本文選題：蟻群算法 + 人眼視覺系統(tǒng)��； 參考：《北京郵電大學(xué)》2016年碩士論文

【摘要】：顯著性檢測(cè)是一種始于生物學(xué)方面,于20世紀(jì)90年代被引入計(jì)算機(jī)領(lǐng)域的圖像和視頻處理方法。根據(jù)人類視覺選擇注意方式,顯著性檢測(cè)方法可以分為兩類:一類是純數(shù)據(jù)驅(qū)動(dòng),獨(dú)立于任務(wù)的自底而上的顯著性檢測(cè)方法,另一類是受意識(shí)支配依賴于任務(wù)的自頂而下的顯著性檢測(cè)方法。顯著性檢測(cè)對(duì)于圖像和視頻的自動(dòng)化處理非常重要,它現(xiàn)在已經(jīng)應(yīng)用到了圖像分割、圖像自適應(yīng)壓縮、圖像識(shí)別、圖像非真實(shí)感繪制等眾多圖像處理研究領(lǐng)域,通過(guò)圖像顯著性信息的引導(dǎo)可以更加精準(zhǔn)高效地進(jìn)行圖像處理工作。雖然圖像顯著性檢測(cè)技術(shù)研究已經(jīng)有了相當(dāng)不錯(cuò)的成果,但是視頻顯著性檢測(cè)的方法還很有限,隨著圖像和視頻處理智能化發(fā)展趨勢(shì)的需求,以及更多領(lǐng)域的使用和普及,顯著性檢測(cè)技術(shù)還有很大的發(fā)展前景本論文考慮了蟻群優(yōu)化算法這一基于螞蟻覓食的生物行為啟發(fā)的算法和視覺顯著性檢測(cè)之間的關(guān)系,以蟻群算法為基礎(chǔ),結(jié)合傳統(tǒng)的顯著性檢測(cè)思想和算法,提出了一種基于蟻群優(yōu)化算法的新的顯著性檢測(cè)模型,并對(duì)圖像和視頻的顯著性檢測(cè)方法進(jìn)行了實(shí)驗(yàn)測(cè)試,對(duì)比其他的算法進(jìn)行了性能和優(yōu)缺點(diǎn)評(píng)估,同時(shí)思考并提出了改進(jìn)措施或改進(jìn)方向。本論文中的算法按照檢測(cè)目標(biāo)分為兩類:非壓縮域圖像和視頻的顯著性檢測(cè)算法研究與壓縮域視頻的顯著性檢測(cè)算法研究。對(duì)于非壓縮域圖像和視頻的顯著性檢測(cè)算法研究,本論文從不同的特征提取方法入手,對(duì)基于不同特征及不同尺度的顯著性檢測(cè)結(jié)果進(jìn)行了分析比較,并從中選出效果最優(yōu)的模型進(jìn)行實(shí)驗(yàn)測(cè)試,與其它經(jīng)典顯著性檢測(cè)算法進(jìn)行性能對(duì)比分析;對(duì)于壓縮域視頻的顯著性檢測(cè)算法研究,本文采用直接從視頻壓縮碼流中提取特征的方法,從殘差變換系數(shù)中提取亮度、色度以及紋理等空域特征,從運(yùn)動(dòng)矢量中直接提取時(shí)域特征,對(duì)不同的特征進(jìn)行顯著性檢測(cè),并采用適應(yīng)人眼視覺系統(tǒng)的融合方法進(jìn)行結(jié)果融合,獲得最終顯著性圖,同時(shí)提出多尺度顯著性檢測(cè)算法,同樣與經(jīng)典顯著性檢測(cè)算法進(jìn)行了性能對(duì)比分析,驗(yàn)證了本論文算法的有效性及可靠性。
[Abstract]:Salience detection is a kind of image and video processing method which started in biology and was introduced into computer field in 1990s. According to the human visual choice of attention, salience detection methods can be divided into two categories: one is purely data-driven, a bottom-up, mission-independent salience detection method. The other is the top-down salience detection method, which is controlled by consciousness. Salience detection is very important for the automatic processing of image and video. It has been applied to many research fields of image processing, such as image segmentation, image adaptive compression, image recognition, image non-realistic rendering and so on. Image processing can be carried out more accurately and efficiently through the guidance of image saliency information. Although the research of image salience detection technology has made quite good achievements, the methods of video salience detection are still very limited. With the development trend of intelligent image and video processing, and the use and popularization of more fields, There is still a great prospect for significance detection. This paper considers the relationship between ant colony optimization, a biological behavior heuristic algorithm based on ant foraging, and visual salience detection, which is based on ant colony algorithm. In this paper, a new salience detection model based on ant colony optimization algorithm is proposed, and the salience detection method of image and video is tested experimentally by combining the traditional salience detection idea and algorithm. Compared with other algorithms, the performance, advantages and disadvantages are evaluated, and the improvement measures or directions are proposed. The algorithms in this paper are divided into two categories according to the detection target: the significance detection algorithm of image and video in uncompressed domain and the salience detection algorithm in compressed video domain. In this paper, we analyze and compare the salience detection results based on different features and scales by using different feature extraction methods to study the salience detection algorithms in uncompressed domain images and video. And select the best model for experimental test, and compare the performance with other classical salience detection algorithms. For compressed video salience detection algorithm research, This paper adopts the method of extracting features directly from video compression bitstream, extracts spatial features such as brightness, chromaticity and texture from residual transform coefficients, extracts temporal features from motion vectors, and detects the salience of different features. The fusion method adapted to human visual system is used to fuse the results and obtain the final significance graph. At the same time, a multi-scale salience detection algorithm is proposed, and the performance of the algorithm is compared with that of the classical salience detection algorithm. The validity and reliability of the algorithm are verified.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 李勇;基于區(qū)域?qū)Ρ榷鹊囊曈X顯著性檢測(cè)算法研究[D];上海交通大學(xué);2013年

2 仇媛媛;基于視覺顯著性的物體檢測(cè)方法研究[D];上海交通大學(xué);2013年

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本文編號(hào)：1899510