A model-based multithreshold method for subgroup identification

Thresholding variable plays a crucial role in subgroup identification for personalizedmedicine. Most existing partitioning methods split the sample basedon one predictor variable. In this paper, we consider setting the splitting rulefrom a combination of multivariate predictors, such as the latent factors, principlecomponents, and weighted sum of predictors. Such a subgrouping methodmay lead to more meaningful partitioning of the population than using a singlevariable. In addition, our method is based on a change point regression modeland thus yields straight forward model-based prediction results. After choosinga particular thresholding variable form, we apply a two-stage multiple changepoint detection method to determine the subgroups and estimate the regressionparameters. We show that our approach can produce two or more subgroupsfrom the multiple change points and identify the true grouping with high probability.In addition, our estimation results enjoy oracle properties. We design asimulation study to compare performances of our proposed and existing methodsand apply them to analyze data sets from a Scleroderma trial and a breastcancer study

Video object detection using fast and accurate change detection and thresholding

Video object detection is an important video processing technique. Change detection and thresholding based video object detection techniques are widely used due to their efficiency. However, change detection and thresholding in real-world video sequences is challenging due to the complexity of video contents and of environmental artifacts. This thesis proposes a color-based change detection and a video-content adaptive thresholding method for accurate and fast video object detection. The proposed color-based change detection algorithm is based on the YUV color model, which has been proved as the most effective color model for object detection. First, frame-differencing is carried out in each channel of a video frame. Then, the pixel intensities in both gray-level channel Y and the color channels U and V of the difference frames are statistically modeled. Second, based on the statistical model of the gray-levels in Y channel, an entropy-based blocks-of-interest scatter estimation algorithm is proposed for locating the frame blocks potentially containing moving objects; and based on the statistical models of the color intensities in color channels, a statistical model of the maximum-intensity between U and V channels are obtained. Third, significance test is applied to the detected blocks-of-interest in both gray-level channel and color channels based on the gray-level statistical model of Y channel and the maximum-intensity statistical model of U and V channels. The gray-levels of the non-significant pixels in Y channel but significant in the U or the V channels are then compensated according to their significance probabilities in the color channels. Finally, change masks can be obtained by a thresholding algorithm. The proposed thresholding algorithm for change detection is based on a change region scatter estimation algorithm and a video-content assessment algorithm to detect the empty frames and estimate the strength of local unimportant changes. According to the proposed video-content assessment, the global threshold of a difference frame is discriminatively computed. For an empty frame, a noise-statistic based thresholding algorithm with a low false alarm is applied to obtain the threshold. Otherwise, the global threshold is obtained by an optimum-thresholding based artifact-robust thresholding algorithm. Experimental results show that (1) with the support from the scatter estimation of the blocks-of-interest, the proposed change detection algorithm is efficient and robust to multiple video contents; (2) the proposed thresholding algorithm clearly outperforms the widely used intensity-distribution based thresholding methods and more efficient and more stable than the state-of-the-art spatial-property based thresholding methods for change detection; and (3) the video object detection technique consisting of the proposed change detection and the proposed thresholding algorithms is robust to artifacts and multiple video contents, and is especially suitable for real-world on-line video applications such as video surveillanc

Thresholding Algorithm Optimization for Change Detection to Satellite Imagery

To detect changes in satellite imagery, a supervised change detection technique was applied to Landsat images from an area in the south of México. At first, the linear regression (LR) method using the first principal component (1-PC) data, the Chi-square transformation (CST) method using first three principal component (PC-3), and tasseled cap (TC) images were applied to obtain the continuous images of change. Then, the threshold was defined by statistical parameters, and histogram secant techniques to categorize as change or unchanged the pixels. A threshold optimization iterative algorithm is proposed, based on the ground truth data and assessing the accuracy of a range of threshold values through the corresponding Kappa coefficient of concordance. Finally, to evaluate the change detection accuracy of conventional methods and the threshold optimization algorithm, 90 polygons (15,543 pixels) were sampled, categorized as real change/unchanged zones, and defined as ground truth, from the interpretation of color aerial photo slides aided by the land cover maps to obtain the omission/commission errors and the Kappa coefficient of agreement. The results show that the threshold optimization is a suitable approach that can be applied for change detection analysis

Flood Extent Mapping for Namibia Using Change Detection and Thresholding with SAR

A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km2, 720 km2, and 673 km2 respectively. Pixels determined to be flooded in vegetation were typically <0.5 % of the entire scene, with the exception of 2009 where the detection of flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes

UNSUPERVISED CHANGE DETECTION FOR MULTISPECTRAL IMAGES

This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images. The proposed approach aims at extracting the change information by jointly analyzing the spectral channels of multitemporal images without any training data. This is accomplished by using a selective Bayesian thresholding for deriving a pseudo training set that is necessary for initializing an adequately defined binary semisupervised support vector machine (S3VM) classifier. Starting from these initial seeds, the S3VM performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a semisupervised learning algorithm. The values of the classifier parameters are then defined according to a novel unsupervised model-selection technique based on a similarity measure between change-detection maps obtained with different settings