Photogrammetric calibration of the NASA-Wallops Island image intensifier system

An image intensifier was designed for use as one of the primary tracking systems for the barium cloud experiment at Wallops Island. Two computer programs, a definitive stellar camara calibration program and a geodetic stellar camara orientation program, were originally developed at Wallops on a GE 625 computer. A mathematical procedure for determining the image intensifier distortions is outlined, and the implementation of the model in the Wallops computer programs is described. The analytical calibration of metric cameras is also discussed

Study Projectile Motion With Different Initial Conditions Using Digital Image

The aim of this research is building algorithms to study projectile motion in tow dimension  and tracking the object in sequence frames of digital image . Computer program has written in visual basic language (version 6) depend on mathematical models to detect a motion of object in two–dimensions (2-D)with different initial conditions like initial velocity, the height of object from the earth and the angle of motion, to calculate important variables in motion such as distance, displacement, velocity, speed and the energy (kinetic and potential). Color digital images of type (bmp) and (RGB) color model were used in the study for easy handling them, after determining the center of the image on the x-axis, and y-axis and tracking movement on the basis of the center, and the results were expected to conform to the movement of the body. Key words: Projectile, Motion, Digital Image

Study Projectile Motion With Different Initial Conditions Using Digital Image

The aim of this research is building algorithms to study projectile motion in tow dimension  and tracking the object in sequence frames of digital image . Computer program has written in visual basic language (version 6) depend on mathematical models to detect a motion of object in two–dimensions (2-D)with different initial conditions like initial velocity, the height of object from the earth and the angle of motion, to calculate important variables in motion such as distance, displacement, velocity, speed and the energy (kinetic and potential). Color digital images of type (bmp) and (RGB) color model were used in the study for easy handling them, after determining the center of the image on the x-axis, and y-axis and tracking movement on the basis of the center, and the results were expected to conform to the movement of the body. Key words: Projectile, Motion, Digital Image

A novel object tracking algorithm based on compressed sensing and entropy of information

Acknowledgments This research is supported by (1) the Ph.D. Programs Foundation of Ministry of Education of China under Grant no. 20120061110045, (2) the Science and Technology Development Projects of Jilin Province of China under Grant no. 20150204007G X, and (3) the Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China.Peer reviewedPublisher PD

Collaborative Artificial Intelligence Algorithms for Medical Imaging Applications

In this dissertation, we propose novel machine learning algorithms for high-risk medical imaging applications. Specifically, we tackle current challenges in radiology screening process and introduce cutting-edge methods for image-based diagnosis, detection and segmentation. We incorporate expert knowledge through eye-tracking, making the whole process human-centered. This dissertation contributes to machine learning, computer vision, and medical imaging research by: 1) introducing a mathematical formulation of radiologists level of attention, and sparsifying their gaze data for a better extraction and comparison of search patterns. 2) proposing novel, local and global, image analysis algorithms. Imaging based diagnosis and pattern analysis are high-risk Artificial Intelligence applications. A standard radiology screening procedure includes detection, diagnosis and measurement (often done with segmentation) of abnormalities. We hypothesize that having a true collaboration is essential for a better control mechanism, in such applications. In this regard, we propose to form a collaboration medium between radiologists and machine learning algorithms through eye-tracking. Further, we build a generic platform consisting of novel machine learning algorithms for each of these tasks. Our collaborative algorithm utilizes eye tracking and includes an attention model and gaze-pattern analysis, based on data clustering and graph sparsification. Then, we present a semi-supervised multi-task network for local analysis of image in radiologists\u27 ROIs, extracted in the previous step. To address missing tumors and analyze regions that are completely missed by radiologists during screening, we introduce a detection framework, S4ND: Single Shot Single Scale Lung Nodule Detection. Our proposed detection algorithm is specifically designed to handle tiny abnormalities in lungs, which are easy to miss by radiologists. Finally, we introduce a novel projective adversarial framework, PAN: Projective Adversarial Network for Medical Image Segmentation, for segmenting complex 3D structures/organs, which can be beneficial in the screening process by guiding radiologists search areas through segmentation of desired structure/organ

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 201

An Intelligent Monitoring System of Vehicles on Highway Traffic

Vehicle speed monitoring and management of highways is the critical problem of the road in this modern age of growing technology and population. A poor management results in frequent traffic jam, traffic rules violation and fatal road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to address this problem is time-consuming, expensive and tedious. This paper presents an efficient framework to produce a simple, cost efficient and intelligent system for vehicle speed monitoring. The proposed method uses an HD (High Definition) camera mounted on the road side either on a pole or on a traffic signal for recording video frames. On the basis of these frames, a vehicle can be tracked by using radius growing method, and its speed can be calculated by calculating vehicle mask and its displacement in consecutive frames. The method uses pattern recognition, digital image processing and mathematical techniques for vehicle detection, tracking and speed calculation. The validity of the proposed model is proved by testing it on different highways.Comment: 5 page