We introduce a comprehensive benchmark for local features and robust estimation
algorithms, focusing on the downstream task—the accuracy of the reconstructed camera …

We introduce a simple modification of local image descriptors, such as SIFT, based on
pooling gradient orientations across different domain sizes, in addition to spatial locations …

In this paper, we propose a new framework to optimally tone map the high dynamic range
(HDR) content for image matching under drastic illumination variations. Since tone map** …

Estimating the optimal representation from sensor data has been one of the most
challenging problems in computer vision research. Given a particular task, an optimal …

Mobile traffics are becoming more dominant due to growing usage of mobile devices and
proliferation of IoT. The influx of mobile traffics introduce some new challenges in traffic …

Decoupled architectures provide a key to the problem of sustained supercomputer
performance through their ability to hide large memory latencies. When a program executes …

Distributed surveillance systems have become popular in recent years due to security
concerns. However, transmitting high dimensional data in bandwidth-limited distributed …

In this paper, we propose a new framework to optimally tone-map a high dynamic range
(HDR) content for image matching under drastic illumination variations. This task is of …

In recent years, a great deal of research has focused on the sparse representation for signal.
Particularly, a dictionary learning algorithm, K-SVD, is introduced to efficiently learn an …

A variation of scale-invariant feature transform (SIFT) based on pooling gradient orientations
across different domain sizes, in addition to spatial locations. The resulting descriptor is …