•A half-Gaussian mixture model is proposed to extract FVC from LARS images (HAGFVC).•HAGFVC is robust to variations of spatial resolution, mixed pixels and vegetated coverage.•HAGFVC outperforms ...previous methods developed for proximal images.
Accurate estimates of fractional vegetation cover (FVC) using remotely sensed images collected using unmanned aerial vehicles (UAVs) offer considerable potential for field measurement. However, most existing methods, which were originally designed to extract FVC from ground-based remotely sensed images (acquired at a few meters above the ground level), cannot be directly used to process aerial images because of the presence of large quantities of mixed pixels. To alleviate the negative effects of mixed pixels, we proposed a new method for decomposing the Gaussian mixture model and estimating FVC, namely, the half-Gaussian fitting method for FVC estimation (HAGFVC). In this method, the histograms of pure vegetation pixels and pure background pixels are firstly fit using two half-Gaussian distributions in the Commission Internationale d’Eclairage (CIE) L*a*b* color space. Then, a threshold is determined based on the parameters of Gaussian distribution to generate a more accurate FVC estimate. We acquired low-altitude remote-sensing (LARS) images in three vegetative growth stages at different flight altitudes over a cornfield. The HAGFVC method successfully fitted the half-Gaussian distributions and obtained stable thresholds for FVC estimation. The results indicate that the HAGFVC method can be used to effectively and accurately derive FVC images, with a small mean bias error (MBE) and with root mean square error (RMSE) of less than 0.04 in all cases. Comparatively, other methods we tested performed poorly (RMSE of up to 0.36) because of the abundance of mixed pixels in LARS images, especially at high altitudes above ground level (AGL) or in the case of moderate vegetation coverage. The results demonstrate the importance of developing image-processing methods that specially account for mixed pixels for LARS images. Simulations indicated that the theoretical accuracy (no errors in fitting the half-Gaussian distributions) of the HAGFVC method reflected an RMSE of less than 0.07. Additionally, this method provides a useful approach to efficiently estimating FVC by using LARS images over large areas.
We propose a new approach which is a three-stage pipeline to fast and accurate segment hand from a single depth image. Firstly, a depth frame is segmented into several regions by histogram-based ...thresholds selection algorithm and tracing the exterior boundaries of objects. We found that MINIMUM, MEAN and MEDIAN are effective ways to separate objects and the threshold in the valley between two maxima similar to MINIMUM algorithm with a minimum error. Then, each segmentation proposal is evaluated by a 3-layers shallow convolutional neural network (CNN) which is trained as a binary classification function to predict whether it is a partition of hand. Finally, all hand components are merged as our hand segmentation result. In our experiment, we use a set of real data containing more than 200,000 frames of depth images. Compared with the results achieved by approaches based on RDF and SegNet, results demonstrate that our approach achieves better performance in high-accuracy (88.34% mean IoU) within shorter processing time (
8 ms).
Leaf area index (LAI) was estimated from vertical gap fraction measurements obtained using top-of-canopy digital colour photography over corn, soybean and wheat canopies. A histogram-based threshold ...technique was used to separate green vegetation tissues from background soil and residue materials in order to derive the canopy vertical gap fraction from the digital photos. The results show that the gap fraction obtained using digital photography was linearly related with the diffuse non-interceptance obtained with a LAI-2000 plant canopy analyzer (
R
2
=
0.78). LAI derived from the photographic method was comparable to the LAI measured using LAI-2000 (
R
2
=
0.83) in the absence of senescence. We recommend using digital photography in addition to conventional equipment for acquiring LAI and gap fraction of agricultural crops, because the approach is less limited by radiation conditions, and the protocol can easily be implemented for extensive sampling at low cost.
Automated lip reading from videos requires lip segmentation. Threshold-based segmentation is straightforward, but it is rarely used. This study proposes a histogram threshold based on the feedback of ...shape information. Both good and bad lip segmentation examples were used to train an
ϵ
-support vector regression model to infer the segmentation accuracy from the region shape. The histogram threshold was optimised to minimise the segmentation error. The proposed method was tested on 895 images from 112 subjects using the AR Face Database. The proposed method, implemented in simple segmentation algorithms, reduced segmentation errors by 23.1%.
Information on sea ice type is an important factor for deriving sea ice parameters from satellite remote sensing data, such as sea ice concentration, extent and thickness. In this study, sea ice in ...the Weddell Sea was classified by the histogram threshold (HT) method, the Spreen model (SM) method from satellite scatterometer data and the strong contrast (SC) method from radiometer data, and this information was compared with Antarctic Sea Ice Processes and Climate (ASPeCt) sea ice-type ship-based observations. The results show that all three methods can distinguish the multi-year (MY) ice and first-year (FY) ice using Ku-band scatterometer data and radiometer data during the ice growth season, while C-band scatterometer data are not suitable for MY ice and FY ice discrimination using HT and SM methods. The SM model has a smaller MY ice classification extent than the HT method from scatterometer data. The classification accuracy of the SM method is the higher compared to ship-based observations. It can be concluded that the SM method is a promising method for discriminating MY ice from FY ice. These results provide a reference for further retrieval of long-term sea ice-type information for the whole of Antarctica.
Purpose
Since pre-processing and initial segmentation steps in medical images directly affect the final segmentation results of the regions of interesting, an automatic segmentation method of a ...parameter-adaptive pulse-coupled neural network is proposed to integrate the above-mentioned two segmentation steps into one. This method has a low computational complexity for different kinds of medical images and has a high segmentation precision.
Methods
The method comprises four steps. Firstly, an optimal histogram threshold is used to determine the parameter
α
for different kinds of images. Secondly, we acquire the parameter
β
according to a simplified pulse-coupled neural network (SPCNN). Thirdly, we redefine the parameter
V
of the SPCNN model by sub-intensity distribution range of firing pixels. Fourthly, we add an offset
A
×
S
off
to improve initial segmentation precision.
Results
Compared with the state-of-the-art algorithms, the new method achieves a comparable performance by the experimental results from ultrasound images of the gallbladder and gallstones, magnetic resonance images of the left ventricle, and mammogram images of the left and the right breast, presenting the overall metric UM of 0.9845, CM of 0.8142, TM of 0.0726.
Conclusion
The algorithm has a great potential to achieve the pre-processing and initial segmentation steps in various medical images. This is a premise for assisting physicians to detect and diagnose clinical cases.
Detecting coastal morphodynamics is a crucial task for monitoring shoreline changes and coastal zone management. However, modern technology viz., Geoinformatics paves the way for long-term monitoring ...and observation with precise output. Therefore, this study aimed to produce explicit shoreline change maps and analyze the historical changes of the coastline at the east coast of the Ampara District in Sri Lanka. The histogram threshold method is used to extract data from satellite images. The time-series satellite images, acquired from 1987 to 2017, toposheet, and Google Earth historical images were compared having adjusted with the ground-truth to find the seashore changes in the study area. The histogram threshold method is used on band 5 (mid-infrared) for separating land from water pixels which means that the water pixel values were classified to one (1) and land pixel values to zero (0). The extracted shoreline vectors were associated with each other to determine the dynamics of changing shoreline of the study area. The Digital Shoreline Analysis System (DSAS) was used to find shoreline movements for each period of time. As a result, it was observed by the cross-section analysis within 100 m shoreline—seaward range along the study area—in which severe erosion has occurred northward of the Oluvil Harbor and anomalous accretion southward of the harbor because of the breakwaters constructed in the port entrance which hinder the long shore sediment transport along the study area. This situation has resulted in many ramifications to the coastal zone of the study area in socio-economic and environmental aspects in which the coastal protection mechanisms have not been well implemented to curb such issues.
Leaf area index (LAI) is an important physiological trait that determines solar radiation interception and thus biomass. In this study leaf area index (LAI) was estimated from vertical gap fraction ...derived from top-of-canopy digital colour photography ofwheat canopies. An improved vegetation index, Excess Green minus Excess Red (ExG-ExR) was compared to the commonly used Excess Green (ExG), Excess Red (ExR) and normalized difference (NDI) indices. A histogram-based threshold technique was used to separate green vegetation tissues from background soil in order to derive the canopy vertical gap fraction. LAI derived from the ExG-ExR, ExG indexed image was comparable to the LAI measured using the commercial plant canopy analyzer (LAI-2200,LI-CORInc., USA) (R2 = 0.68 and 0.66 for ExG-ExRand ExG, respectively) with RMSE of 0.63 and 0.79, respectively.However, NDI was overestimated while Ex Rwas found to be under estimated LAI as compared with that measured using the commercial plant canopy analyzer(R2 = 0.47 and 0.35 for NDI and ExR, respectively) with RMSE of 4.09 and 2.19, respectively. Thus, digital photography based ExG-ExRmethod can be used as low cost, non-destructive high through put method for assessing LAI, early vigour and gap fraction of wheat and potentially other cereal crops.
Threshold-based segmentation methods provide a simple and efficient way to implement lip segmentation. However, automatic computation of robust thresholds presents a major challenge. This research ...proposes an adaptive method for selecting the histogram threshold, based on feedback of shape information. The proposed method reduces unnecessary overhead by first comparing the initial segmentation to a reference lip shape model to decide if optimisation is required. In cases where optimisation is required, the algorithm adjusts the threshold until the segmentation is sufficiently similar to a reference shape model. The algorithm is tested on the AR Face Database by comparing the segmentation accuracy before and after optimisation. The proposed method increases the number of segmentations classified as ‘good’ (overlap above 90 %) by 7.1 % absolute, and significantly improves the segmentation in challenging cases containing facial hair.
Focused on static high-definition sequence images captured on the highway bayonet, this paper proposes a new approach for vehicle detection and shadow elimination based on average background ...modeling, which uses average background model and background subtraction to locate vehicle roughly, eliminates shadow of the vehicle using canny edge detection with dynamic histogram threshold determined by the histogram of the image. Experiments show that this method can locate the position of vehicle quickly and accurately.
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