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Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence digital objects and collections over time. One of its integral pieces is provenance filtering, which consists of searching a potentially large pool of objects for the most related ones with respect to a given query, in terms of possible ancestors (donors or contributors) and descendants.

Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through doctored visual media. Nonetheless, most recent image phylogeny solutions cannot properly address cases of forged composite images with multiple donors, an area known as multiple parenting phylogeny (MPP). This paper presents a preliminary undirected graph construction solution for MPP, without any strict assumptions.

A novel data-driven reconstruction algorithm for quantum image sensors (QIS) is proposed. Observations are efficiently decoded by modeling the reconstruction structure as a two-layer neural network, where optimal coefficients are obtained via error backpropagation. Our model encapsulates the structure of state-of-the-art algorithms, yet it presents a faster alternative which adapts to input examples without a priori statistical information.

We propose a fixed-map semi-direct visual odometry (FSVO) algorithm for Micro Aerial Vehicles (MAVs). The proposed approach does not need computationally expensive feature extraction and matching techniques for motion estimation at each frame. Instead, we extract and match ORiented Brief (ORB) features between keyframes and assist-frames. We replace the incremental map generation step in traditional algorithms with fixed map generation at keyframe and assist- frame only in our algorithm, resulting in reduced storage memory and higher flexibility for relocalization.

We propose a fixed-map semi-direct visual odometry (FSVO) algorithm for Micro Aerial Vehicles (MAVs). The proposed approach does not need computationally expensive feature extraction and matching techniques for motion estimation at each frame. Instead, we extract and match ORiented Brief (ORB) features between keyframes and assist-frames. We replace the incremental map generation step in traditional algorithms with fixed map generation at keyframe and assist- frame only in our algorithm, resulting in reduced storage memory and higher flexibility for relocalization.

Structure-from-Motion approaches could be broadly divided into two classes: incremental and global. While incremental manner is robust to outliers, it suffers from error accumulation and heavy computation load. The global manner has the advantage of simultaneously estimating all camera poses, but it is usually sensitive to epipolar geometry outliers. In this paper, we propose an adaptive community-based SfM (CSfM) method which takes both robustness and efficiency into consideration. First, the epipolar geometry graph is partitioned into separate communities.