We present the application of different nonlinear image deformation models to the task of image recognition The deformation models are especially suited for local changes as they often occur in the presence of image object variability We show that, among the discussed models, there is one approach that combines simplicity of implementation, low-computational complexity, and highly competitive performance across various real-world image recognition tasks We show experimentally that the model performs very well for four different handwritten digit recognition tasks and for the classification of medical images, thus showing high generalization capacity In particular, an error rate of 054 percent on the MNIST benchmark is achieved, as well as the lowest reported error rate, specifically 126 percent, in the 2005 international ImageCLEF evaluation of medical image specifically categorization

/pdf/deformation-models-for-image-recognition-4y6w31onnf.pdf

Deformation Models for Image Recognition

https://www.cs.wustl.edu/~taoju/research/jnm_volrecon.pdf

3D volume reconstruction of a mouse brain from histological sections using warp filtering

This paper presents a survey of elastic matching (EM) techniques employed in handwritten character recognition. EM is often called deformable template, flexible matching, or nonlinear template matching, and defined as the optimization problem of two-dimensional warping (2DW) which specifies the pixel-to-pixel correspondence between two subjected character image patterns. The pattern distance evaluated under optimized 2DW is invariant to a certain range of geometric deformations. Thus, by using the EM distance as a discriminant function, recognition systems robust to the deformations of handwritten characters can be realized. In this paper, EM techniques are classified according to the type of 2DW and the properties of each class are outlined. Several topics around EM, such as the category-dependent deformation tendency of handwritten characters, are also discussed.

/pdf/a-survey-of-elastic-matching-techniques-for-handwritten-4e3ckj40fk.pdf

A Survey of Elastic Matching Techniques for Handwritten Character Recognition

http://www-i6.informatik.rwth-aachen.de/PostScript/InterneArbeiten/Keysers_Elastic_Image_Matching_is_NP_complete_Pattern_Recognition_Letters_2003.pdf

Elastic image matching is NP-complete

This paper proposes a new method for speaker feature extraction based on Formants, Wavelet Entropy and Neural Networks denoted as FWENN.In the first stage, five formants and seven Shannon entropy wavelet packets are extracted from the speakers' signals as the speaker feature vector.In the second stage, these 12 feature extraction coefficients are used as inputs to feed-forward neural networks.In contrast to conventional speaker identification methods that extract features from sentences (or words), the proposed method extracts the features from vowels.Advantages of using vowels include the ability to identify speakers when only partially-recorded words are available. This may be useful for deaf-mute persons. This paper proposes a new method for speaker feature extraction based on Formants, Wavelet Entropy and Neural Networks denoted as FWENN. In the first stage, five formants and seven Shannon entropy wavelet packet are extracted from the speakers' signals as the speaker feature vector. In the second stage, these 12 feature extraction coefficients are used as inputs to feed-forward neural networks. Probabilistic neural network is also proposed for comparison. In contrast to conventional speaker recognition methods that extract features from sentences (or words), the proposed method extracts the features from vowels. Advantages of using vowels include the ability to recognize speakers when only partially-recorded words are available. This may be useful for deaf-mute persons or when the recordings are damaged. Experimental results show that the proposed method succeeds in the speaker verification and identification tasks with high classification rate. This is accomplished with minimum amount of information, using only 12 coefficient features (i.e. vector length) and only one vowel signal, which is the major contribution of this work. The results are further compared to well-known classical algorithms for speaker recognition and are found to be superior.

Speaker identification using vowels features through a combined method of formants, wavelets, and neural network classifiers

The effectiveness of piecewise linear 2D warping, a dynamic programming-based elastic image matching technique, in handwritten character recognition is investigated. The technique presented is capable of providing compensation for most variations in character patterns with tractable computation. The superiority of the present technique over several conventional 2D warping techniques in variation compensation is experimentally justified. Another comparison with monotonic and continuous 2D warping, a more flexible matching technique, reveals that the method presented takes far less computation than the latter, yet provides almost the same recognition accuracy for most categories.

/pdf/handwritten-character-recognition-using-piecewise-linear-two-1a2xz24dv5.pdf

Handwritten character recognition using piecewise linear two-dimensional warping

Deformations in handwritten characters have class-dependent tendencies. For example, characters of class "A" are often deformed by global slant transformation and never deformed to be similar to "R". In this paper, the extraction and the utilization of such tendencies called eigen-deformations are investigated for better performance of elastic matching based recognition systems. The eigen-deformations are extracted by the principal component analysis of actual deformations automatically collected by elastic matching. From experimental results it was shown that the extracted eigen-deformations represent typical deformations of each class. It was also shown that the recognition performance can be improved significantly by using the eigen-deformations in detecting overfitting, which often results in misrecognition.

/pdf/using-eigen-deformations-in-handwritten-character-37nttqi68r.pdf

M.A. Ronee

Papers

Handwritten character recognition using piecewise linear two-dimensional warping

Using eigen-deformations in handwritten character recognition

Handwritten Character Recognition Using Piecewise Linear Two-Dimensional Warping