The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

Template Matching Techniques in Computer Vision: Theory and Practice

Basic concepts in probability are employed to develop analytic formulae for both the expectation (bias) and variance for image motions obtained during subset-based pattern matching. Specifically, the expectation and variance in image motions in the presence of uncorrelated Gaussian intensity noise for each pixel location are obtained by optimising a least squares intensity matching metric. Results for both 1D and 2D image analyses clearly quantify both the bias and the covariance matrix for image motion estimates as a function of: (a) interpolation method, (b) sub-pixel motion, (c) intensity noise, (d) contrast, (e) level of uniaxial normal strain and (f) subset size. For 1D translations, excellent agreement is demonstrated between simulations, theoretical predictions and experimental measurements. The level of agreement confirms that the analytical formulae can be used to provide a priori estimates for the ‘quality’ of local, subset-based measurements achievable with a given pattern. For 1D strain with linear interpolation, theoretical predictions are provided for the expectation and co-variance matrix for the local displacement and strain parameters. For 2D translations with bi-linear interpolation, theoretical predictions are provided for both the expectation and the co-variance matrix for both displacement components. Theoretical results in both cases show that the expectations for the local parameters are biased and a function of: (a) the interpolation difference between the translated and reference images, (b) magnitude of white noise, (c) decimal part of the motion and (d) intensity pattern gradients. For 1D strain, the biases and the covariance matrix for both parameters are directly affected by the strain parameter p1 as the deformed image is stretched by (1 + p1). For 2D rigid body motion case, the covariance matrix for measured motions is shown to have coupling between the motions, demonstrating that the directions of maximum and minimum variability do not generally coincide with the x and y directions.

Quantitative Error Assessment in Pattern Matching: Effects of Intensity Pattern Noise, Interpolation, Strain and Image Contrast on Motion Measurements

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.

Autonomous topological modeling of a home environment and topological localization using a sonar grid map

Previous images matching uses algorithm based on SIFT which has lots of calculating works and low time-efficiency. In this paper, we use SURF algorithm to detect and descript the interest points, and match the interest points by using high time-efficient KD-tree nearest neighbor searching method. The experimental result shows that it has good time efficiency and excellent robustness.

Research of image matching algorithm based on SURF features

[] Model Deformation Measurement is becoming a major topic in wind tunnel testing, especially for commercial applications. Onera has developed a solution which is used in several wind tunnels. The system uses two cameras in stereoscopic arrangement and the model shape is measured with markers. The main steps are: camera calibration, initial shape recognition, alignment of the calibration body reference system on the model reference system, wind tunnel testing and post processing. The wing twist uncertainty is lower than 0.05° which is the main requirement. The system can work at is best in real time conditions and can be easily adapted to each test section.

Model Deformation Measurement (MDM) at Onera

The need for accurate and efficient computation of template matching prevails in many applications. However, a challenge in tem- plate matching is to obtain high accuracy that involves acceptable com- putation complexity and is robust to rotation. A new subpixel template matching approach that combines the parametric template method and the ring projection transform process is proposed. It not only achieves subpixel accuracy in location, but also offers rotation invariance in the subpixel template matching. Furthermore, our approach is conceptually simple, easy to implement, and very efficient because no iterative steps are involved. The simulated results show that our approach enjoys very high precision in the presence of image rotations. Experiments with real- world scenes demonstrate that the proposed method can reach subpixel accuracy for finding the distance between two target objects in the pres- ence of rotations and translations. This indicates that our approach is suitable for accurate on-line template matching with scene rotations and translations. © 2006 Society of Photo-Optical Instrumentation Engineers.

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Yi Hsien Lin

Papers

New method for subpixel image matching with rotation invariance by combining the parametric template method and the ring projection transform process