JP4830712B2 - Image processing device - Google Patents

Description

The present invention relates to an image processing apparatus .
More specifically, the present invention relates to a technique for detecting a suspicious object or a suspicious person from an image obtained by imaging a predetermined monitoring place by a monitoring camera.

2. Description of the Related Art Conventionally, a monitoring system that monitors a predetermined monitoring target by visually recognizing an image captured by a monitoring camera via a predetermined monitoring monitor is known.
In such a monitoring camera, it is difficult for a 24-hour monitor to continue to monitor the monitor as the number of places to be monitored increases. In video recorders connected to surveillance cameras, there is an urgent need to shorten the actual operation time, such as recording only the minimum necessary video.
For this reason, establishment of the technique in which a machine detects a suspicious object or a suspicious person from an input image is calculated | required.
For example, in a bank ATM, the above-described requirements can be satisfied relatively easily if there is a sensor for detecting a human body.
However, such a sensor cannot be used when detecting a suspicious ship or the like from a distant landscape such as the seaside.
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Conventionally, in such a situation, a method of comparing with the immediately preceding image data has been often employed.
When an object enters the video, the brightness of the object portion in the video data changes. Therefore, an object can be detected by detecting a region having a difference in luminance value in the video as a difference region. However, in the case of natural scenery such as the sea, desert, grassland, and sky, water, sand, grass, clouds, etc. other than the object to be detected are also moving. For this reason, there has been a problem that they are erroneously detected as being different from the previous image data.

One method for solving this problem is, for example, a technique described in Patent Document 1.
In Patent Document 1, a difference value between an image captured at the current time and an image captured in the past is created and binarized by threshold processing. At that time, a background image in which the threshold value is changed is created based on the accumulated result of past difference values. In this way, attempts are made to reduce false detection of fluctuations caused by trees and water existing in the background image.
However, with this technique, when the change in the luminance value due to the fluctuation of the tree or the like is large, the case where the threshold value becomes too large can be considered. In such a case, there is a possibility that a detection omission may occur when an important intruder or the like enters.

  The present invention has been made in view of the above points, and from image data obtained by a monitoring camera that monitors a place where a photographed image fluctuates mainly due to a natural phenomenon or the like, such as a sea, a desert, or a grassland. The purpose is to stably infiltrate a suspicious person or a suspicious object.

In order to solve the above-described problems, an image processing apparatus according to the present invention includes an image storage unit that stores input image data, and an array that includes an address indicating each pixel constituting the input image data in the image storage unit as an element. Obtained by a feature amount calculation unit that calculates a co-occurrence probability matrix, a processing region setting unit that sets an exclusion region that is not subject to the calculation of the co-occurrence probability matrix, and a feature amount calculation unit. The number of elements of the co-occurrence probability matrix obtained is compared with a predetermined threshold value, and a pixel determination unit that determines whether or not the background is present, and based on the determination result obtained by the pixel determination unit, the presence or absence of a subject that is not the background And a pixel set determining unit for outputting .

The inventor can compare the background and the suspicious object, etc. with respect to the object to be photographed while the natural scenery such as the sea has a certain regularity by comparison with the immediately preceding image data known in the prior art. The method was judged to be inappropriate. Therefore, the inventor sought a technique for discriminating a background from a suspicious object using only input image data. As a result, I came up with the idea of using a co-occurrence probability matrix as a technique for discriminating between background and suspicious objects. In the case of an image processing method using a co-occurrence probability matrix, even if there is a change caused by a natural phenomenon such as the sea or the sky, it can be determined that the background has a certain feature including this.
Further, in the present invention, as a discrimination based on the co-occurrence probability matrix, unlike the conventional technique, the threshold value is calculated from the obtained matrix without comparing with the matrix data captured and held in advance. Based on the background, it is determined whether or not it is a background. This discrimination method itself is a new approach not found in the prior art.

According to the present invention, it is possible to provide an image processing apparatus that is extremely suitable for a monitoring apparatus that monitors a landscape with a certain fluctuation, such as the sea or grassland.
Further, the present invention requires less computation amount and storage capacity of a storage device that is required in advance as compared with the same type of device of the prior art.
Therefore, compared to the conventional technology, the amount of computation is extremely small, the required storage capacity is small, and natural image processing such as ocean waves and sky clouds can be recognized as the background without misrecognizing the background. Equipment can be provided.

  Hereinafter, embodiments of the present invention will be described with reference to FIGS.

FIG. 1 is an overall block diagram of an image processing apparatus according to a first embodiment of the present invention. FIG. 1A is a block diagram corresponding to the contents of an actual device, and FIG. 1B is a virtual block diagram focusing on functions.
In FIG. 1A, the image processing apparatus 101 detects the presence / absence of a suspicious object from image data obtained from the imaging camera 102, and outputs a binary signal (alarm output) informing the presence / absence of a suspicious object or a predetermined process. The image signal subjected to is output. The imaging camera 102 is a well-known camera that outputs an image signal, for example, composed of a CCD imaging device or the like. The output image signal is sent to another host device via a large-capacity storage such as an HDD or a communication line.
The image processing apparatus 101 is mainly composed of a microcomputer. The CPU 103, ROM 104, RAM 105 and bus 106 constituting the microcomputer perform predetermined processing on the image data obtained from the imaging camera 102 and output a predetermined signal or the like through the output interface 107.

In FIG. 1B, each unit is a software program executed on a microcomputer, except for an image storage unit 112 whose entity is a RAM 105. These software are stored in the ROM 104.
The image signal obtained from the imaging camera 102 is once held in the image storage unit 112 as a still image.
The feature amount calculation unit 113 creates feature amount data from the still image data in the image storage unit 112. This is array data composed of relative addresses of image data created by a co-occurrence probability matrix described later.
The pixel determination unit 114 creates bitmap data that identifies a suspicious object in still image data based on the array data created by the feature amount calculation unit 113.
The pixel set determination unit 115 calculates the total area and center coordinates of an image portion that seems to be a suspicious object in the still image data from the created bitmap data.

The image processing apparatus 101 according to the present embodiment is made for the purpose of being installed mainly on the coast together with the imaging camera 102. The imaging camera 102 is reciprocally driven by a driving mechanism such as a motor (not shown) so as to overlook the coastal sea area.
To detect suspicious objects on the seaside, you have to increase the magnification of the camera. Increasing the magnification of the camera means that the shooting angle becomes smaller, and for this reason, it is necessary to drive the camera back and forth.
Reciprocating the camera is difficult to compare the previous still image with the current still image and find a suspicious object or the like from the difference between the image data. The invention according to the present embodiment is based on such a background situation.

FIG. 2 schematically illustrates the inside of the RAM 105. Inside the RAM 105, there are an input image bitmap data storage area 202 that can be called an image storage unit 112, an array data area 203 for a co-occurrence probability matrix, and an image flag bitmap data area 204.
FIG. 3 illustrates data stored in the RAM 105.
FIG. 3A shows input image bitmap data stored in the input image bitmap data storage area 202 of the RAM 105. That is, it is still image data itself captured by the imaging camera 102. Each pixel is data representing luminance from 0 to 255, for example. In FIG. 3A, a sea 302, a sky 303, and a ship 304 that is a suspicious object are shown.
FIG. 3B shows image flag bitmap data stored in the image flag bitmap data area 204 of the RAM 105. In the processing described later, the suspicious object region is detected from the input image bitmap data. In this figure, a bit in an area corresponding to the ship 304 is set.
FIG. 3C is an enlarged view of a part of the image flag bitmap data. Each pixel consists of 1 bit. If the bit is set (if the bit value is 1), it indicates a suspicious pixel, and if the bit is down (if the bit value is 0) ), Which shows the pixel that seems to be the background.

FIG. 4 schematically shows array data stored in the array data area 203 of the RAM 105. Actually, the contents of the RAM 105 do not have such a shape, but it should be considered that the RAM 105 is virtually constructed in such a shape by a software program.
In FIG. 4A, array data 401 is a collection of variable-length array data based on a co-occurrence probability matrix. The co-occurrence probability matrix is a square matrix composed of 256 elements each having a vertical k and a horizontal l of 0 to 255. The vertical k and horizontal l correspond to the luminance of the image data. A cube is stacked on each element on this matrix. The cube stores a relative address indicating one pixel of image data.

(K, l) [1] in FIG. 4A is the first array element of the array data including the matrix elements of the l-th column in the k-th row.
Similarly, (k, l) [2] is the second array element of array data composed of matrix elements of k rows and 1 columns.
The array data (k, l) has five array elements and exists up to (k, l) [5].
(K, l-1) [1] in FIG. 4A is the first array element of the array data including the matrix elements of the l-1st column in the kth row.
Similarly, (k, 1-5) [1] is the first array element of array data composed of matrix elements of k rows and 1-5 columns.
Similarly, (k−3, l) [1] is the first array element of array data composed of matrix elements of k−3 rows and l columns.
In the cubes stacked in this way, relative addresses indicating one pixel of the image data stored in the input image bitmap data area 202 are stored.

FIG. 4 (a) shows only a part of the array data that is piled up high due to space limitations. However, it can be estimated that the shape is close to a well-known Gaussian curved surface as shown in FIG.
A detailed method for creating array data based on the co-occurrence probability matrix will be described later.

FIG. 5 is a diagram schematically showing the relationship between the RAM 105 and each data stored in the RAM 105.
A background portion showing a large area in the input image bitmap data area 202 is concentrated in a portion having many elements in the array data area 203 constituting the co-occurrence probability matrix. The relative address of the image data stored as the array element of the concentrated portion corresponds to the relative address of the background portion of the image flag bitmap data area 204, and the flag at that location is lowered.
In FIG. 5, image data corresponding to a part surrounded by a dotted line, that is, an empty part of an image showing a part of the input image bitmap data area 202 is respectively placed in the stacked cubes of the array data area 203. It corresponds. In other words, the array addresses forming these cubes store the relative addresses of the pixels surrounded by the dotted line portion in the image data as elements. These relative addresses lower the flag of the corresponding part of the image flag bitmap data area 204 from 1 to 0.

Here, the outline of the co-occurrence probability matrix will be described.
FIG. 6 is a diagram for explaining the outline of the co-occurrence probability matrix.
In FIG. 6A, P1 and P2 are two arbitrary pixels in the image data. The pixel P1 is separated from the pixel P2 by a distance r and an angle θ. Let r and θ be the relative position function δ. δ = (r, θ).
In FIG. 6B, when δ1 = (1,0 °), the pixel P2 indicates the next adjacent pixel with respect to the pixel P1.
In FIG. 6C, when δ1 = (1, 90 °), the pixel P2 indicates the adjacent pixel immediately above the pixel P1.
The co-occurrence probability matrix is a mechanism that adds a combination of the luminances of two pixels separated by a certain relative position function δ to a square matrix having luminance as the number of elements.

FIG. 7 shows simplified image data and a simplified co-occurrence probability matrix for explanation.
In FIG. 7A, each pixel in the image data composed of 4 × 4 16 pixels has four levels of brightness from 0 to 3.
FIG. 7B is a co-occurrence probability matrix that is a 4 × 4 square matrix having four levels of luminance. Here, a procedure for creating a co-occurrence probability matrix from the image data in FIG. 7A in the relationship of relative position function δ = (r, θ) = (1,0) is shown below.
If (x, y) = (0, 0) in FIG. 7A is the pixel P1, the pixel P2 is (x, y) = (1, 0). At this time, the luminance i of the pixel P1 is 0, and the luminance j of the pixel P2 is 0. Therefore, 1 is added to (i, j) = (0,0). Furthermore, since round trip counting is performed, 1 is added to (i, j) = (0, 0).
If (x, y) = (1, 0) in FIG. 7A is the pixel P1, the pixel P2 is (x, y) = (2, 0). At this time, the luminance i of the pixel P1 is 0, and the luminance j of the pixel P2 is 1. Therefore, 1 is added to (i, j) = (0,1). Furthermore, since round trip counting is performed, 1 is added to (i, j) = (1, 0).
As described above, the operation of counting up the elements of the matrix corresponding to the combination of the luminance of each pixel separated by the relative position function δ is performed for all the pixels. That is, this square matrix is a matrix of counters indicating the number of appearances of the combination of the luminance values of two pixels.
Note that it is up to the designer to perform a round-trip count.

That is, the array data is created from the input image bitmap data in FIG. 5 by the method for creating the co-occurrence probability matrix described in FIGS. 6 and 7.
However, this embodiment takes an approach different from the conventionally known co-occurrence probability matrix handling.
In the prior art, the co-occurrence probability matrix is used as a method for comparing non-defective products with defective products during the manufacturing process, as disclosed in Patent Document 2, for example. A good product is photographed in advance and a co-occurrence probability matrix is stored, and a product flowing into the production line is photographed. A co-occurrence probability matrix is created from the photographed image data, and compared with a non-defective product matrix that holds this, abnormalities such as product scratches and dirt are discriminated from the differences. This is the use of a co-occurrence probability matrix that has been well known.
In the present embodiment, each matrix element is compared with this co-occurrence probability matrix using a predetermined threshold value. Then, it is determined that the matrix elements equal to or greater than the threshold value are regions that occupy a large area in the image data, that is, the background. Then, an area in the image data corresponding to the background is excluded.
For this purpose, it is necessary to use matrix elements as array data and store the relative addresses of the pixels in the image data in the array elements.
After all the processes are completed, it is determined that the portion where the flag is not lowered in the image flag data is a narrow portion showing luminance different from the background in the image data, that is, a region indicating a suspicious object.
As an image, it can be considered that the Gaussian curved surface of FIG. 4B is cut right by a plane parallel to the xy plane indicating the threshold value. The relative address of the image data corresponding to the matrix element cut by the threshold plane, that is, touching the threshold plane is the background.

8 and 9 are flowcharts showing the operation of the image processing apparatus 101 of this embodiment.
When the process is started (S801), a relative position function δ, which is a rule for creating a co-occurrence probability matrix for the first time, is set (S802). Next, the examination target address a of the input image bitmap data is set at the head of the image data (S803). That is, the address a is a relative address in the input image bitmap data, and an initial value of the relative address is given in this process.
Next, it is verified whether a pixel exists at a point separated from the relative address a by the relative position function δ (S804). For example, when the relative position function δ = (1,0 °), if the relative address a is the right end of the input image bitmap data, the relative address b = δ (a) does not exist.
If there is a relative address b = δ (a), the luminance of the relative address a is substituted for k, and the luminance of the relative address b is substituted for l. Then, the relative address a is added to the array of matrix elements (k, l) in the array data area 203 (S805). That is, one cube shown in FIG. 4A is stacked and the relative address a is stored therein. If there is no b = δ (a) in step S804, the process of step S805 is skipped because the co-occurrence probability matrix cannot be added.
Next, the relative address a is advanced by one (S806). Next, it is verified whether or not the relative address a exists (S807). If there is, the process is repeated again from step S804. If not, it is the end of the input image bitmap data, and since the processing of the co-occurrence probability matrix is finished, the process proceeds to the next process.

When processing of the co-occurrence probability matrix is completed for all input image bitmap data, a threshold value is calculated using the matrix elements (S808). There are various methods for calculating the threshold value. For example, it is conceivable to use a preset fixed value or to average all of one or more matrix elements.
When the threshold value is obtained, initial values of matrix elements to be evaluated in the array data area 203 are set (S909).
Next, the number of matrix elements is compared with a threshold value (S910). If the number of elements is equal to or greater than the threshold value, the flag of the corresponding address in the image flag bitmap data area 204 corresponding to the relative address of the input image bitmap data in the array element belonging to the matrix element is lowered (S911). . In other words, it is a judgment that there is a background. If the number of elements is less than the threshold, nothing is done. In other words, it is a judgment that there is a suspicious object.
Then, the matrix element to be evaluated is advanced by one (S912).
Next, it is verified whether there is a matrix element to be evaluated (S913). If there is, the evaluation is performed again (S910). If not, it means the end of processing.
Finally, the area and central coordinates of the flagged area remaining in the image flag bitmap data area 204, that is, the area indicating the suspicious object are calculated (S914), and the process ends (S915).

In FIG. 8, the process consisting of steps S802, S803, S804, S805, S806, and S807 corresponds to the process of the feature amount calculation unit 113 in FIG. That is, it is a calculation process of a co-occurrence probability matrix.
In FIG. 8, the process consisting of step S808, and in FIG. 9, steps S909, S910, S911, S912, and S913 corresponds to the process of the pixel determination unit 114 of FIG. That is, it is a process for determining the address of the suspicious object.
In FIG. 9, the process consisting of step S914 corresponds to the process of the pixel set determining unit 115 of FIG. That is, this is a process of counting the number of suspicious object addresses and calculating the center of the area indicating the suspicious object address.

Next, a second embodiment of the present invention will be described.
FIG. 10 is a schematic diagram illustrating the relative position function δ used in the image processing apparatus according to the second embodiment.
In the present embodiment, the internal configuration of the image processing apparatus is almost the same as the configuration shown in the block diagram of FIG. 1 described in the first embodiment. A co-occurrence probability matrix is created.
In the first embodiment described above, the relative position function δ = (1,0 °) has been described as an example. By the way, it is well known in the prior art that a co-occurrence probability matrix is obtained by a plurality of relative position functions δ. This is a measure for more accurately quantifying the characteristics of image data having a certain pattern.
In FIG. 10, it can be seen that there are 8 pixels even in the vicinity of a certain pixel.
δ ₁ has a distance of 1 and an angle of 0 °.
δ ₂ has a distance of √2 and an angle of 45 °.
Similarly, δ ₃ = (1,90 °), δ ₄ = (√2,135 °), δ ₅ = (1,180 °), δ ₆ = (√2,225 °), δ ₇ = ( 1,270 °) and δ ₈ = (√2,315 °).
That is, eight relative position functions δ _{1 to} δ ₈ can be created.
Eight co-occurrence probability matrices can be created using these eight relative position functions δ _{1 to} δ ₈ .

FIG. 11 is a flowchart of the image processing apparatus according to the present embodiment.
When the process is started (S1101), the counter variable i is first initialized to 0 (S1102). Next, the existence of the relative position function δ (i), which is a rule for creating the co-occurrence probability matrix, is confirmed (S1103).
If δ (i) exists, it is set (S1104), and based on this, a feature amount calculation process based on the co-occurrence probability matrix is performed (S1105). Next, pixel determination processing is performed using the obtained arrangement data of the co-occurrence probability matrix (S1106).
Here, step S1105 is equal to steps S803, S804, S805, S806, and S807 of FIG.
Step S1106 is equivalent to step S808 in FIG. 8 and steps S909, S910, S911, S912, and S913 in FIG.
Next, the examination target address a of the input image bitmap data is set at the head of the image data (S803). That is, the address a is a relative address in the input image bitmap data, and an initial value of the relative address is given in this process.
That is, steps S1105 and S1106 are equivalent to the subroutine R822 enclosed by the dotted line in FIG. This is a process of creating array data including a co-occurrence probability matrix and determining a pixel of a suspicious object in the image flag bitmap data area 204 therefrom.

When the process is finished, the counter variable i is advanced (S1107). Then, the presence / absence of the next relative position function δ (i) is confirmed (S1103), and if it is set (S1104), the processing is continued. If not, the processing is completed for all the relative position functions δ (i).
When i image flag bitmap data are obtained for all the relative position functions δ (i), these are integrated and calculated (S1108). The operation may be a logical product of all the bits, or all the bits may be added and compared with an appropriate threshold value. This calculation process increases the accuracy of determining the suspicious object region.
Finally, the area and center coordinates of the suspicious object region are calculated (S1109), and the process ends (S1110).

Summarize what has been described above.
The image processing apparatus 101 of this embodiment is mainly composed of software that runs on a microcomputer.
When still image data is input to the image processing apparatus 101, it is expanded in the input image bitmap data area 202 in the RAM 105. Thereafter, array data is created in the array data area 203 by the algorithm of the co-occurrence probability matrix. Next, a threshold value is calculated from the obtained array data, and an image pixel corresponding to a number of matrices satisfying the threshold value is determined to be the background. The relative address of the pixel of the image determined to be the background is read from the array data, and the flag of the corresponding address in the image flag bitmap data area 204 is cleared.
Through the above processing, the background and other portions can be discriminated from the co-occurrence probability matrix generated from the still image data.

Next, a third embodiment of the present invention will be described.
FIG. 12 is an example of a still image taken by the imaging camera 102.
Compare with the image in Figure 3 (a). In FIG. 3, the suspicious object 304 which is a ship is shown, but in FIG. 12, a shore 1202 and a lighthouse 1203 are shown on the shore 1202. It is clear that these are not suspicious objects, but there is a high possibility that they will be mistaken as suspicious objects as they are in the first embodiment. This is because these are not recognized as the background because they occupy a small area when viewed from the whole image data. Therefore, it is necessary to perform a process of previously removing a part that may be mistaken as a suspicious object from the object of the process for creating the co-occurrence probability matrix in advance.

FIG. 13A is an overall block diagram of an image processing apparatus according to the third embodiment. An image processing apparatus 1301 shown in FIG. 13A is almost the same as the image processing apparatus 101 in FIG. 1B in the first embodiment. In FIG. 13 (a), parts having the same functions as those in FIG. 1 (b) are denoted by the same reference numerals, and detailed description thereof is omitted.
The image processing apparatus 1301 according to the present embodiment performs a process of excluding a specific area in the image data from a target for determining a suspicious object in advance, based on the captured still image data, before the actual operation. Therefore, compared with the first embodiment shown in FIG. 1B, a processing region setting unit 1302, a display device 1303, and an input device 1304 are newly provided.
The display device 1303 is, for example, a well-known LCD display. The input device 1304 is, for example, a known mouse.
Based on the result obtained from the pixel determining unit 1314, the processing region setting unit 1302 causes the display device 1303 to display an area that appears to be a suspicious object and overlaps with image data held in the image storage unit 112. The user operates the input device 1304 to correct the still image displayed on the display device 1303 and the excluded area displayed in an overlapped state on the still image.
As described above, the exclusion region set in the feature amount calculation unit 1313 by the processing region setting unit 1302 is excluded from the calculation of the co-occurrence probability matrix performed in the feature amount calculation unit 1313.
Taking FIG. 12 as an example, an exclusion region surrounding the shore 1202 and the lighthouse 1203 is created by the processing region setting unit 1302 and held in the feature amount calculation unit 1313. The feature amount calculation unit 1313 forcibly regards a pixel corresponding to the exclusion region as the background without performing the calculation of the co-occurrence probability matrix among the pixels of the image data.

Next, a fourth embodiment of the present invention will be described.
FIG. 13B is an overall block diagram of an image processing apparatus according to the fourth embodiment. An image processing device 1311 shown in FIG. 13B is substantially the same as the image processing device 1301 shown in FIG. 13A in the third embodiment. In FIG. 13 (b), parts having the same functions as those in FIG. 13 (a) are denoted by the same reference numerals, and detailed description thereof is omitted.
Similar to FIG. 13A, the image processing apparatus 1311 according to the present embodiment starts from a target for determining a suspicious object in advance in a specific area in image data based on still image data that has been captured before actual operation. Perform the removal process. The difference from the third embodiment is that the operator automatically sets the excluded area without performing the correction process of the excluded area. For this reason, the display device 1303 and the input device 1304 are not provided as compared to the third embodiment shown in FIG. In addition, the processing region setting unit 1303 is connected to the pixel set determination unit 1315.
The processing region setting unit 1303 regards the result obtained from the pixel set determination unit 1315 as an exclusion region and sets it in the feature amount calculation unit 1313.
The set exclusion region is excluded from the calculation of the co-occurrence probability matrix performed in the feature amount calculation unit 1313.

As described above, the image processing apparatuses described in the first, second, third, and fourth embodiments monitor the single background such as the sea as described in FIGS. 3 and 12. Is particularly preferred. For this reason, the imaging camera is often rotated to monitor a wide range.
FIG. 14A shows an external view of the imaging camera in the present embodiment. FIG. 14B is a block diagram showing a combination of the imaging camera and the image processing apparatus in FIG.
In FIG. 14A, the imaging camera 102 is installed in the turning device 1402 and is driven to rotate left and right in order to monitor a wide range.
In FIG. 14B, inside the swivel device 1402, a motor 1403 for rotating the imaging camera 102 and an angle detector 1405 attached to the shaft 1404 of the motor and driven to rotate together with the imaging camera 102 are provided. ing. Examples of the angle detector 1405 include a tachometer using optical detection, magnetic detection, and the like.
The detection signal of the angle detector 1405 is input to the microcomputer bus 106 that constitutes the main component of the image processing apparatus 101, and a still image obtained from the imaging camera 102 is taken into the RAM 105 at every predetermined angle. The above processing is performed.
In the second and fourth embodiments, the exclusion area is set in advance for each shooting angle of the imaging camera 102.

The following application examples can be considered in the present embodiment.
(1) Instead of mounting by a microcomputer, a programmable logic device (PLD) may be used.
(2) In the third and fourth embodiments described above, the exclusion region set by the processing region setting units 1302 and 1303 is reflected in the feature amount calculation unit 1313. Instead, the same effect can be obtained by reflecting the excluded region in the pixel set determining unit 1315. That is, the part corresponding to the excluded area is regarded as the background, and the flag of the corresponding part in the image flag bitmap data area 204 is lowered.
(3) Although the input image bitmap data area 202 and the image flag bitmap data area 204 are described as different areas in the RAM 105 in FIG. 2, they can be integrated.
For example, a structure that stores luminance data and flag data is configured for each pixel that configures image data. In this way, since the absolute address for each pixel can be directly stored in the array data 401, the arithmetic processing for converting the relative address becomes unnecessary.
(4) Various methods for expressing the co-occurrence probability matrix configured by the array data area 203 are conceivable. For example, it can be realized using a relational database or an associative array implemented in perl which is a well-known interpreter language processing system in Non-Patent Document 1.

In the present embodiment, an object different from the background can be determined from still image data. This processing is different from the method based on comparison with the immediately preceding image data, which is well known in the prior art, and is determined using only the input image data.
In this embodiment, as a technique for discrimination, discrimination is performed using a co-occurrence probability matrix. For this reason, even if there is a change caused by a natural phenomenon such as the sea or the sky, it can be determined as a background having certain characteristics including this.
Furthermore, in this embodiment, as a discrimination based on the co-occurrence probability matrix, unlike the conventional technique, the threshold value is calculated from the obtained matrix without comparing with the matrix data captured and held in advance. Based on the above, it is determined whether or not the background. This discrimination method itself is a new approach not found in the prior art. In addition, it is easy to see that the amount of calculation is small compared with the comparison between the co-occurrence probability matrices in the prior art. Furthermore, as compared with the comparison between the co-occurrence probability matrices, it can be easily understood that the storage capacity of the storage device required in advance can be reduced because the co-occurrence probability matrix does not need to be held in advance.
Therefore, by combining the image processing apparatus of the present embodiment with an imaging camera, the amount of calculation is extremely small compared with the prior art, and the required storage capacity is also reduced. Natural phenomena such as ocean waves and sky clouds It is possible to provide an excellent monitoring device that can be recognized as a background without erroneous recognition.

  The embodiment of the present invention has been described above. However, the present invention is not limited to the above-described embodiment, and other modifications may be made without departing from the gist of the present invention described in the claims. It goes without saying that application examples are included.

1 is an overall block diagram of an image processing apparatus according to a first embodiment of the present invention. It is the schematic which shows the inside of RAM of 1st Embodiment. It is an image figure which shows roughly the input image bit map data and image flag bit map data comprised in RAM of 1st Embodiment. It is an image figure which shows roughly the co-occurrence probability matrix comprised in RAM of 1st Embodiment. It is an image figure which shows roughly the correspondence of each data stored in RAM of 1st Embodiment. It is a figure explaining the principle of a co-occurrence probability matrix. It is a figure explaining the principle of a co-occurrence probability matrix. 3 is a flowchart illustrating processing contents of the image processing apparatus according to the first embodiment. 3 is a flowchart illustrating processing contents of the image processing apparatus according to the first embodiment. It is an image figure which shows the correspondence of the pixel of the image data and relative position function (delta) (i) by the 2nd Embodiment of this invention. It is a flowchart which shows the processing content of the image processing apparatus of 2nd Embodiment. It is an image figure which shows roughly the input image bitmap data comprised in RAM by the 2nd Embodiment of this invention. It is a whole block diagram of the image processing apparatus by the 3rd and 4th embodiment of this invention. 1 is an overall block diagram of an image processing apparatus applied to each embodiment of the present invention combined with an example of an imaging camera. FIG.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 101 ... Image processing apparatus, 102 ... Imaging camera, 103 ... CPU, 104 ... ROM, 105 ... RAM, 106 ... Bus, 107 ... Output interface, 112 ... Image preservation | save part, 113 ... Feature-value calculation part, 114 ... Pixel determination part 115 ... Pixel set determination unit

Claims (1)

An image storage unit for storing input image data;
A feature amount calculating unit that calculates a co-occurrence probability matrix in an array having an address indicating each pixel constituting the input image data in the image storage unit;
A processing region setting unit that sets an exclusion region that is not subject to the calculation of the co-occurrence probability matrix for the feature amount calculation unit;
And the number of elements of the co-occurrence probability matrix obtained by the feature amount calculation unit with a predetermined threshold value, the pixel determination unit for determining whether the background,
An image processing apparatus comprising: a pixel set determining unit that outputs the presence / absence of an object that is not the background based on a determination result obtained by the pixel determining unit.

Images