Video Motion: Finding Complete Motion Paths for Every Visible Point

<p>The problem of understanding motion in video has been an area of intense research in computer vision for decades. The traditional approach is to represent motion using optical flow fields, which describe the two-dimensional instantaneous velocity at every pixel in every frame. We present a new approach to describing motion in video in which each visible world point is associated with a sequence-length video motion path. A video motion path lists the location where a world point would appear if it were visible in every frame of the sequence. Each motion path is coupled with a vector of binary visibility flags for the associated point that identify the frames in which the tracked point is unoccluded.</p><p>We represent paths for all visible points in a particular sequence using a single linear subspace. The key insight we exploit is that, for many sequences, this subspace is low-dimensional, scaling with the complexity of the deformations and the number of independent objects in the scene, rather than the number of frames in the sequence. Restricting all paths to lie within a single motion subspace provides strong regularization that allows us to extend paths through brief occlusions, relying on evidence from the visible frames to hallucinate the unseen locations.</p><p>This thesis presents our mathematical model of video motion. We define a path objective function that optimizes a set of paths given estimates of visible intervals, under the assumption that motion is generally spatially smooth and that the appearance of a tracked point remains constant over time. We estimate visibility based on global properties of all paths, enforcing the physical requirement that at least one tracked point must be visible at every pixel in the video. The model assumes the existence of an appropriate path motion basis; we find a sequence-specific basis through analysis of point tracks from a frame-to-frame tracker. Tracking failures caused by image noise, non-rigid deformations, or occlusions complicate the problem by introducing missing data. We update standard trackers to aggressively reinitialize points lost in earlier frames. Finally, we improve on standard Principal Component Analysis with missing data by introducing a novel compaction step that associates these relocalized points, reducing the amount of missing data that must be overcome. The full system achieves state-of-the-art results, recovering dense, accurate, long-range point correspondences in the face of significant occlusions.</p>Dissertatio

16th century Persian tiles in dialogue with 21st century digital tiles in the Sadrian universe

This article brings together tiles of 16th century Persian architecture and 21st century digital tiles of moving image to explore new potentials beyond the perceived image. As minimal parts of a bigger image, they both appear still and motionless. However, Persian Islamic philosopher, Mulla Sadrā Shirazi’s (1571-1640) theory of ‘substantial motion’ (al-harakat al-jawhariyya) argues that, at the level of substance, an invisible internal motion and change takes place. Due to this internal change, aspects of the Divine Being constantly manifest in the existence of entities. Sadrā’s unique view on existence suggests that all living and non-living entities, as manifestations of the Divine Being, have certain experiences of the universe. To think that an image, a tile, or a pixel, as an existing entity, has certain experiences can unfold new avenues for creative thinking/making in digital moving image that can reveal what is hidden from human perception

Head Tracking via Robust Registration in Texture Map Images

A novel method for 3D head tracking in the presence of large head rotations and facial expression changes is described. Tracking is formulated in terms of color image registration in the texture map of a 3D surface model. Model appearance is recursively updated via image mosaicking in the texture map as the head orientation varies. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. Parameters are estimated via a robust minimization procedure; this provides robustness to occlusions, wrinkles, shadows, and specular highlights. The system was tested on a variety of sequences taken with low quality, uncalibrated video cameras. Experimental results are reported

Full Reference Objective Quality Assessment for Reconstructed Background Images

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated Database: https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing (Email for permissions at: ashrotreasuedu