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

【摘要】：顯著性檢測是一種始于生物學方面,于20世紀90年代被引入計算機領域的圖像和視頻處理方法。根據(jù)人類視覺選擇注意方式,顯著性檢測方法可以分為兩類:一類是純數(shù)據(jù)驅(qū)動,獨立于任務的自底而上的顯著性檢測方法,另一類是受意識支配依賴于任務的自頂而下的顯著性檢測方法。顯著性檢測對于圖像和視頻的自動化處理非常重要,它現(xiàn)在已經(jīng)應用到了圖像分割、圖像自適應壓縮、圖像識別、圖像非真實感繪制等眾多圖像處理研究領域,通過圖像顯著性信息的引導可以更加精準高效地進行圖像處理工作。雖然圖像顯著性檢測技術研究已經(jīng)有了相當不錯的成果,但是視頻顯著性檢測的方法還很有限,隨著圖像和視頻處理智能化發(fā)展趨勢的需求,以及更多領域的使用和普及,顯著性檢測技術還有很大的發(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之間的關系,以蟻群算法為基礎,結(jié)合傳統(tǒng)的顯著性檢測思想和算法,提出了一種基于蟻群優(yōu)化算法的新的顯著性檢測模型,并對圖像和視頻的顯著性檢測方法進行了實驗測試,對比其他的算法進行了性能和優(yōu)缺點評估,同時思考并提出了改進措施或改進方向。本論文中的算法按照檢測目標分為兩類:非壓縮域圖像和視頻的顯著性檢測算法研究與壓縮域視頻的顯著性檢測算法研究。對于非壓縮域圖像和視頻的顯著性檢測算法研究,本論文從不同的特征提取方法入手,對基于不同特征及不同尺度的顯著性檢測結(jié)果進行了分析比較,并從中選出效果最優(yōu)的模型進行實驗測試,與其它經(jīng)典顯著性檢測算法進行性能對比分析;對于壓縮域視頻的顯著性檢測算法研究,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,從運動矢量中直接提取時域特征,對不同的特征進行顯著性檢測,并采用適應人眼視覺系統(tǒng)的融合方法進行結(jié)果融合,獲得最終顯著性圖,同時提出多尺度顯著性檢測算法,同樣與經(jīng)典顯著性檢測算法進行了性能對比分析,驗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.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41

【參考文獻】

相關碩士學位論文 前2條

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

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

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本文編號：1899510