Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

On-board three-dimensional object tracking: Software and hardware solutions

We describe a real time system for recognition and tracking 3D objects such as UAVs, airplanes, fighters with the optical sensor. Given a 2D image, the system has to perform background subtraction, recognize relative rotation, scale and translation of the object to sustain a prescribed topology of the fleet. In the thesis a comparative study of different algorithms and performance evaluation is carried out based on time and accuracy constraints. For background subtraction task we evaluate frame differencing, approximate median filter, mixture of Gaussians and propose classification based on neural network methods. For object detection we analyze the performance of invariant moments, scale invariant feature transform and affine scale invariant feature transform methods. Various tracking algorithms such as mean shift with variable and a fixed sized windows, scale invariant feature transform, Harris and fast full search based on fast fourier transform algorithms are evaluated. We develop an algorithm for the relative rotations and the scale change calculation based on Zernike moments. Based on the design criteria the selection is made for on-board implementation. The candidate techniques have been implemented on the Texas Instrument TMS320DM642 EVM board. It is shown in the thesis that 14 frames per second can be processed; that supports the real time implementation of the tracking system under reasonable accuracy limits

Deep Learning based Novel Anomaly Detection Methods for Diabetic Retinopathy Screening

Programa Oficial de Doutoramento en Computación. 5009V01[Abstract] Computer-Aided Screening (CAS) systems are getting popularity in disease diagnosis. Modern CAS systems exploit data driven machine learning algorithms including supervised and unsupervised methods. In medical imaging, annotating pathological samples are much harder and time consuming work than healthy samples. Therefore, there is always an abundance of healthy samples and scarcity of annotated and labelled pathological samples. Unsupervised anomaly detection algorithms can be implemented for the development of CAS system using the largely available healthy samples, especially when disease/nodisease decision is important for screening. This thesis proposes unsupervised machine learning methodologies for anomaly detection in retinal fundus images. A novel patchbased image reconstructor architecture for DR detection is presented, that addresses the shortcomings of standard autoencoders-based reconstructors. Furthermore, a full-size image based anomaly map generation methodology is presented, where the potential DR lesions can be visualized at the pixel-level. Afterwards, a novel methodology is proposed to extend the patch-based architecture to a fully-convolutional architecture for one-shot full-size image reconstruction. Finally, a novel methodology for supervised DR classification is proposed that utilizes the anomaly maps