An Optimized Method for Terrain Reconstruction Based on Descent Images

An optimization method is proposed to perform high-accuracy terrain reconstruction of the landing area of Chang'e III. First, feature matching is conducted using geometric model constraints. Then, the initial terrain is obtained and the initial normal vector of each point is solved on the basis of the initial terrain. By changing the vector around the initial normal vector in small steps a set of new vectors is obtained. By combining these vectors with the direction of light and camera, the functions are set up on the basis of a surface reflection model. Then, a series of gray values is derived by solving the equations. The new optimized vector is recorded when the obtained gray value is closest to the corresponding pixel. Finally, the optimized terrain is obtained after iteration of the vector field. Experiments were conducted using the laboratory images and descent images of Chang'e III. The results showed that the performance of the proposed method was better than that of the classical feature matching method. It can provide a reference for terrain reconstruction of the landing area in subsequent moon exploration missions

Nonrigid Registration of Brain Tumor Resection MR Images Based on Joint Saliency Map and Keypoint Clustering

This paper proposes a novel global-to-local nonrigid brain MR image registration to compensate for the brain shift and the unmatchable outliers caused by the tumor resection. The mutual information between the corresponding salient structures, which are enhanced by the joint saliency map (JSM), is maximized to achieve a global rigid registration of the two images. Being detected and clustered at the paired contiguous matching areas in the globally registered images, the paired pools of DoG keypoints in combination with the JSM provide a useful cluster-to-cluster correspondence to guide the local control-point correspondence detection and the outlier keypoint rejection. Lastly, a quasi-inverse consistent deformation is smoothly approximated to locally register brain images through the mapping the clustered control points by compact support radial basis functions. The 2D implementation of the method can model the brain shift in brain tumor resection MR images, though the theory holds for the 3D case

An improved classification approach for echocardiograms embedding temporal information

Cardiovascular disease is an umbrella term for all diseases of the heart. At present, computer-aided echocardiogram diagnosis is becoming increasingly beneficial. For echocardiography, different cardiac views can be acquired depending on the location and angulations of the ultrasound transducer. Hence, the automatic echocardiogram view classification is the first step for echocardiogram diagnosis, especially for computer-aided system and even for automatic diagnosis in the future. In addition, heart views classification makes it possible to label images especially for large-scale echo videos, provide a facility for database management and collection. This thesis presents a framework for automatic cardiac viewpoints classification of echocardiogram video data. In this research, we aim to overcome the challenges facing this investigation while analyzing, recognizing and classifying echocardiogram videos from 3D (2D spatial and 1D temporal) space. Specifically, we extend 2D KAZE approach into 3D space for feature detection and propose a histogram of acceleration as feature descriptor. Subsequently, feature encoding follows before the application of SVM to classify echo videos. In addition, comparison with the state of the art methodologies also takes place, including 2D SIFT, 3D SIFT, and optical flow technique to extract temporal information sustained in the video images. As a result, the performance of 2D KAZE, 2D KAZE with Optical Flow, 3D KAZE, Optical Flow, 2D SIFT and 3D SIFT delivers accuracy rate of 89.4%, 84.3%, 87.9%, 79.4%, 83.8% and 73.8% respectively for the eight view classes of echo videos

Quantitative analysis of defects in composite material by means of optical lockin thermography

In the aerospace industry, carbon-fiber reinforced plastic (CFRP) materials are becoming increasingly popular. Due to mechanical fracture and hence safety related issues, CFRP components must be inspected for defects with non-destructive methods. This thesis focuses on non-destructive testing of CFRP materials with optical lockin thermography. The field of quantitative analysis of thermographic measurements is enhanced. The data of geometrical parameters e.g. depth, size and shape of defects in structures of globally homogeneous and anisotropic CFRP materials is required for fracture mechanics. To evaluate defects in a quantitative way, image processing algorithms are applied to thermographic phase images in order to get panoramic views of extended aircraft parts and to compare measurements before and after a fatigue load in order to determine potential defect growth. Images of lockin and ultrasound excited thermography are combined with data-fusion techniques to get improved information on defects such as impacts. The image formation process can be modeled through a point-spread function, which depends on the depth of the defect and the modulation frequency. A function is computed by using Green\u27s functions and is adapted to anisotropic materials. The quantities depth, size and shape of a defect are determined through inverse numerical filters. Measurements are compared to numerical simulations and a reconstruction algorithm of planar subsurface defects is validated.In der Luft- und Raumfahrt werden verstärkt kohlefaserverstärkte Kunststoffe (CFK) eingesetzt, die mit Methoden der zerstörungsfreien Prüftechnik auf Defekte hin überprüft werden müssen. Diese Dissertation befasst sich mit optischer Lockin Thermographie als zerstörungsfreie Prüftechnik für CFK Werkstoffe. Im Rahmen der Arbeit wurde die quantitative Analyse von Defekten in global homogenem und anisotropem CFK Material erweitert. Im Rahmen der quantitativen Bestimmung von Defekten werden Bildverarbeitungsalgorithmen auf thermische Bilder angewandt, um Panorama-Bilder von großen, langen Bauteilen zu erzeugen. Messungen vor- und nach einer Belastung werden verglichen, um ein potentielles Defektwachstum zu bestimmen. Thermische Bilder der Lockin und der Ultraschall angeregten Thermographie werden im Sinne von "Data-Fusion" überlagert, um bessere quantitative Informationen über Defekte wie Impaktschäden zu erzielen. Die thermischen Bilder werden durch eine Punktbildfunktion, die von der Tiefe des Defektes und der Modulationsfrequenz abhängt, modelliert. Die Funktion wird mit Hilfe Green\u27scher Funktionen aufgestellt und an anisotropem Material adaptiert. Die Parameter Tiefenlage, Größe und Form eines Defektes werden über die Lösung eines inversen Problem mit numerischen Filter bestimmt. Die Messungen werden mit numerischen Simulationen verglichen. Ein Algorithmus zur Rekonstruktion flacher Defekte wird validiert

A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision

Higher dimensional data such as video and 3D are the leading edge of multimedia retrieval and computer vision research. In this survey, we give a comprehensive overview and key insights into the state of the art of higher dimensional features from deep learning and also traditional approaches. Current approaches are frequently using 3D information from the sensor or are using 3D in modeling and understanding the 3D world. With the growth of prevalent application areas such as 3D games, self-driving automobiles, health monitoring and sports activity training, a wide variety of new sensors have allowed researchers to develop feature description models beyond 2D. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms. Due to the major importance of the evaluation process, we also present an overview of the current datasets and benchmarks. Moreover, based on more than 330 papers from this study, we present the major challenges and future directions. Computer Systems, Imagery and Medi

Enhanced Ultrasound Visualization for Procedure Guidance

Intra-cardiac procedures often involve fast-moving anatomic structures with large spatial extent and high geometrical complexity. Real-time visualization of the moving structures and instrument-tissue contact is crucial to the success of these procedures. Real-time 3D ultrasound is a promising modality for procedure guidance as it offers improved spatial orientation information relative to 2D ultrasound. Imaging rates at 30 fps enable good visualization of instrument-tissue interactions, far faster than the volumetric imaging alternatives (MR/CT). Unlike fluoroscopy, 3D ultrasound also allows better contrast of soft tissues, and avoids the use of ionizing radiation.Engineering and Applied Science

Image-Based Localization Using the Plenoptic Function

In this report we study the ways to exploit the vast amount of information inherent in the plenoptic space and constraints of the plenoptic function to improve the efficiency of image retrieval, recognition and matching techniques. The specific application we are concerned with is image-based location recognition on mobile devices. The plenoptic space is formed by extending the notion of traditional two-dimensional by adding more dimensions for viewing direction, time and wavelength. Using current mobile devices' built-in cameras, one can easily capture a large sequence of pictures from a single static scene by moving the camera in one direction, which form a three dimensional plenoptic function