Encoder-Decoder-Based Intra-Frame Block Partitioning Decision

The recursive intra-frame block partitioning decision process, a crucial component of the next-generation video coding standards, exerts significant influence over the encoding time. In this paper, we propose an encoder-decoder neural network (NN) to accelerate this process. Specifically, a CNN is utilized to compress the pixel data of the largest coding unit (LCU) into a fixed-length vector. Subsequently, a Transformer decoder is employed to transcribe the fixed-length vector into a variable-length vector, which represents the block partitioning outcomes of the encoding LCU. The vector transcription process adheres to the constraints imposed by the block partitioning algorithm. By fully parallelizing the NN prediction in the intra-mode decision, substantial time savings can be attained during the decision phase. The experimental results obtained from high-definition (HD) sequences coding demonstrate that this framework achieves a remarkable 87.84\% reduction in encoding time, with a relatively small loss (8.09\%) of coding performance compared to AVS3 HPM4.0

Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

Large-Scale Light Field Capture and Reconstruction

This thesis discusses approaches and techniques to convert Sparsely-Sampled Light Fields (SSLFs) into Densely-Sampled Light Fields (DSLFs), which can be used for visualization on 3DTV and Virtual Reality (VR) devices. Exemplarily, a movable 1D large-scale light field acquisition system for capturing SSLFs in real-world environments is evaluated. This system consists of 24 sparsely placed RGB cameras and two Kinect V2 sensors. The real-world SSLF data captured with this setup can be leveraged to reconstruct real-world DSLFs. To this end, three challenging problems require to be solved for this system: (i) how to estimate the rigid transformation from the coordinate system of a Kinect V2 to the coordinate system of an RGB camera; (ii) how to register the two Kinect V2 sensors with a large displacement; (iii) how to reconstruct a DSLF from a SSLF with moderate and large disparity ranges. To overcome these three challenges, we propose: (i) a novel self-calibration method, which takes advantage of the geometric constraints from the scene and the cameras, for estimating the rigid transformations from the camera coordinate frame of one Kinect V2 to the camera coordinate frames of 12-nearest RGB cameras; (ii) a novel coarse-to-fine approach for recovering the rigid transformation from the coordinate system of one Kinect to the coordinate system of the other by means of local color and geometry information; (iii) several novel algorithms that can be categorized into two groups for reconstructing a DSLF from an input SSLF, including novel view synthesis methods, which are inspired by the state-of-the-art video frame interpolation algorithms, and Epipolar-Plane Image (EPI) inpainting methods, which are inspired by the Shearlet Transform (ST)-based DSLF reconstruction approaches

RELLISUR: A Real Low-Light Image Super-Resolution Dataset

The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution. The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution

Macro-and Micro-Expressions Facial Datasets: A Survey

Automatic facial expression recognition is essential for many potential applications. Thus, having a clear overview on existing datasets that have been investigated within the framework of face expression recognition is of paramount importance in designing and evaluating effective solutions, notably for neural networks-based training. In this survey, we provide a review of more than eighty facial expression datasets, while taking into account both macro-and micro-expressions. The proposed study is mostly focused on spontaneous and in-the-wild datasets, given the common trend in the research is that of considering contexts where expressions are shown in a spontaneous way and in a real context. We have also provided instances of potential applications of the investigated datasets, while putting into evidence their pros and cons. The proposed survey can help researchers to have a better understanding of the characteristics of the existing datasets, thus facilitating the choice of the data that best suits the particular context of their application

Estimation of Driver's Gaze Region from Head Position and Orientation using Probabilistic Confidence Regions

A smart vehicle should be able to understand human behavior and predict their actions to avoid hazardous situations. Specific traits in human behavior can be automatically predicted, which can help the vehicle make decisions, increasing safety. One of the most important aspects pertaining to the driving task is the driver's visual attention. Predicting the driver's visual attention can help a vehicle understand the awareness state of the driver, providing important contextual information. While estimating the exact gaze direction is difficult in the car environment, a coarse estimation of the visual attention can be obtained by tracking the position and orientation of the head. Since the relation between head pose and gaze direction is not one-to-one, this paper proposes a formulation based on probabilistic models to create salient regions describing the visual attention of the driver. The area of the predicted region is small when the model has high confidence on the prediction, which is directly learned from the data. We use Gaussian process regression (GPR) to implement the framework, comparing the performance with different regression formulations such as linear regression and neural network based methods. We evaluate these frameworks by studying the tradeoff between spatial resolution and accuracy of the probability map using naturalistic recordings collected with the UTDrive platform. We observe that the GPR method produces the best result creating accurate predictions with localized salient regions. For example, the 95% confidence region is defined by an area that covers 3.77% region of a sphere surrounding the driver.Comment: 13 Pages, 12 figures, 2 table