Optimization for automated assembly of puzzles

The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The puzzle pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is formulated as an optimization problem where the optimum assembly of the pieces is achieved by maximizing the total affinity measure. An fft based image registration technique is used to speed up the alignment of the pieces. Experimental results are presented on real and artificial data sets

A Comparison on Features Efficiency in Automatic Reconstruction of Archeological Broken Objects

Automatic reconstruction of archeological broken objects is an invaluable tool for restoration purposes and personnel. In this paper, we assume that broken pieces have similar characteristics on their common boundaries, when they are correctly combined. In this paper we work in a framework for the full reconstruction of the original objects using texture and surface design information on the sherd. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. We present a quantitative and qualitative comparison over a large set of features and over a large set of synthetic and real archeological broken objects

Debris Tracking In A Semistable Background

Object Tracking plays a very pivotal role in many computer vision applications such as video surveillance, human gesture recognition and object based video compressions such as MPEG-4. Automatic detection of any moving object and tracking its motion is always an important topic of computer vision and robotic fields. This thesis deals with the problem of detecting the presence of debris or any other unexpected objects in footage obtained during spacecraft launches, and this poses a challenge because of the non-stationary background. When the background is stationary, moving objects can be detected by frame differencing. Therefore there is a need for background stabilization before tracking any moving object in the scene. Here two problems are considered and in both footage from Space shuttle launch is considered with the objective to track any debris falling from the Shuttle. The proposed method registers two consecutive frames using FFT based image registration where the amount of transformation parameters (translation, rotation) is calculated automatically. This information is the next passed to a Kalman filtering stage which produces a mask image that is used to find high intensity areas which are of potential interest

FFT-based estimation of large motions in images: a robust gradient-based approach

A fast and robust gradient-based motion estimation technique which operates in the frequency domain is presented. The algorithm combines the natural advantages of a good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation schemes. Experimentation with real images taken from a popular database showed that, unlike any other Fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors in the range 4-6

FFT-based estimation of large motions in images: a robust gradient-based approach

A fast and robust gradient-based motion estimation technique which operates in the frequency domain is presented. The algorithm combines the natural advantages of a good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation schemes. Experimentation with real images taken from a popular database showed that, unlike any other Fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors in the range 4-6

Towards a multi-device versión of the HYFMGPU Algorithm

Proceedings of the 18th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2018, July 9–14, 2018The task consisting on estimating the translation, rotation and scaling of an image with respect to another take of the same scene obtained at different times, viewpoints and/or lightning conditions is known as image registration. Applications like environmental disasters management or rescue operations depend on real-time hyperspectral images registration, but most of the current FFT-based techniques ignore such performance needs. Ordóñez et al. proposed HYFMGPU [1], a single-GPU algorithm whose performance makes it suitable for real-time use cases. As hyperspectral sensors improve, both the size of images and the wavelength ranges covered are expected to increase, so that a multi-GPU implementation is proposed to satisfy such growing needsThis work has been partially supported by Regional Government of Castilla y León (Spain) and ERDF program of European Union: PROPHET project (JCYL-VA082P17

2x1D Image Registration and Comparison

This paper presents a novel 2x1D phase correlation based image registration method for verification of printer emulator output. The method combines the basic phase correlation technique and a modified 2x1D version of it to achieve both high speed and high accuracy. The proposed method has been implemented and tested using images generated by printer emulators. Over 97% of the image pairs were registered correctly, accurately dealing with diverse images with large translations and image cropping