Absolute Difference Algorithm Research Articles

Algorithm for Creating 3d Scenes of Recognized Objects from Depth Maps

Purpose of research. Development of an algorithm for constructing 3d scenes of recognized objects from synthesized depth maps in order to improve the speed of real-time image processing.Methods. The 3d scene construction algorithm is based on the method of stereo image construction using a threelevel fuzzy depth map construction model. At the first level of this model the boundaries of objects are determined using a modified Canny algorithm, at the second level the values of disparity are calculated on the basis of the sum of absolute differences algorithm modified by fuzzy logic methods, and at the final level the gradients of distances from the boundaries of images to the edges of recognized objects are calculated first and then according to the obtained values of disparity at the second and third levels of the fuzzy hierarchical model, the refined values of disparity are calculated, which are used to carry out the analysis of the depth map.Results. An algorithm for constructing 3d scenes of recognized objects using synthesized depth maps has been developed. It was determined that the proposed algorithm has better performance compared to existing depth map algorithms such as conjugate point algorithm and pyramidal algorithm.Conclusion. The experimental results showed that the proposed algorithm has a lower complexity compared to the analyzed algorithms (conjugate points and pyramidal). The minimum average execution time of the 3d scene construction operation was about 1-2 minutes, which is almost 120 times better compared to the conjugate point algorithms.

Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features

Visual odometry is critical in visual simultaneous localization and mapping for robot navigation. However, the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight. Herein, a new human visual attention mechanism for point-and-line stereo visual odometry, which is called point-line-weight-mechanism visual odometry (PLWM-VO), is proposed to describe scene features in a global and balanced manner. A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism, where sufficient attention is assigned to position-distinctive objects (sparse features in the environment). Furthermore, the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features. Compared with the state-of-the-art method (ORB-VO), PLWM-VO show a 36.79% reduction in the absolute trajectory error on the Kitti and Euroc datasets. Although the time consumption of PLWM-VO is higher than that of ORB-VO, online test results indicate that PLWM-VO satisfies the real-time demand. The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry, but also quantitatively demonstrates the superiority of the human visual attention mechanism.

A Distributed Adaptive Algorithm Based on the Asymmetric Cost of Error Functions

In this paper, a family of novel diffusion adaptive estimation algorithms is proposed from the asymmetric cost function perspective by combining diffusion strategy and the linear–linear cost, quadratic-quadratic cost, and linear-exponential cost at all distributed network nodes, and named diffusion LLCLMS (DLLCLMS), diffusion QQCLMS (DQQCLMS), and diffusion LECLMS (DLECLMS), respectively. Then, the stability of mean estimation error and computational complexity of those three diffusion algorithms are analyzed theoretically. Finally, several experiment simulation results are designed to verify the superiority of those three proposed diffusion algorithms. Results show that DLLCLMS, DQQCLMS, and DLECLMS algorithms are more robust to the input signal and impulsive noise than the diffusion sign-error LMS, diffusion robust variable step-size least mean square (DRVSSLMS), and least mean logarithmic absolute difference algorithms. In brief, theoretical analysis and experiment results show that those proposed DLLCLMS, DQQCLMS, and DLECLMS algorithms perform better when estimating the unknown linear system under the changeable impulsive noise environments and different environments types of input signals.

Parallel field programmable gate array implementation of the sum of absolute differences algorithm used in the stereoscopic system

Stereo vision is a popular method for an artificial vision-based environment perception system used in various applications such as intelligent transportation. With two cameras, the disparity map is calculated to find the distance and depth of objects in front of a moving vehicle. The key element of the stereoscopic system is based on the sum of absolute differences (SAD) algorithm, which is the most repeated operation in the stereo matching subsystem; however, this algorithm requires a very intensive processing time, statistical analysis show that the SAD block can consume more than 80% of the overall processing time of the algorithm. In this paper we propose a highly efficient hardware architecture of the SAD algorithm for real time stereo matching, the proposed architecture is established by a hierarchical parallel architecture of the SAD block, and verified by simulation and successfully implemented in Cyclone IV field programmable gate array (FPGA), it provides a significant reduction of processing time and the performance of the stereo imaging system is able to achieve 30 frames per second of 640×480 resolution color images.

Spectrum Sensing using Energy Measurement in Wireless Telemetry Networks using Logarithmic Adaptive Learning

<p>To identify primary user signals in cognitive radios spectrum sensing method is used. Due to statistical variances in received signal, noise is present in primary user signals, this noise powers are varied due to random nature of noise signals and leads to noise uncertainty problem in the performance of energy detection. The task of energy measurement and further detecting the unused frequency spectrum is a key task in cognitive radio applications. For avoiding these problems, least logarithmic absolute difference (LLAD) algorithm is proposed in which noise powers are adjusted at sensing point of licensed users. With help of proposed method, estimated noise signals are eliminated. Sign regressor version of LLAD algorithm is considered due to it reduces computational complexity and convergence rate is improved. Further probability of detection (<em>P</em><sub>od</sub>), probability of false alarm (<em>P</em><sub>ofa</sub>) is estimated to know threshold value. From results, it is clear that good performance in terms of <em>P</em><sub>ofa</sub> versus <em>P</em><sub>od</sub> in range of low signal to noise ratio in multiple nodes. Therefore, the proposed energy measurement-based spectrum sensing method is useful in remote health care monitoring, medical telemetry applications by sharing the un-used spectrum.</p>

Kernel Least Logarithmic Absolute Difference Algorithm

Kernel adaptive filtering (KAF) algorithms derived from the second moment of error criterion perform very well in nonlinear system identification under assumption of the Gaussian observation noise; however, they inevitably suffer from severe performance degradation in the presence of non-Gaussian impulsive noise and interference. To resolve this dilemma, we propose a novel robust kernel least logarithmic absolute difference (KLLAD) algorithm based on logarithmic error cost function in reproducing kernel Hilbert spaces, taking into account of the non-Gaussian impulsive noise. The KLLAD algorithm shows considerable improvement over the existing KAF algorithms without restraining impulsive interference in terms of robustness and convergence speed. Moreover, the convergence condition of KLLAD algorithm with Gaussian kernel and fixed dictionary is presented in the mean sense. The superior performance of KLLAD algorithm is confirmed by the simulation results.

Kernel least logarithm absolute difference algorithm based on P-norm

The kernel adaptive filtering is an efficient and nonlinear approximation method which is developed in reproducing kernel Hilbert space (RKHS). Kernel function is used to map input data from original space to RKHS space, thus solving nonlinear problems is efficient.Impulse noise and non-Gaussian noise exist in the real application environment, and the probability density distribution of these noise characteristics shows a relatively heavy trailing phenomenon in the statistical sense. α stable distribution can be used to model this kind of non-Gaussian noise well. The kernel least mean square(KLMS) algorithms usually perform well in Gaussian noise, but the mean square error criterion only captures the second-order statistics of the error signal, this type of algorithm is very sensitive to outliers, in other words, it lacks robustness in α stable distribution noise. The kernel least logarithm absolute difference(KLLAD) algorithm can deal with outliers well, but it has the problem of slow convergence.In order to further improve the convergence speed of nonlinear adaptive filtering algorithm in α stable distributed noise background, a new kernel least logarithm absolute difference algorithm based on p-norm (P-KLLAD) is presented in this paper. The algorithm combining least logarithm absolute difference algorithm and p norm, on the one hand, the least logarithm difference criteria is ensure the algorithm to have good robustness in α stable distribution noise environment, and on the other hand, add p norm on the absolute value of error.The steepness of the cost function is controlled by p norm and a posititive constant ɑ to improve the convergence speed of the algorithm.The computer simulation results of Mackey-Glass chaotic time series prediction and nonlinear system identification show that this algorithm improves the convergence speed with good robustness,and the convergence speed and robustness better than the kernel least mean square algorithm,the kernel fractional lower power algorithm, the kernel least logarithm absolute difference algorithm and the kernel least mean p-norm algorithm.

Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images

Feature extraction plays an important role in pattern recognition because band-to-band registration and geometric correction from different satellite images have linear image distortion. However, new near-equatorial orbital satellite system (NEqO) images is different because they have nonlinear distortion. Conventional techniques cannot overcome this type of distortion and lead to the extraction of false features and incorrect image matching. This research presents a new method by improving the performance of the Scale-Invariant Feature Transformation (SIFT) with a significantly higher rate of true extracted features and their correct matching. The data in this study were obtained from the RazakSAT satellite covering a part of Penang state, Malaysia. The method consists of many stages: image band selection, image band compression, image sharpening, automatic feature extraction, and applying the sum of absolute difference algorithm with an experimental and empirical threshold. We evaluate a refined features scenario by comparing the result of the original extracted SIFT features with corresponding features of the proposed method. The result indicates accurate and precise performance of the proposed method from removing false SIFT extracted features of satellite images and remain only true SIFT extracted features, that leads to reduce the extracted feature from using three frame size: (1) from 2000 to 750, 552 and 92 for the green and red bands image, (2) from 678 extracted control points to be 193, 228 and 73 between the green and blue bands, and (3) from 1995 extracted CPs to be 656, 733, and 556 between the green and near-infrared bands, respectively.

Infrared target detection based on the single-window average absolute gray difference algorithm

Infrared target detection based on the single-window average absolute gray difference algorithm

Local Stereo Matching: An Adaptive Weighted Guided Image Filtering-Based Approach

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms.

Design and Development of Multirotor UAV with Automatic Collision Avoidance using Stereo Camera

The operation of drones in cluttered environments like forests and hilly areas is extremely difficult; it is impossible to use drones autonomously without having built-in information to detect and avoid obstacles. The vision based obstacle avoidance algorithm is presented in this paper, with extensions to UAV navigation. The proposed method is incorporated on a stereo vison multi copter using a block matching algorithm. The stereo vision baseline is based on horizontal configuration and computes the depth using a sum of absolute difference algorithm. The image processing node (LabVIEW vi) and the controller node are run on a remote laptop. This vi computes the distance between the multirotor and an obstacle and transmits depth data to an onboard flight controller through the MAVLink protocol. The algorithm efficiency was tested using the software in the loop on Gazebo simulator to analyze the performance of the UAV. The hardware in loop results are also shown in this paper after the successful flight test.

Diffusion-Probabilistic Least Mean Square Algorithm

In this paper, a novel diffusion estimation algorithm is proposed from a probabilistic perspective by combining the diffusion strategy and the probabilistic least mean square (LMS) at all distributed network nodes. The proposed method, namely diffusion-probabilistic LMS (DPLMS), is more robust to the input signal and impulsive noise than previous algorithms like the diffusion sign-error LMS (DSE-LMS), diffusion robust variable step-size LMS (DRVSSLMS), and diffusion least logarithmic absolute difference (DLLAD) algorithms. Instead of minimizing the estimation error, the DPLMS algorithm is based on approximating the posterior distribution with an isotropic Gaussian distribution. In this paper, the stability of the mean estimation error and the computational complexity of the DPLMS algorithm are analyzed theoretically. Simulation experiments are conducted to explore the mean estimation error for the DPLMS algorithm with varied conditions for input signals and impulsive interferences, compared to the DSE-LMS, DRVSSLMS, and DLLAD algorithms. Both results from the theoretical analysis and simulation suggest that the DPLMS algorithm has superior performance than the DSE-LMS, DRVSSLMS, and DLLAD algorithms when estimating the unknown linear system under the changeable impulsive noise environments.

Fast and robust small infrared target detection using absolute directional mean difference algorithm

Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.

Design of Robust Infrared Image Sequence (IR-Video) Target Tracking Model

As regards protection, monitoring a certain objective has become a primary goal and thus has helped to incorporate the video technology into the industry. However, owing to its zigzag trajectory or shifts of position, it is often difficult to trace the imprevisible existence of the goal. This article therefore proposed the use of infrared cameras as sensors to monitor the object. The infrared camera is effective when the object changes its direction, like its contemporaries. Therefore, a pattern mapping technique was used for infrarouge goal monitoring. Ultimately, there was a distinction among the traditional Mean Absolute Difference (MAD) algorithm and the evolved V-SQ -track model used here. The findings show that the method of V-SQ -track model performs stronger than the MAD algorithm. Even if the modern algorithm is unable to do so, the artifacts can be monitored

A Method for Broccoli Seedling Recognition in Natural Environment Based on Binocular Stereo Vision and Gaussian Mixture Model.

Illumination in the natural environment is uncontrollable, and the field background is complex and changeable which all leads to the poor quality of broccoli seedling images. The colors of weeds and broccoli seedlings are close, especially under weedy conditions. The factors above have a large influence on the stability, velocity and accuracy of broccoli seedling recognition based on traditional 2D image processing technologies. The broccoli seedlings are higher than the soil background and weeds in height due to the growth advantage of transplanted crops. A method of broccoli seedling recognition in natural environments based on Binocular Stereo Vision and a Gaussian Mixture Model is proposed in this paper. Firstly, binocular images of broccoli seedlings were obtained by an integrated, portable and low-cost binocular camera. Then left and right images were rectified, and a disparity map of the rectified images was obtained by the Semi-Global Matching (SGM) algorithm. The original 3D dense point cloud was reconstructed using the disparity map and left camera internal parameters. To reduce the operation time, a non-uniform grid sample method was used for the sparse point cloud. After that, the Gaussian Mixture Model (GMM) cluster was exploited and the broccoli seedling points were recognized from the sparse point cloud. An outlier filtering algorithm based on k-nearest neighbors (KNN) was applied to remove the discrete points along with the recognized broccoli seedling points. Finally, an ideal point cloud of broccoli seedlings can be obtained, and the broccoli seedlings recognized. The experimental results show that the Semi-Global Matching (SGM) algorithm can meet the matching requirements of broccoli images in the natural environment, and the average operation time of SGM is 138 ms. The SGM algorithm is superior to the Sum of Absolute Differences (SAD) algorithm and Sum of Squared Differences (SSD) algorithms. The recognition results of Gaussian Mixture Model (GMM) outperforms K-means and Fuzzy c-means with the average running time of 51 ms. To process a pair of images with the resolution of 640×480, the total running time of the proposed method is 578 ms, and the correct recognition rate is 97.98% of 247 pairs of images. The average value of sensitivity is 85.91%. The average percentage of the theoretical envelope box volume to the measured envelope box volume is 95.66%. The method can provide a low-cost, real-time and high-accuracy solution for crop recognition in natural environment.

New Exon Prediction Techniques Using Adaptive Signal Processing Algorithms for Genomic Analysis

Substantial research and monetary aids to healthcare establishments are provided by cloud computing. A benign position to store and handle vast genome data is offered by cloud services. Labs for gene sequencing send out raw and contingent data over the Internet to multiple sequence collections under conservative flow of gene information. The use of cloud services also reduces the storage costs of deoxyribonucleic acid (DNA) sequencing. Here, an efficient and new bio-informatics genomic system is proposed by the use of cloud services from Amazon to access the stored gene data and process it. A key task in bio-informatics is to locate protein-coding sections in a gene sequence based on three base periodicity (TBP) is for disease diagnosis and design drugs. Here, a novel cloud-based adaptive exon predictor (AEP) using Amazon cloud services is proposed to improve the accuracy in exon finding ability as well as aimed at superior convergence. Noise in the input gene sequence given to the proposed AEPs is pre-processed using normalized LMS filtering. Computational complexity can be reduced using proposed data normalized form of least logarithmic absolute difference (NLLAD) algorithm and its error normalized variants. It was shown that sign regressor NLLAD (SRNLLAD) dependent AEP is efficient in exon forecast applications using different metrics for a performance like sensitivity 0.8037, precision 0.8052 along with specificity 0.8146 by different gene sequences considered from the National Center for Biotechnology Information (NCBI) databank. The proposed AEPs have shown upright performance than typical LMS and other AEPs in terms of exon prediction accuracy, convergence, and computational complexity. Their less computational complexity will be found attractive, and they are suitable to use in bio-informatics nano devices.

Intelligent Information System for Suspicious Human Activity Detection in Day and Night

The detection of human beings in a camera attracts more attention because of its wide range of applications such as abnormal event detection, person counting in a dense crowd, person identification, fall detection for care to elderly people, etc. Over the time, various techniques have evolved to enhance the visual information. This article presents a novel 3-D intelligent information system for identifying abnormal human activity using background subtraction, rectification, morphology, neural networks and depth estimation with a thermal camera and a pair of hand held Universal Serial Bus (USB) camera to visualize un-calibrated images. The proposed system detects strongest points using Speed-Up Robust Features (SURF). The Sum of Absolute Difference (SAD) algorithm match the strongest points detected by SURF. 3-D object model and image stitching from image sequences are carried out in the proposed work. A series of images captured from different cameras are stitched into a geometrically consistent mosaic either horizontally/vertically based on the image acquisition. 3-D image and depth estimation of un-calibrated stereo images are acquired using rectification and disparity. The background is separated from the scene using threshold approach. Features are extracted using morphological operators in order to get the skeleton. Junction points and end points of the skeleton image are obtained from the skeleton. Data set of abnormal human activity is created using supervised learning such as neural network with a thermal camera and a pair of webcam. The feature vector of an activity is compared with already created data set, if a match occurs the classifier detects abnormal human activity. Additionally the proposed algorithm performs depth estimation to measure real time distance of objects dynamically. The system use thermal camera, Intel computing stick, converter, video graphics array (VGA) to high-definition multimedia interface (HDMI) and webcams. The proposed novel intelligent information system gives 94% maximum accuracy and 89% minimum accuracy for different activities, thus it effectively detects suspicious activity during day and night.

Person Re-Identification Based on SAD and Histogram in a Non-Overlapping Camera Network

In this paper, we present the conception and implementation of a system for person re-identification in a camera network, based on the appearance. This system aims to build an online database that contains the history of every person that enters the field of view of the cameras. This system is able to associate an identifier to each detected person, which keeps this identifier in the same camera and in other cameras even if he or she disappears and then appears again. Our system comprises a moving objects detection step that is implemented using the Mixture of Gaussians method and a proposed difference method, to improve the detection results. It also comprises a tracking step that is implemented using the sum of absolute differences algorithm. The re-identification stage is realized using three steps: the tracking for the temporal association, the histogram and the new smart exploitation of the interpolation technique for comparison. The global system was tested on a real data set collected by three cameras. The experimental results show that our approach gives very satisfactory results.

A family of robust adaptive filtering algorithms based on sigmoid cost

In this paper, a new framework of cost function for designing robust adaptive filtering algorithms is developed. This new cost framework, called sigmoid cost function, results from imbedding the conventional cost function into the sigmoid framework. Utilizing this proposed sigmoid cost framework, several important members (e.g., the sigmoid least mean square, sigmoid least absolute difference, sigmoid least mean fourth, sigmoid least mean logarithmic square, sigmoid least logarithmic absolute difference algorithms) of this family of robust adaptive filtering algorithms are proposed. In the proposed sigmoid cost framework with a steepness parameter α, a greater (smaller) value of α results in a slower (faster) convergence. Thus, an adaptive α is proposed which is based on an exponential function with respect to the L1-norm of the system error. In addition, the mean-square deviation analysis of the proposed algorithms is also carried out and their accuracies are verified via system identification experiment. Simulations in system identification and acoustic echo-cancellation scenarios have demonstrated that the proposed algorithms outperform the corresponding order's generalized maximum correntropy criterion, normalized least mean square using step-size scaler, sign algorithm and least logarithmic absolute difference algorithms.

Diffusion least logarithmic absolute difference algorithm for distributed estimation

The popular distributed estimation algorithms based on the mean-square error criterion is not robust against impulsive noise in the adaptive networks. To address the problem, the diffusion least logarithmic absolute difference (LLAD) algorithm is proposed in this article, which adopts both the logarithm operation and sign operation to the error. The algorithm can elegantly and gradually adjust the conventional cost functions in its optimization based on the error variation. Compared with centralized LLAD algorithm, the diffusion LLAD algorithm performs a good balance between communications and performance. The theoretical stability of mean and mean-square performance of the algorithm is analyzed. Simulation results indicate that the algorithm achieves a better performance, compared with diffusion LMS and diffusion sign-error LMS algorithms, even in the impulsive noise environment.