本文選題：時(shí)頻復(fù)合加權(quán) + 抗噪柱狀圖�。� 參考：《西北大學(xué)》2016年博士論文

【摘要】：計(jì)算機(jī)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(jì)算和分析方法對這類圖像數(shù)據(jù)進(jìn)行分析處理時(shí)往往計(jì)算代價(jià)過高,甚至?xí)耆�。通常在對高維復(fù)雜圖像數(shù)據(jù)進(jìn)行分析和處理之前,需要對樣本數(shù)據(jù)集進(jìn)行特征提取操作,提取與樣本相關(guān)性強(qiáng)的特征點(diǎn),并且要去除噪聲特征點(diǎn)和與樣本數(shù)據(jù)集不相關(guān)的冗余特征點(diǎn),以便為后續(xù)的處理工作提供高價(jià)值的數(shù)據(jù)。通常圖像特征提取方法所獲取的特征點(diǎn),在表征圖像時(shí)存在優(yōu)劣之分,同時(shí)特征點(diǎn)的優(yōu)劣直接影響著實(shí)際應(yīng)用中圖像處理的結(jié)果。圖像特征提取是進(jìn)行圖像配準(zhǔn)、圖像目標(biāo)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(dá)能力強(qiáng)、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(diǎn)和熱點(diǎn)之一。本文在對常用的特征提取方法進(jìn)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(jìn)行了深入研究,提出了一系列改進(jìn)算法,以提高特征提取的有效性和實(shí)用性。具體內(nèi)容如下：1提出了一種約束優(yōu)化進(jìn)化的夜間圖像時(shí)頻復(fù)合特征提取方法。對多幀夜間模糊圖像的時(shí)域與頻域同時(shí)加權(quán)處理,是實(shí)現(xiàn)深度提取夜間模糊圖像特征的先進(jìn)方法。傳統(tǒng)的夜間圖像特征提取方法大多只單獨(dú)提取時(shí)域特征,沒有對多幀夜間低質(zhì)量圖像的頻譜特性進(jìn)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(shí)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(yùn)用約束優(yōu)化進(jìn)化算法對圖像特征提取的結(jié)果不斷進(jìn)行循環(huán)優(yōu)化,最終達(dá)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(jìn)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(zhǔn)確率降低。為了進(jìn)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(jìn)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(jī)變量的原點(diǎn)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(biāo)的航空圖像準(zhǔn)確檢索的問題。由于航空圖像特定區(qū)域中的目標(biāo)呈隨機(jī)性分布,干擾目標(biāo)與識別目標(biāo)交錯(cuò)分布。傳統(tǒng)的圖像檢索算法的思路都是以目標(biāo)為線索,根據(jù)特定的目標(biāo)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(jī)械,在目標(biāo)眾多環(huán)境下,排除過程極其復(fù)雜,檢索效率與準(zhǔn)確性都很低。為了避免上述缺陷,本文方法利用局部二進(jìn)制(LBP)方法進(jìn)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(jìn)行航空圖像檢索,提高了檢索效率和準(zhǔn)確性。4提出了一種基于SIFT描述符的圖像角點(diǎn)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(jī)采樣一致性原則刪除異常值,提高圖像角點(diǎn)特征配準(zhǔn)精度。5為實(shí)現(xiàn)對圖像的實(shí)時(shí)邊緣檢測,研究了基于嵌入式多DSP的實(shí)時(shí)邊緣檢測系統(tǒng)。設(shè)計(jì)了基于3個(gè)DSP兩兩相連的圖像邊緣檢測運(yùn)算模塊,并通過FPGA對圖像進(jìn)行預(yù)處理來提高檢測效率,保證了邊緣檢測的實(shí)時(shí)性。采用改進(jìn)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(xì)節(jié)信息的檢測準(zhǔn)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(xué)位授予單位】：西北大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

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