本文選題：生物特征 + 紋理�。� 參考：《農(nóng)業(yè)工程學(xué)報》2017年18期

【摘要】：為了將生物特征識別技術(shù)應(yīng)用于追溯系統(tǒng)中的果品標(biāo)識,該文提出了一種采用西瓜蒂外圍紋理信息標(biāo)識果品個體的方法。該方法首先采集西瓜圖像并在瓜蒂外圍構(gòu)造一個環(huán)形區(qū)域;將環(huán)形區(qū)域歸一化后利用Gabor濾波器對圖像紋理進行特征提取及編碼;然后通過計算碼間的Hamming距進行紋理編碼的匹配。在試驗中,為100個西瓜在采后貯藏初始、第7天和第14天各采集一幅圖像并計算出3組每組100個紋理編碼,將第7天、第14天的每一個編碼與初始階段的每一個編碼兩兩比對,累計完成20 000次比對證實西瓜紋理具有唯一性,不同個體的紋理特征各不相同。使用成對數(shù)據(jù)的假設(shè)檢驗證實在采收后14 d內(nèi)紋理特征不隨時間的推移產(chǎn)生顯著變化(P0.05)。通過理論和試驗證實算法對平移、尺度和旋轉(zhuǎn)具有適應(yīng)性,即西瓜在圖像中的平移、尺度和旋轉(zhuǎn)對Hamming距的計算不會產(chǎn)生顯著影響(P0.05)。使用最大類間方差法在50個西瓜的紋理編碼內(nèi)訓(xùn)練出判別閾值,并用該閾值判別另外50個西瓜3個時間點間相互比對生成的100個來自同一個西瓜的Hamming距和4 900個來自不同西瓜的Hamming距,結(jié)果表明上述Hamming距均能正確判別,準確率與召回率為100%。該研究為農(nóng)產(chǎn)品追溯系統(tǒng)的標(biāo)識技術(shù)提供新的思路。
[Abstract]:In order to apply biometric identification technology to fruit identification in traceability system, this paper proposes a method of identifying fruit individuals by using the texture information of watermelon pedicle periphery. Firstly, the watermelon image is collected and a ring region is constructed around Guarty. After normalization, Gabor filter is used to extract and encode the texture of the image, and then the matching of texture coding is carried out by calculating hamming distance between codes. In the experiment, 100 watermelons were stored at the beginning of postharvest storage. One image was collected on the 7th and 14th days, and three groups of 100 texture codes were calculated. Each encoding on the 7th and 14th day was compared with each other in the initial stage. The cumulative completion of 20,000 comparison shows that watermelon texture is unique, and the texture characteristics of different individuals are different. The hypothesis test using paired data confirmed that the texture features did not change significantly with the passage of time within 14 days after harvest (P0.05). Theoretical and experimental results show that the algorithm is adaptive to translation, scale and rotation, that is, the translation of watermelon in the image, scale and rotation have no significant impact on the calculation of hamming distance (P0.05). The maximum inter-class variance method was used to train the discriminant threshold in the texture coding of 50 watermelons. The hamming distance of 100 watermelons from the same watermelon and 4,900 hamming distances from different watermelons were determined by the threshold value. The results show that the hamming distances mentioned above can be correctly judged, and the accuracy and recall rate are 100. This research provides a new idea for the identification technology of agricultural product traceability system.
【作者單位】： 華中農(nóng)業(yè)大學(xué)信息學(xué)院;
【基金】：國家自然科學(xué)基金項目(31571351) 農(nóng)業(yè)部農(nóng)業(yè)技術(shù)試驗示范項目(2015-2130106)
【分類號】：TP391.41

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