scholarly journals Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Zelang Miao ◽  
Bin Wang ◽  
Wenzhong Shi ◽  
Hao Wu ◽  
Yiliang Wan
Keyword(s):  
Image Analysis ◽  
Extraction Method ◽  
Mean Shift ◽  
Major Axis ◽  
Gaussian Mixture ◽  
Initial Result ◽  
Robust Solution ◽  
Centerline Extraction ◽  
Common Problems ◽  
Classified Image

The extraction of road centerline from the classified image is a fundamental image analysis technology. Common problems encountered in road centerline extraction include low ability for coping with the general case, production of undesired objects, and inefficiency. To tackle these limitations, this paper presents a novel accurate centerline extraction method using Gaussian mixture model (GMM) and subspace constraint mean shift (SCMS). The proposed method consists of three main steps. GMM is first used to partition the classified image into several clusters. The major axis of the ellipsoid of each cluster is extracted and deemed to be taken as the initial centerline. Finally, the initial result is adjusted using SCMS to produce precise road centerline. Both simulated and real datasets are used to validate the proposed method. Preliminary results demonstrate that the proposed method provides a comparatively robust solution for accurate centerline extraction from a classified image.

scholarly journals Analytical Study of Colour Spaces for Plant Pixel Detection

2017 ◽  
Author(s):  
Pankaj Kumar ◽  
Stanley J. Miklavcic
Keyword(s):  
Image Analysis ◽  
Analytical Study ◽  
Digital Images ◽  
Mean Shift ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Space Representation ◽  
Colour Space ◽  
Colour Image ◽  
Constant Bandwidth

Segmentation of a region of interest is an important pre-processing step for many colour image analysis techniques. Similarly segmentation of plant in digital images is an important preprocessing step in phenotying plants by image analysis. In this paper we present an analytical study to statistically determine the suitability of colour space representation of an image to best detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation in which the separation of the distributions representing plant pixels and background pixels is maximized would be the best for detection of plant pixels. The two classes of pixels are modelled as a Gaussian mixture model (GMM). In our GM modelling we don't make any prior assumption about the number of Gaussians in the model. Rather a constant bandwidth mean-shift filter is used to cluster the data and the number of clusters and hence the number of Gaussians is automatically determined. Here we have analysed following representative colour spaces like $RGB$, $rgb$, $HSV$, $Ycbcr$ and $CIE-Lab$. This is because these colour spaces represent several other similar colour spaces and also an exhaustive study of all the colour space will be too voluminous. We also analyse the colour space feature from the two-class variance ratio perspective and compare the results of our hypothesis with this metric. The dataset for this empirical study consist of 378 digital images of plants and their manual segmentation. Dataset consist of various species of plants (arabidopsi, tobacco, wheat, rye grass etc.) imaged under different lighting conditions, indoor and outdoor, controlled and uncontrolled background. In results we obtain better segmentation of the plants in $HSV$ colour space, which is supported by its Earth mover distance (EMD) on the GMM distribution of plant and background pixels.

Using High-Speed Image Analysis to Estimate Trash in Cotton

1985 ◽  
Vol 107 (2) ◽  
pp. 206-219 ◽  
Author(s):  
R. A. Taylor
Keyword(s):  
Image Analysis ◽  
High Speed ◽  
Market Quality ◽  
Foreign Matter ◽  
Black And White ◽  
Television Camera ◽  
Cotton Sample ◽  
Common Problems ◽  
Surface Scan ◽  
Dimensional Surface

Two new scanning type cotton trashmeters are being developed to indicate the amount of trash and foreign matter in lint cotton. These instruments are primarily intended to replace the current visual method of grading cotton for market quality. They both perform a two dimensional surface scan using a black and white television camera. High-speed microprocessors provide an analysis of the TV signal at video scan rates. Only a fraction of a second of time is required to complete all scanning, signal processing, and data analysis for each cotton sample exposure. This article discusses some common problems in TV image analysis and how they relate to cotton scanning. Also discussed are instrument precision and design features and a method of calibrating each instrument.

scholarly journals Continuously Adaptive Mean-shift Tracking Algorithm Based on Improved Gaussian Model

2020 ◽  
Vol 13 (5) ◽  
pp. 50-57
Author(s):  
Jinping Sun ◽  
◽  
Enjie Ding ◽  
Dan Li ◽  
Kailiang Zhang ◽  
...  
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Mean Shift ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Target Color ◽  
Color Model ◽  
Background Color ◽  
Complex Scenes

In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.

Color Quantization Based on Gaussian Mixture Mode

2012 ◽  
Vol 457-458 ◽  
pp. 650-654
Author(s):  
Qiu Chun Jin ◽  
Xiao Li Tong
Keyword(s):  
Image Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Image ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Color Quantization ◽  
Quantization Method ◽  
Color Palette ◽  
Image Quantization

Color quantization is an important technique for image analysis that reduces the number of distinct colors for a color image. A novel color image quantization algorithm based on Gaussian mixture model is proposed. In the approach, we develop a Gaussian mixture model to design the color palette. Each component in the GMM represents a type of color in the color palette. The task of color quantization is to group pixels into different component. Experimental results show that our quantization method can obtain better results than other methods.

scholarly journals Object-Based Image Analysis Applied to Low Altitude Aerial Imagery for Potato Plant Trait Retrieval and Pathogen Detection

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5477 ◽  
Author(s):  
Jasper Siebring ◽  
João Valente ◽  
Marston Heracles Domingues Franceschini ◽  
Jan Kamp ◽  
Lammert Kooistra
Keyword(s):  
Image Analysis ◽  
Large Scale ◽  
Pathogen Detection ◽  
Mean Shift ◽  
Human Consumption ◽  
Object Based Image Analysis ◽  
Potato Plants ◽  
Plant Trait ◽  
Object Based ◽  
Low Altitude

There is a growing demand in both food quality and quantity, but as of now, one-third of all food produced for human consumption is lost due to pests and other pathogens accounting for roughly 40% of pre-harvest loss in potatoes. Pathogens in potato plants, like the Erwinia bacteria and the PVYNTN virus for example, exhibit symptoms of varying severity that are not easily captured by pixel-based classes (as these ignore shape, texture, and context in general). The aim of this research is to develop an object-based image analysis (OBIA) method for trait retrieval of individual potato plants that maximizes information output from Unmanned Aerial Vehicle (UAV) RGB very high resolution (VHR) imagery and its derivatives, to be used for disease detection of the Solanum tuberosum. The approach proposed can be split in two steps: (1) object-based mapping of potato plants using an optimized implementation of large scale mean-shift segmentation (LSMSS), and (2) classification of disease using a random forest (RF) model for a set of morphological traits computed from their associative objects. The approach was proven viable as the associative RF model detected presence of Erwinia and PVY pathogens with a maximum F1 score of 0.75 and an average Matthews Correlation Coefficient (MCC) score of 0.47. It also shows that low-altitude imagery acquired with a commercial UAV is a viable off-the-shelf tool for precision farming, and potato pathogen detection.

Co-registration of White Matter Tractographies by Adaptive-Mean-Shift and Gaussian Mixture Modeling

2010 ◽  
Vol 29 (1) ◽  
pp. 132-145 ◽  
Author(s):  
O. Zvitia ◽  
A. Mayer ◽  
R. Shadmi ◽  
S. Miron ◽  
H.K. Greenspan
Keyword(s):  
White Matter ◽  
Mean Shift ◽  
Gaussian Mixture ◽  
Mixture Modeling ◽  
Gaussian Mixture Modeling

scholarly journals Vehicle Speed Estimation Using Gaussian Mixture Model and Kalman Filter

2021 ◽  
Vol 16 (4) ◽  
Author(s):  
Ahmed Abdulwahab Tayeb ◽  
Rabah Wasel Aldhaheri ◽  
Muhammad Shehzad Hanif
Keyword(s):  
Kalman Filter ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Vehicle Detection ◽  
Gaussian Mixture ◽  
Automated System ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Robust Solution ◽  
Geometry Model

Many countries use traffic enforcement camera to monitor the speed limit and capture over speed violations. The main objective of such a system is to enforce the speed limits which results in the reduction of number of accidents, fatalities, and serious injuries. Traditionally, the task is carried out manually by the enforcement agencies with the help of specialized hardware such as radar and camera. To automate the process, an efficient and robust solution is needed. Vehicle detection, tracking and speed estimation are the main tasks in an automated system which are not trivial. In this paper, we address the problem of vehicle detection, tracking, and speed estimation using a single fixed camera. A background subtraction method based on the Gaussian Mixture Model (GMM) is employed to detect vehicles because of its capability in dealing with complex backgrounds and variations in the appearance due to illumination and scale. Next, the detected vehicles are tracked in each frame by using the Kalman Filter. Finally, an estimate the speed of each vehicle is determined by using the perspective geometry model. The complete system is tested at our university campus and the results are promising.

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