Deep Depth From Focus

Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end learning approach to this problem. One of the main challenges we face is the hunger for data of deep neural networks. In order to obtain a significant amount of focal stacks with corresponding groundtruth depth, we propose to leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us to digitally create focal stacks of varying sizes. Compared to existing benchmarks our dataset is 25 times larger, enabling the use of machine learning for this inverse problem. We compare our results with state-of-the-art DFF methods and we also analyze the effect of several key deep architectural components. These experiments show that our proposed method `DDFFNet' achieves state-of-the-art performance in all scenes, reducing depth error by more than 75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201

Research and development of algorithm of solid image resolution

In this final master work was developed algorithm for focal plane stacking, to increase depth of field in photo. From 28 mathematical methods was chosen 8, which showed best performance in information detection in photos. These methods was used to detect focal plane in photos. For focal plane stacking was used Laplace pyramid. Was measured quality of results, using mathematical methods. Was set the most appropriate method for detecting focal plane

Automation of Microraft Arrays for Stem Cell Analysis and Sorting

Induced pluripotent stem cells (iPSCs) are reprogrammed somatic cells with the potential to revolutionize personalized medicine, disease modeling, and tissue engineering. Emerging therapies based upon human iPSCs (hiPSCs) are already under development for degenerative diseases such as age-related macular degeneration (AMD). Despite the ready availability of hiPSCs, their enormous clinical and research potential is limited by the need to purify the cells during generation, genetic editing, and differentiation using tedious manual methodologies. This dissertation describes the automation and further development of microraft arrays to perform the isolation and splitting of hiPSCs colonies, which is the primary bottleneck in hiPSC purification pipelines. Microraft arrays are elastomeric microwell arrays with releasable magnetic cell culture and transfer elements, or “microrafts,” held within each microwell. Microraft arrays enable the identification of cells by imaging cytometry and the isolation of cells and their associated microrafts by dislodgement from the microarray, followed by magnetic manipulation into secondary vessels. The microraft array platform has been previously shown to be automatable and able to sort cells with exceptional viability and efficiencies. However, previous platforms have lacked the speed and robustness to perform large-scale microraft releases. Furthermore, previous microraft array designs were not designed to culture isolated microcolonies of hiPSCs. In this dissertation, microraft arrays were redesigned to isolate hundreds of microcolonies of cells, each within a nested grid of microrafts. Novel microarray microfabrication and computational modeling methods were developed to enable automated and robust imaging of microraft arrays. Image acquisition and analysis software was created to perform label-free detection of hiPSC microcolonies on microraft arrays and, in a separate application, to monitor colonic organoids. Additionally, a high-throughput automated microraft release and collection platform was developed that, for the first time, made used of real-time imaging to intelligently maximize the robustness and speed of microraft releases. This platform was utilized to isolate, culture, monitor, and successfully split hundreds of hiPSC microcolonies, thus demonstrating its utility for hiPSC purification.Doctor of Philosoph