【摘要】：豬肉是我國居民的主要肉食品種,雖然政府付出了大量的人力物力來對豬肉生產(chǎn)的各個環(huán)節(jié)進(jìn)行監(jiān)管,但是近年來還是出現(xiàn)了一系列嚴(yán)重的豬肉食品安全事故。目前我國豬肉的追溯主要依靠豬皮上的合格印章和各個環(huán)節(jié)工作人員進(jìn)行的信息記錄,并沒有依靠豬肉自身的特征來實現(xiàn)豬肉的追溯。毛孔作為帶皮豬肉自身所具備的特征在豬皮上是普遍存在的,并且在流通過程中能夠基本保持性狀的不變。本研究致力于探索和提取豬皮圖像中毛孔所攜帶的具有唯一性的特征信息,并基于此來進(jìn)行局部豬皮圖像與總體豬皮圖像的匹配從而實現(xiàn)帶皮豬肉的追溯。本文的主要研究內(nèi)容和結(jié)論如下。(1)對高清豬皮圖像的毛孔特征提取的研究。針對本研究的目標(biāo)應(yīng)用場景到市場上購買豬皮樣本,并進(jìn)行高清豬皮圖像的采集。將采集的高清豬皮圖像作為研究對象,對豬皮圖像中的豬皮毛孔進(jìn)行建模。根據(jù)豬皮圖像的毛孔特性和目標(biāo)應(yīng)用場景,本研究在毛孔特征提取算法PSIFT(Pore Scale Invariant Feature Transform)的基礎(chǔ)上提出了多方向的毛孔特征提取算法MPSIFT(Multi-orientation Pore Scale Invariant Feature Transform),使得最終所提取的毛孔特征具有一定的旋轉(zhuǎn)不變性。利用MPSIFT算法對豬皮圖像的毛孔進(jìn)行檢測和特征提取,最終為每一個毛孔特征點至少生成一個特征點描述向量。(2)局部豬皮圖像與總體豬皮圖像的匹配研究�；贛PSIFT算法提取的高清豬皮圖像的毛孔尺度特征,本文進(jìn)行了局部豬皮圖像與總體豬皮圖像的匹配研究。本研究利用特征點描述向量的夾角作為豬皮圖像毛孔特征點的相似度度量,利用不同特征點描述向量與被匹配特征點描述向量夾角大小的比值來衡量特征點之間的匹配度。在兩張豬皮圖像的毛孔特征匹配完成之后,本文利用離群點檢測的方法對匹配成功的特征點對進(jìn)行篩選,剔除錯誤匹配的特征點對,從而保證匹配成功的毛孔特征點對的質(zhì)量。(3)基于局部豬皮圖像與總體豬皮圖像的匹配研究本文提出了基于毛孔尺度特征分析的帶皮豬肉追溯技術(shù),并給出了帶皮豬肉的追溯原理與該技術(shù)的應(yīng)用場景。(4)豬皮圖像毛孔特征提取與匹配的實驗與分析。本研究通過實驗探明了不同數(shù)據(jù)特性對局部豬皮圖像與總體豬皮圖像匹配效果的影響,并最終在所采集的全部豬皮圖像中實現(xiàn)了94.14%的局部豬皮圖像與總體豬皮圖像的匹配正確率,在保證追溯成功結(jié)果的可靠性情況下實現(xiàn)了局部豬皮圖像86.73%的追溯成功率。
[Abstract]:Pork is the main meat food in our country. Although the government has paid a lot of manpower and material resources to supervise the pork production, there have been a series of serious pork food safety accidents in recent years. At present, the traceability of pork in our country mainly depends on the qualified seal on the skin of the pig and the information recorded by the personnel in every link, and it does not depend on the characteristics of the pork itself to realize the tracing of the pork. Pores, as the characteristics of skinned pork, are common in pig skin, and can basically maintain the same characters in the circulation process. The purpose of this study is to explore and extract the unique feature information carried by pores in porcine skin images and to match the local porcine skin images with the overall pig skin images so as to achieve the traceability of skinned pork. The main contents and conclusions of this paper are as follows: (1) the study of pore feature extraction from high-definition porcine skin image. According to the target of this study, the pig skin samples were purchased from the market, and the high-definition pig skin images were collected. The porcine pores in the high-definition pig skin images were modeled. Based on the pore characteristics of porcine skin image and the target application scene, a multi-directional pore feature extraction algorithm, MPSIFT (Multi-orientation Pore Scale Invariant Feature Transform), is proposed based on the pore feature extraction algorithm (PSIFT (Pore Scale Invariant Feature Transform). The resulting pore features have a certain rotation invariance. MPSIFT algorithm is used to detect and extract pores of porcine skin image, and at least one feature point description vector is generated for each pore feature point. (2) matching between local pig skin image and total pig skin image. Based on the pore size feature of high-definition pig skin image extracted by MPSIFT algorithm, the matching between local pig skin image and total pig skin image is studied in this paper. In this study, the angle of feature point description vector is used as the similarity measure of porcine skin image pore feature point, and the ratio of different feature point description vector and matching feature point description vector angle is used to measure the matching degree between feature points. After the pore feature matching of two porcine skin images is completed, the outlier detection method is used to screen the matching feature pairs, and the false matching feature pairs are eliminated. So as to ensure the quality of the matching pore feature points. (3) based on the matching between local porcine skin image and total porcine skin image, a new technique of pork traceability based on pore size feature analysis is proposed in this paper. The traceability principle of skinned pork and the application scene of this technique are also given. (4) the experiment and analysis of porcine skin image pore feature extraction and matching. In this study, the effects of different data characteristics on the matching effect between local pig skin image and total pig skin image were investigated. Finally, 94.14% of the local pig skin image and the total pig skin image are matched correctly in all the pig skin images collected. The success rate of local pig skin image was 86.73%.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS251.51;TP391.41

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