Discriminating Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Review.

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral-spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral-spatial representations for classification. In this article, the crucial factors to discriminate spectral-spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral-spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.

Analyzing Data Modalities for Cattle Weight Estimation Using Deep Learning Models.

We investigate the impact of different data modalities for cattle weight estimation. For this purpose, we collect and present our own cattle dataset representing the data modalities: RGB, depth, combined RGB and depth, segmentation, and combined segmentation and depth information. We explore a recent vision-transformer-based zero-shot model proposed by Meta AI Research for producing the segmentation data modality and for extracting the cattle-only region from the images. For experimental analysis, we consider three baseline deep learning models. The objective is to assess how the integration of diverse data sources influences the accuracy and robustness of the deep learning models considering four different performance metrics: mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2). We explore the synergies and challenges associated with each modality and their combined use in enhancing the precision of cattle weight prediction. Through comprehensive experimentation and evaluation, we aim to provide insights into the effectiveness of different data modalities in improving the performance of established deep learning models, facilitating informed decision-making for precision livestock management systems.

Propagation Measurements for IQRF Network in an Urban Environment.

Recently, IQRF has emerged as a promising technology for the Internet of Things (IoT), owing to its ability to support short- and medium-range low-power communications. However, real world deployment of IQRF-based wireless sensor networks (WSNs) requires accurate path loss modelling to estimate network coverage and other performances. In the existing literature, extensive research on propagation modelling for IQRF network deployment in urban environments has not been provided yet. Therefore, this study proposes an empirical path loss model for the deployment of IQRF networks in a peer-to-peer configured system where the IQRF sensor nodes operate in the 868 MHz band. For this purpose, extensive measurement campaigns are conducted outdoor in an urban environment for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) links. Furthermore, in order to evaluate the prediction accuracy of well-known empirical path loss models for urban environments, the measurements are compared with the predicted path loss values. The results show that the COST-231 Walfisch-Ikegami model has higher prediction accuracy and can be used for IQRF network planning in LoS links, while the COST-231 Hata model has better accuracy in NLoS links. On the other hand, the effects of antennas on the performance of IQRF transceivers (TRs) for LoS and NLoS links are also scrutinized. The use of IQRF TRs with a Straight-Line Dipole Antenna (SLDA) antenna is found to offer more stable results when compared to IQRF (TRs) with Meander Line Antenna (MLA) antenna. Therefore, it is believed that the findings presented in this article could offer useful insights for researchers interested in the development of IoT-based smart city applications.

Fast parallel vessel segmentation.

BACKGROUND AND OBJECTIVE: Accurate and fast vessel segmentation from liver slices remain challenging and important tasks for clinicians. The algorithms from the literature are slow and less accurate. We propose fast parallel gradient based seeded region growing for vessel segmentation. Seeded region growing is tedious when the inter connectivity between the elements is unavoidable. Parallelizing region growing algorithms are essential towards achieving real time performance for the overall process of accurate vessel segmentation. METHODS: The parallel implementation of seeded region growing for vessel segmentation is iterative and hence time consuming process. Seeded region growing is implemented as kernel termination and relaunch on GPU due to its iterative mechanism. The iterative or recursive process in region growing is time consuming due to intermediate memory transfers between CPU and GPU. We propose persistent and grid-stride loop based parallel approach for region growing on GPU. We analyze static region of interest of tiles on GPU for the acceleration of seeded region growing. RESULTS: We aim fast parallel gradient based seeded region growing for vessel segmentation from CT liver slices. The proposed parallel approach is 1.9x faster compared to the state-of-the-art. CONCLUSION: We discuss gradient based seeded region growing and its parallel implementation on GPU. The proposed parallel seeded region growing is fast compared to kernel termination and relaunch and accurate in comparison to Chan-Vese and Snake model for vessel segmentation.

GPU acceleration of liver enhancement for tumor segmentation.

BACKGROUND AND OBJECTIVE: Medical image segmentation plays a vital role in medical image analysis. There are many algorithms developed for medical image segmentation which are based on edge or region characteristics. These are dependent on the quality of the image. The contrast of a CT or MRI image plays an important role in identifying region of interest i.e. lesion(s). In order to enhance the contrast of image, clinicians generally use manual histogram adjustment technique which is based on 1D histogram specification. This is time consuming and results in poor distribution of pixels over the image. Cross modality based contrast enhancement is 2D histogram specification technique. This is robust and provides a more uniform distribution of pixels over CT image by exploiting the inner structure information from MRI image. This helps in increasing the sensitivity and accuracy of lesion segmentation from enhanced CT image. The sequential implementation of cross modality based contrast enhancement is slow. Hence we propose GPU acceleration of cross modality based contrast enhancement for tumor segmentation. METHODS: The aim of this study is fast parallel cross modality based contrast enhancement for CT liver images. This includes pairwise 2D histogram, histogram equalization and histogram matching. The sequential implementation of the cross modality based contrast enhancement is computationally expensive and hence time consuming. We propose persistence and grid-stride loop based fast parallel contrast enhancement for CT liver images. We use enhanced CT liver image for the lesion or tumor segmentation. We implement the fast parallel gradient based dynamic seeded region growing for lesion segmentation. RESULTS: The proposed parallel approach is 104.416 ( ±  5.166) times faster compared to the sequential implementation and increases the sensitivity and specificity of tumor segmentation. CONCLUSION: The cross modality approach is inspired by 2D histogram specification which incorporates spatial information existing in both guidance and input images for remapping the input image intensity values. The cross modality based liver contrast enhancement improves the quality of tumor segmentation.

Weakly-Supervised Network for Detection of COVID-19 in Chest CT Scans.

Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily rely on large labeled data sets, which are difficult to acquire in this pandemic situation. Therefore, weakly-supervised approaches are in demand. In this paper, we propose an end-to-end weakly-supervised COVID-19 detection approach, ResNext+, that only requires volume level data labels and can provide slice level prediction. The proposed approach incorporates a lung segmentation mask as well as spatial and channel attention to extract spatial features. Besides, Long Short Term Memory (LSTM) is utilized to acquire the axial dependency of the slices. Moreover, a slice attention module is applied before the final fully connected layer to generate the slice level prediction without additional supervision. An ablation study is conducted to show the efficiency of the attention blocks and the segmentation mask block. Experimental results, obtained from publicly available datasets, show a precision of 81.9% and F1 score of 81.4%. The closest state-of-the-art gives 76.7% precision and 78.8% F1 score. The 5% improvement in precision and 3% in the F1 score demonstrate the effectiveness of the proposed method. It is worth noticing that, applying image enhancement approaches do not improve the performance of the proposed method, sometimes even harm the scores, although the enhanced images have better perceptual quality.

Efficient Enhancement of Stereo Endoscopic Images Based on Joint Wavelet Decomposition and Binocular Combination.

The success of minimally invasive interventions and the remarkable technological and medical progress have made endoscopic image enhancement a very active research field. Due to the intrinsic endoscopic domain characteristics and the surgical exercise, stereo endoscopic images may suffer from different degradations which affect its quality. Therefore, in order to provide the surgeons with a better visual feedback and improve the outcomes of possible subsequent processing steps, namely, a 3-D organ reconstruction/registration, it would be interesting to improve the stereo endoscopic image quality. To this end, we propose, in this paper, two joint enhancement methods which operate in the wavelet transform domain. More precisely, by resorting to a joint wavelet decomposition, the wavelet subbands of the right and left views are simultaneously processed to exploit the binocular vision properties. While the first proposed technique combines only the approximation subbands of both views, the second method combines all the wavelet subbands yielding an inter-view processing fully adapted to the local features of the stereo endoscopic images. Experimental results, carried out on various stereo endoscopic datasets, have demonstrated the efficiency of the proposed enhancement methods in terms of perceived visual image quality.