本文關(guān)鍵詞： 圖像處理 同態(tài)濾波 圖像分割 牛體檢測 混淆矩陣　出處：《西北農(nóng)林科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：肉牛養(yǎng)殖業(yè)在我國的國民經(jīng)濟(jì)建設(shè)中占有非常重要的地位,是高效、節(jié)糧、與民生息息相關(guān)的產(chǎn)業(yè),同時(shí)也是我國調(diào)整農(nóng)業(yè)結(jié)構(gòu)的戰(zhàn)略方向。產(chǎn)肉量作為評(píng)定肉牛質(zhì)量的重要指標(biāo),更是迫切需要實(shí)現(xiàn)自動(dòng)化測定。牛體檢測作為其一項(xiàng)前期準(zhǔn)備工作,有著十分重要的意義,本文針對(duì)這一問題,對(duì)牛體圖像進(jìn)行分析處理,主要通過濾波處理、牛體檢測、形態(tài)學(xué)運(yùn)算以及評(píng)價(jià)對(duì)比等方法將牛體與復(fù)雜的自然背景分離,并獲得了較好的分離效果。本文的主要研究內(nèi)容有以下幾個(gè)方面:(1)圖像預(yù)處理:由于牛體圖像在獲取的過程中會(huì)受到光照影響,所以要對(duì)圖像進(jìn)行對(duì)比度增強(qiáng)和光照去除處理。本文首先在YCbCr空間上對(duì)牛體圖像進(jìn)行分解,得到其Y通道圖像,然后用同態(tài)濾波對(duì)其進(jìn)行濾波處理以增強(qiáng)圖像對(duì)比度。但處理結(jié)果對(duì)環(huán)境光照影響去除效果不明顯,因此對(duì)上述Y通道圖像進(jìn)行二級(jí)小波分解及同態(tài)濾波處理,使圖像基本信息在最低分辨率層得到體現(xiàn),且圖像峰值信噪比達(dá)到了24.35。因此,在YCbCr空間Y通道下采用小波變換及同態(tài)濾波方法,可以降低牛體檢測過程中強(qiáng)自然光照的影響,且增強(qiáng)了圖像的對(duì)比度,為下一步牛體檢測做好了準(zhǔn)備工作。(2)牛體檢測:本文通過兩種方法來進(jìn)行牛體檢測,分別是貝葉斯分類器和改進(jìn)大津算法,即分別利用皮膚檢測和圖像分割的方式將牛體從復(fù)雜的背景中分離開。貝葉斯分類器采用了RGB和HSV兩個(gè)顏色空間實(shí)現(xiàn)牛體的檢測,通過統(tǒng)計(jì)在不同顏色通道上的檢測效果建立了相應(yīng)的訓(xùn)練集和測試集。(3)圖像優(yōu)化及評(píng)價(jià):牛體所處環(huán)境和其自身毛色均較為復(fù)雜導(dǎo)致牛體檢測結(jié)果存在背景噪聲,因此需對(duì)圖像進(jìn)行優(yōu)化處理。本文運(yùn)用形態(tài)學(xué)運(yùn)算對(duì)牛體檢測圖像進(jìn)行修復(fù)和優(yōu)化,得到了較為完整的牛體信息。然后,采用混淆矩陣對(duì)優(yōu)化后的圖像進(jìn)行評(píng)價(jià)和分析。通過對(duì)最終處理得到的20幅牛體圖像進(jìn)行分析和計(jì)算,結(jié)果表明,貝葉斯分類器和改進(jìn)大津算法的牛體提取平均準(zhǔn)確率分別為86.17%和80.07%。本實(shí)驗(yàn)較好的解決了牛體與復(fù)雜背景分離的問題,基本實(shí)現(xiàn)了牛體的完整提取,為后續(xù)牛體自動(dòng)化測量提供前期準(zhǔn)備工作。
[Abstract]:Beef cattle farming occupies a very important position in the national economic construction of our country. It is an industry that is efficient, grain saving and closely related to people's livelihood. At the same time, it is also the strategic direction of adjusting the agricultural structure of our country. As an important index to evaluate the quality of beef cattle, it is urgent to realize automatic determination. In order to solve this problem, this paper analyzes and processes the bovine body image, mainly separates the bovine body from the complex natural background by filtering, detecting, morphological operation and evaluation and comparison, etc. The main contents of this paper are as follows: image preprocessing: the bovine body image will be affected by light during the process of acquisition. Therefore, contrast enhancement and illumination removal should be carried out on the image. Firstly, the bovine body image is decomposed in YCbCr space, and the Y channel image is obtained. Then the homomorphic filter is used to filter the image to enhance the contrast of the image. However, the effect of the processing result on the environmental illumination is not obvious, so the Y-channel image is processed by two-level wavelet decomposition and homomorphic filtering. The basic information of the image is reflected in the lowest resolution layer, and the peak signal-to-noise ratio (PSNR) of the image reaches 24.35.Therefore, wavelet transform and homomorphic filtering in YCbCr space Y channel can reduce the influence of strong natural illumination in the process of bovine body detection. And enhanced the contrast of the image, prepared for the next step of bovine body detection. 2) Bovine body detection: this paper through two methods to carry out cattle body detection, respectively, Bayesian classifier and improved Otsu algorithm, That is to say, cattle body is separated from complex background by skin detection and image segmentation. Bayesian classifier uses two color spaces, RGB and HSV, to detect bovine body. The image optimization and evaluation of training set and test set on different color channels were established by statistical analysis. The results showed that the environment of cattle body and its coat color were more complex, which resulted in the background noise in the result of bovine body detection. Therefore, it is necessary to optimize the image processing. In this paper, the image of bovine body detection is repaired and optimized by morphological operation, and the complete information of bovine body is obtained. The confusion matrix is used to evaluate and analyze the optimized images. The analysis and calculation of 20 bovine body images obtained from the final processing show that, The average accuracy of bovine body extraction by Bayesian classifier and improved Otsu algorithm is 86.17% and 80.07 respectively. This experiment has solved the problem of separating cattle body from complex background and basically realized the complete extraction of bovine body. To provide early preparation for follow-up automatic measurement of cattle body.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：S823;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 周顏軍,王雙成,王輝;基于貝葉斯網(wǎng)絡(luò)的分類器研究[J];東北師大學(xué)報(bào)(自然科學(xué)版);2003年02期

2 董立巖;苑森淼;劉光遠(yuǎn);賈書洪;;基于貝葉斯分類器的圖像分類[J];吉林大學(xué)學(xué)報(bào)(理學(xué)版);2007年02期

3 熊杰;周明全;耿國華;韓麗娜;;使用同態(tài)分解和小波變換增強(qiáng)真彩圖像[J];計(jì)算機(jī)工程與應(yīng)用;2010年04期

4 張海林;李榕;常鴻森;;基于YCbCr模型和形態(tài)學(xué)的瞳孔分割及人臉檢測[J];計(jì)算機(jī)仿真;2006年10期

5 鄧國取;;解析我國農(nóng)區(qū)畜牧業(yè)發(fā)展戰(zhàn)略模式[J];農(nóng)村經(jīng)濟(jì);2007年04期

6 馬龍?zhí)?張成義;;基于Matlab的同態(tài)濾波器的優(yōu)化設(shè)計(jì)[J];應(yīng)用光學(xué);2010年04期

7 周進(jìn)登;王曉丹;周紅建;;基于混淆矩陣的自適應(yīng)糾錯(cuò)輸出編碼多類分類方法[J];系統(tǒng)工程與電子技術(shù);2012年07期

相關(guān)碩士學(xué)位論文 前3條

1 劉亞男;二維離散小波變換的算法研究及有效實(shí)現(xiàn)[D];武漢理工大學(xué);2002年

2 黃君冉;基于Web的奶牛圖像識(shí)別及圖像信息管理系統(tǒng)的研究[D];河北農(nóng)業(yè)大學(xué);2006年

3 陳廣華;奶牛信息采集系統(tǒng)設(shè)計(jì)與開發(fā)[D];河北農(nóng)業(yè)大學(xué);2009年

，

本文編號(hào)：1556268