654 research outputs found
Fast Normalized Cross-Correlation for Template Matching with Rotations
Normalized cross-correlation is the reference approach to carry out template
matching on images. When it is computed in Fourier space, it can handle
efficiently template translations but it cannot do so with template rotations.
Including rotations requires sampling the whole space of rotations, repeating
the computation of the correlation each time.
This article develops an alternative mathematical theory to handle
efficiently, at the same time, rotations and translations. Our proposal has a
reduced computational complexity because it does not require to repeatedly
sample the space of rotations. To do so, we integrate the information relative
to all rotated versions of the template into a unique symmetric tensor template
-which is computed only once per template-. Afterward, we demonstrate that the
correlation between the image to be processed with the independent tensor
components of the tensorial template contains enough information to recover
template instance positions and rotations.
Our proposed method has the potential to speed up conventional template
matching computations by a factor of several magnitude orders for the case of
3D images
Personal Identification Using Ear Recognition
 Biometric authentication for personal identification is very popular now a days. Human ear recognition system is a new technology in this field. The change of appearance with the expression was a major problem in face biometrics but in case of ear biometrics the shape and appearance is fixed. That is why it is advantageous to use it for personal identification. In this paper, we have proposed a new approach for an automated system for human ear identification. Our proposed method consists of three stages. In the first stage, preprocessing of ear image is done for its contrast enhancement and size normalization. In the second stage, features are extracted through Haar wavelets followed by ear identification using fast normalized cross correlation in the third stage. The proposed method is applied on USTB ear image database and IIT Delhi. Experimental results show that our proposed system achieves an average accuracy of 97.2% and 95.2% on these databases respectively
3D RECONSTRUCTION USING MULTI-VIEW IMAGING SYSTEM
This thesis presents a new system that reconstructs the 3D representation of dental casts. To maintain the integrity of the 3D representation, a standard model is built to cover the blind spots that the camera cannot reach. The standard model is obtained by scanning a real human mouth model with a laser scanner. Then the model is simplified by an algorithm which is based on iterative contraction of vertex pairs. The simplified standard model uses a local parametrization method to obtain the curvature information. The system uses a digital camera and a square tube mirror in front of the camera to capture multi-view images. The mirror is made of stainless steel in order to avoid double reflections. The reflected areas of the image are considered as images taken by the virtual cameras. Only one camera calibration is needed since the virtual cameras have the same intrinsic parameters as the real camera. Depth is computed by a simple and accurate geometry based method once the corresponding points are identified. Correspondences are selected using a feature point based stereo matching process, including fast normalized cross-correlation and simulated annealing
Motion Detection in Low Resolution Grayscale Videos Using Fast Normalized Cross Correrelation on GP-GPU
Motion estimation (ME) has been widely used in many computer vision applications, such as object tracking, object detection, pattern recognition and video compression. The most popular block based similarity measures are the sum of absolute differences (SAD), the sum of squared differences (SSD) and the normalized cross correlation (NCC). Similarity measure obtained using NCC is more robust under varying illumination changes as compared to SAD and SSD. However NCC is computationally expensive and application of NCC using full or exhaustive search method further increases required computational time. Relatively efficient way of calculating the NCC is to pre-compute sum-tables to perform the normalization referred to as fast NCC (FCC). In this paper we propose real time implementation of full search FCC algorithm applied to gray scale videos using NVIDIA’s Compute Unified Device Architecture (CUDA). We present fine-grained optimization techniques for fully exploiting computational capacity of CUDA. Novel parallelization strategies adopted for extracting data parallelism substantially reduce computational time of exhaustive FCC. We show that by efficient utilization of global, shared and texture memories available on CUDA, we can obtain the speedup of the order of 10x as compared to the sequential implementation of FCC
Dual-fisheye lens stitching for 360-degree imaging
Dual-fisheye lens cameras have been increasingly used for 360-degree
immersive imaging. However, the limited overlapping field of views and
misalignment between the two lenses give rise to visible discontinuities in the
stitching boundaries. This paper introduces a novel method for dual-fisheye
camera stitching that adaptively minimizes the discontinuities in the
overlapping regions to generate full spherical 360-degree images. Results show
that this approach can produce good quality stitched images for Samsung Gear
360 -- a dual-fisheye camera, even with hard-to-stitch objects in the stitching
borders.Comment: ICASSP 17 preprint, Proc. of the 42nd IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, March
201
General Defocusing Particle Tracking: fundamentals and uncertainty assessment
General Defocusing Particle Tracking (GDPT) is a single-camera,
three-dimensional particle tracking method that determines the particle depth
positions from the defocusing patterns of the corresponding particle images.
GDPT relies on a reference set of experimental particle images which is used to
predict the depth position of measured particle images of similar shape. While
several implementations of the method are possible, its accuracy is ultimately
limited by some intrinsic properties of the acquired data, such as the
signal-to-noise ratio, the particle concentration, as well as the
characteristics of the defocusing patterns. GDPT has been applied in different
fields by different research groups, however, a deeper description and analysis
of the method fundamentals has hitherto not been available. In this work, we
first identity the fundamental elements that characterize a GDPT measurement.
Afterwards, we present a standardized framework based on synthetic images to
assess the performance of GDPT implementations in terms of measurement
uncertainty and relative number of measured particles. Finally, we provide
guidelines to assess the uncertainty of experimental GDPT measurements, where
true values are not accessible and additional image aberrations can lead to
bias errors. The data were processed using DefocusTracker, an open-source GDPT
software. The datasets were created using the synthetic image generator
MicroSIG and have been shared in a freely-accessible repository
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