scholarly journals NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency

2020 ◽  
Vol 10 (12) ◽  
pp. 4415 ◽  
Author(s):  
Cheng Li ◽  
Baolong Guo ◽  
Geng Wang ◽  
Yan Zheng ◽  
Yang Liu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Computational Efficiency ◽  
Batch Processing ◽  
Experimental Results ◽  
Processing Strategy ◽  
Trade Off ◽  
Quantitative Metrics ◽  
Visual Tasks ◽  
Pixel Correlation ◽  
Region Description

Superpixels intuitively over-segment an image into small compact regions with homogeneity. Owing to its outstanding performance on region description, superpixels have been widely used in various computer vision tasks as the substitution for pixels. Therefore, efficient algorithms for generating superpixels are still important for advanced visual tasks. In this work, two strategies are presented on conventional simple non-iterative clustering (SNIC) framework, aiming to improve the computational efficiency as well as segmentation performance. Firstly, inter-pixel correlation is introduced to eliminate the redundant inspection of neighboring elements. In addition, it strengthens the color identity in complicated texture regions, thus providing a desirable trade-off between runtime and accuracy. As a result, superpixel centroids are evolved more efficiently and accurately. For further accelerating the framework, a recursive batch processing strategy is proposed to eliminate unnecessary sorting operations. Therefore, a large number of neighboring elements can be assigned directly. Finally, the two strategies result in a novel synergetic non-iterative clustering with efficiency (NICE) method based on SNIC. Experimental results verify that it works 40% faster than conventional framework, while generating comparable superpixels for several quantitative metrics—sometimes even better.

scholarly journals Center Symmetric Local Multilevel Pattern Based Descriptor and Its Application in Image Matching

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Zeng ◽  
Xiuqing Wang ◽  
Yu Gu
Keyword(s):  
Computational Efficiency ◽  
Image Matching ◽  
State Of The Art ◽  
Experimental Results ◽  
Image Region ◽  
Local Intensity ◽  
Region Description ◽  
Local Image

This paper presents an effective local image region description method, called CS-LMP (Center Symmetric Local Multilevel Pattern) descriptor, and its application in image matching. The CS-LMP operator has no exponential computations, so the CS-LMP descriptor can encode the differences of the local intensity values using multiply quantization levels without increasing the dimension of the descriptor. Compared with the binary/ternary pattern based descriptors, the CS-LMP descriptor has better descriptive ability and computational efficiency. Extensive image matching experimental results testified the effectiveness of the proposed CS-LMP descriptor compared with other existing state-of-the-art descriptors.

scholarly journals Content-Sensitive Superpixel Generation with Boundary Adjustment

2020 ◽  
Vol 10 (9) ◽  
pp. 3150
Author(s):  
Dong Zhang ◽  
Gang Xie ◽  
Jinchang Ren ◽  
Zhe Zhang ◽  
Wenliang Bao ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computational Efficiency ◽  
Experimental Results ◽  
Evaluation Metrics ◽  
Local Entropy ◽  
Generation Algorithm ◽  
Amount Of Information ◽  
Adjustment Strategy ◽  
Computer Vision Applications

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in the image, and the amount of information was evenly distributed to each seed. It placed more seeds to achieve the lower under-segmentation in content-dense regions, and placed the fewer seeds to increase computational efficiency in content-sparse regions. Second, the Prim algorithm was adopted to generate uniform superpixels efficiently. Third, a boundary adjustment strategy with the adaptive distance further optimized the superpixels to improve the performance of the superpixel. Experimental results on the Berkeley Segmentation Database show that our method outperforms competing methods under evaluation metrics.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments

2021 ◽  
Vol 11 (9) ◽  
pp. 3921
Author(s):  
Paloma Carrasco ◽  
Francisco Cuesta ◽  
Rafael Caballero ◽  
Francisco J. Perez-Grau ◽  
Antidio Viguria
Keyword(s):  
Sensor Fusion ◽  
Computational Efficiency ◽  
Probabilistic Approach ◽  
Laser Scanner ◽  
Industrial Applications ◽  
Experimental Results ◽  
Added Value ◽  
Aerial Robots ◽  
3D Localization ◽  
High Level

The use of unmanned aerial robots has increased exponentially in recent years, and the relevance of industrial applications in environments with degraded satellite signals is rising. This article presents a solution for the 3D localization of aerial robots in such environments. In order to truly use these versatile platforms for added-value cases in these scenarios, a high level of reliability is required. Hence, the proposed solution is based on a probabilistic approach that makes use of a 3D laser scanner, radio sensors, a previously built map of the environment and input odometry, to obtain pose estimations that are computed onboard the aerial platform. Experimental results show the feasibility of the approach in terms of accuracy, robustness and computational efficiency.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Experimental and Analytical Decentralized Adaptive Control of a 7-DOF Robot Manipulator

2020 ◽  
Author(s):  
Alexander Bertino ◽  
Peiman Naseradinmousavi ◽  
Atul Kelkar
Keyword(s):  
Adaptive Control ◽  
Control Strategy ◽  
Computational Efficiency ◽  
Tracking Control ◽  
Robot Manipulator ◽  
Problem Formulation ◽  
Experimental Results ◽  
Model Free ◽  
Trajectory Simulation ◽  
Adaptive Control Strategy

Abstract In this paper, we study the analytical and experimental control of a 7-DOF robot manipulator. A model-free decentralized adaptive control strategy is presented for the tracking control of the manipulator. The problem formulation and experimental results demonstrate the computational efficiency and simplicity of the proposed method. The results presented here are one of the first known experiments on a redundant 7-DOF robot. The efficacy of the adaptive decentralized controller is demonstrated experimentally by using the Baxter robot to track a desired trajectory. Simulation and experimental results clearly demonstrate the versatility, tracking performance, and computational efficiency of this method.

Multi-Resolution-Based Contour Corner Extraction Algorithm for Computer Vision-Based Measurement

2018 ◽  
Author(s):  
Jianhua Li ◽  
Lin Liao
Keyword(s):  
Computer Vision ◽  
Experimental Results ◽  
Original Algorithm ◽  
Industry Standard ◽  
Extraction Algorithm ◽  
Corner Points ◽  
Multiple Resolutions ◽  
Key Steps

Corner-based registration of the industry standard contour and the actual product contour is one of the key steps in industrial computer vision-based measurement. However, existing corner extraction algorithms do not achieve satisfactory results in the extraction of the standard contour and the deformed contour of the actual product. This paper proposes a multi-resolution-based contour corner extraction algorithm for computer vision-based measurement. The algorithm first obtains different corners in multiple resolutions, then sums up the weighted corner values, and finally chooses the corner points with the appropriate corner values as the final contour corners. The experimental results show that the proposed algorithm, based on multi-resolution, outperforms the original algorithm in the aspect of the corner matching situation and helps in subsequent product measurements.

scholarly journals DMC: Decentralized Mixer with Channel for Transaction Privacy Protection on Ethereum

2021 ◽  
Author(s):  
Su Liu ◽  
Jian Wang
Keyword(s):  
Transaction Cost ◽  
Privacy Protection ◽  
Batch Processing ◽  
Experimental Results ◽  
Smart Contract ◽  
Mixing Schemes ◽  
High Transaction Cost ◽  
Privacy Issues

Ethereum is a public blockchain platform with smart contract. However, it has transaction privacy issues due to the openness of the underlying ledger. Decentralized mixing schemes are presented to hide transaction relationship and transferred amount, but suffer from high transaction cost and long transaction latency. To overcome the two challenges, we propose the idea of batch accounting, adopting batch processing at the time of accounting. For further realization, we introduce payment channel technology into decentralized mixer. Since intermediate transactions between two parties do not need network consensus, our scheme can reduce both transaction cost and transaction latency. Moreover, we provide informal definitions and proofs of our scheme's security. Finally, our scheme is implemented based on zk-SNARKs and Ganache, and experimental results show that the higher number of transactions in batch, the better our scheme performs.

scholarly journals Consistency-Check Edge Refinement for Deep Stereo Matching

2020 ◽  
Author(s):  
Fangrui Wu ◽  
Menglong Yang
Keyword(s):  
Computational Efficiency ◽  
Stereo Matching ◽  
Information Aggregation ◽  
Experimental Results ◽  
Global Information ◽  
Consistency Check ◽  
Filtering Method ◽  
Tightly Coupled ◽  
End To End ◽  
Public Datasets

Recent end-to-end CNN-based stereo matching algorithms obtain disparities through regression from a cost volume, which is formed by concatenating the features of stereo pairs. Some downsampling steps are often embedded in constructing cost volume for global information aggregation and computational efficiency. However, many edge details are hard to recover due to the imprudent upsampling process and ambiguous boundary predictions. To tackle this problem without training another edge prediction sub-network, we developed a novel tightly-coupled edge refinement pipeline composed of two modules. The first module implements a gentle upsampling process by a cascaded cost volume filtering method, aggregating global information without losing many details. On this basis, the second module concentrates on generating a disparity residual map for boundary pixels by sub-pixel disparity consistency check, to further recover the edge details. The experimental results on public datasets demonstrate the effectiveness of the proposed method.

Mobile Platform Challenges in Interactive Computer Vision

2017 ◽  
pp. 47-73
Author(s):  
Miguel Bordallo López
Keyword(s):  
Computer Vision ◽  
Energy Efficiency ◽  
User Interfaces ◽  
High Performance ◽  
Heterogeneous Computing ◽  
Low Power Consumption ◽  
Mobile Environment ◽  
Trade Off ◽  
And Performance ◽  
Important Design

Computer vision can be used to increase the interactivity of existing and new camera-based applications. It can be used to build novel interaction methods and user interfaces. The computing and sensing needs of this kind of applications require a careful balance between quality and performance, a practical trade-off. This chapter shows the importance of using all the available resources to hide application latency and maximize computational throughput. The experience gained during the developing of interactive applications is utilized to characterize the constraints imposed by the mobile environment, discussing the most important design goals: high performance and low power consumption. In addition, this chapter discusses the use of heterogeneous computing via asymmetric multiprocessing to improve the throughput and energy efficiency of interactive vision-based applications.

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