Deep learning-based 3D local feature descriptor from Mercator projections

Point clouds provide rich geometric information about a shape and a deep neural network can be used to learn effective and robust features. In this paper, we propose a novel local feature descriptor, which employs a Siamese network to directly learn robust features from the point clouds. We use a data representation based on the Mercator projection, then we use a Siamese network to map this projection into a 32-dimensional local descriptor. To validate the proposed method, we have compared it with seven state-of-the-art descriptor methods. Experimental results show the superiority of the proposed method compared to existing methods in terms of descriptiveness and robustness against noise and varying mesh resolutions

Text to image synthesis for improved image captioning

Generating textual descriptions of images has been an important topic in computer vision and natural language processing. A number of techniques based on deep learning have been proposed on this topic. These techniques use human-annotated images for training and testing the models. These models require a large number of training data to perform at their full potential. Collecting human generated images with associative captions is expensive and time-consuming. In this paper, we propose an image captioning method that uses both real and synthetic data for training and testing the model. We use a Generative Adversarial Network (GAN) based text to image generator to generate synthetic images. We use an attention-based image captioning method trained on both real and synthetic images to generate the captions. We demonstrate the results of our models using both qualitative and quantitative analysis on popularly used evaluation metrics. We show that our experimental results achieve two fold benefits of our proposed work: i) it demonstrates the effectiveness of image captioning for synthetic images, and ii) it further improves the quality of the generated captions for real images, understandably because we use additional images for training

A High-Performance Spectral-Spatial Residual Network for Hyperspectral Image Classification with Small Training Data

In this paper, we propose a high performance Two-Stream spectral-spatial Residual Network (TSRN) for hyperspectral image classification. The first spectral residual network (sRN) stream is used to extract spectral characteristics, and the second spatial residual network (saRN) stream is concurrently used to extract spatial features. The sRN uses 1D convolutional layers to fit the spectral data structure, while the saRN uses 2D convolutional layers to match the hyperspectral spatial data structure. Furthermore, each convolutional layer is preceded by a Batch Normalization (BN) layer that works as a regularizer to speed up the training process and to improve the accuracy. We conducted experiments on three well-known hyperspectral datasets, and we compare our results with five contemporary methods across various sizes of training samples. The experimental results show that the proposed architecture can be trained with small size datasets and outperforms the state-of-the-art methods in terms of the Overall Accuracy, Average Accuracy, Kappa Value, and training time

A reinforcement learning-based approach for imputing missing data

Missing data is a major problem in real-world datasets, which hinders the performance of data analytics. Conventional data imputation schemes such as univariate single imputation replace missing values in each column with the same approximated value. These univariate single imputation techniques underestimate the variance of the imputed values. On the other hand, multivariate imputation explores the relationships between different columns of data, to impute the missing values. Reinforcement Learning (RL) is a machine learning paradigm where the agent learns by taking actions and receiving rewards in response, to achieve its goal. In this work, we propose an RL-based approach to impute missing data by learning a policy to impute data through an action-reward-based experience. Our approach imputes missing values in a column by working only on the same column (similar to univariate single imputation) but imputes the missing values in the column with different values thus keeping the variance in the imputed values. We report superior performance of our approach, compared with other imputation techniques, on a number of datasets

Moisture evolution, thermal properties and energy consumption of drying spent grain pellets from a blend of some cereals for small scale bio-energy utilization: Modelling and Experimental study

© 2022 Springer Nature Switzerland AG. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s13399-022-02846-xA fixed bed convective dryer was used to assess the influence of drying temperature and geometry deformation on moisture and thermo-physical property evolution of solid fraction pellets (spent grain) from wet milling of cereal blends for bio-energy generation for small homes. The aim is to study the physical mechanism of drying the pellets that includes temperature and moisture behaviour, transport phenomena, the response rate to varying process conditions, drying time, and energy utilization which can be applied in the development of a fixed bed dryer for drying the pellets at a lower scale. The modified Cranck's diffusion model was used to study moisture loss by introducing shrinkage. The verification of the model gave the mean absolute error (MAE) for moisture content with shrinkage as 0.0366 - 0.1500 while for without shrinkage was 0.0729 - 0.1500 for 60- 80 oC. The effective moisture diffusivity with integrating shrinkage is lower than non- shrinkage though these values varied with drying time. Fitting the moisture ratio with the exponential drying curve equations shows that logarithmic equations were the best model for drying at 60 and 70 oC while Henderson and Pabis's model was better at 80 oC isothermal drying. Thermophysical analysis showed that the average specific heat capacity ranges from 5423.387 to 5198.197J/kgK while the thermal conductivity ranged from 0.115281to 0.136882W/mK at 60-80 oC. The energy and specific energy consumption ranged from 0.41 to 0.494 kWh and 108.39 to 119.29MJ/kg. The shrinkage ratios, effective diffusivity and energy and specific energy consumption were empirically presented as a function of moisture, temperature and or air velocity variations with a high degree of association.Peer reviewe

Geometric distortion measurement for shape coding: a contemporary review

Geometric distortion measurement and the associated metrics involved are integral to the rate-distortion (RD) shape coding framework, with importantly the efficacy of the metrics being strongly influenced by the underlying measurement strategy. This has been the catalyst for many different techniques with this paper presenting a comprehensive review of geometric distortion measurement, the diverse metrics applied and their impact on shape coding. The respective performance of these measuring strategies is analysed from both a RD and complexity perspective, with a recent distortion measurement technique based on arc-length-parameterisation being comparatively evaluated. Some contemporary research challenges are also investigated, including schemes to effectively quantify shape deformation

Relationship detection based on object semantic inference and attention mechanisms

Detecting relations among objects is a crucial task for image understanding. However, each relationship involves different objects pair combinations, and different objects pair combinations express diverse interactions. This makes the relationships, based just on visual features, a challenging task. In this paper, we propose a simple yet effective relationship detection model, which is based on object semantic inference and attention mechanisms. Our model is trained to detect relation triples, such as , . To overcome the high diversity of visual appearances, the semantic inference module and the visual features are combined to complement each others. We also introduce two different attention mechanisms for object feature refinement and phrase feature refinement. In order to derive a more detailed and comprehensive representation for each object, the object feature refinement module refines the representation of each object by querying over all the other objects in the image. The phrase feature refinement module is proposed in order to make the phrase feature more effective, and to automatically focus on relative parts, to improve the visual relationship detection task. We validate our model on Visual Genome Relationship dataset. Our proposed model achieves competitive results compared to the state-of-the-art method MOTIFNET