Real-time Mosaicking Research Articles

Real-time mosaicking for infrared videos from an oblique sweeping camera

Image mosaicking is widely used in Geographic Information Systems (GISs) for large-scale ground surface analysis. However, most existing mosaicking methods can only be used in offline processing due to the enormous amounts of computation. In this paper, we propose a novel and practical algorithm for real-time infrared video mosaicking. To achieve this, a novel fast template matching algorithm based on Sum of Cosine Differences (SCD) is proposed to coarsely match the sequential images. The high speed of the proposed template matching algorithm is obtained by computing correlation with Fast Fourier Transform (FFT). We also propose a novel fast Least Squares Matching (LSM) algorithm for inter-frame fine registration, which can significantly reduce the computation without degrading the matching accuracy. In addition, the proposed fast LSM can effectively adapt for noise degradation and geometric distortion. Based on the proposed fast template matching algorithm and fine registration algorithm, we develop a practical real-time mosaicking approach which can produce seamless mosaic image highly efficiently. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithm is not just computationally efficient but also robust against various noise distortions.

Real-time video mosaicking to guide handheld in vivo microscopy.

Handheld and endoscopic optical-sectioning microscopes are being developed for noninvasive screening and intraoperative consultation. Imaging a large extent of tissue is often desired, but miniature in vivo microscopes tend to suffer from limited fields of view. To extend the imaging field during clinical use, we have developed a real-time video mosaicking method, which allows users to efficiently survey larger areas of tissue. Here, we modified a previous post-processing mosaicking method so that real-time mosaicking is possible at >30 frames/second when using a device that outputs images that are 400 × 400 pixels in size. Unlike other real-time mosaicking methods, our strategy can accommodate image rotations and deformations that often occur during clinical use of a handheld microscope. We perform a feasibility study to demonstrate that the use of real-time mosaicking is necessary to enable efficient sampling of a desired imaging field when using a handheld dual-axis confocal microscope.

BlockRFC: Real-Time Rolled Fingerprint Construction and Distortion Rectification

Compared with a flat fingerprint, the rolled fingerprint has a larger fingerprint area and can be extracted more minutiae. It has high requirements in many fields, not only in the military environment or the police field but also in many civil application fields. The challenge that has been troubled for a long time is that contact-based rolled fingerprint registration is easy to cause obvious distortion without human experts’ supervision, which has a negative impact on fingerprint recognition performance. Due to the elastic deformation of fingertips, the mosaicking gaps in the rolled fingerprint are usually visible but challenging to locate. To address these problems, we propose a novel rolled fingerprint construction algorithm called BlockRFC ( Block -based R olled F ingerprint C onstruction) in this article. BlockRFC’s core idea is to use the fingerprint image block as a processing unit for mosaicking and distortion rectification. BlockRFC is based on a real-time mosaicking framework, which makes it possible to construct a rolled fingerprint while collecting fingerprint images. One distinctive advantage of BlockRFC is that it does not require minutiae or ridge information, but fully utilizes the gray-scale information and foreground area in the fingerprint image block. In this article, we first propose a metric called Mosaicking Gap Rate (MGR), which can effectively quantify the mosaicking gaps in the rolled fingerprints. Experimental results show that the proposed method can not only effectively locate and eliminate the mosaicking gaps but also have better recognition accuracy than previous methods.

Multichannel correlation improves the noise tolerance of real-time hyperspectral microimage mosaicking.

.Live-subject microscopies, including microendoscopy and other related technologies, offer promise for basic biology research as well as the optical biopsy of disease in the clinic. However, cellular resolution generally comes with the trade-off of a microscopic field-of-view. Microimage mosaicking enables stitching many small scenes together to aid visualization, quantitative interpretation, and mapping of microscale features, for example, to guide surgical intervention. The development of hyperspectral and multispectral systems for biomedical applications provides motivation for adapting mosaicking algorithms to process a number of simultaneous spectral channels. We present an algorithm that mosaics multichannel video by correlating channels of consecutive frames as a basis for efficiently calculating image alignments. We characterize the noise tolerance of the algorithm by using simulated video with known ground-truth alignments to quantify mosaicking accuracy and speed, showing that multiplexed molecular imaging enhances mosaic accuracy by leveraging observations of distinct molecular constituents to inform frame alignment. A simple mathematical model is introduced to characterize the noise suppression provided by a given group of spectral channels, thus predicting the performance of selected subsets of data channels in order to balance mosaic computation accuracy and speed. The characteristic noise tolerance of a given number of channels is shown to improve through selection of an optimal subset of channels that maximizes this model. We also demonstrate that the multichannel algorithm produces higher quality mosaics than the analogous single-channel methods in an empirical test case. To compensate for the increased data rate of hyperspectral video compared to single-channel systems, we employ parallel processing via GPUs to alleviate computational bottlenecks and to achieve real-time mosaicking even for video-rate multichannel systems anticipated in the future. This implementation paves the way for real-time multichannel mosaicking to accompany next-generation hyperspectral and multispectral video microscopy.

Economical and compact briefcase spectral-domain optical coherence tomography system for primary care and point-of-care applications.

Development of low-cost and portable optical coherence tomography (OCT) systems is of global interest in the OCT research community. Such systems enable utility broadly throughout a clinical facility, or in remote areas that often lack clinical infrastructure. We report the development and validation of a low-cost, portable briefcase spectral-domain optical coherence tomography (SD-OCT) system for point-of-care diagnostics in primary care centers and/or in remote settings. The self-contained briefcase OCT contains all associated optical hardware, including light source, spectrometer, hand-held probe, and a laptop. Additionally, this system utilizes unique real-time mosaicking of surface video images that are synchronized with rapid A-scan acquisition to eliminate the need for lateral scanning hardware, and enable the construction of cross-sectional B-mode images over extended lateral distances. The entire briefcase system weighs 9kg and costs ∼USD$8000 using off-the-shelf components. System performance was validated by acquiring images of in vivo human skin on the fingertip, palm, and nail fold. The efficiency, portability, and low-cost enable accessibility and utility in primary care centers and low-resource settings.

画幅扫描红外成像实时拼接中的光束法平差

画幅扫描红外成像实时拼接中的光束法平差

画幅扫描红外成像实时拼接中的光束法平差

画幅扫描红外成像实时拼接中的光束法平差

A fast mosaic method for airborne images: the new Template-Convolution Speed-Up Robust Features (TSURF) algorithm

In this paper, we present a new feature extraction algorithm termed Template-Convolution Speed-Up Robust Features (TSURF), which uses template convolution to extract points of interest based on the Speed-Up Robust Features (SURF) algorithm. Feature extraction is applied to extract corresponding characteristics in overlapping fields from adjacent images. The characteristics include area, line, and point features. As the point feature is easy to compute and is invariant to image scale, rotation, intensity, and so on, we use it to register and mosaic images widely. By filtering redundant points of interest, TSURF can greatly reduce the running time and keep the mosaic quality good during real-time mosaicking. SURF and TSURF are applied to airborne images to compare the efficiency of each algorithm in constructing a mosaic. We calculate the average coordinate error and angle error for evaluating mosaic precision. We also use the average gradient, standard deviation, and forecast root mean-square error to compare the mosaic quality of SURF with that of TSURF. The first mosaic experiment consisting of 20 groups of airborne images showed that the mosaic speed of TSURF is three times faster than that of SURF and maintains comparative mosaic accuracy. In the second experiment, a series of continuous thumbnail images were mosaicked using the SURF and TSURF algorithms fully automatically. The TSURF speed was 63.40% faster than that of SURF and the accuracy remained consistent.

An Algorithm Based on SURF for Surveillance Video Mosaicing

Using the SIFT algorithm for image mosaicing is the study hotspot in recent years, which is in a wide range of applications. SIFT algorithm of large amount of data and the time-consuming calculation method is not applicable in higher real-time video mosaicing. Firstly using SURF extracts feature points, secondly using the nearest matching method, RANSAC and least-square method solve the homography matrix between images, and finally using normalized covariance related function for obtaining the best effect of the homography matrix. The algorithm not only meets the accuracy requirement of parameter estimation, but also has smaller computation and faster speed than SIFT. It has proved that the algorithm used in this paper has good real-time performance, high accuracy and the ideal effect, which can satisfy the requirement of real-time mosaicking.

Multi-resolution and wavelet representations for identifying signatures of disease.

Identifying physiological and anatomical signatures of disease in signals and images is one of the fundamental challenges in biomedical engineering. The challenge is most apparent given that such signatures must be identified in spite of tremendous inter and intra-subject variability and noise. Crucial for uncovering these signatures has been the development of methods that exploit general statistical properties of natural signals. The signal processing and applied mathematics communities have developed, in recent years, signal representations which take advantage of Gabor-type and wavelet-type functions that localize signal energy in a joint time-frequency and/or space-frequency domain. These techniques can be expressed as multi-resolution transformations, of which perhaps the best known is the wavelet transform. In this paper we review wavelets, and other related multi-resolution transforms, within the context of identifying signatures for disease. These transforms construct a general representation of signals which can be used in detection, diagnosis and treatment monitoring. We present several examples where these transforms are applied to biomedical signal and imaging processing. These include computer-aided diagnosis in mammography, real-time mosaicking of ophthalmic slit-lamp imagery, characterization of heart disease via ultrasound, predicting epileptic seizures and signature analysis of the electroencephalogram, and reconstruction of positron emission tomography data.