頻域內基于權重系數的木材圖像增強及識別
發(fā)布時間：2018-11-13 09:54
【摘要】：在木材行業(yè)中,最常用的識別木材的方法以視覺觀測為主,但是利用計算機進行識別更為準確。在圖像采集過程中由于環(huán)境以及采集設備的限制因素,導致人們所采集到的圖像并不理想,因此會使得提取的木材中的紋理信息丟失,這樣就會導致在利用圖像進行木材圖像的識別時出現誤差。本文現有的圖像增強算法的基礎上,提出了基于權重系數的木材圖像增強算法,分析了低高頻、高低頻和高高頻等波段的小波變換的性能,從而有針對性的消除圖像中的混淆部分,提高圖像質量。論文研究的主要內容包括:1.本文重點介紹了直方圖均衡化算法、直方圖規(guī)定化算法、低通濾波算法、高通濾波算法和小波變換算法這五種傳統的圖像增強算法理論。2.在現有的圖像增強算法的基礎上,提出了基于權重系數的木材圖像增強算法,此方法主要是對圖像中混淆備份的定向篩選,即將整幅圖像分為LL、LH、HL和HH四個不同的波段,對LL子帶進行歸一化處理。并利用計算權重系數的方法獲得相似模塊,同時結合自適應濾波模塊進行混淆檢測;對LH、HL和HH子帶采用局部方差法進行混淆檢測。最后將其利用方向自適應小波收縮進行混淆消除,通過小波逆變換完成混淆消除恢復圖像。3.將基于權重系數的木材圖像增強算法與直方圖均衡化算法、直方圖規(guī)定化算法、低通濾波算法、高通濾波算法等方法進行實驗對比,并對所得實驗結果進行主觀和客觀分析。4.將基于權重系數的圖像增強算法應用到木材圖像識別中,以榆木和榆木樹皮作為識別試樣,分別用基于權重系數的木材圖像增強算法和傳統的圖像增強對榆木木片和榆木樹皮圖像進行預處理,并用BP神經網絡對圖像進行識別,且對其識別結果進行分析和比較。
[Abstract]:In the timber industry, the most commonly used method of wood identification is visual observation, but it is more accurate to identify wood by computer. In the process of image acquisition, because of the limited factors of environment and acquisition equipment, the image collected by people is not ideal, so the texture information of extracted wood will be lost. This will lead to errors in the recognition of wood images using images. Based on the existing image enhancement algorithms, a wood image enhancement algorithm based on weight coefficient is proposed, and the performance of wavelet transform in low high frequency, high and low frequency and high frequency bands is analyzed. Thus the obfuscation part of the image is eliminated and the image quality is improved. The main contents of this paper are as follows: 1. This paper focuses on five traditional image enhancement algorithms: histogram equalization algorithm, histogram specification algorithm, low-pass filtering algorithm, high-pass filtering algorithm and wavelet transform algorithm. 2. On the basis of existing image enhancement algorithms, a wood image enhancement algorithm based on weight coefficient is proposed. This method is mainly used for directional filtering of obfuscation backups in images, that is to say, the whole image is divided into four different bands: LL,LH,HL and HH. The LL subbands are normalized. The similarity module is obtained by calculating the weight coefficient, and the aliasing detection is carried out by combining the adaptive filtering module, and the local variance method is used to detect the LH,HL and HH subbands. Finally, it uses directional adaptive wavelet shrinkage to eliminate confusion, and accomplishes the obfuscation elimination and restoration image by inverse wavelet transform. The wood image enhancement algorithm based on weight coefficient is compared with histogram equalization algorithm, histogram specification algorithm, low-pass filter algorithm, high-pass filtering algorithm and so on. The experimental results are analyzed subjectively and objectively. 4. The image enhancement algorithm based on weight coefficient is applied to wood image recognition. Elm and elm bark are used as the recognition samples. The wood image enhancement algorithm based on weight coefficient and the traditional image enhancement algorithm are used to preprocess the elm and elm bark images, and the BP neural network is used to identify the images, and the recognition results are analyzed and compared.
【學位授予單位】：內蒙古農業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
本文編號：2328754
[Abstract]:In the timber industry, the most commonly used method of wood identification is visual observation, but it is more accurate to identify wood by computer. In the process of image acquisition, because of the limited factors of environment and acquisition equipment, the image collected by people is not ideal, so the texture information of extracted wood will be lost. This will lead to errors in the recognition of wood images using images. Based on the existing image enhancement algorithms, a wood image enhancement algorithm based on weight coefficient is proposed, and the performance of wavelet transform in low high frequency, high and low frequency and high frequency bands is analyzed. Thus the obfuscation part of the image is eliminated and the image quality is improved. The main contents of this paper are as follows: 1. This paper focuses on five traditional image enhancement algorithms: histogram equalization algorithm, histogram specification algorithm, low-pass filtering algorithm, high-pass filtering algorithm and wavelet transform algorithm. 2. On the basis of existing image enhancement algorithms, a wood image enhancement algorithm based on weight coefficient is proposed. This method is mainly used for directional filtering of obfuscation backups in images, that is to say, the whole image is divided into four different bands: LL,LH,HL and HH. The LL subbands are normalized. The similarity module is obtained by calculating the weight coefficient, and the aliasing detection is carried out by combining the adaptive filtering module, and the local variance method is used to detect the LH,HL and HH subbands. Finally, it uses directional adaptive wavelet shrinkage to eliminate confusion, and accomplishes the obfuscation elimination and restoration image by inverse wavelet transform. The wood image enhancement algorithm based on weight coefficient is compared with histogram equalization algorithm, histogram specification algorithm, low-pass filter algorithm, high-pass filtering algorithm and so on. The experimental results are analyzed subjectively and objectively. 4. The image enhancement algorithm based on weight coefficient is applied to wood image recognition. Elm and elm bark are used as the recognition samples. The wood image enhancement algorithm based on weight coefficient and the traditional image enhancement algorithm are used to preprocess the elm and elm bark images, and the BP neural network is used to identify the images, and the recognition results are analyzed and compared.
【學位授予單位】：內蒙古農業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
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