Deformation Models for Image Recognition

Keysers, Daniel; Deselaers, Thomas; Gollan, Christian; Ney, Hermann

doi:10.1109/tpami.2007.1153

Cited by 213 publications

(182 citation statements)

References 42 publications

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“…Keysers et al [5] presented several deformation models for image classification, and the Image Distortion Model (IDM) represented the best compromise between computational complexity and evaluation accuracy. For this reason, we chose the IDM to perform a matching between two images.…”

Section: Image-matching Model (Idm)mentioning

confidence: 99%

“…The IDM is a zero-order model of image variability [5]. This model uses a mapping function with absolute constraints; hence, it is computationally much simpler than a 2-dimensional warping.…”

Section: Image-matching Model (Idm)mentioning

confidence: 99%

“…Starting from the IDM-distance algorithm presented in [5], we proposed the Binary IDM (BIDM) evaluation algorithm shown in Fig. 1.…”

Section: The Evaluation Algorithm (Bidm)mentioning

confidence: 99%

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An Image-Based Measure for Evaluation of Mathematical Expression Recognition

Alvaro

Sánchez

Benedí

2013

Pattern Recognition and Image Analysis

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Abstract. Mathematical expression recognition is an active research field that is related to document image analysis and typesetting. In this study, we present a novel global performance evaluation measure for mathematical expression recognition based on image matching. Using an image representation for evaluation tries to overcome the representation ambiguity as human beings do. The results of a recent competition were used to perform several experiments in order to analyze the benefits and drawbacks of this measure.

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Section: Image-matching Model (Idm)mentioning

confidence: 99%

Section: Image-matching Model (Idm)mentioning

confidence: 99%

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An Image-Based Measure for Evaluation of Mathematical Expression Recognition

Alvaro

Sánchez

Benedí

2013

Pattern Recognition and Image Analysis

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“…We call this approach zero-order warping (ZOW), which has been introduced e.g. as Image Distortion Model in [17]. Approximative Energy Minimisation.…”

Section: Problem Statementmentioning

confidence: 99%

Warp that smile on your face: Optimal and smooth deformations for face recognition

Gass

Pishchulin

Dreuw

et al. 2011

Face and Gesture 2011

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Abstract-In this work, we present novel warping algorithms for full 2D pixel-grid deformations for face recognition.Due to high variation in face appearance, face recognition is considered a very difficult task, especially if only a single reference image, for example a mug-shot, per face is available.Usually model-based approaches with additional training data are used to cope with several types of variation occurring in facial imaging. Image warping contrarily yields a distance measure which is invariant with regard to several types of variation. This allows for precise recognition even using only very few reference observations. Due to the computationally complex problem of optimal 2D warping, pseudo-2D warpingbased approaches in the past represented strong approximations of the original problem, and were mainly successful on data with low variability or rectified images.We propose a novel 2D warping method which is globally optimal and makes no prior assumtions on the data variability besides two-dimensional smootheness constraints which both avoid local mirroring and gaps and significantly speed up the optimization. Furthermore, we show that occlusion handling is imperative to obtain smooth warpings in a variety of domains.We evaluate our novel algorithm on various well known databases, such as the AR-Face and CMU-PIE database, and provide a detailed comparison to existing warping approaches. We show that by using simple relative 2D constraints, strong local features and a kernel, which is robust w.r.t. occlusions, our computationally complex approaches outperform state-ofthe-art results for recognizing faces under varying expressions, occlusions and poses. Most interestingly, we achieve higher accuracy using fewer training instances per class compared to methods learning a model of the 3D shape.

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“…In general, pattern recognition schemes can directly handle the samples which are represented in a vector space. In most neural networks system, such as character recognition [20] and traffic signs recognition [21], the samples can be easily converted into feature vectors after normalizing the size of the input images.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Triaxial Accelerometer-Based Energy Efficient Human Activity Recognition Using Smartphone

2017

IJAERD

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Deformation Models for Image Recognition

Cited by 213 publications

References 42 publications

An Image-Based Measure for Evaluation of Mathematical Expression Recognition

An Image-Based Measure for Evaluation of Mathematical Expression Recognition

Warp that smile on your face: Optimal and smooth deformations for face recognition

Real-Time Triaxial Accelerometer-Based Energy Efficient Human Activity Recognition Using Smartphone

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