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WO2010032298A1 - Dispositif de traitement d'image, procédé de traitement d'image et programme de traitement d'image - Google Patents

Dispositif de traitement d'image, procédé de traitement d'image et programme de traitement d'image Download PDF

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Publication number
WO2010032298A1
WO2010032298A1 PCT/JP2008/066791 JP2008066791W WO2010032298A1 WO 2010032298 A1 WO2010032298 A1 WO 2010032298A1 JP 2008066791 W JP2008066791 W JP 2008066791W WO 2010032298 A1 WO2010032298 A1 WO 2010032298A1
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WIPO (PCT)
Prior art keywords
face
image
area
face image
region
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English (en)
Japanese (ja)
Inventor
悟 牛嶋
雅芳 清水
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Fujitsu Ltd
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Fujitsu Ltd
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Priority to PCT/JP2008/066791 priority Critical patent/WO2010032298A1/fr
Priority to JP2010529530A priority patent/JP4947216B2/ja
Publication of WO2010032298A1 publication Critical patent/WO2010032298A1/fr
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation

Definitions

  • the present invention relates to an image processing apparatus, an image processing method, and an image processing program for detecting an object from image data.
  • an image property model is extracted from the area where the face image exists, and the extracted image property model is used.
  • a technique for improving the accuracy of object detection by detecting a face image of a subsequent frame is also known (see, for example, Patent Document 1).
  • image information is quantized based on the frequency conversion (wavelet ⁇ Wavelet> conversion) of image data and converting the conversion coefficient resulting from the conversion (or the difference in pixel value between adjacent pixels) to quantum.
  • There is a technique for performing a digitization process see, for example, Non-Patent Document 1).
  • the criteria for determining whether or not the image is a facial image has a large number of criteria, so various face images are detected.
  • the present invention has been made to solve the above-described problems caused by the prior art, and an object thereof is to provide an image processing apparatus, an image processing method, and an image processing program capable of preventing erroneous detection of an object. To do.
  • the image processing apparatus includes a storage unit that stores dictionary data having facial image characteristics, an input unit that receives input of a moving image, and the moving image.
  • a face image indicating a region including a feature of a face image from the processing target frame based on the image data and the dictionary data included in the processing target frame, with one frame being a processing target frame among a plurality of frames.
  • An extraction means for extracting a candidate area; and when the plurality of face image candidate areas are extracted from the first area in the processing target frame by the extraction means, the first area is converted into a face image.
  • determining means for determining that the area is included.
  • the first region is determined to be a region including a face image.
  • the face image can be accurately extracted, and erroneous detection of the face image can be prevented.
  • FIG. 1 is a diagram for explaining the outline and features of the image processing apparatus according to the present embodiment.
  • FIG. 2 is a diagram for explaining a face detection method based on successive frames.
  • FIG. 3 is a functional block diagram of the configuration of the image processing apparatus according to the present embodiment.
  • FIG. 4 is a diagram illustrating an example of the data structure of the face learning data.
  • FIG. 5 is a diagram illustrating an example of the data structure of non-face learning data.
  • FIG. 6 is a diagram illustrating an example of a data structure of comparison target data.
  • FIG. 7 is a diagram for explaining an example of processing of the face detection processing unit.
  • FIG. 8 is a diagram illustrating an example of the data structure of the actual detection result management table.
  • FIG. 9 is a diagram for explaining how the detection range moves.
  • FIG. 9 is a diagram for explaining how the detection range moves.
  • FIG. 10 is a flowchart of the process procedure of the image processing apparatus according to the present embodiment.
  • FIG. 11 is a flowchart illustrating a processing procedure of detection result aggregation processing.
  • FIG. 12 is a flowchart (1) showing the processing procedure of the overlap determination processing.
  • FIG. 13 is a flowchart (2) illustrating the processing procedure of the overlap determination processing.
  • FIG. 14 is a flowchart showing the processing procedure of the first overlapping area processing.
  • FIG. 15 is a flowchart illustrating a processing procedure of the second overlapping area processing.
  • FIG. 16 is a diagram illustrating a hardware configuration of a computer constituting the image processing apparatus according to the present embodiment.
  • FIG. 1 is a diagram for explaining the outline and features of the image processing apparatus according to the present embodiment.
  • the image processing apparatus according to the present embodiment first learns the features of the face to be detected and creates learning data, and compares the learning data with the input image data for each predetermined detection area, thereby Extract regions that may contain.
  • an area that may contain a face is referred to as a face candidate area.
  • the image processing apparatus determines whether or not a face image is included in the input image data based on whether or not a plurality of face candidate regions are extracted for a predetermined region. For example, as shown on the left side of FIG. 1, when there are a plurality of face candidate areas in a predetermined area, an area where the plurality of face candidate areas are concentrated is determined as a face image. On the other hand, as shown on the right side of FIG. 1, when a plurality of face candidate areas are not included in the predetermined area, that is, when a single face candidate area is included, a single face candidate is included. It is determined that the face image is not included in the area where the area exists.
  • the cutout region when the cutout region is changed while gradually shifting the vicinity of the face image, if the cutout region includes facial features such as eyes, nose, and mouth, the cutout region is detected as a face image. . Therefore, as shown on the left side of FIG. 1, since each face is detected as a face in each cut-out area set by being shifted little by little, a plurality of areas on the face image are detected as face candidate areas.
  • a subject that is not a real face, such as a flower may momentarily have features in the part corresponding to the eyes, nose, or mouth of the face image due to external light during shooting. is there.
  • the image processing apparatus extracts a face image depending on whether or not there are a plurality of face candidate areas in a predetermined area. Detection can be prevented. Further, such an image processing apparatus can prevent erroneous detection by a single frame without referring to a plurality of frames.
  • the image processing apparatus detects a face candidate area for each successive frame of the moving image, and based on the positional relationship between the face candidate areas detected in the preceding and succeeding frames, the face image is input to the input image data. Determine whether data is included.
  • FIG. 2 is a diagram for explaining a face detection method based on successive frames.
  • the image processing apparatus extracts the face candidate areas A and B in the frame 1, the position of the face candidate area detected in the subsequent frame 2, and the face candidate A extracted in the frame 1. , B on the basis of the position of the face image.
  • face candidate area C is detected, and face candidate area C exists in the vicinity of face candidate area A (in the vicinity of the position corresponding to face candidate area A detected in frame 1). If it is, the face candidate area C is determined to be a face image.
  • a face candidate area located in the vicinity of the face candidate area B is not detected in the frame 2
  • face candidate areas are continuously extracted for the actual face, and face candidate areas are intermittently extracted for objects other than the face (for example, flowers). Therefore, as described above, it is possible to determine whether or not a face image is accurately included by determining whether or not the face is based on the positional relationship between the face candidate regions that are continuously extracted. .
  • the image processing apparatus extracts the face image from the positional relationship between the face candidate regions existing between the previous and next frames. Therefore, the face image can be accurately extracted, and erroneous detection of the face image can be performed. Can be prevented.
  • FIG. 3 is a functional block diagram of the configuration of the image processing apparatus according to the present embodiment.
  • the image processing apparatus 100 includes a conversion processing unit 110, an analysis processing unit 120, a storage unit 130, and a face detection processing unit 140.
  • the conversion processing unit 110 is a processing unit that acquires face image collection data and non-face image collection data, and performs frequency conversion processing (for example, wavelet conversion) on the acquired face image collection data and non-face image collection data. is there.
  • the frequency conversion process for the image data is the same as the known technique.
  • the face image collection data is image data obtained by collecting face images of various persons, and is an image group in which a face is captured on the entire surface with a vertical width substantially including the head from the chin and a horizontal width substantially including both ears. It is configured.
  • Non-face image collection data is image data obtained by collecting various images other than face images.
  • the conversion processing unit 110 acquires face image collection data and non-face image collection data from an input device, a storage device (not shown), or the like.
  • the conversion processing unit 110 outputs the face image collection data and non-face image collection data subjected to frequency conversion to the analysis processing unit 120.
  • frequency-converted face image collection data is referred to as converted face image collection data
  • frequency-converted non-face image collection data is referred to as converted non-face image collection data.
  • the analysis processing unit 120 creates face learning data based on the converted image collection data, and generates non-face based on the converted non-face image collection data. It is a processing unit that creates face learning data.
  • the analysis processing unit 120 extracts single image data from the image data included in the converted face image collection data. Then, the analysis processing unit 120 quantizes the corresponding transform coefficient value by comparing each transform coefficient with a quantization threshold. It is assumed that the quantization threshold is set in the analysis processing unit 120 in advance.
  • the analysis processing unit 120 also quantizes the remaining image data included in the converted face image collection data by the above method. Then, the analysis processing unit 120 compares the transform coefficient values of the quantized image data (transform coefficient values corresponding to the same position of the image data), and determines the face learning data based on the frequency of the transform coefficient values. create. For example, when each image data is compared with respect to the conversion coefficient value at the position (x1, y1) in the image data, it is more than the frequency of other conversion coefficient values (for example, conversion coefficient values “0”, “2”, etc.). When the frequency of the conversion coefficient value “1” is larger, the conversion coefficient value at the position (x1, y1) in the face learning data is set to “1”.
  • FIG. 4 is a diagram showing an example of the data structure of face learning data.
  • the face learning data stores position information and conversion coefficient values in association with each other.
  • the data structure of the face learning data is not limited to that shown in FIG. 4.
  • conversion coefficient values may be stored in association with each block of conversion coefficients (for example, 8 coefficients) to be compared at the time of object detection. good.
  • the analysis processing unit 120 extracts single image data from the image data included in the converted non-face image collection data. Then, the analysis processing unit 120 quantizes the pixel value of the corresponding block by comparing each transform coefficient with a quantization threshold.
  • the analysis processing unit 120 also quantizes the remaining image data included in the converted non-face image collection data by the above method. Then, the analysis processing unit 120 compares the pixel values of the quantized image data (pixel values corresponding to the same position of the image data), and creates non-face learning data based on the frequency of the pixel values. For example, when the pixel values at the position (x1, y1) in the image data are compared with each other and the frequency of the pixel value “1” is greater than the frequency of the other pixel values, The pixel value at the position (x1, y1) in the learning data is set to “1”.
  • FIG. 5 is a diagram showing an example of the data structure of non-face learning data.
  • the non-face learning data stores position information and coefficient values in association with each other.
  • coefficient values may be stored in association with each block of transform coefficients (eg, 8 coefficients) to be compared at the time of object detection. good.
  • the storage unit 130 is a storage unit that stores the face learning data 130a, the non-face learning data 130b output from the analysis processing unit 120, the comparison target data 130c output from the face detection processing unit 140, and the actual detection result management table 130d. is there. Among these, the comparison target data 130c and the actual detection result management table 130d will be described later.
  • the face detection processing unit 140 is a processing unit that detects a face image from image data included in each frame of the acquired moving image when the moving image data is acquired.
  • the face detection processing unit 140 outputs the detection result to a higher-level device (not shown).
  • a higher-level device not shown.
  • the face detection processing unit 140 extracts a single frame from the moving image data, and executes frequency conversion processing (for example, wavelet conversion) on the extracted frame image data. Then, after performing the frequency conversion, the face detection processing unit 140 quantizes the image data subjected to the frequency conversion, thereby creating the comparison target data 130c.
  • frequency conversion processing for example, wavelet conversion
  • the face detection processing unit 140 quantizes the pixel value of the corresponding block by comparing each transform coefficient with a quantization threshold. Next, sequentially, a rectangular size having the same size as the dictionary is cut out from the quantization result, and comparison target data 130c is created.
  • the input image since only a face having a certain size can be detected, the input image may be reduced in advance and a detection process may be performed on images of a plurality of sizes. If detection is performed on the reduced image, a face image of a large size is actually detected.
  • FIG. 6 is a diagram illustrating an example of the data structure of the comparison target data 130c.
  • the comparison target data 130 c stores position coordinates and coefficient values in association with each other.
  • the data structure of the comparison target data 130c is not limited to that shown in FIG. 6, and may be stored in association with the number of pixels for each block (for example, 8 coefficients) of conversion coefficients to be compared at the time of object detection. good.
  • the face detection processing unit 140 compares the face learning data 130a stored in the storage unit 130, the non-face learning data 130b, and the comparison target data 130c, and extracts face candidate regions.
  • FIG. 7 is a diagram for explaining an example of processing of the face detection processing unit 140.
  • the face detection unit 140 sets a detection range, and compares the pixel values of each block included in the detection range at the same position in the face learning data 130a, the non-face learning data 130b, and the comparison target data 130c.
  • the face detection processing unit 140 determines whether the detection range of the comparison target data is similar to the face learning data or similar to the non-face learning data.
  • the comparison target data 130c has 8 hits with respect to the face learning data 130a and one hit with respect to the non-face learning data. It is determined that the face learning data 130a is similar.
  • FIG. 9 is a diagram for explaining how the detection range moves.
  • the actual detection result management table 130d is a table for managing face candidate area information.
  • FIG. 8 is a diagram showing an example of the data structure of the actual detection result management table 130d.
  • the actual detection result management table 130d stores face candidate area identification numbers, frame identification numbers, GID (Group Identification), coordinates, and coordinate averages in association with each other.
  • GID Group Identification
  • the face candidate area identification number is a number for identifying each face candidate area extracted by the face detection processing unit 140.
  • the frame identification number is a number for identifying each frame included in the moving image.
  • GID is information for identifying each face candidate area to be aggregated.
  • the face candidate areas having the face candidate area identification numbers “1 to 3” are collected into the same group with the GID “1”. A method of collecting each face candidate area will be described later.
  • the initial value of GID is set to 0.
  • the coordinates are the coordinates of the face candidate area.
  • the coordinates (X1, Y1) of the upper left corner of the face candidate area and the coordinates (X2, Y2) of the lower right corner of the face candidate area are stored.
  • the coordinate average is an average value of coordinates of each face candidate region belonging to the same GID.
  • the face detection processing unit 140 extracts a face candidate area while cutting out a rectangular area from the conversion coefficient, and registers a face candidate area identification number, a frame identification number, and coordinates in the actual detection result management table 130.
  • the face detection processing unit 140 compares the pixel values in the detection range, the positions of the detection ranges arranged on the face learning data 130a, the non-face learning data 130b, and the comparison target data 130c are unified. It shall be.
  • the face detection processing unit 140 extracts face candidate areas from the comparison target data 130c, then refers to the actual detection result management table 130d, compares the face candidate areas, and aggregates the face candidate areas.
  • the face candidate area A and the face candidate area B are compared will be described.
  • R be the overlapping region of the face candidate region A and the face candidate region B.
  • the coordinates of the face candidate area A are expressed as (A.X1, A.Y1), (A.X2, A.Y2).
  • the coordinates of the face candidate area B are expressed as (B.X1, B.Y1), (B.X2, B.Y2).
  • the coordinates of the overlapping region R are expressed as (B.X1, B.Y1), (B.X2, B.Y2).
  • the face detection processing unit 140 sets the same value as that of the face candidate region B as the initial value of the coordinates of the overlapping region R.
  • the face detection processing unit 140 Width of face candidate area A / width of face candidate area B ⁇ threshold When at least one of the GID of the face candidate area A or the GID of the face candidate area B is 0, the face candidate areas A and B are targeted for aggregation.
  • the face detection processing unit 140 excludes the face candidate areas A and B from the aggregation targets.
  • the face detection processing unit 140 determines the coordinates (A.X1, A.Y1), (A.X2, A.Y2) of the face candidate areas. Then, based on the coordinates of the face candidate area B (B.X1, B.Y1) and (B.X2, B.Y2), the overlapping area is extracted.
  • the face detection processing unit 140 compares the coordinates of the face candidate area A with the coordinates of the face candidate area B, Y1 is B.I. Y1 and B.I. Y2. X1 is B.I. X1 and B.I. If it exists between X2, it is determined that the upper left of the face candidate area A overlaps with the face candidate area B; R. of the overlapping region R. X1 includes A.I. Substituting X1, R. of the overlapping region R. A. Y. By substituting Y1, each coordinate of the overlapping region R is obtained.
  • the face detection processing unit 140 compares the coordinates of the face candidate area A with the coordinates of the face candidate area B, Y1 is B.I. Y1 and B.I. Y2. X2 is B.I. X1 and B.I. If it exists during X2, it is determined that the upper right of the face candidate area A overlaps with the face candidate area B, R. of the overlapping region R. X2. Substituting X2, R. of the overlapping region R. A. Y. By substituting Y1, each coordinate of the overlapping region R is obtained.
  • the face detection processing unit 140 compares the coordinates of the face candidate area A with the coordinates of the face candidate area B, Y2 is B.I. Y1 and B.I. Y2.
  • X1 is B.I. X1 and B.I. If it exists between X2, it is determined that the lower left of the face candidate area A overlaps with the face candidate area B; R. of the overlapping region R.
  • X1 includes A.I. Substituting X1, R. of the overlapping region R. Y2. By substituting Y2, each coordinate of the overlapping region R is obtained.
  • the face detection processing unit 140 compares the coordinates of the face candidate area A with the coordinates of the face candidate area B, Y2 is B.I. Y1 and B.I. Y2. X2 is B.I. X1 and B.I. If it exists between X2, it is determined that the lower right corner of the face candidate area A overlaps with the face candidate area B; R. of the overlapping region R. X2. Substituting X2, R. of the overlapping region R. Y2. By substituting Y2, each coordinate of the overlapping region R is obtained.
  • the face detection processing unit 140 calculates the area of the overlap region R / the area of the face candidate region A after performing the above overlap determination and extraction of the overlap region R. If the calculation result is equal to or greater than the threshold, the following rule The GIDs of the face candidate area A and the face candidate area B are determined according to the above.
  • the face detection processing unit 140 assigns a common number to the GID of the face candidate area A and the GID of the face candidate area B.
  • the face candidate areas A and B are collected.
  • the GID assigned to the GID of the face candidate area A and the GID of the face candidate area B is a number that does not overlap with other groups.
  • the non-zero GID is assigned to another GID.
  • the GID of the face candidate area A is 1 and the GID of the face candidate area B is 0, the GID of the face candidate area B is set to 1.
  • the face detection processing unit 140 extracts the next frame from the moving image data when the extraction of the face candidate regions and the aggregation of the face candidate regions are completed for the frame image extracted from the moving image data, and the processing described above. repeat.
  • the face detection processing unit 140 determines whether the face candidate area detected from the image data in the frame is a face area based on the actual detection result management table 130d. Specifically, the face detection processing unit 140 selects a corresponding face candidate area from the actual detection result management table 130d, and when another face candidate area belongs to the GID to which the selected face candidate area belongs ( When there are a plurality of face image areas in a predetermined area), the selected face candidate area is determined as a face image area, and the determination result is output.
  • the face candidate detection unit 140 determines whether or not the face candidate area is a face image based on the coordinates of the face candidate area of each successive frame.
  • the face candidate image detected in the first frame is set as the first face candidate area
  • the face candidate area detected in the second frame next to the first frame is set as the second face candidate area.
  • the face candidate detection unit 140 compares the coordinates of the first face candidate area with the coordinates of the second face candidate area, and determines that the second face candidate area is a face area when the distance between the coordinates is less than a threshold value. Judges and outputs the judgment result. Further, the face candidate detection unit 140 calculates a coordinate average by calculating an average value of the coordinates of each group, and registers the calculated coordinate average in the actual detection result management table 130d.
  • the face candidate detection unit 140 when the face candidate detection unit 140 extracts a face image by the above-described method, the face candidate detection unit 140 lowers the threshold value for determining that it is likely to be a face when extracting a face candidate region around the face image of the next frame. Also good.
  • the face candidate detection unit 140 sets a detection range in the area where the face image is extracted in the previous frame, and compares the comparison target data 130c, the face learning data 130a, and the non-face learning data 130b as shown in FIG.
  • a predetermined value may be added to the number of hits of each face learning data 130a and comparison target data 130c to facilitate detection of the face candidate area.
  • FIG. 10 is a flowchart illustrating the processing procedure of the image processing apparatus 100 according to the present embodiment.
  • the image processing apparatus 100 scans input image data (step S101), and executes detection result aggregation processing (step S102).
  • the image processing apparatus 100 stores the actual detection result (step S103), takes out one actual detection result (step S104), and overlaps coordinates in the previous actual detection result (or the distance of each coordinate is less than the threshold value). Is determined (step S105).
  • step S106 If there is an overlapping coordinate in the previous actual detection result (step S106, Yes), the image processing apparatus 100 determines that the area corresponding to the actual detection result is a face area (step S107). The process proceeds to S110.
  • the image processing apparatus 100 determines whether a plurality of face candidate areas are dense (step S106). S108).
  • step S109 When a plurality of face candidate areas are densely packed (step S109, Yes). On the other hand, when a plurality of face candidate areas are not dense (step S109, No), it is determined whether or not there is a remaining actual detection result (step S110).
  • step S111 The image processing apparatus 100 proceeds to step S104 when there is a remaining actual detection result (step S111, Yes). On the other hand, if there is no remaining actual detection result (No at step S111), the process is terminated.
  • FIG. 11 is a flowchart illustrating a processing procedure of detection result aggregation processing.
  • the image processing apparatus 100 initializes GID to 0 (step S201), initializes MaxGID to 0 (step S202), and sets ii to 0 (step S203).
  • the image processing apparatus 100 determines whether ii + 1 is smaller than N (step S204).
  • N the number of face candidate regions obtained as a result of scanning the input image in step S101 of FIG. 10 is registered.
  • the image processing apparatus 100 sequentially sets the value of MaxGID to GID while increasing MaxGID by 1 to the result of GID 0 in the actual detection result management table 130d. (Step S206). Then, the image processing apparatus 100 calculates a coordinate average for each group and outputs the result after aggregation (step S207).
  • step S205 if ii + 1 is smaller than N (step S205, Yes), the image processing apparatus 100 initializes jj to ii + 1 (step S208), and determines whether jj is smaller than N (step S209). ).
  • step S210 When the jj is not smaller than N (step S210, No), the image processing apparatus 100 adds 1 to ii (step S211), and proceeds to step S204. On the other hand, if jj is smaller than N (step S210, Yes), an overlap determination process is executed (step S212), jj is incremented by 1 (step S213), and the process proceeds to step S209.
  • step S212 of FIG. 11 are flowcharts showing the processing procedure of the overlap determination process.
  • the image processing apparatus 100 sets the smaller area of the face candidate area identification number [ii] and the face candidate identification number [jj] to A and the larger area to B (step S301), it is determined whether or not the width of A / the width of B is smaller than the threshold (step S302).
  • the image processing apparatus 100 ends the overlap determination process when the width of A / width of B is smaller than the threshold (Yes in step S303). On the other hand, if the A width / B width is equal to or greater than the threshold (No in step S303), it is determined whether both the A GID and the B GID are values other than 0 (step S304).
  • step S305 Yes
  • the image processing apparatus 100 ends the overlap determination process.
  • the GID of A and the GID of B are not values other than 0 (No in step S305)
  • the R.D. X1, R.I. Y1, R.I. X2, R.I. Y2 to B.I. X1, B.I. Y1, B.I. X2, B.I. Y2 is set (step S306).
  • Y1 is B.I. Y1 and B.I. It is determined whether or not it exists between Y2 (step S307), and if it exists (step S308, Yes), the first overlapping area process is executed (step S309), and the process proceeds to step S310.
  • the image processing apparatus 100 includes A. Y1 is B.I. Y1 and B.I. If it does not exist during Y2 (No at Step S308), the process proceeds to Step S310.
  • the image processing apparatus 100 includes A.
  • Y2 is B.I. Y1 and B.I. It is determined whether or not it exists between Y2 (step S310), and if it exists (step S311, Yes), the second overlapping region process is executed (step S312), and the area / A of the overlapping region R is determined. It is determined whether or not the area is equal to or greater than a threshold (step S313).
  • the image processing apparatus 100 ends the overlap determination process when the area of the overlapping region R / the area of A is less than the threshold (No in step S314). On the other hand, if it is equal to or greater than the threshold value (step S314, Yes), it is determined whether both the GID of A and the GID of B are 0 (step S315).
  • step S316, No the image processing apparatus 100 substitutes the value of the non-zero GID into the other GID (step S317), and determines overlap. End the process.
  • step S316 when both the GID of A and the GID of B are 0 (step S316, Yes), the image processing apparatus 100 adds 1 to MAXGID (step S318), and sets MAXGID to the GID of A and the GID of B. (Step S319), and the overlap determination process ends.
  • FIG. 14 is a flowchart showing the processing procedure of the first overlapping area processing.
  • X1 is B.I. X1 and B.I. X2 is determined (step S401). If it exists (step S402, Yes), A.X. X1 is R.I. X. Y1 is changed to R.I. Substitute for Y1 (step S403). If it does not exist (step S402), the process proceeds to step S404.
  • step S404 the image processing apparatus 100 is connected to the A.D. X2 is B.I. X1 and B.I. X2 is determined (step S404). If it exists (step S405, Yes), A.X. X2 to R.I. X. Y1 is changed to R.I. Substituting for Y1 (step S406), the first overlapping area process is terminated. If it does not exist (step S405, No), the first overlapping area process is terminated.
  • FIG. 15 is a flowchart illustrating a processing procedure of the second overlapping area processing.
  • X1 is B.I. X1 and B.I. X2 is determined (step S501). If it exists (step S502, Yes), A.X. X1 is R.I. X. Y2 to R.I. Substitute for Y2 (step S503). When it does not exist (step S502, No), the process proceeds to step S504.
  • step S504 the image processing apparatus 100 is connected to the A.D. X2 is B.I. X1 and B.I. X2 is determined (step S504). If it exists (step S505, Yes), A.X. X2 to R.I. X. Y2 to R.I. Substituting for Y2 (step S506), the second overlapping area process is terminated. If it does not exist (step S505, No), the second overlapping area process is terminated.
  • the image processing apparatus 100 determines whether a plurality of face candidate areas exist in a predetermined area (whether another face candidate area belongs to the GID to which the face candidate area belongs). )), The face image is extracted, so that the face image can be accurately extracted and erroneous detection of the face image can be prevented. Further, such an image processing apparatus can prevent erroneous detection by a single frame without referring to a plurality of frames.
  • the image processing apparatus 100 extracts the face image from the positional relationship between the face candidate areas existing between the previous and next frames, so that the face image can be extracted accurately and the face image is not erroneously detected. can do.
  • the process of detecting the face candidate area has been described in detail, but the process of detecting the face candidate area is not limited to this method.
  • the candidate area may be set using [Non-Patent Document 1] H. Schneiderman and T. Kanade, Object Detection Using the Statistics of Parts To appear in International Journal of Computer Vision, 2002.
  • each component of the image processing apparatus 100 shown in FIG. 3 is functionally conceptual and does not necessarily need to be physically configured as illustrated.
  • the various processing procedures described in this embodiment can be realized by executing a program prepared in advance on a computer such as a personal computer or a workstation.
  • FIG. 16 is a diagram illustrating a hardware configuration of a computer constituting the image processing apparatus 100 (image processing apparatus 200) according to the present embodiment.
  • this computer (image processing device) 30 communicates with other devices via an input device 31, a monitor 32, a RAM (Random Access Memory) 33, a ROM (Read Only Memory) 34, and a network.
  • a communication control device 35, a medium reading device 36 that reads data from a storage medium, a camera 37, a CPU (Central Processing Unit) 38, and an HDD (Hard Disk Drive) 39 are connected by a bus 40.
  • the HDD 39 stores an image processing program 39b that exhibits the same function as that of the image processing apparatus 100 described above.
  • the image processing process 38a is activated.
  • the image processing process 38a corresponds to the conversion processing unit 110, the analysis processing unit 120, and the face detection processing unit 140 of FIG.
  • the HDD 39 also stores various data 39a corresponding to information stored in the storage unit 130 of the image processing apparatus 100.
  • the CPU 38 reads various data 39 a stored in the HDD 39, stores it in the RAM 33, and detects a face image using the various data 33 a stored in the RAM 33.
  • the image processing program 39b shown in FIG. 16 is not necessarily stored in the HDD 39 from the beginning.
  • a “portable physical medium” such as a flexible disk (FD), a CD-ROM, a DVD disk, a magneto-optical disk, or an IC card inserted into a computer, or a hard disk drive (HDD) provided inside or outside the computer.
  • the image processing program 39b is stored in the “fixed physical medium” of “the computer”, and “another computer (or server)” connected to the computer via the public line, the Internet, LAN, WAN, etc.
  • the computer may read and execute the image processing program 39b from these.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

Un dispositif de traitement d'image (100) selon la présente invention apprend une caractéristique d'un visage qui est une cible de détection, génère des données apprises, et extrait une zone possible dans laquelle le visage peut être inclus en comparant les données apprises avec des données d'image entrées pour chaque zone de détection prédéterminée. Le dispositif de traitement d'image (100) évite une détection erronée d'une image de visage en extrayant l'image de visage selon qu'une pluralité de zones de visage candidates existent ou non dans une zone prédéterminée (selon que l'autre zone de visage candidate appartienne ou non à un GID auquel la zone de visage candidate appartient).
PCT/JP2008/066791 2008-09-17 2008-09-17 Dispositif de traitement d'image, procédé de traitement d'image et programme de traitement d'image Ceased WO2010032298A1 (fr)

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JP2010529530A JP4947216B2 (ja) 2008-09-17 2008-09-17 画像処理装置および画像処理方法

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JP2012114561A (ja) * 2010-11-22 2012-06-14 Casio Comput Co Ltd 被写体検出装置、被写体検出方法及びプログラム
JP2014123991A (ja) * 2014-03-20 2014-07-03 Casio Comput Co Ltd 被写体検出装置、被写体検出方法及びプログラム
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JP2001175869A (ja) * 1999-12-07 2001-06-29 Samsung Electronics Co Ltd 話し手位置検出装置及びその方法

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Publication number Priority date Publication date Assignee Title
WO2012002048A1 (fr) * 2010-06-30 2012-01-05 Ｎｅｃソフト株式会社 Procédé de détection de tête, dispositif de détection de tête, procédé de détermination d'attributs, dispositif de détermination d'attributs, programme, support d'enregistrement et système de détermination d'attributs
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JP2012114561A (ja) * 2010-11-22 2012-06-14 Casio Comput Co Ltd 被写体検出装置、被写体検出方法及びプログラム
JP2014123991A (ja) * 2014-03-20 2014-07-03 Casio Comput Co Ltd 被写体検出装置、被写体検出方法及びプログラム
CN105389794A (zh) * 2015-10-08 2016-03-09 西安电子科技大学 基于先验场景知识的sar目标检测虚警去除方法
US10699169B2 (en) 2017-10-26 2020-06-30 Samsung Sds Co., Ltd. Machine learning-based object detection method and apparatus
WO2020012756A1 (fr) * 2018-07-13 2020-01-16 マクセル株式会社 Dispositif de formation d'images stéréoscopiques
JP2020014067A (ja) * 2018-07-13 2020-01-23 マクセル株式会社 ステレオ撮像装置
JP7140580B2 (ja) 2018-07-13 2022-09-21 マクセル株式会社 ステレオ撮像装置
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CN116724332A (zh) * 2021-01-27 2023-09-08 富士通株式会社 判定方法、判定程序、以及信息处理装置

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