Video-Tag Detection and Recognition

2011 ◽  
Vol 58-60 ◽  
pp. 2528-2533
Author(s):  
Li Hua Ye ◽  
Hai Ming Yin
Keyword(s):  
Image Matching ◽  
Detection Method ◽  
Median Filter ◽  
Recognition Method ◽  
Standard Size ◽  
High Recall ◽  
Matching Algorithm ◽  
Analysis Algorithm ◽  
Better Than ◽  
Detection And Recognition

In this work, we present a video-tag detection and recognition method. According to the duration of the video, choose an appropriate strategy to sample the frames. After the candidate tag of every frame is computed, a median filter algorithm is employed to get the tag boundary. At last the binary video-tag is determined by a multi-frame-based analysis algorithm. After scaling the binary tag image to the standard size, a full image-matching algorithm is used to recognize the tag. The experimental results indicate the proposed video-tag detection method has high recall ratio and precision ratio, and the image-matching-based video-tag recognition method performs much better than the traditional OCR methods.

Vehicle Seat Detection Based on Improved RANSAC-SURF Algorithm

2020 ◽  
pp. 2155004
Author(s):  
Xiaoguang Li ◽  
Juan Zhu ◽  
Yiming Ruan
Keyword(s):  
Median Filter ◽  
Perspective Transformation ◽  
Feature Points ◽  
Matching Algorithm ◽  
Absolute Value ◽  
Mean Filter ◽  
Intersection Points ◽  
The Mean ◽  
Vehicle Seat ◽  
Better Than

In order to detect the type of vehicle seat and the missing part of the spring hook, this paper proposes an improved RANSAC-SURF method. First, the image is filtered by a Gauss filter. Second, an improved RANSAC-SURF algorithm is used to detect the types of vehicle seats. Extract the feature points of vehicle seats. The feature points are matched according to the improved RANSAC-SURF algorithm. Third, the image distortion of the vehicle seat is corrected by the method of perspective transformation. Determine whether the seat’s spring hook is missing or not according to the absolute value of the gray difference between the image collected by the camera and the image of the normal installation. The experimental results show that the MSE of the Gauss filter under a 5 [Formula: see text] 5 template is 19.0753, and the PSNR is 35.3261, which is better than that of the mean filter and the median filter. The total matching logarithm of feature points and the number of intersection points are 188 and 18, respectively, in the improved RANSAC-SURF matching algorithm.

A New Recognition Method for Visualizing Music Emotion

2017 ◽  
Vol 7 (3) ◽  
pp. 1246
Author(s):  
Van Loi Nguyen ◽  
Donglim Kim ◽  
Phi Van Ho ◽  
Younghwan Lim
Keyword(s):  
Detection Method ◽  
Emotion Detection ◽  
Dimensional Approach ◽  
Recognition Method ◽  
Categorical Approach ◽  
Training Models ◽  
Discrete Emotion ◽  
Selection Strategies ◽  
Better Than ◽  
Arousal And Valence

This paper proposes an emotion detection method using a combination of dimensional approach and categorical approach. Thayer’s model is divided into discrete emotion sections based on the level of arousal and valence. The main objective of the method is to increase the number of detected emotions which is used for emotion visualization. To evaluate the suggested method, we conducted various experiments with supervised learning and feature selection strategies. We collected 300 music clips with emotions annotated by music experts. Two feature sets are employed to create two training models for arousal and valence dimensions of Thayer’s model. Finally, 36 music emotions are detected by proposed method. The results showed that the suggested algorithm achieved the highest accuracy when using RandomForest classifier with 70% and 57.3% for arousal and valence, respectively. These rates are better than previous studies.

Based on Image Matching and Tracking of Moving Objects

2012 ◽  
Vol 505 ◽  
pp. 352-356
Author(s):  
Jian Xiong Wang ◽  
Yu Lan Wang ◽  
Bin Hong ◽  
Hong Ling Zhang
Keyword(s):  
Image Matching ◽  
Moving Objects ◽  
Linear Prediction ◽  
Prediction Method ◽  
Moving Target ◽  
Target Image ◽  
Gray Scale ◽  
Matching Algorithm ◽  
Template Image ◽  
Better Than

According to the traditional gray-scale value matching algorithm and its defect of high time complexity, this paper presents a matching algorithm based background subtraction. First we find moving target in the background by subtraction, and then match the template image to the target image of motion, which greatly reduces the amount of calculation and improves the matching speed. We effectively predict the target’s location in the next frame by five-point linear prediction method. Simulation shows that the algorithm is better than the traditional gray-scale matching algorithm and it has strong robustness.

An Image Matching Algorithm Based on SCCH Feature Descriptor

2011 ◽  
Vol 33 (9) ◽  
pp. 2152-2157 ◽  
Author(s):  
Yong-he Tang ◽  
Huan-zhang Lu ◽  
Mou-fa Hu
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Matching Algorithm

Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection

2019 ◽  
Vol 15 (8) ◽  
pp. 769-776
Author(s):  
Kalaivani Subramani ◽  
Shantharajah Periyasamy ◽  
Padma Theagarajan
Keyword(s):  
Pattern Matching ◽  
Error Rate ◽  
Median Filter ◽  
Identification System ◽  
Double Line ◽  
Cultivation System ◽  
Matching Algorithm ◽  
Non Local ◽  
Mining Methods ◽  
Blight Disease

Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.

scholarly journals Recognition of Bimolecular Logic Operation Pattern Based on a Solid-State Nanopore

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 33
Author(s):  
Han Yan ◽  
Zhen Zhang ◽  
Ting Weng ◽  
Libo Zhu ◽  
Pang Zhang ◽  
...  
Keyword(s):  
Self Assembly ◽  
Detection Method ◽  
Logic Gate ◽  
Detection Methods ◽  
Label Free ◽  
Recognition Method ◽  
Operation Pattern ◽  
Simple Detection ◽  
Dna Tetrahedron ◽  
Marker Molecule

Nanopores have a unique advantage for detecting biomolecules in a label-free fashion, such as DNA that can be synthesized into specific structures to perform computations. This method has been considered for the detection of diseased molecules. Here, we propose a novel marker molecule detection method based on DNA logic gate by deciphering a variable DNA tetrahedron structure using a nanopore. We designed two types of probes containing a tetrahedron and a single-strand DNA tail which paired with different parts of the target molecule. In the presence of the target, the two probes formed a double tetrahedron structure. As translocation of the single and the double tetrahedron structures under bias voltage produced different blockage signals, the events could be assigned into four different operations, i.e., (0, 0), (0, 1), (1, 0), (1, 1), according to the predefined structure by logic gate. The pattern signal produced by the AND operation is obviously different from the signal of the other three operations. This pattern recognition method has been differentiated from simple detection methods based on DNA self-assembly and nanopore technologies.

An Image Matching Algorithm Based on SIFT and Invariability of Feature Points Set

2011 ◽  
Vol 121-126 ◽  
pp. 701-704
Author(s):  
Xue Tong Wang ◽  
Yao Xu ◽  
Feng Gao ◽  
Jing Yi Bai
Keyword(s):  
Image Matching ◽  
Singular Value ◽  
Feature Points ◽  
Matching Algorithm ◽  
Candidate Feature

Feature points can be used to match images. Candidate feature points are extracted through SIFT firstly. Then feature points are selected from candidate points through singular value decomposing. Distance between feature points sets is computed According to theory of invariability of feature points set, images are matched if the distance is less than a threshold. Experiment showed that this algorithm is available.

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