隨著工業4.0、智慧工廠、智慧製造、機械視覺、人工智慧等等先進領域的蓬勃發展，無人且自動化產線成了現今的趨勢，而圖像辨識更是其各個先進領域共同的需求，因此本論文根據生命科學領域中DNA序列的K-mer二維編碼分析方法，提出一套全新的二維圖像K-mer頻率編碼方式，其為一項高適應性且智慧化的『人工智慧影像辨識演算法』。根據2D圖像中形狀特徵的出現機率，建立圖像形貌辨識的分類編碼，並加入各種分類器，建立圖像的特徵數據庫，進而辨識圖像形貌。我們先使用知名的MNIST數字手寫圖像庫進行測試，實驗結果發現，本論文提出的K-mer二維編碼方法，在未使用複雜的類神經網路人工學習架構下，僅採納一套有系統的圖像形貌出現機率之編碼方式便能夠達到約90%的圖像辨識正確率。建立在數字辨識的良好成果後，我們挑戰使用EMNIST英文手寫字庫中的Balanced，隨機挑選不同數量的英文字母數據集進行測試，在兼具時間與精準度的條件下進行參數最佳化，而此最佳化方法本論文提出採用兩階段的多保真度設計優化，搭配基因演算法（GA），利用大數據集與小數據集的來回切換計算精準度，在10000張手寫英文數據集中得到了87%的圖像辨識正確率，更在60000張手寫英文數據集得到了90%的圖像辨識正確率。之後也與目前常用的方法Histogram of Oriented Gradient（HOG）方向梯度特徵和Local binary patterns（LBP）局部二值化直方圖特徵做性能比較，得到精準度比其兩者相對高且速度也相對快的結果。同時也基於不同大小的數據集和Convolutional Neural Networks（CNN）做性能比較，也得到了精準度比其相對高的結果。本論文所提出之方法不受到圖像尺度或其他幾何參數的影響，具有良好的圖像形貌編碼能力。我們相信此具有適應性的圖像K-mer頻率編碼方式，能夠廣泛地被用於許多不同的影像分析及特徵辨識之應用中。

With the flourishing development of advanced fields such as Industry 4.0, Smart Factory, Smart Manufacturing, Machine Vision, Artificial Intelligence etc., unmanned and automation production line has become the current trends. And the technology of pattern recognition plays an important role among these fields. Based on the K-mer frequency analysis method of DNA sequences in the field of life sciences, this paper proposes a new set and highly adaptive of two-dimensional array that K-mer frequency coding methods, and it is an "Artificial Intelligence Image Recognition Algorithm". According to the appearance probability of shape features in 2D images, we establish classification codes for image shape recognition and add it into various classifiers. First, we use the MNIST database of handwritten digits for testing. The experimental results show that the K-mer frequency coding method proposed in this paper only use a set of systematic coding methods with appearance probability of image appearance without a complex neural network-like artificial learning architecture. However, it can reach about 90% of the accuracy of image recognition. With the good results of digital recognition, we challenged EMNIST English handwriting Balanced library. We randomly chose different amount of English alphabet for testing and then optimized the parameter for time and accuracy. In this paper, the method of optimization that we proposed is two-stepped multi-fidelity optimization with genetic algorithm. Switching big and small dataset to calculate the accuracy. It achieved an accuracy of 87% for image recognition with 10,000 images of English alphabet and 90% for 60,000 images. Furthermore, the performance is compared with the ordinary methods, HOG directional gradient feature and LBP feature, and its accuracy is higher than both methods and it is also faster. The performance compared to CNN based on different size of datasets, and it also has obtained relatively good results. The method proposed in this paper is not affected by image scale or other geometric parameters, and has good image shape coding ability. We believe that the adaptive image K-mer frequency encoding method can be widely applied in different kinds of image analysis and feature recognition.

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