scholarly journals Self-Similarity Based Corresponding-Point Extraction from Weakly Textured Stereo Pairs

2014 ◽  
Vol 2014 ◽  
pp. 1-20
Author(s):  
Min Mao ◽  
Kuang-Rong Hao ◽  
Yong-Sheng Ding
Keyword(s):  
Detection Method ◽  
Detection Algorithm ◽  
Second Step ◽  
Self Similarity ◽  
Detection Mechanism ◽  
Matching Algorithm ◽  
Geometric Transformations ◽  
Point Set ◽  
Image Pairs ◽  
Point Detection

For the areas of low textured in image pairs, there is nearly no point that can be detected by traditional methods. The information in these areas will not be extracted by classical interest-point detectors. In this paper, a novel weakly textured point detection method is presented. The points with weakly textured characteristic are detected by the symmetry concept. The proposed approach considers the gray variability of the weakly textured local regions. The detection mechanism can be separated into three steps: region-similarity computation, candidate point searching, and refinement of weakly textured point set. The mechanism of radius scale selection and texture strength conception are used in the second step and the third step, respectively. The matching algorithm based on sparse representation (SRM) is used for matching the detected points in different images. The results obtained on image sets with different objects show high robustness of the method to background and intraclass variations as well as to different photometric and geometric transformations; the points detected by this method are also the complement of points detected by classical detectors from the literature. And we also verify the efficacy of SRM by comparing with classical algorithms under the occlusion and corruption situations for matching the weakly textured points. Experiments demonstrate the effectiveness of the proposed weakly textured point detection algorithm.

scholarly journals On-line detection algorithm of ore grade change in grinding grading system

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 701-709
Author(s):  
Jianjun Zhao ◽  
Junwu Zhou
Keyword(s):  
Detection Method ◽  
Control Process ◽  
Detection Algorithm ◽  
Operating Conditions ◽  
Grading System ◽  
Process Data ◽  
Practical Applications ◽  
Grade Change ◽  
On Line ◽  
Point Detection

AbstractIn process industry control, process data is critical for both control and fault diagnosis. Timely detection of outliers and mutation point in process data can quickly adjust control parameters or discover potential failures throughout the system. Aiming at the shortcomings of the traditional mutation point detection method, such as the detection time delay and not being suitable for the process industrial data that mixed outliers, this paper proposes a mutation point and outliers detection method that is suitable for the grinding grading system using the wavelet method to analyze the “Efficient Scoring Vector.” In this algorithm, to distinguish between outliers and mutation points in the detection process, we propose a detection framework based on the relationship between Lipschitz index and wavelet coefficients. Under this framework, the detection algorithm proposed in this paper can detect outliers and mutation points simultaneously. The advantage of this is that the accuracy of the mutation point detection is not affected by the outliers. This means that the method can process grinding grading system process data containing outliers and mutation points under actual operating conditions and is more suitable for practical applications. Finally, the effectiveness and practicability of the proposed detection method are proved by simulation results.

scholarly journals A New Semantic-Based Tool Detection Method for Robots

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Wenbai Chen ◽  
Chao He ◽  
Chen W.Z. ◽  
Chen Q.L. ◽  
Wu P.L.
Keyword(s):  
Detection Method ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Recognition Ability ◽  
Home Helper ◽  
Point Detection ◽  
Functional Components ◽  
Instance Segmentation ◽  
Better Than

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms.

Design of Swimming Pool Temperature Dot Matrix Screen Display

2014 ◽  
Vol 556-562 ◽  
pp. 2208-2211
Author(s):  
Xue Feng Yang
Keyword(s):  
Water Temperature ◽  
Real Time ◽  
Room Temperature ◽  
Detection Method ◽  
Swimming Pool ◽  
Real Time Monitoring ◽  
Real Time Processing ◽  
The Core ◽  
Swimming Pool Water ◽  
Point Detection

According to the need of the real-time monitoring and displaying of the environment in many areas,to put forward a method of temperature monitoring and displaying, using STC11F32XE microcontroller as the core controller, DS18B20 as temperature acquisition chip, 32X64LED dot matrix screen as a display screen,using the mothod of multi point detection method,real-time monitoring of swimming pool water temperature and room temperature, real-time displaying of Multipoint collecting information, Real time processing the detected temperature, the page display to multipoint temperature display through the wireless remote control module,the system will alarm When the water temperature is too high or too low, to remind managers of real-time processing.To design a clear temperature display for the swimming pool,real time monitoring and controlling is very convenient,after the experimental verification, the system reaches the anticipative goal,the system is an ideal and effective.

Analysis of Cluttered Scenes Using an Elastic Matching Approach for Stereo Images

2006 ◽  
Vol 18 (6) ◽  
pp. 1441-1471 ◽  
Author(s):  
Christian Eckes ◽  
Jochen Triesch ◽  
Christoph von der Malsburg
Keyword(s):  
Graph Matching ◽  
Recognition Performance ◽  
Stereo Image ◽  
Matching Algorithm ◽  
Automatic Interpretation ◽  
Color Features ◽  
Occluded Objects ◽  
Elastic Graph Matching ◽  
Image Pairs ◽  
Object Models

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.

Self-similarity based compression of point set surfaces with application to ray tracing

2008 ◽  
Vol 32 (2) ◽  
pp. 221-234 ◽  
Author(s):  
Erik Hubo ◽  
Tom Mertens ◽  
Tom Haber ◽  
Philippe Bekaert
Keyword(s):  
Ray Tracing ◽  
Self Similarity ◽  
Point Set ◽  
Point Set Surfaces
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