Research on distortion-tolerant, controllable, parametric image matching

研究成果の概要 (和文) : １．2枚の濃淡画像間で正規化相互相関値を最大化する最適な2次元射影変換（独立な8パラメータを含む）を決定するパラメトリックな変形耐性画像マッチング法を提案し、GPT相関法と命名した。２．GPT相関法とk近傍法を組合わせ、公開手書き数字データベースMNISTの認識実験を行った結果、世界最高性能のエラー率0.30%を達成した。３．上記２．の実験で用いたC言語プログラムのソースコードをWeb上で公開した。４．GPT相関法の定式化を厳密化し、さらに計算モデルも改良することで、最適解への収束性を大きく向上した。これを強化されたGPT相関法と命名した。研究成果の概要 (英文) : 1. A new technique of 2D projection transformation (PT) invariant template matching, GPT (Global Projection Transformation) correlation, was proposed. The GPT correlation method determines optimal eight parameters of PT that maximize a normalized cross-correlation value between an input image and the PT-superimposed template. 2. Recognition experiments made on the well-known MNIST handwritten digit database via a combination of the GPT correlation and k-NN techniques achieved the lowest error rate of 0.30% ever reported for k-NN based classification. 3. The programs in C language with source codes used in the above-mentioned experiments were published on the Web. 4. The GPT correlation method was greatly enhanced to stabilize and accelerate convergence to the optimal solution of eight parameters via strict formalization of the objective function and refinement of its computational model

Recognition of Color Characters in Scene Images via Optimal Binarization and Distortion-tolerant Image Matching

In this paper, we propose the method of recognition of color characters in scene images. In this method, we apply two key ideas to recognition. The first idea is optimal binarization, which separates the image into the character region and the background region. We use K-means clustering in Lab color space to generate multiple segmented images. After generation, these segmented images are classified to correctly binarized character image or not, by the convolutional neural network. All of the segmented images are classified, one of binarized image is selected as the optimal binarized character image among them. The second idea is distortion-tolerant image matching. Distortion indicates rotation, expansion, shearing, and translation, expressed by an affine transformation. In this matching, the correlation value is calculated between the target image and the template images first. Then, the optimal affine transformation is applied to the template images, to make a higher correlation value. These steps are repeated while the maximum correlation values are gained. Finally, we select k samples which gained higher correlation values and classify the target image using k-NN classification method. Experimental results made on the public color character image dataset “the Chars74K” show that the proposed method achieves 96.6% of correctly binarized character image selection rates and 73.9% of character recognition rates