Quantized Residual Preference Based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting.

In this paper, quantized residual preference is proposed to represent the hypotheses and the points for model selection and inlier segmentation in multi-structure geometric model fitting. First, a quantized residual preference is proposed to represent the hypotheses. Through a weighted similarity measurement and linkage clustering, similar hypotheses are put into one cluster, and hypotheses with good quality are selected from the clusters as the model selection results.

Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting.

Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting.

Motion Segmentation Based on Model Selection in Permutation Space for RGB Sensors.

Motion segmentation is aimed at segmenting the feature point trajectories belonging to independently moving objects. Using the affine camera model, the motion segmentation problem can be viewed as a subspace clustering problem-clustering the data points drawn from a union of low-dimensional subspaces. In this paper, we propose a solution for motion segmentation that uses a multi-model fitting technique.

Blind UAV Images Deblurring Based on Discriminative Networks.

Unmanned aerial vehicles (UAVs) have become an important technology for acquiring high-resolution remote sensing images. Because most space optical imaging systems of UAVs work in environments affected by vibrations, the optical axis motion and image plane jitter caused by these vibrations easily result in blurring of UAV images. In the paper; we propose an advanced UAV image deblurring method based on a discriminative model comprising a classifier for blurred and sharp UAV images which is embedded into the maximum a posteriori framework as a regularization term that constantly optimizes ill-posed problem of blind image deblurring to obtain sharper UAV images.

An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks.

Unmanned aerial vehicles (UAVs) are an inexpensive platform for collecting remote sensing images, but UAV images suffer from a content loss problem caused by noise. In order to solve the noise problem of UAV images, we propose a new methods to denoise UAV images. This paper introduces a novel deep neural network method based on generative adversarial learning to trace the mapping relationship between noisy and clean images.

A region-based segmentation method for ultrasound images in HIFU therapy.

Purpose: Precisely and efficiently locating a tumor with less manual intervention in ultrasound-guided high-intensity focused ultrasound (HIFU) therapy is one of the keys to guaranteeing the therapeutic result and improving the efficiency of the treatment. The segmentation of ultrasound images has always been difficult due to the influences of speckle, acoustic shadows, and signal attenuation as well as the variety of tumor appearance. The quality of HIFU guidance images is even poorer than that of conventional diagnostic ultrasound images because the ultrasonic probe used for HIFU guidance usually obtains images without making contact with the patient's body.

Computational imaging from non-uniform degradation of staggered TDI thermal infrared imager.

For the Time Delay Integration (TDI) staggered line-scanning thermal infrared imager, a Computational Imaging (CI) approach is developed to achieve higher spatial resolution images. After a thorough analysis of the causes of non-uniform image displacement and degradation for multi-channel staggered TDI arrays, the study aims to approach one-dimensional (1D) sub-pixel displacement estimation and superposition of images from time-division multiplexing scanning lines. Under the assumption that a thermal image is 2D piecewise C(2) smooth, a sparse-and-smooth deconvolution algorithm with L1-norm regularization terms combining the first and second order derivative operators is proposed to restore high frequency components and to suppress aliasing simultaneously.

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy.

High-intensity focused ultrasound (HIFU) therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy.

Segmentation of tumor ultrasound image in HIFU therapy based on texture and boundary encoding.

It is crucial in high intensity focused ultrasound (HIFU) therapy to detect the tumor precisely with less manual intervention for enhancing the therapy efficiency. Ultrasound image segmentation becomes a difficult task due to signal attenuation, speckle effect and shadows. This paper presents an unsupervised approach based on texture and boundary encoding customized for ultrasound image segmentation in HIFU therapy.

Stereo vision calibration based on GMDH neural network.

In order to improve the accuracy and stability of stereo vision calibration, a novel stereo vision calibration approach based on the group method of data handling (GMDH) neural network is presented. Three GMDH neural networks are utilized to build a spatial mapping relationship adaptively in individual dimension. In the process of modeling, the Levenberg-Marquardt optimization algorithm is introduced as an interior criterion to train each partial model, and the corrected Akaike's information criterion is introduced as an exterior criterion to evaluate these models.

[Application of support vector machine in the detection of early cancer].

Support Vector Machine (SVM) is an efficient novel method originated from the statistical learning theory. It is powerful in machine learning to solve problems with finite samples. Due to the deficiency of cancer cells, character of patient and noise in the raw data, it is very difficult to diagnose early cancer accurately.