CN100378752C - Segmentation method of natural image in robustness - Google Patents
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Abstract
The present invention relates to the technical field image segmentation and comprehension, particularly to a segmentation method of a natural image in robustness. The method comprises the following procedures: step 1, a frame image is acquired to be stored in a memory; step 2, color band width is counted by the YUV component of the image according to a selecting method of the optimal band width in multivariate non-parameter kernel density estimation, and the space band width is determined according to an upper layer visual task; step 3, bilateral filtering operation is carried out to the image; step 4, the estimated color band width is used for further estimating the probability density of each picture element part; step 5, the process of local mode detection is carried out, and the process is also a picture element clustering process which belongs to that the picture element points of the same local mode are clustered into the same class; step 6, the process of merging local modes is carried out, the clustered result is merged again, and blocks with similar colors in the image are merged; step 7, the influence of textural characteristics to the segmentation result is eliminated; step 8, the area of which the area is smaller than 100 picture element points is eliminated; step 9, the result is outputted.
Description
Technical field
The present invention relates to the natural image dividing method of image segmentation and image understanding technical field, particularly a kind of robust.
Background technology
The applied environment of indoor and outdoors irregularity is the common situation that robot visual guidance runs into.In order to adapt to these environment, the artificial target of some special uses can be set during navigation.Adopt artificial target to be subjected to certain restriction, because be difficult to be provided with artificial target in a lot of application scenarios as its application of mode of continental embankment navigation.By contrast, the navigate mode that detects with identification based on natural target has more general applicability, and it is applicable to the robot navigation under the scene at random.Natural target detect with identification in, natural image cut apart and the stability (robustness) of segmentation performance is crucial.Therefore, the natural image dividing method of Shandong group has important use value in the mobile robot visual navigation.
The existing natural image partition method comprises: conventional segmentation methods, based on the method for the dividing method of region-competitive, based target model, based on the dividing method of average drifting, based on the dividing method of graph theory (Graphic theory), based on the dividing method of study etc.In above natural image dividing method, the dividing method of Fusion of Color, profile and textural characteristics is realistic picture structure simultaneously, the development trend of having represented natural image to cut apart.In these methods, be the optimizing process of the recurrence of the overall situation based on the dividing method of region-competitive, object module, graph theory, the extraction of textural characteristics and handle and need bigger calculated amount, so calculation of complex are difficult to use in real time environment.Yet, based on the dividing method of average drifting because split image on the color only, describe from the angle of data analysis and to cut apart, under asymptotic intergration square error (AMISE) meaning, select bandwidth, keep the original distribution of data, distortion is little, and computing velocity is fast, robustness is good, has been used to motion target tracking.Exactly because the shortcoming of average drifting method is not considered texture information yet in cutting apart, cause its easy over-segmentation image, particularly texture image.Simultaneously, the average drifting method is a kind of dividing method from bottom to top, and it carries out cluster from the distribution of data itself, yet actual visual task often needs top-down processing mode, wishes that segmentation result needs to change with the upper strata.Therefore, above method can't satisfy the needs of actual visual processes task.
Summary of the invention
The object of the present invention is to provide a kind of natural image dividing method of robust.
The natural image dividing method of robust is exactly the method for any natural image of gathering under the energy stable segmentation different illumination conditions, its main task is that the image of random acquisition is cut apart, all will obtain stable segmentation result, and control segmentation result according to the visual task on upper strata.
The natural image dividing method of robust is at first gathered a two field picture to internal memory, then the image of gathering is carried out color bandwidth estimation, filtering, density Estimation, local mode detection, local mode fusion successively, eliminates textural characteristics etc., eliminate the zone of area at last less than 100 picture elements, and the output segmentation result.
Bandwidth is selected: bandwidth comprises spatial bandwidth and color bandwidth.Spatial bandwidth is exactly the window size that is used to estimate density and search local mode, and the size of spatial bandwidth has determined the processing speed of algorithm.Along with the increase of spatial bandwidth, the time of algorithm consumption will increase fast.Simultaneously, the size of spatial bandwidth also can influence detected local mode number.Be not that the big more segmentation result of spatial bandwidth is just good more, spatial bandwidth will be determined according to corresponding visual task.The color bandwidth is to calculate from the angle of data analysis and according to the Plug-in rule, and it makes the color probability density distribution of estimation and the progressive integration square error minimum between the real color probability density distribution.Yet actual visual task is not each details that will keep image, only image segmentation need be become several main parts sometimes and ignores most of details, therefore need adjust the color bandwidth in certain scope.Usually, the color bandwidth is big more, and detected local mode number is just few more, but when the color bandwidth surpassed certain value, detected local mode number just no longer changed.The color bandwidth is big more, and details many more in the image will be left in the basket, and in this sense, the color bandwidth can be regarded as cuts apart resolution.Big color bandwidth is equivalent to see buildings at a distance with a less magnifier of enlargement factor, can only sees a fuzzy profile; Little color bandwidth is equivalent to a magnifier that enlargement factor is higher, can see the detail section of target.
Filtering: adopted self-adaptive smooth filtering, when keeping object boundary, eliminated The noise.
Density Estimation: the kernel function that is used for density Estimation is selected multivariate independence gauss of distribution function.After bandwidth was determined, the density Estimation value only was subjected to the influence that colour-difference changes between pixel.And the variation range of color difference in yuv space is within 0 to 512, therefore, can be according to the bandwidth of selecting, list the density value form that a color difference changes in advance between 0 to 512, only need table look-up to add up when carrying out density Estimation gets final product, and greatly reduces the calculation cost of kernel function density Estimation like this.
Local mode detects: local mode detects can be divided into direct density way of search and average drifting way of search, and the present invention has adopted direct density way of search.Directly the density Estimation searching method is finished in two steps: at first be the probability density of each picture element of size estimation of window with the spatial bandwidth, then with the window search local mode of identical size.The average drifting method is then directly utilized the pixel in the window to calculate the drift vector and is searched for.Obviously, directly search is finished in the density Estimation search in two steps, and its calculated amount is bigger than the calculated amount of average drifting.But directly the density Estimation search has utilized 2 times of interior pixel informations of window ranges to search for, and the average drifting search only depends on the pixel information in the very little window ranges of spatial bandwidth mark, so its Search Results will be better than average drifting searching method.
Local mode merges: adopted the local mode amalgamation mode based on global criteria, i.e. the foundation that merges as local mode with the information of entire image.Adopt the local mode fusion under the global criteria to also help according to the final cut zone number of upper strata visual task control.
Eliminate textural characteristics: on detected local mode basis, the influence of the over-segmentation that the removal of images textural characteristics brings.
Eliminate the zone of area: be used to eliminate some little zones less than 100 picture elements.
Characteristics of the present invention and effect: compare with other natural image dividing methods,, computing velocity is fast.The natural image dividing method of the robust that the present invention proposes has following difference:
● be from top to bottom and the visual processing method that combines from bottom to top, computing velocity is fast.
● adopted direct local mode way of search, the local mode under the global criteria merges,
Under detected local mode, eliminate the influence of textural characteristics.
● under the different illumination conditions and have the natural image of texture, can obtain stable segmentation result, robust performance is better.
● the needs according to the upper strata visual task are adjusted partitioning parameters, obtain required segmentation result.
● segmentation effect is better than the average drifting method, and computing time and average drifting are suitable.
Description of drawings
Fig. 1 is the natural image dividing method procedure chart of robust of the present invention.
Embodiment
The natural image cutting procedure of robust as shown in Figure 1.Whole cutting procedure is made up of seven parts: color bandwidth estimation, filtering, density Estimation, local mode detect, local mode merges, eliminate textural characteristics, eliminate the zone less than 100 pixels.
Concrete steps comprise:
Step S1 gathers a two field picture to internal memory;
Step S2, the system of selection of the optimum bandwidth in estimating according to the multivariate norm of nonparametric kernel density counts the color bandwidth by the YUV component of image, and determines spatial bandwidth according to the upper strata visual task;
Step S3 carries out bilateral filtering to image;
Step S4 uses the color bandwidth that estimates further to estimate the probability density at each picture element place;
Step S5, local mode detects, on the probability density of estimating, adopt local directly density way of search, search local density maximum of points, the corresponding local mode of each local density's maximum of points, this process also is the process of pixel cluster, the picture element cluster that belongs to same local mode in same class, a corresponding localized mass;
Step S6, local mode merges, and the local mode after the cluster is merged once more the close piece of color in the combined diagram picture;
Step S7 eliminates the influence of textural characteristics to segmentation result;
Step S8 eliminates the zone of area less than 100 picture elements;
Step S9, the output result.
At first, gather a two field picture, count optimum color bandwidth under the plug-in meaning, and determine spatial bandwidth according to actual visual task by the YUV component of image to internal memory.
Utilize color bandwidth and the adaptive filter method estimated, image is carried out filtering.
The kernel function that is used for density Estimation is selected multivariate independence gauss of distribution function.Because under the prerequisite that kernel function has been selected and bandwidth has been estimated, density value only is subjected to the influence of colour-difference between pixel, and, the variation range of color difference in yuv space is within 0 to 2 * 256 between pixel, therefore, list the density value form that changes the colour-difference from 0 to 2 * 256 before density Estimation, only need table look-up to add up when carrying out density Estimation gets final product, and has reduced the calculation cost of kernel function density Estimation like this.
Local mode detects and adopts direct density way of search.At first be the size of search window with the spatial bandwidth, the density maximum point in the search window.Search for once more with the density extreme point in the window that searches then, up to converging to local density's maximum point.All picture elements that converge to same local density maximum point constitute a local mode.Through the part
After the mode detection, piece image can be decomposed into some local modes, the corresponding localized mass of each local mode.
The local mode fusion is to merge under the rule of the divergence minimization of entire image, perhaps merges according to the number of regions after merging, and is decided by concrete visual task.During fusion, the adjacent local mode of color difference within threshold value merged, up to reaching the number of regions that divergence minimizes or expects.
Eliminating texture region handles on detected local mode.By adding up the color distortion between each local mode and the adjacent local mode, estimate the color independence of each local mode, carry out the merging of local mode according to color independence, eliminate of the influence of most of textural characteristics to segmentation result.
Eliminate the zone of area less than 100 picture elements, and the output result.
Claims (1)
1. the natural image dividing method of a robust, whole cutting procedure is made up of seven part steps: color bandwidth estimation, filtering, density Estimation, local mode detect, local mode merges, eliminate textural characteristics, eliminate the zone less than 100 pixels, and its step is as follows:
Step S1 gathers a two field picture to internal memory;
Step S2, the system of selection of the optimum bandwidth in estimating according to the multivariate norm of nonparametric kernel density counts the color bandwidth by the YUV component of image, and determines spatial bandwidth according to the upper strata visual task;
Step S3 according to the final cut zone number of upper strata visual task control, carries out bilateral filtering to image;
Step S4 uses the color bandwidth that estimates further to estimate the probability density at each picture element place;
Step S5, local mode detects, on the probability density of estimating, adopt local directly density way of search, search local density maximum of points, the corresponding local mode of each local density's maximum of points, this process also is the process of pixel cluster, the picture element cluster that belongs to same local mode in same class, a corresponding localized mass;
Step S6, local mode merges, and the local mode after the cluster is merged once more the close piece of color in the combined diagram picture;
Step S7 eliminates the influence of textural characteristics to segmentation result;
Step S8 eliminates the zone of area less than 100 picture elements;
Step S9, the output result.
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| CN101216886B (en) * | 2008-01-11 | 2010-06-09 | 北京航空航天大学 | A shot clustering method based on spectral segmentation theory |
| CN101510310B (en) * | 2009-02-19 | 2010-12-29 | 上海交通大学 | Method for segmentation of high resolution remote sensing image based on veins clustering constrain |
| JP5169997B2 (en) * | 2009-05-29 | 2013-03-27 | ソニー株式会社 | Filter circuit, image processing apparatus, imaging apparatus, image processing method, and program |
| CN105578021B (en) * | 2014-10-15 | 2019-03-26 | 上海弘视通信技术有限公司 | The imaging method and its device of binocular camera |
| CN104835174B (en) * | 2015-05-25 | 2017-12-15 | 厦门大学 | Robust Model approximating method based on Hypergraph model search |
| CN114018271B (en) * | 2021-10-08 | 2024-07-09 | 北京控制工程研究所 | Accurate fixed-point landing autonomous navigation method and system based on landmark image |
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| WO2011160309A1 (en) * | 2010-06-25 | 2011-12-29 | 中国科学院自动化研究所 | Method for brain tumor segmentation in multimodal three-dimensional magnetic resonance images based on robust statistical information propagation |
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