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.

scholarly journals Reconstruction of Corrupted Image From Salt And Pepper Noise From Median Filter

2012 ◽  
pp. 4-6
Author(s):  
Vishal Gautam ◽  
Tarun Varma
Keyword(s):  
Median Filter ◽  
Median Filtering ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Noise Density ◽  
Mean Filter ◽  
Salt And Pepper ◽  
Image Details ◽  
Lower Noise ◽  
Better Than

- Inthis paper,we propose an improved median filtering algorithm. Here, we introduced salt and pepper noise for the image corruption and reconstruct original image using different filters i.e. mean, median and improved median filter. The performance of improved median filter is good at lower noise density levels.The mean filter suppresses little noise and gets the worst results.The experimental resultsshow that our improved median filter is better than previousmedian filterfor lower noise density (upto 60%). It removes most of the noises effectively while preserving image details very well.

scholarly journals AN ADAPTIVE MORPHOLOGICAL MEAN FILTER FOR VERY HIGH-RESOLUTION REMOTE SENSING IMAGE PROCESSING

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1269-1274
Author(s):  
Z. Lv ◽  
J. Shi ◽  
Y. Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Median Filter ◽  
Remote Sensing Image ◽  
Mean Filter ◽  
Ground Object ◽  
The Mean ◽  
Remote Sensing Image Processing ◽  
Very High

Very high resolution (VHR) remote sensing imagery can reveal the ground object in greater detail, depicting their color, shape, size and structure. However, VHR also leads much original noise in spectra, and this original noise may reduce the reliability of the classification’s result. This paper presents an Adaptive Morphological Mean Filter (AMMF) for smoothing the original noise of VHR imagery and improving the classification’s performance. AMMF is a shape-adaptive filter which is constructed by detecting gradually the spectral similarity between a kernel-anchored pixel and its contextual pixels through an extension-detector with 8-neighbouring pixels, and the spectral value of the kernel-anchored pixel is instead by the mean of group pixels within the adaptive region. The classification maps based on the AMMF are compared with the classification of VHR images based on the homologous filter processing, such as Mean Filter (MF) and Median Filter(MedF). The experimental results suggest the following: 1) VHR image processed using AMMF can not only preserve the detail information among inter-classes but also smooth the noise within intra-class; 2) The proposed AMMF processing can improve the classification’s performance of VHR image, and it obtains a better visual performance and accuracy while comparing with MF and MedF.

A Combination Approach to Noise Reduction in Digital Image Processing

2013 ◽  
Vol 278-280 ◽  
pp. 1359-1365
Author(s):  
Jing Dong ◽  
Zhi Chai ◽  
Ke Wen Xia
Keyword(s):  
Noise Reduction ◽  
Digital Image ◽  
Gaussian Noise ◽  
Median Filter ◽  
The Other ◽  
Median Filtering ◽  
Mean Filter ◽  
The Mean ◽  
Combination Approach ◽  
Mean Filtering

In order to reduce Gaussian and Salt & Pepper noises, a combination approach to noise reduction is presented by combining the median filter with the mean filter. The detail simulations show that the mode which the median filtering first and then the mean filtering is superior to that of the simply single filtering, or the mean filtering first and then the median filtering when the image obviously contain the Salt & Pepper noise. On the other hand, it is not necessarily the optimal scheme to use the mode which the mean filtering first and then the median filtering when the digital image obviously contains the Gaussian noise.

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.

Relation of Statistically Significant, Abnormal, and Typical WAIS-R VIQ-PIQ Discrepancies to Full Scale IQs

2000 ◽  
Vol 16 (2) ◽  
pp. 107-114 ◽  
Author(s):  
Louis M. Hsu ◽  
Judy Hayman ◽  
Judith Koch ◽  
Debbie Mandell
Keyword(s):  
Graphical Method ◽  
The United States ◽  
Full Scale ◽  
True Score ◽  
Absolute Value ◽  
Normative Population ◽  
The Us ◽  
The Mean ◽  
And Performance ◽  
Quadratic Models

Summary: In the United States' normative population for the WAIS-R, differences (Ds) between persons' verbal and performance IQs (VIQs and PIQs) tend to increase with an increase in full scale IQs (FSIQs). This suggests that norm-referenced interpretations of Ds should take FSIQs into account. Two new graphs are presented to facilitate this type of interpretation. One of these graphs estimates the mean of absolute values of D (called typical D) at each FSIQ level of the US normative population. The other graph estimates the absolute value of D that is exceeded only 5% of the time (called abnormal D) at each FSIQ level of this population. A graph for the identification of conventional “statistically significant Ds” (also called “reliable Ds”) is also presented. A reliable D is defined in the context of classical true score theory as an absolute D that is unlikely (p < .05) to be exceeded by a person whose true VIQ and PIQ are equal. As conventionally defined reliable Ds do not depend on the FSIQ. The graphs of typical and abnormal Ds are based on quadratic models of the relation of sizes of Ds to FSIQs. These models are generalizations of models described in Hsu (1996) . The new graphical method of identifying Abnormal Ds is compared to the conventional Payne-Jones method of identifying these Ds. Implications of the three juxtaposed graphs for the interpretation of VIQ-PIQ differences are discussed.

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.

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples

2019 ◽  
Vol 73 (12) ◽  
pp. 1436-1450 ◽  
Author(s):  
Fabiola León-Bejarano ◽  
Martin O. Méndez ◽  
Miguel G. Ramírez-Elías ◽  
Alfonso Alba
Keyword(s):  
Empirical Mode Decomposition ◽  
Raman Spectra ◽  
Biological Samples ◽  
Biological Material ◽  
Noise Levels ◽  
Synthetic Material ◽  
High Noise ◽  
Mode Decomposition ◽  
Mean Filter ◽  
The Mean

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA ([Formula: see text]) outperforms VRA ([Formula: see text]) for moderate to high noise levels whereas mVRA outperformed EMD ([Formula: see text]) for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results ([Formula: see text]. For Raman spectra of synthetic material, good performance of the three methods ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.

Quantitative analysis of static equilibrium in women after mastectomy

2014 ◽  
Vol 14 (1) ◽  
pp. 81-87
Author(s):  
Maciej Rachwał ◽  
Justyna Drzał-Grabiec ◽  
Katarzyna Walicka-Cupryś ◽  
Aleksandra Truszczyńska
Keyword(s):  
Mean Amplitude ◽  
Control Group ◽  
Foot Pressure ◽  
Locomotor System ◽  
Center Of Foot Pressure ◽  
The Mean ◽  
Almost All ◽  
Posture Stability ◽  
Mastectomy Group ◽  
Better Than

Abstract Background: The post-mastectomy changes to the locomotor system are related to the scar and adhesion or to the lymphatic edema after amputation which, in turn, lead to local and global distraction of the work of the muscles. These changes lead to body statics disturbance that changes the projection of the center of gravity and worsens motor response due to changing of the muscle sensitivity. Objective: The aim of the study was to evaluate the static balance of women after undergoing mastectomy. Methods: The study included 150 women, including 75 who underwent mastectomy (mean age: 60±7.6) years, mean body mass index (BMI): 26 (±3.6) kg/m2) and 75 who were placed in the control group with matched age and BMI. The study was conducted using a tensometric platform. Results: Statistically significant differences were found for almost all parameters between the post-mastectomy group and group of healthy women, regarding center of foot pressure (COP) path length in the Y and X axes and the mean amplitude of COP. Conclusions: First, the findings revealed that balance in post-mastectomy women is significantly better than in the control group. Second, physiotherapeutic treatment of post-mastectomy women may have improved their posture stability compared with their peers.

scholarly journals Heterobeltiosis in French Marigold (Tagetes patula L.)

2016 ◽  
Vol 8 (8) ◽  
pp. 182
Author(s):  
Kanwar Priyanaka ◽  
Y. C. Gupta ◽  
S. R. Dhiman ◽  
R. K. Dogra ◽  
Sharma Madhu ◽  
...  
Keyword(s):  
Rainy Season ◽  
Quantitative Traits ◽  
Block Design ◽  
Tagetes Patula ◽  
Male Sterile ◽  
Randomized Block Design ◽  
Parental Material ◽  
The Mean ◽  
Parental Lines ◽  
Better Than

<p>The studies on heterosis were carried with four male sterile lines namely; ms<sub>7</sub>, ms<sub>8</sub>, ms<sub>9,</sub> ms<sub>10</sub> and 18 diverse pollinators as tester by using line × tester crossing programme. The 72 F<sub>1</sub> hybrids were produced and evaluated along with 22 parental lines during summer 2009 and rainy season 2009 in Randomized Block Design. Observations were recorded on nine quantitative traits during both the seasons. Highly significant variances for all the traits indicated the sufficient variability in the parental material for all the characters under study. The performance of F<sub>1</sub> hybrids was much better than the mean performance of parents during both the crop seasons. Appreciable heterosis was observed in all the characters, except flower weight in summer and plant height in rainy season.</p>

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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