To achieve precise positioning and accurate recognition of dot-distribution encoding points while enhancing efficiency, this study proposes an approach combining improved grayscale centroid and point pattern matching. The algorithm identifies encoded points using multiple constraints and optimizes grayscale centroid via dynamic threshold calculation. It accurately extracts encoded points centers. An advanced clustering method reduces parameter sensitivity and a novel decoding method employs geometric and computer vision principles. Experimental outcomes demonstrate sub-pixel accuracy, robustness to angles, and a 90% correct recognition rate at a 60-degree angle, holding practical significance.
Constructing descriptors for key points in image matching is a crucial task in computer vision, visual tracking, and pattern recognition. Highly rated descriptors such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF) utilize the grayscale gradient information of the square regions around the key points. Nevertheless, these famous descriptors fail to take advantage of the image color information. Furthermore, their performances will be severely degraded for distorted images and the error due to rotation of their square windows. To address the problems, we proposed Circular Coordinate Combining Shape–Color Descriptor Under Distortion Based SURF (CSCD-SURF) that achieves competitive performance for colorful images with distortion contamination and numerical similar structures. The proposed approach can generate scale-space by adaptive filter to rectify distortion and integrate the SURF descriptor and shape–color information into our own custom concentric circular coordinate with flying colors. Experiments with Institut National de Recherche en Infomatique et Automatique dataset and spherical radial distortion-SIFT distorted image datasets prove that the ability of distortion rectification as well as matching performance against rotation, scaling, viewpoints, and blurring is more competitive than the state-of-the-art methods and its computation cost is acceptable.
Monocular depth estimation refers to recovering the depth information of a 3D scene from a single 2D image taken by a camera. A multi-task training framework combining of semantic segmentation and depth estimation is developed to improve the monocular depth estimation performance in this paper. Nevertheless, joint annotations, namely semantic labels and depth annotations, are necessary for training dataset in the traditional joint training framework of semantics and depth. Unluckily, scarcely any large public datasets that provide the joint annotations can be accessed. To address the problem, a training framework having the feature correlation screening and linkage mechanism based on the linear independence of Gram matrix called GSFA-MDEN (Gram Semantic-Feature-Aided Monocular Depth Estimation Network), which is trained through the TSTB (Two-Stages-Two-Branches) training strategy, is studied and developed. GSFA-MDEN is composed with two brunches namely DepthNet and SemanticsNet, which are firstly trained through two different large datasets having its own respective annotation. Subsequently, the overall network is constructed through the feature fusion of the two brunches based on the Gram nonlinear correlation, which can establish the quantitative representation of the correlation between semantic features and depth features. Compared to the original DepthNet, on the KITTI dataset, GSFAMDEN decreases Root Mean Square Error (RMSE) from 5.808m to 5.370m by adding SemanticsNet assisted depth estimation, and the RMSE is further decreased to 5.167m by creatively employing Gram nonlinear correlation to excavate correlation of different task features. The series experimental results illustrate the superiority of GSFA-MDEN.
A new set of gratings with medium resolution (R ∼ 7500) has been mounted on the LAMOST spectrographs, and the wavelength windows range in 490 ∼ 540nm and 640 ∼ 690 nm respectively for blue and red spectrograph arm. Commissioning observation has been conducted to test the survey based on 16 spectrographs and 4000 fibers. Meanwhile, a spectral analysis pipeline has been developing to get more precise stellar parameters, radial velocities and abundance of chemical elements. Instrument profiles are calculated for each fiber at each exposure according to emission lines both from arc lamp. A template grid spectra with R ∼ 7500 for fundamental parameter (Teff, logg, and [Fe/H] ) are selected from Elodie. During the commissioning observation, each star have been visited for several times, and a fraction targets include APOGEE, Kepler and PASTEL objects which have high precisely measured parameters. With the commissioning spectra, we can understand instrument performance, intrinsic precision of repeat observations, and the accuracy of the pipeline.
A color image super-resolution (SR) reconstruction based on an improved Projection onto Convex Sets (POCS) in YCbCr space is proposed. Compared with other methods, the POCS method is more intuitive and generally simple to implement. However, conventional POCS algorithm is strict to the accuracy of movement estimation and it is not conducive to the resumption of the edge and details of images. Addressed to these two problems, we on one hand improve the LOG operator to detect edges with the directions of ±0°, ±45°, ±90°, ±135° in order to inhibit the edge degradation. Then, by using the edge information, we proposed a self-adaptive edge-directed interpolation and a modified adaptive direction PSF to construct a reference image as well as to reduce the edge oscillation when revising the reference respectively. On the other hand, instead of block-matching, the Speeded up Robust Feature (SURF) matching algorithm, which can accurately extract the feature points with invariant to affine transform, rotation, scale, illumination changes, are utilized to improve the robustness and real-time in motion estimation. The performance of the proposed approach has been tested on several images and the obtained results demonstrate that it is competitive or rather better in quality and efficiency in comparison with the traditional POCS.
Constructing appropriate descriptors for interest points in image matching is a critical aspect task in computer vision and pattern recognition. A method as an extension of the scale invariant feature transform (SIFT) descriptor called shape–color alliance robust feature (SCARF) descriptor is presented. To address the problem that SIFT is designed mainly for gray images and lack of global information for feature points, the proposed approach improves the SIFT descriptor by means of a concentric-rings model, as well as integrating the color invariant space and shape context with SIFT to construct the SCARF descriptor. The SCARF method developed is more robust than the conventional SIFT with respect to not only the color and photometrical variations but also the measuring similarity as a global variation between two shapes. A comparative evaluation of different descriptors is carried out showing that the SCARF approach provides better results than the other four state-of-the-art related methods.
A new full-parameter singular value decomposition-based image quality assessment (IQA) method, which aims at capturing the loss of structural content instead of measuring the distortion of pixel intensity value, is proposed. Both the singular vectors and the singular value are considered as features and weight for quantifying major information, respectively, to evaluate the distortion degree in images. Extensive validation experiments are conducted with two kinds of test images, one of which is the LIVE database supplied by the University of Texas and the other is created from our own simulation. The prediction performance of the presented metrics, such as accuracy, monotonicity, and consistency, is measured. The experiment results show that, compared to several state-of-the-art image quality metrics, the performance of the proposed IQA is in better alignment with the perception of the human visual system in predicting image quality, particularly when comparing images containing different types of distortions.
KEYWORDS: Digital signal processing, Video processing, Signal processing, Video, Image processing, Electromagnetic coupling, Dielectrics, Signal analyzers, Data modeling, Detection theory
On account of high performance requirement of video processing systems and the shortcoming of conventional circuit
design method, a design methodology based on the signal integrity (SI) theory for the high-speed video processing
system with TI's digital signal processor TMS320DM642 was proposed. The PCB stack-up and construction of the
system as well as transmission line characteristic impedance are set and calculated firstly with the impedance control tool
Si8000 through this methodology. And then some crucial signals such as data lines of SDRAM are simulated and
analyzed with the IBIS models so that reasonable layout and routing rules are established. Finally the system's highdensity
PCB design is completed on Cadence SPB15.7 platform. The design result shows that this methodology can
effectively restrain signal reflection, crosstalk, rail collapse noise and electromagnetic interference (EMI). Thus it
significantly improves stability of the system and shortens development cycles.
Focused on the problem of distortion correction for those images having simple contour, the synthetic method was
described. In advance, without known camera parameters and any known world coordinates point in 3D space, the first
order radial distortion coefficient can be estimated based on the fundamental property: a camera follows the pinhole
model if and only if the projection of every line in space onto the camera is a line. Then the proposed distortion
correction method, which characterized by its fast computing speed and good accuracy, maps not only pixel coordinates
but also the grey values between the distortion image and non-distortion image. Several experiments and analysis were
performed and presented on this paper. Finally, a comparative analysis about the accuracy and computational load with
real data between this new method and the existed methods was put forward.
3-D motion estimation method based on computer vision theory is employed to implement a vision guide algorithm for UAV in this paper. First, the image sequences of landing target are taken by the camera mounted on UAV with known focal length and the Lucas-Kanade method is adopted to estimate two successive frame optical flow; then a hierarchical approach is described to effectively decompose the nonlinearities of the 3-D motion estimation into two linear subsystems; finally 3-D motion and structure(depth) information of landing target relative to UAV is recovered without using features of landing target. Experiments using both computer simulated images and real video images demonstrate the correctness and effectiveness of our method.
This paper lays emphasis on the mechanisms of optical bistabilities in thin films (TF) or organic dye solutions (OS). The absorption of laser beam in these materials strongly depends upon not only their detailed electronic structures but also the photon energy (HBAR (omega) ) of laser beam. The third-order nonlinear optical characteristics of TF or OS are presented and discussed.
High density and high data rate are two key points in the development of ODS (optical data storage) which has advanced remarkably in recent years. The aim of this contribution is to report on the migration from CD-E to DVD-RAM by changing: 1) the recording media from heat-mode material to photo-mode material, 2) the recording laser from near infrared to green or blue wavelength, and 3) the recording methods from CD- format to sectorial format and from PPM-RLL to PWM-RLL, where PPM and PWM are pulse position modulation and pulse width modulation respectively, of which all have a potential to achieve an ultrafast recording rate-60ps/bit.
KEYWORDS: Standards development, Optical discs, Compact discs, Digital video discs, Molybdenum, Optical storage, Video compression, Electron holes, Roads, Information technology
International Standards of Optical disks: CD-ROM, CD-R, CD- E, magneto-optical disk and heat-mode phase-change optical disk are presented. DVD-RAM based on photon excited effects: electron-hole pair generation and recombination in nonlinear materials is discussed.
An analysis of the RLL (1,7)-PWM method for magneto-optical disks and the RLL (2,7)-PPM method for phase-change disks is presented. The reasons why they exist simultaneously is the light of the characteristics of recording media are discussed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.