A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning.

This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning.

Soil Moisture, Organic Carbon, and Nitrogen Content Prediction with Hyperspectral Data Using Regression Models.

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.

Correction of geometrically distorted underwater images using shift map analysis.

In underwater imaging, water waves cause severe geometric distortions and blurring of the acquired short-exposure images. Corrections for these distortions have been tackled reasonably well by previous efforts but still need improvement in the estimation of pixel shift maps to increase restoration accuracy. This paper presents a new algorithm that efficiently estimates the shift maps from geometrically distorted video sequences and uses those maps to restore the sequences. A nonrigid image registration method is employed to estimate the shift maps of the distorted frames against a reference frame. The sharpest frame of the sequence, determined using a sharpness metric, is chosen as the reference frame. A k-means clustering technique is employed to discard too-blurry frames that could result in inaccuracy in the shift maps' estimation. The estimated pixel shift maps are processed to generate the accurate shift map that is used to dewarp the input frames into their nondistorted forms. The proposed method is applied on several synthetic and real-world video sequences, and the obtained results exhibit significant improvements over the state-of-the-art methods.

Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning.

Bidirectional mapping-based generalized zero-shot learning (GZSL) methods rely on the quality of synthesized features to recognize seen and unseen data. Therefore, learning a joint distribution of seen-unseen classes and preserving the distinction between seen-unseen classes is crucial for GZSL methods. However, existing methods only learn the underlying distribution of seen data, although unseen class semantics are available in the GZSL problem setting. Most methods neglect retaining seen-unseen classes distinction and use the learned distribution to recognize seen and unseen data. Consequently, they do not perform well. In this work, we utilize the available unseen class semantics alongside seen class semantics and learn joint distribution through a strong visual-semantic coupling. We propose a bidirectional mapping coupled generative adversarial network (BMCoGAN) by extending the concept of the coupled generative adversarial network into a bidirectional mapping model. We further integrate a Wasserstein generative adversarial optimization to supervise the joint distribution learning. We design a loss optimization for retaining distinctive information of seen-unseen classes in the synthesized features and reducing bias towards seen classes, which pushes synthesized seen features towards real seen features and pulls synthesized unseen features away from real seen features. We evaluate BMCoGAN on benchmark datasets and demonstrate its superior performance against contemporary methods.

Fast Mode Decision in the HEVC Video Coding Standard by Exploiting Region with Dominated Motion and Saliency Features.

The emerging High Efficiency Video Coding (HEVC) standard introduces a number of innovative and powerful coding tools to acquire better compression efficiency compared to its predecessor H.264. The encoding time complexities have also increased multiple times that is not suitable for realtime video coding applications. To address this limitation, this paper employs a novel coding strategy to reduce the time complexity in HEVC encoder by efficient selection of appropriate block-partitioning modes based on human visual features (HVF). The HVF in the proposed technique comprise with human visual attention modelling-based saliency feature and phase correlation-based motion features. The features are innovatively combined through a fusion process by developing a content-based adaptive weighted cost function to determine the region with dominated motion/saliency (RDMS)- based binary pattern for the current block. The generated binary pattern is then compared with a codebook of predefined binary pattern templates aligned to the HEVC recommended block-paritioning to estimate a subset of inter-prediction modes. Without exhaustive exploration of all modes available in the HEVC standard, only the selected subset of modes are motion estimated and motion compensated for a particular coding unit. The experimental evaluation reveals that the proposed technique notably down-scales the average computational time of the latest HEVC reference encoder by 34% while providing similar rate-distortion (RD) performance for a wide range of video sequences.

Video coding focusing on block partitioning and occlusion.

Among the existing block partitioning schemes, the pattern-based video coding (PVC) has already established its superiority at low bit-rate. Its innovative segmentation process with regular-shaped pattern templates is very fast as it avoids handling the exact shape of the moving objects. It also judiciously encodes the pattern-uncovered background segments capturing high level of interblock temporal redundancy without any motion compensation, which is favoured by the rate-distortion optimizer at low bit-rates. The existing PVC technique, however, uses a number of content-sensitive thresholds and thus setting them to any predefined values risks ignoring some of the macroblocks that would otherwise be encoded with patterns. Furthermore, occluded background can potentially degrade the performance of this technique. In this paper, a robust PVC scheme is proposed by removing all the content-sensitive thresholds, introducing a new similarity metric, considering multiple top-ranked patterns by the rate-distortion optimizer, and refining the Lagrangian multiplier of the H.264 standard for efficient embedding. A novel pattern-based residual encoding approach is also integrated to address the occlusion issue. Once embedded into the H.264 Baseline profile, the proposed PVC scheme improves the image quality perceptually significantly by at least 0.5 dB in low bit-rate video coding applications. A similar trend is observed for moderate to high bit-rate applications when the proposed scheme replaces the bi-directional predictive mode in the H.264 High profile.