發(fā)布時間：2018-06-18 00:42

  本文選題：時頻復(fù)合加權(quán) + 抗噪柱狀圖　； 參考：《西北大學》2016年博士論文

【摘要】：計算機視覺和圖像處理領(lǐng)域普遍存在數(shù)據(jù)維度高,圖像數(shù)據(jù)類型日益復(fù)雜的情形,經(jīng)典的計算和分析方法對這類圖像數(shù)據(jù)進行分析處理時往往計算代價過高,甚至會完全失效。通常在對高維復(fù)雜圖像數(shù)據(jù)進行分析和處理之前,需要對樣本數(shù)據(jù)集進行特征提取操作,提取與樣本相關(guān)性強的特征點,并且要去除噪聲特征點和與樣本數(shù)據(jù)集不相關(guān)的冗余特征點,以便為后續(xù)的處理工作提供高價值的數(shù)據(jù)。通常圖像特征提取方法所獲取的特征點,在表征圖像時存在優(yōu)劣之分,同時特征點的優(yōu)劣直接影響著實際應(yīng)用中圖像處理的結(jié)果。圖像特征提取是進行圖像配準、圖像目標識別、圖像檢索等應(yīng)用的關(guān)鍵步驟,從圖像數(shù)據(jù)中提取表達能力強、抗噪能力好的圖像特征仍然是圖像處理領(lǐng)域的研究難點和熱點之一。本文在對常用的特征提取方法進行深入分析的基礎(chǔ)上,針對若干特定應(yīng)用領(lǐng)域的圖像特征提取方法進行了深入研究,提出了一系列改進算法,以提高特征提取的有效性和實用性。具體內(nèi)容如下：1提出了一種約束優(yōu)化進化的夜間圖像時頻復(fù)合特征提取方法。對多幀夜間模糊圖像的時域與頻域同時加權(quán)處理,是實現(xiàn)深度提取夜間模糊圖像特征的先進方法。傳統(tǒng)的夜間圖像特征提取方法大多只單獨提取時域特征,沒有對多幀夜間低質(zhì)量圖像的頻譜特性進行分析與特征提取。本文方法針對多幀原始圖像,分別在頻域和時域提取多幀圖像之間的相關(guān)信息；通過加權(quán)處理形成新的圖像特征；運用約束優(yōu)化進化算法對圖像特征提取的結(jié)果不斷進行循環(huán)優(yōu)化,最終達到更好的特征提取效果。2提出了一種基于柱狀圖特征描述的含噪圖像特征提取方法。在基于內(nèi)容的圖像檢索方法中,通常將圖像的內(nèi)容表示成柱狀圖,根據(jù)圖像柱狀圖之間的相似性進行檢索。由于數(shù)碼圖像中包含噪聲,往往使得柱狀圖變得平滑,圖像之間變得更為相似,導(dǎo)致返回結(jié)果中圖像數(shù)量增加,檢索準確率降低。為了進一步提高圖像檢索方法性能,本文提出了一種對噪聲不敏感的柱狀圖特征描述符,并用該特征描述符進行圖像檢索。將圖像中的噪聲描述為平穩(wěn)附加高斯白噪聲,并給出相應(yīng)的柱狀圖表示。通過隨機變量的原點矩定義了柱狀圖的特征描述符,并且分析了如何應(yīng)用特征描述符恢復(fù)原始圖像的柱狀圖。3提出了一種基于LBP和人眼視覺感覺模型的航空圖像特征提取方法,研究非單一目標的航空圖像準確檢索的問題。由于航空圖像特定區(qū)域中的目標呈隨機性分布,干擾目標與識別目標交錯分布。傳統(tǒng)的圖像檢索算法的思路都是以目標為線索,根據(jù)特定的目標像素特征,或者關(guān)聯(lián)特征完成圖像的檢索。干擾排除也以特征對比為主,方法較為機械,在目標眾多環(huán)境下,排除過程極其復(fù)雜,檢索效率與準確性都很低。為了避免上述缺陷,本文方法利用局部二進制(LBP)方法進行航空圖像的特征提取,并將上述不同種類的特征作為人眼視覺感覺航空圖像檢索的基礎(chǔ)數(shù)據(jù)。通過建立人眼視覺感覺模型,進行航空圖像檢索,提高了檢索效率和準確性。4提出了一種基于SIFT描述符的圖像角點特征提取方法。該方法使用最近距離比的方法來獲得初始匹配特征描述符對,有效減少異常值的影響。此外,利用尺度方向的聯(lián)合限制尋找假匹配SIFT描述符對。并利用隨機采樣一致性原則刪除異常值,提高圖像角點特征配準精度。5為實現(xiàn)對圖像的實時邊緣檢測,研究了基于嵌入式多DSP的實時邊緣檢測系統(tǒng)。設(shè)計了基于3個DSP兩兩相連的圖像邊緣檢測運算模塊,并通過FPGA對圖像進行預(yù)處理來提高檢測效率,保證了邊緣檢測的實時性。采用改進的萬有引力邊緣檢測算法,降低噪聲對檢測結(jié)果的影響,提高對細節(jié)信息的檢測準確度。
[Abstract]:In the field of computer vision and image processing, the data dimension is high and the image data type is increasingly complex. The classical calculation and analysis methods often have high cost and even complete failure when analyzing and processing this kind of image data. Usually, before the analysis and processing of the high dimensional complex image data, the sample needs to be matched. This data set extracts the feature extraction operation, extracts the feature points with strong correlation with the sample, and removes the noise feature points and the redundant feature points which are not related to the sample data set so as to provide the high value data for the subsequent processing work. At the same time, the advantages and disadvantages of feature points directly affect the results of image processing in actual applications. Image feature extraction is the key step in image registration, image target recognition, image retrieval and so on. The extraction of image features from image data is strong, and the image features with good noise resistance are still the difficult and hot topics in the field of image processing. Firstly, based on the in-depth analysis of the commonly used feature extraction methods, this paper makes a thorough study of the image feature extraction methods in certain application fields, and proposes a series of improved algorithms to improve the effectiveness and practicability of feature extraction. The specific contents are as follows: 1 a night map with constrained optimization evolution is proposed. The time frequency complex feature extraction method is an advanced method for the time domain and frequency domain weighting of multi frame nocturnal blurred images. It is an advanced method to extract the features of the nocturnal blurred image. Most of the traditional night image feature extraction methods only extract the time domain features alone, and do not analyze the spectrum characteristics of the multi frame low quality images. Feature extraction. This method extracts the related information between multi frame images in frequency domain and time domain for multi frame original images. A new image feature is formed by weighted processing, and the result of image feature extraction is continuously optimized by constrained optimization evolutionary algorithm, and a better feature extraction effect.2 is proposed at the end. In the content based image retrieval method, the content of the image is usually expressed as a columnar graph and retrieved according to the similarity between the image histogram. Because of the noise in the digital image, the histogram becomes smooth and the image becomes more similar. In order to further improve the performance of the image retrieval method, this paper presents a feature descriptor of the histogram that is insensitive to noise, and uses the feature descriptor to retrieve the image. The noise in the image is described as a stationary additional Gauss white noise, and the corresponding column is given. The feature descriptors of the histogram are defined by the origin moment of random variables, and the histogram of the original image which is used to restore the original image by the feature descriptor.3 is analyzed. An aerial image feature extraction method based on the LBP and human visual sense model is proposed, and the problem of accurate retrieval of the non single target aerial images is studied. Due to the random distribution of the target in the specific area of the aerial image, the interference target and the recognition target are interlaced. The traditional image retrieval algorithm is based on the target as the clue, and completes the image retrieval according to the specific target pixel features, or the associated features. The interference elimination is also dominated by the feature contrast, and the method is more mechanical and in the eye. In a large number of environment, the removal process is extremely complex and the retrieval efficiency and accuracy are very low. In order to avoid the above defects, this method uses the local binary (LBP) method to extract the characteristics of the aerial images, and takes the different kinds of features as the basic data of the visual sense of the human eye sense. Through the establishment of human visual sense, the visual sense of human eye is established. An image retrieval method is used to improve the retrieval efficiency and accuracy..4 proposes an image corner feature extraction method based on the SIFT descriptor. This method uses the nearest distance ratio method to obtain the initial matching feature descriptor pair, effectively reducing the effect of the abnormal value. In addition, the joint restriction of the scale direction is used to find the false. Matching the SIFT descriptor pair, and using the random sampling consistency principle to delete abnormal values and improve the registration accuracy of the image corner feature.5 in order to realize real-time edge detection of images, the real-time edge detection system based on embedded multi DSP is studied. A graph image edge detection operation module based on 3 DSP 22 is designed, and a FPGA pair diagram is used. In order to improve the detection efficiency and ensure the real-time performance of the edge detection, an improved universal gravitational edge detection algorithm is adopted to reduce the impact of noise on the detection results and improve the accuracy of the detection of details.
【學位授予單位】：西北大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.41

【參考文獻】

相關(guān)期刊論文 前10條

1 左艷麗;馬志強;左憲禹;;基于小波域的壓縮感知超分辨率重建方法[J];電視技術(shù);2015年09期

2 Yibing Li;Qiang Fu;Fang Ye;Hayaru Shouno;;Dark channel prior based blurred image restoration method using total variation and morphology[J];Journal of Systems Engineering and Electronics;2015年02期

3 丁軍娣;董燕;戶衛(wèi)東;;基于角點特征的圖像檢索新方法[J];計算機應(yīng)用與軟件;2015年03期

4 陳長偉;;基于角點檢測與SIFT算法的快速匹配方法[J];計算機應(yīng)用與軟件;2014年07期

5 馬忠麗;文杰;梁秀梅;陳虹麗;趙新華;;無人艇視覺系統(tǒng)多類水面目標特征提取與識別[J];西安交通大學學報;2014年08期

6 王波;;基于細胞膜優(yōu)化的圖像邊緣檢測算法研究[J];計算機仿真;2013年06期

7 劉丹;胡永健;劉t2貝;;聯(lián)合SIFT特征點和CS-LBP特征描述子的復(fù)制粘貼篡改檢測[J];合肥工業(yè)大學學報(自然科學版);2012年03期

8 趙西安;陳志學;呂京國;靖常峰;;具有尺度與旋轉(zhuǎn)不變性的立體影像自動匹配研究[J];測繪學報;2012年01期

9 周樹道;王敏;劉志華;梁妙元;葉松;;基于多方向小波變換及形態(tài)學重構(gòu)的SAR圖像邊緣檢測[J];解放軍理工大學學報(自然科學版);2011年05期

10 鄭秀清;何坤;周激流;;圖像局部方向均值邊緣檢測算法[J];電子科技大學學報;2011年05期

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