Due to the new crown and other epidemic diseases that make people wear masks to travel, the accuracy of the original face recognition system is affected. To address this challenge, a mask-wearing face recognition system based on an improved attention mechanism is proposed. First, Adding a maximum pooling operation to the CA (Coordinate Attention) attention module, then, placing attention module in the residual unit to form a feature extraction network. LResNet18E-IR is selected as the backbone network. Finally, the ArcFace loss and occlusion probability loss are combined to establish a multi-task network, which further promotes the accuracy of occluded face recognition. The results demonstrate that the system effectively increases the recognition accuracy of masked face and maintains almost the same accuracy as the original model on the unmasked dataset.
Advances in computer technology and artificial intelligence have led to a surge in the volume and complexity of multimedia data, and content-based image retrieval systems (CBIR) have become widely popular to extract useful information from this data. Traditional CBIR systems are implemented by inter-image features and cannot perform associative image retrieval. Therefore, in this paper, we propose a new method for associative image retrieval by mimicking the human brain mechanism. By using an improved deep residual network to extract different kinds of image features, and using a loss function to maximize the feature distance loss between non-associative image groups and minimize the loss between associative image groups, and finally using the trained model to achieve multi-objective associative retrieval.
As the hot and difficult issue in computer vision, binocular stereo vision is an important form of computer vision,which has a broad application prospects in many computer vision fields,such as aerial mapping,vision navigation,motion analysis and industrial inspection etc.In this paper, a research is done into binocular stereo camera calibration, image feature extraction and stereo matching. In the binocular stereo camera calibration module, the internal parameters of a single camera are obtained by using the checkerboard lattice of zhang zhengyou the field of image feature extraction and stereo matching, adopted the SURF operator in the local feature operator and the SGBM algorithm in the global matching algorithm are used respectively, and the performance are compared. After completed the feature points matching, we can build the corresponding between matching points and the 3D object points using the camera parameters which are calibrated, which means the 3D information.
At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.
This paper focuses on the study of implementing feature-based image registration by System on a Programmable Chip (SoPC) hardware platform. We solidify the image registration algorithm on the FPGA chip, in which embedded soft core processor Nios II can speed up the image processing system. In this way, we can make image registration technology get rid of the PC. And, consequently, this kind of technology will be got an extensive use. The experiment result indicates that our system shows stable performance, particularly in terms of matching processing which noise immunity is good. And feature points of images show a reasonable distribution.
KEYWORDS: Digital signal processing, Wavelets, Signal processing, Algorithm development, Filtering (signal processing), Field programmable gate arrays, Fusion energy, System on a chip, Simulink, Linear filtering
With the development of FPGA, DSP Builder is widely applied to design system-level algorithms. The algorithm of CL multi-wavelet is more advanced and effective than scalar wavelets in processing signal decomposition. Thus, a system of CL multi-wavelet based on DSP Builder is designed for the first time in this paper. The system mainly contains three parts: a pre-filtering subsystem, a one-level decomposition subsystem and a two-level decomposition subsystem. It can be converted into hardware language VHDL by the Signal Complier block that can be used in Quartus II. After analyzing the energy indicator, it shows that this system outperforms Daubenchies wavelet in signal decomposition. Furthermore, it has proved to be suitable for the implementation of signal fusion based on SoPC hardware, and it will become a solid foundation in this new field.
The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.
In this paper, we present new methods for image fusion based on intuitionistic index in spatial domain and contourlet transform domain, furthermore we adopt two ways to fuse images in contourlet domain. When constructing an intuitionistic fuzzy set, we use the Gamma function to get the membership degree, and the Sugeno complementation to get the non-membership degree. Based on the information theory, the larger the hesitancy is, the more information it has. So we set up a fusion rule, by which the larger hesitancy will be chosen, to get a fused image from multi-focus images or remote sensing ones. We compare these new algorithms to some classical image fusion algorithms. The results show, for multi-focus image, these new algorithms are better comparing to other algorithms, and they can get a good fusion result, especially the contourlet transformation algorithm using the intuitionistic index. For remote sensing image, these new algorithms are not the best, but they can also get a well fusion result.
Satellite data can adequately capture forest dynamics over larger areas. Firstly, the Landsat ground surface reflectance
(GSR) images from 1974 to 2013 were collected and processed based on 6S atmospheric transfer code and a relative
reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the
forest change history (afforestation and deforestation) from the dense time-series Landsat GSR images, and the
afforestation age was successfully retrieved from the Landsat time-series stacks in the last forty years and shown to be
consistent with the surveyed tree ages. Then, the above ground biomass (AGB) regression models were greatly improved
by integrating the simple ratio vegetation index (SR) and tree age. Finally, the forest AGB images were mapped at eight
epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in six counties of Yulin District
increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360%. For the forest area, the
forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 1 t/ha. The
results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.
KEYWORDS: Image fusion, Image processing, Digital signal processing, Wavelet transforms, Field programmable gate arrays, Embedded systems, Multispectral imaging, Logic, Wavelets, System on a chip
Combining the theory of wavelet transform based image fusion and SOPC design method, the authors uses SOPC as the core device to design and implement a image fusion system. The fusion system adopts the Verilog hardware description language, Dsp builder and Quartus II development platform together with macro module to complete the logic design and timing control of each module. In the fusion system, we can achieve simple pixel-level image fusion of two registered images. This design not only builds up an image fusion system based on SOPC in accident, but also provides a hardware design principle in SoPC for the future design and Implementation of more comprehensive function of image processing.
Multi-wavelet, extended from single wavelet theory, is able to provide a more accurate image processing analysis method than wavelet. After studying the features of multi-wavelet transform, we realize the core algorithms of an image processing system with GHM multi-wavelet system, by which we can fuse two multi-spectra images by the matching degree-based fusion method. In the phase of image de-noising, we get rid of the noise in the fused image based on the integrated threshold de-noising method. In the stage of image compression, the de-noised image will be compressed and decompressed in adopting the method of Shannon, Fano, Huff-man, and SPIHT (Set Partitioning In Hierarchical Tree) respectively in three different proportional. At last, the results of various stages will be shown in the integrated processing system. This paper uses the MFC mode of VC+ + 6.0 to build a visualization interface (UI) model, to make the interface of our multi-wavelet image processing system concise which occupies less resource and easy to operate. That is, the system consists of three sub-systems, namely: image fusion, image de-noising and image compression. The realization of the sub-system’s functions is independent of each other which enhance portability and stability of the whole system.
A new image fusion method based on intuitionistic fuzzy clustering is proposed in this paper. In this method, two multisource
images are decomposed firstly by multiwavelet filter groups. Secondly, intuitionistic fuzzy similarity is used to
cluster the decomposing sub-images respectively. Thirdly, decomposition coefficients of fused image are formed
according to the clustering results. The experimental results show that there is a noticeable improvement in the
information and visual quality of the fused images.
A novel pixel-level image fusion algorithm based on adaptive PCNN model is presented by authors. The algorithm
combines the advantages of multiwavelet and PCNN. It is worth mentioning that the algorithm adopts adaptive PCNN
model, in which some of the model parameters are determined according to image characteristics or statistical values.
The decomposition coefficients of fused image are formed according to the PCNN output number of pulses. The
simulation results show that this algorithm is effective. There is a noticeable improvement in the information and visual
quality of the fused images.
This paper studies the algorithm fusing IR/Visible images used in imaging guidance field. Firstly, the differences between IR image and Visible one are introduced curtly. Then we introduce the essential multiwavelet theory and explain the reason why we choose multiwavelet analysis as a tool fusing images. We give the framework of image fusion based on discrete multiple wavelet transform (DMWT) which core principle is still based on multiresolution analysis (MRA) and Mallat algorithm. A novel image fusion method based on multiwavelet transform and directional contrasts information is presented.
A new anti-jamming method is proposed combining adaptive array with direct sequence spread spectrum (DSSS)
techniques in mobile satellite communication system. Different with the conventional depreading process, the proposed
method modifies DSSS despreador, in which the output data are blindly processed. It overcomes the problem that DSSS
signal often has very lower SNR, which is difficult to be captured in constant modulus array processing, especially when
strong interference/jamming signals exist coincidentally.
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