A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in segmentation and system. In segmentation, an improved histogram method which can calculate threshold automatically accurately Meanwhile, the regional growth true color processing combined with this to improve accuracy intelligence. While creating system, feature extraction utilized. After evaluating results different training libraries, proved have effective...

Point cloud registration is a fundamental technique in 3-D computer vision with applications graphics, autonomous driving, and robotics. However, tasks under challenging conditions, which noise or perturbations are prevalent, can be difficult. We propose robust point approach that leverages graph neural partial differential equations (PDEs) heat kernel signatures. Our method first uses PDE modules to extract high-dimensional features from clouds by aggregating information the neighborhood,...

Autonomous navigation in farmlands is one of the key technologies for achieving autonomous management maize fields. Among various techniques, visual using widely available RGB images a cost-effective solution. However, current mainstream methods crop row detection often rely on highly specialized, manually devised heuristic rules, limiting scalability these methods. To simplify solution and enhance its universality, we propose an innovative annotation strategy. This strategy, by simulating...

Group sparse representation has shown great potential in image restoration, which can be considered as a low-rank matrix approximation problem. The nuclear norm minimization method, convex relaxation of the rank minimization, shrinks all singular values simultaneously. Recent advances have suggested truncated method to better approximate matrix. In this paper, we connect group with application restoration. Then, an implementation fast convergence via alternating direction multipliers is...

Recently, the residual learning strategy has been integrated into convolutional neural network (CNN) for single image super-resolution (SISR), where CNN is trained to estimate images. Recognizing that a usually consists of high-frequency details and exhibits cartoon-like characteristics, in this paper, we propose deep shearlet (DSRLN) images based on transform. The proposed transform-domain which provides an optimal sparse approximation image. Specifically, address large statistical...

Sparse sensing schemes based on matrix completion for data collection have been proposed to reduce the power consumption of data-sensing and transmission in wireless sensor networks (WSNs). While extensive efforts made improve recovery accuracy from sparse samples, it is usually at cost running time. Moreover, most data-collection methods are difficult implement with low sampling ratio because communication limit. In this paper, we design a novel method including Rotating Random Sampling...

In wireless sensor networks (WSNs), data recovery is an indispensable operation for loss or energy constrained WSNs using sparse sampling. However, the accuracy not satisfying with various types due to neglect of correlation among multi-attribute data. this paper, we propose a novel method joint sparsity and low-rank constraints based on tensor completion in WSNs. The proposed represents high-dimensional as tensors effectively exploit that exists utilization spatiotemporal signal emphasized...

Data gathering is one of the key technologies in wireless sensor networks (WSNs). Total variation regularization has been extensively used image processing, showing its merit. Inspired by this, a compressive multi-timeslots data method with total proposed this letter. The effectiveness for WSNs exploited. In method, sink gets small-sized sample matrix through sensing and then recovers original from samples using an alternating direction method. Simulations verify that outperforms...

Wireless Sensor Networks (WSNs) have been deeply studied by many researchers and widely used in fields. Since a large amount of energy for WSNs is sensing transmitting, come up with methods to reduce the number sensed transmitted data packets. Compressive Data Gathering (CDG) well-known method gather data, but it does not realize sparse as needs sense all compress them. The efficiency Low-rank TV regularizations recovering has demonstrated, however, they are combined enable utilization...

An effective way to reduce the energy consumption of constrained wireless sensor networks is reducing number collected data, which causes recovery problem. In this paper, we propose a novel data method based on low-rank tensors for heterogeneous with various types. The proposed represents high-dimensional as effectively exploit spatiotemporal correlation that exists in data. Furthermore, an algorithm alternating direction multipliers developed solve resultant optimization problem...

It has been shown that iterative reweighted strategies will often improve the performance of many sparse reconstruction algorithms. Iterative Framework for Sparse Reconstruction Algorithms (IFSRA) is a recently proposed method which iteratively enhances any given arbitrary algorithm. However, IFSRA assumes sparsity level known. Forward-Backward Pursuit (FBP) algorithm an approach where each iteration consists consecutive forward and backward stages. Based on IFSRA, this paper proposes (IFBP)...

Point cloud registration is a crucial technique in 3D computer vision with wide range of applications. However, this task can be challenging, particularly large fields view dynamic objects, environmental noise, or other perturbations. To address challenge, we propose model called PosDiffNet. Our approach performs hierarchical based on window-level, patch-level, and point-level correspondence. We leverage graph neural partial differential equation (PDE) Beltrami flow to obtain...

The Orthogonal Matching Pursuit (OMP) algorithm has been intensively applied for its simple structure and reconstruction efficiency. However, the accuracy of OMP still needs to be improved. Though OMP-based algorithms, LAOMP KOMP proposed in recent years have significantly improved performance while it can lead fairly high computational complexity. In this paper, we propose an method algorithms using fusing strategy demonstrate superiority. We use double intersection fuse estimated support...

This paper designs and implements a parallel correlation algorithm for ultra-long sequence in large-scale field programmable gate array (FPGA).The operation speed is 64 times faster than traditional algorithms.Combining multiple data values one RAM address improves the efficiency avoids time consumption caused by reading repeatedly.The multi-group multiply accumulator used to calculate read according certain rules, which fully utilizes advantages of FPGA reduces time.This overcomes...

Group sparse representation (GSR) has shown great potential in image Compressive Sensing (CS) recovery, which can be considered as a low rank matrix approximation problem. The nuclear norm minimization only minimize all the singular values simultaneously. Recent advances have suggested truncated (TNNM) to better approximate rank. In this paper, we connect group with for CS recovery. Then, an implementation of fast convergence via alternating direction method multipliers (ADMM) is developed...

Group sparse representation has raised lots of powerful signal recovery techniques in various compressive sensing studies, which can be considered as a low rank matrix approximation problem. Recent advances have suggested the adaptive singular value thresholding for under affine constraints. In this paper, we propose an group image recovery. A framework based on alternating direction method multipliers is presented, where introduced to solve method, threshold adaptively decreases during...

Compressed Sensing (CS) is intended to recover a high-dimensional but sparse vector by small number of linear sampling. Seeking an appropriate domain great importance achieve high enough degree sparsity. In this paper, we propose new scheme for image using multiscale strategy and structural group representation, which efficiently characterizes the sparsity multi-scale self-similarity natural images in adaptive domain. Then, constraint are exploited simultaneously under unified framework. A...

Reducing energy consumption and prolonging lifetime are the main goals of protocol data collection methods in wireless sensor networks. As for Low Energy Adaptive Clustering Hierarchy method, cluster head nodes randomly selected a round which is similar to sampling method matrix completion theory. Combining sensing with compression, we propose Sparse based on completion. Through setting multiple thresholds, selection process heads can be completed at same time, after that, only portion...