Chung-Ching Lin

Adaptive as-natural-as-possible image stitching

The goal of image stitching is to create natural-looking mosaics free of artifacts that may occur due to relative camera motion, illumination changes, and optical aberrations. In this paper, we propose a novel stitching method, that uses a smooth stitching field over the entire target image, while accounting for all the local transformation variations. Computing the warp is fully automated and uses a combination of local homography and global similarity transformations, both of which are estimated with respect to the target. We mitigate the perspective distortion in the non-overlapping regions by linearizing the homography and slowly changing it to the global similarity. The proposed method is easily generalized to multiple images, and allows one to automatically obtain the best perspective in the panorama. It is also more robust to parameter selection, and hence more automated compared with state-of-the-art methods. The benefits of the proposed approach are demonstrated using a variety of challenging cases.

IBM system z10 design for RAS

The IBM System z10™ server reliability, availability, and serviceability (RAS) design continues to reduce the sources of server outages through innovative RAS architecture and techniques. The z10™ server introduced functional improvements that challenged the RAS design. Increases were made in the performance of each processor, the total number of processors, the total size of the memory, the amount of cache, the bandwidth of the I/O, the thermal density, and the exposure to soft errors. These changes demanded stronger RAS functions to prevent unscheduled outages. Significant improvements were made to the IBM e-business on demand® functions (concurrent, customer-requested upgrades) that enable customers to better manage capacity without having to take planned outages. The hypervisor simplified configuration changes, such as adding cryptography or channel subsystems to logical partitions, by eliminating the need for preplanning. Single-core checkstopping and single transparent CPU (central processing unit) sparing were added. The RAS functions reduced the number of scheduled outages. Product improvements were complemented by improvements in RAS modeling. This paper describes these RAS improvements and how they provide value to the customer.

Resilient mobile cognition: Algorithms, innovations, and architectures

The importance of the internet-of-things (IOT) is now an established reality. With that backdrop, the phenomenal emergence of cameras/sensors mounted on unmanned aerial, ground and marine vehicles (UAVs, UGVs, UMVs) and body worn cameras is a notable new development. The swarms of cameras and real-time computing thereof are at the heart of new technologies like connected cars, drone-based city-wide surveillance and precision agriculture, etc. Smart computer vision algorithms (with or without dynamic learning) that enable object recognition and tracking, supported by baseline video content summarization or 2D/3D image reconstruction of the scanned environment are at the heart of such new applications. In this article, we summarize our recent innovations in this space. We focus primarily on algorithms and architectural design considerations for video summarization systems.

Moving camera analytics: Emerging scenarios, challenges, and applications

In recent years, more than one billion cameras have been actively used on moving platforms, and various video analytics applications for moving cameras are emerging in diverse areas. Each of these applications can generate very large datasets to analyze. In this paper, we present practical approaches to handle and summarize the video content from moving cameras and use the application of unmanned aerial vehicles (UAVs) as an example. Our system is enabled by a geographic information system (GIS), and video summarization is used to generate efficient representations of videos on the basis of moving object detection and tracking. The approach automatically creates a panorama for videos and includes a novel registration method we developed for normalizing 3D distortion. Our experimental results on the UAV dataset show that we can accomplish a 10,000-fold data reduction without losing significant activities of interest. We also present a summary of the emerging landscape of mobile video analytics and how it may evolve in conjunction with other distributed mobile and static sensor networks.

Resilience characterization of a vision analytics application under varying degrees of approximation

In this work we study a state-of-the-art video summarization application that serves as a representative emerging workload for the domain of real time edge computing. We further examine three different approximation techniques to improve the power and performance efficiency of the workload. A detailed resiliency study of the application as well as its approximate versions show that the approximations do not degrade the resiliency of the baseline algorithm. Thus, we conclude that it is possible to realize safe, yet efficient approximations for this state-of-the-art video summarization algorithm from the point of view of performance, energy and reliability.