A Coherent Wideband Acoustic Source Localization Using a Uniform Circular Array.

In modern applications such as robotics, autonomous vehicles, and speaker localization, the computational power for sound source localization applications can be limited when other functionalities get more complex. In such application fields, there is a need to maintain high localization accuracy for several sound sources while reducing computational complexity. The array manifold interpolation (AMI) method applied with the Multiple Signal Classification (MUSIC) algorithm enables sound source localization of multiple sources with high accuracy. However, the computational complexity has so far been relatively high. This paper presents a modified AMI for uniform circular array (UCA) that offers reduced computational complexity compared to the original AMI. The complexity reduction is based on the proposed UCA-specific focusing matrix which eliminates the calculation of the Bessel function. The simulation comparison is done with the existing methods of iMUSIC, the Weighted Squared Test of Orthogonality of Projected Subspaces (WS-TOPS), and the original AMI. The experiment result under different scenarios shows that the proposed algorithm outperforms the original AMI method in terms of estimation accuracy and up to a 30% reduction in computation time. An advantage offered by this proposed method is the ability to implement wideband array processing on low-end microprocessors.

Shearlet Transform based Light Field Compression Under Low Bitrates.

Light field (LF) acquisition devices capture spatial and angular information of a scene. In contrast with traditional cameras, the additional angular information enables novel postprocessing applications, such as 3D scene reconstruction, the ability to refocus at different depth planes, and synthetic aperture. In this paper, we present a novel compression scheme for LF data captured using multiple traditional cameras. The input LF views were divided into two groups: key views and decimated views. The key views were compressed using the multi-view extension of high-efficiency video coding (MV-HEVC) scheme, and decimated views were predicted using the shearlet-transform-based prediction (STBP) scheme. Additionally, the residual information of predicted views was also encoded and sent along with the coded stream of key views. The proposed scheme was evaluated over a benchmark multi-camera based LF datasets, demonstrating that incorporating the residual information into the compression scheme increased the overall peak signal to noise ratio (PSNR) by 2 dB. The proposed compression scheme performed significantly better at low bit rates compared to anchor schemes, which have a better level of compression efficiency in high bit-rate scenarios. The sensitivity of the human vision system towards compression artifacts, specifically at low bit rates, favors the proposed compression scheme over anchor schemes.

Scalable Coding of Plenoptic Images by Using a Sparse Set and Disparities.

One of the light field capturing techniques is the focused plenoptic capturing. By placing a microlens array in front of the photosensor, the focused plenoptic cameras capture both spatial and angular information of a scene in each microlens image and across microlens images. The capturing results in a significant amount of redundant information, and the captured image is usually of a large resolution. A coding scheme that removes the redundancy before coding can be of advantage for efficient compression, transmission, and rendering. In this paper, we propose a lossy coding scheme to efficiently represent plenoptic images. The format contains a sparse image set and its associated disparities. The reconstruction is performed by disparity-based interpolation and inpainting, and the reconstructed image is later employed as a prediction reference for the coding of the full plenoptic image. As an outcome of the representation, the proposed scheme inherits a scalable structure with three layers. The results show that plenoptic images are compressed efficiently with over 60 percent bit rate reduction compared with High Efficiency Video Coding intra coding, and with over 20 percent compared with an High Efficiency Video Coding block copying mode.

A weighted optimization approach to time-of-flight sensor fusion.

Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.