The uniqueness of an individual can be identified according to one’s behavior, qualities, skills, characteristics, social connectivity, knowledge which all resemble to a single term called, “Person...

Personality analysis through handwriting recognition

Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu’s algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu’s method, MAOTSU_2D, and two latest 1D Otsu’s methods (Cao’s method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%.

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Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation

The solar radiation estimation is important parameter of interest in the Photovoltaic system installation, maintenance and performance evaluation. In the present work support vector regression (SVR...

Estimation of solar radiation using support vector regression

In this paper, a model for facial expression recognition (FER) fusing local and global features is proposed. Local features are extracted by dividing the face into multiple regions and carefully se...

Facial expression recognition using feature level fusion

The directional feature is considered suitable for handwriten chinese character recognition,and it has been widely used as one of the main feature extraction method.Meshing method is one of the key factors of meshing direction feature.According to stroke distributing characterisitic and topologic correlation of chinese characters,we present a new method based on elastic mesh and related fuzzy feature.A more stable feature vector with more information is extracted.The experiment based on the handwritten legal amount on Chinese bank check shows that the method is more effective than other meshing direction features,and recognition rate has been up to 97.64%.

A Feature Extracting Method for Handwritten Chinese Character Recognition Based on Elastic Mesh and Fuzzy Feature of Block

An uneven-lighting image binarization technique using support vector machines (SVM) is proposed. High-pass filtering first transforms the grayscale image into a contour image, which categorizes some pixels as edge pixels. The grayscale image is partitioned into numerous blocks. In each block, the edge pixel with largest gradient is designated as the feature edge pixel of the block, and its feature white pixel is then synthesized based on the feature black pixel. The features of a feature pixel can include its coordinates, gray level and even gradient. The trained SVM can accurately binarize the grayscale image even with uneven lighting disturbance.

Efficient uneven-lighting image binarization by support vector machines

This paper proposes a high-accuracy on-line structure-based handwritten Chinese phonetic symbol recognition system. Each reference symbol consists of visible and hidden line segments, where two line segments intersecting each other form an X-shape structure, and those forming a crucial T-shape structure mark a T-shape structure. The first-stage recognition performs the structure-based dynamic programming to match the input handwriting with reference symbols. The second-stage recognition performs the partialmerge dynamic programming to reevaluate the similarity between the input handwriting and the candidate symbols in a confusing set. The third-stage recognition performs the structure-constrained confirmation to determine the best candidate.

On-line structure-based handwritten Chinese phonetic symbol recognition

The research proposes an efficient high-accuracy SVM-based handwritten Chinese character recognition system. The 304-dimensional feature vector of a handwritten Chinese character consists of the co...

Off-line handwritten chinese character recognition by using support vector machines

An efficient global-contour-guided binarization technique is proposed. First, a high-pass convolution processes the gray-level image to get its contour binary image. The gray-level image ...

Cheng-Huang Tung

Papers

Efficient uneven-lighting image binarization by support vector machines

On-line structure-based handwritten Chinese phonetic symbol recognition

Off-line handwritten chinese character recognition by using support vector machines

Efficient global-contour-guided binarization