of distribution. [0144] Step S253 creates an image (an image with excessive contrast) in which luminance levels are quantized into four levels, by replacing pixels having the luminance levels of the sections obtained in step S252 with respect to the input image with a representative level. In a case of the portrait image photographed at night shown in FIG. 4, a quantized image as shown in FIG. 28 is obtained. Then, the image is determined as the type E in a case where an average luminance of a background area of the quantized image and that of an object area (a black area in FIG. 28) of the same are calculated and compared with each other, and where the average luminance of the background area is lower than that of the object area (in a case where the background is relatively darker than the object). In cases other than the above-mentioned case, the image is determined as the type F. The reason for quantizing the image is to simply recognize the relative contrast relationship between the brightness of the background and that of the object. [0145] The image determined as the type E is an image in which the background is relatively darker than the object. That is, images such as a night portrait image, a night scene image, an underexposed image photographed normally and the like. The images determined as the type F are such as an image photographed not completely against light but against light in which the background is brighter than the object, a correctly exposed image photographed normally, an underexposed image photographed normally and the like. [0146] There are two reasons for further performing the type determination by the second determination part 203 after the first determination part 202 determines whether or not an input image is an image photographed completely against light (the type D). A first reason is that erroneous determination hardly occurs in determining whether or not an image is photographed completely against light since it is easily determined. Additionally, the second determination part 203 calculates the dynamic ranges for the type determination in order to extract a relatively bright part and a dark part in the image. This is for simplifying the relationship between the brightness of the background and that of the object, by clarifying the bright part and the dark part in the image that has unsatisfactory contrast due to underexposure or the like. Accordingly, when the same quantization is performed on the type D image that has the originally and absolutely bright background and is considered to be an image photographed against light, the image tends to be mistaken for the type F image that is correctly exposed. For this reason, it is necessary to perform the determination of the type D by the first determination part 202, and this is a second reason. [0147] Further, in the above-mentioned determination process of the image type, decimation may be suitably performed in calculating the high-luminance white light area and luminance histogram. In addition, the description is given of the example where the RGB information is used for determining whether or not the background area mainly includes the high-luminance white light pixels. However, a color space other than RGB, such as luminance color difference information or brightness color difference information may be used for the determination. Furthermore, the second determination part 203 calculates the relatively dark part in the image using the luminance information. However, color information such as brightness information or G signals may also be used. Additionally, it is also possible to vary the determination parameters (the threshold values in the above-mentioned condition (5)) of the high-luminance white light and the determination parameter (cumulative frequency 1%) of the dynamic ranges for the type determination. [0148] Furthermore, an operator may handle the determination of the first determination part 202. For example, the determination may be made such that, by preparing means for inputting a decision result of the operator such as a �backlight correction key�, the process proceeds regarding an image as the type D when the backlight correction key is pressed, and the process proceeds to the determination process of the second determination part 203 when the backlight correction key is not pressed. [0149] Next, a description will be given of the processing part 204 that performs steps S203, S204 and S206 in FIG. 22, by referring to a flow chart in FIG. 29. [0150] Step S260 extracts a determination area and a control area. The determination area is an area for determining an exposure correction amount. The control area is an area used when controlling the correction. All pixels are divided into these two areas as described below. [0151] For the types D and F where the background is brighter than the object, the object area is the determination area. Additionally, highlight pixels of the background area and object area (a whole image, namely) form the control area. On the other hand, for the type E where the background is darker than the object (step S204 in FIG. 22), the object area is the determination area and the highlight pixels in the object area form the control area. The highlight pixels refer to pixels belonging to the section 3 of the quantized luminance histogram as shown in FIG. 14, for example. [0152] Step S261 determines an initial value of the correction amount in accordance with information of the determination area, for example, a luminance median value, and the type of an image (this corresponds to the determination of the exposure). Various methods may be employed for the determination. For example, when the luminance median value is smaller than a first threshold value, a first predetermined value is set to the initial value when the image is the type D, and the initial value is set to a value calculated by a first equation using the luminance median value when the image is the type E or F. In a case where the luminance median value is greater than the first threshold value and smaller than a second threshold value, irrespective of the type, the initial value is set to a value calculated by a second equation using the luminance median value. In a case where the luminance median value is greater than the second threshold value, the initial value is set to a second predetermined value irrespective of the type. Then, using the initial value of the correction amount a gradation correction table f0(x) of an initial stage is created in step S261. f(0)x={(x/255)^ δ}�255 (11) [0153] Next, step S262 evaluates whether or not the destruction of the gradation or the deterioration of the gradation is within a permissible range when the gradation correction is performed using a current gradation correction table. More specifically, using the current gradation correction table, step S262 performs conversion of pixels belonging to the control area by employing the same color conversion method of step S265 that will be described later. Then, the destruction of the gradation of image data after the gradation correction is evaluated. For example, with respect to the saturated pixels that exceed the reproducing range (that is, over level 255) of the output device, an average transcendental value is calculated. That is, with respect to �k� saturated pixels, the maximum level lj (j=1, 2, . . . K) of a saturated color component is obtained for each of the pixels, and an average value Oave that is the difference between the maximum level and the upper limit of the reproducing range is calculated as a degree of saturation (chroma). Oave=Σ(lj−255)/K (12) [0154] where j 1, 2 . . . K [0155] Then, when the degree of saturation is no less than a certain threshold value, it is determined that the degree of saturation, that is, the destruction of the gradation, is not permitted. [0156] Further, the degree of saturation may be the ratio K/M of the number of the saturated pixels (K) in the control area to the number of pixels (M) in the control area, or the ratio K/N of the number of the saturated pixels (K) to the number of pixels (N) in the whole area of the image. [0157] When step S262 determines that the destruction of the gradation is not permitted (NO in step S262), step S263 adjusts the correction amount so that the correction amount becomes a smaller value, step S264 creates the gradation correction table fi(x) using the correction amount, and step S262 determines again whether or not the destruction of the gradation is permitted. This update process of the gradation correction table is repeated until it is determined that the gradation correction is permitted (the above-mentioned update process of the gradation correction table according to a loop of steps S262 through S264 is the exposure control). FIG. 30 shows examples of the gradation correction table f0(x) in the initial stage, and the gradation correction table f1(x) after the first update. [0158] Step S265 performs color conversion on the input image by using the final gradation correction table fn(x) obtained by the above-mentioned procedure. In the following, the color conversion with respect to RGB data of the input image will be explained. [0159] A gradation correction coefficient C1(j) is calculated by defining an output luminance value Y2(j) after the correction according to the gradation correction table fn(x), with respect to a luminance value Y1(j) (j=1, 2, . . . , N where N is a total number of pixels). [0160] Then, by using this gradation correction coefficient, color image signals (R1(j), G1(j) and B1(j)) are converted so as to obtain output color signals (R2(j), G2(j) and B2(j)). (R2(j), G2(j), B2(j))=C1(j)�(R1(j), G1(j), B1(j)) (14) [0161] The above-described image processing apparatus 201 may be realized not only as a separate apparatus but also as a built-in apparatus. In addition, the image processing apparatus 201 may be realized by any of hardware, software and a combination of hardware and software. [0162] A description will be given of a case where the image processing apparatus 201 is realized by software, by referring to FIG. 16. For example, in the image processing system 101 as shown in FIG. 16, it is possible to provide the correction program 117 to the image processing application 118 that operates on the computer 107 for realizing the functions (the procedure shown in FIG. 22) of the image processing apparatus 201 shown in FIG. 21 on the computer 107. When performing the image processing by the image processing application 118, the correction program 117 performs the gradation correction process on the image data. The image data after the gradation correction are output to the display 114 through the display driver 119, or output to the printer 115 through the printer driver 120. Further, when the gradation correction process is performed only when outputting an image, the printer driver 120 and the display driver 119, or the printer 115 and the display 114 may include the same functions as those of the correction program 117 has. The present invention includes programs for realizing the functions for such contrast correction and computer-readable information storage media on which the programs are recorded such as a magnetic disk, an optical disk, a magnetic optical disk, a semiconductor memory element and the like. [0163] <Fourth Embodiment>
[0164]FIG. 31 is a block diagram showing the structure of a fourth embodiment of the present invention. An image output system shown in FIG. 31 includes a plurality of personal computers (PC) 231 a and 231 b and a plurality of printers 236 a and 236 b, and can output an image from an arbitrary one of the personal computers to an arbitrary one of the printers. The image output system further includes image processing apparatuses 232 a and 232 b corresponding to the printers 236 a and 236 b, respectively. [0165] In this image output system, when an operator selects the printer 236 a and inputs an output instruction thereto, for example, the personal computer 231 a sends image data captured by imaging an image or the like and image data created by various DTP software to the image processing apparatus 232 a. The image processing apparatus 232 a performs the gradation correction process on the sent image data, thereafter performs the color conversion process so as to convert the image data into a plurality of output color components C (cyan), M (magenta), Y (yellow) and K (black), for example, and to generate print data. The print data are sent to the printer 236 a and printed. [0166] Further, the image processing apparatuses 232 a and 232 b are shown as separate apparatuses. However, as will be described later more specifically, printer drivers that operate on the personal computers 231 a and 231 b may include all the functions of the image processing apparatuses 232 a and 232 b, respectively. Additionally, it is also possible that printer drivers include the partial functions of the image forming apparatuses 232 a and 232 b and the printers 236 a and 236 b include the rest of the functions. As mentioned above, various realizing forms may be employed. [0167] Each of the image processing apparatuses 232 a and 232 b includes a color conversion part 233, a drawing part 234 that performs the halftone process and the like, and an image storing part 235 for temporarily storing a print image for one page drawn by the drawing part 234. [0168] As shown in FIG. 32, the color conversion part 233 includes a correction process part 250, an interpolation calculation part 251, and a color conversion table storing part 252. The correction process part 250 includes a set parameter table 207 and a permissible level table 208, besides a first determination part 202, a second determination part 203 and a processing part 204 that are similar to those corresponding parts in the image processing apparatus 201 in the third embodiment. [0169] In the following, a description will be given of the process of the image processing apparatus 232 a for a case where the personal computer 231 a or 231 b sends RGB image data to the image processing apparatus 232 a, the image data are converted into CMYK print data, and the printer 236 a outputs the print data as an image, for example. [0170] When the RGB image data are sent to the image processing apparatus 232 a, in the correction process part 250 in the color conversion part 233, the first determination part 202 and the second determination part 203 determine the type of the image. The processing part 204 creates a gradation correction table corresponding to the determined type, and performs the gradation correction using the created table. [0171] Since different printers have different reproducing ranges, each printer has a different permissible range of the destruction of the gradation. Accordingly, the correction process part 250 keeps parameter information relating to the printer (236 a, in this case) from which an output will be made as a table. More specifically, the correction process part 250 keeps, in a set parameter table 207, the parameters defining the high-luminance white light pixels in the above-mentioned condition (5) and cumulative frequency parameters in setting the dynamic range. In addition, the permissible level of the destruction of the gradation varies depending on the ink characteristics of the printer and the halftone process of the drawing part 251. For this reason, the correction process part 250 keeps, in the set parameter table, determination threshold values of the degree of saturation (chroma) for permission evaluation of the destruction of the gradation. The first determination part 202 and the second determination part 203 read and use the parameters kept in the set parameter table 207. The processing part 204 reads and uses the parameters kept in the permissible level table 208. [0172] Next, in the color conversion part 233, an optimum table is selected from among the color conversion tables stored in the color conversion table storing part 252, and the selected optimum table is sent to the interpolation calculation part 251. The interpolation calculation part 251 performs a memory map interpolation that is similar to that of the second embodiment on input RGB of a memory map of the selected color conversion table, converts RGB image data into CMYK print data, and sends the CMYK print data to the drawing part 234. [0173] The print data converted into CMYK by the interpolation calculation part 251 are sent to the drawing part 234. Print image data on which the halftone process and the like are performed by the drawing part 234 are temporarily stored in the image storing part 235 page by page, and are finally printed out by the printer 236 a. [0174] Of course, the above-described image processing apparatuses 232 a and 232 b may be realized as separate hardware apparatuses. However, it is also possible for the printer 236 a and 236 b to include all the functions or the partial functions of the image processing apparatuses 232 a and 232 b, respectively. In addition, it is also possible for software to include all the functions or the partial functions. For example, printer drivers that operate on the personal computers 231 a and 231 b may include all the functions or the partial functions of the image processing apparatus. In such a case, among the functions of the interpolation calculation part 251, if the printer drivers include the functions up to (C, Y, K) data generation and the printers include the converting function to (C, M, Y, K) data, the transmission data amount from the personal computer to the printer is reduced, and, generally, there is an advantage in speeding up the process. The present invention also includes programs for such printer drivers and various information recording media (computer-readable recording media), such as the CD-ROM 121 shown in FIG. 16, on which the programs are recorded. [0175] As described in the second embodiment, in a case where print data for one page includes different kinds of objects such as a nature image and a graphic image, it is necessary that the image data are sent to the image processing apparatuses 232 a and 232 b by attaching the correction coefficient to the header of the image data. Additionally, in a case where the printer driver includes the above-mentioned functions, it is also possible to configure the printer driver to read and use a device profile that is standardized by the ICC (Inter Color Consortium). [0176] The present invention is not limited to the specifically disclosed embodiments, and variations and modifications may be made without departing from the scope of the present invention. [0177] The present application is based on Japanese priority application No. 2001-363785 filed on Nov. 29, 2001, the entire contents of which are hereby incorporated by reference. Referenced byCiting PatentFiling datePublication dateApplicantTitleUS7221807 *Mar 29, 2002May 22, 2007Sharp Laboratories Of America, Inc.Methods and systems for digital image characteristic adjustment using a neural networkUS7327504 *Dec 6, 2002Feb 5, 2008Eastman Kodak CompanyMethod of detecting clipped image pixelsUS7359573 *Nov 4, 2003Apr 15, 2008Samsung Electronics Co., Ltd.Contrast compensation apparatus and method thereofUS7406193 *Mar 17, 2004Jul 29, 2008Oki Data CorporationImage processing method and image processing apparatus for making luminance correctionUS7486836Jul 26, 2005Feb 3, 2009Casio Computer Co., Ltd.Image pickup device with brightness correcting function and method of correcting brightness of imageUS7510275Sep 13, 2006Mar 31, 2009Ricoh Company, Ltd.Image forming apparatus and image forming methodUS7545417 *May 11, 2006Jun 9, 2009Sony CorporationImage processing apparatus, image processing method, and image processing programUS7545435 *Apr 15, 2005Jun 9, 2009Lifesize Communications, Inc.Automatic backlight compensation and exposure controlUS7551334 *Jul 20, 2005Jun 23, 2009Xerox CorporationBackground suppression method and apparatusUS7646406 *Feb 24, 2005Jan 12, 2010Fujifilm CorporationImage taking apparatusUS7664319 *Apr 7, 2006Feb 16, 2010Fujitsu LimitedMethod and device for color balance correction, and computer productUS7684096 *Apr 1, 2003Mar 23, 2010Avid Technology, Inc.Automatic color correction for sequences of imagesUS7912282Sep 22, 2006Mar 22, 2011Fujifilm CorporationImage processing apparatus for correcting an input image and image processing method thereforUS8023744 *Mar 15, 2007Sep 20, 2011Hoya CorporationPattern matching system and targeted object pursuit system using light quantities in designated areas of images to be comparedUS8106965 *Sep 17, 2008Jan 31, 2012Olumpus CorporationImage capturing device which corrects a target luminance, based on which an exposure condition is determinedUS8115852 *Jun 11, 2008Feb 14, 2012Olympus Imaging Corp.Contrast control for use in image display apparatus and image pickup apparatusUS8212871 *Nov 3, 2009Jul 3, 2012Omron CorporationImage processing deviceUS8285041 *Sep 7, 2005Oct 9, 2012Olympus CorporationImage processing apparatus, image recording apparatus, and image processing methodUS8330970Dec 17, 2008Dec 11, 2012Ricoh Company, Ltd.Image processing device, image processing method, and recording mediumUS8373754 *Aug 28, 2008Feb 12, 2013VALEO Schalter Sensoren GmbHMethod and system for evaluating brightness values in sensor images of image-evaluating adaptive cruise control systemsUS8477234Jul 22, 2010Jul 2, 2013Panasonic Electric Works Co., Ltd.Brightness sensing system and illumination system using the sameUS20060056688 *Sep 7, 2005Mar 16, 2006Tetsuya ToyodaImage processing apparatus, image recording apparatus, and image processing methodUS20060182352 *Jan 31, 2006Aug 17, 2006Tetsuya MurakamiEncoding apparatus and method, decoding apparatus and method, recording medium, and image processing system and methodUS20100110180 *Nov 3, 2009May 6, 2010Omron CorporationImage processing deviceUS20100260415 *Mar 22, 2010Oct 14, 2010Canon Kabushiki KaishaImage processing apparatus and method for controlling the apparatusUS20110109743 *Aug 28, 2008May 12, 2011Valeo Schalter Und Sensoren GmbhMethod and system for evaluating brightness values in sensor images of image-evaluating adaptive cruise control systemsEP1850584A1 *Jan 29, 2007Oct 31, 2007Huawei Technologies Co., Ltd.Method for acquiring and controlling automatic exposure control parameters and imaging deviceEP2288141A1 *Jul 20, 2010Feb 23, 2011Panasonic Electric Works Co., Ltd.Brightness sensing system and illumination system using sameWO2005104032A2 *Feb 1, 2005Nov 3, 2005Eastman Kodak CoAutomatic in vivo image adjustmentWO2006011687A2Jul 29, 2005Feb 2, 2006Casio Computer Co LtdImage pickup device with brightness correcting function and method of correcting brightness of image* Cited by examinerClassifications U.S. Classification382/274, 348/E05.119, 348/251, 382/224, 382/228, 348/234, 382/168, 348/254International ClassificationH04N5/20, H04N1/407, H04N5/57, G09G5/10, G09G5/00, G06T5/00Cooperative ClassificationH04N5/57, H04N1/4074, H04N5/2355European ClassificationH04N5/235N, H04N1/407B2, H04N5/57Legal EventsDateCodeEventDescriptionJul 16, 2010FPAYFee paymentYear of fee payment: 4Feb 14, 2003ASAssignmentOwner name: RICOH COMPANY, LTD., JAPANFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:MATSUSHIMA, YUKI;REEL/FRAME:013764/0335Effective date: 20020926RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google