Medical image segmentation entails assigning each pixel in an image to its corresponding class label, a challenging task given the considerable anatomical variations in different cases. The encoder-decoder approach, exemplified by architectures such as U-Net, has emerged as the predominant framework for medical imaging segmentation tasks. In recent years, diverse modifications to the U-Net architecture have been explored, giving rise to distinct models that showcase noteworthy results in comparison to the conventional U-Net design. In this paper, we introduce a novel architectural framework, which we refer to as the Polyhierarchical Residual Network ( PolyRes-Net ). Each encoder step comprises a Multi-Level Residual Block ( MLR-block ) designed to extract local and global feature maps. Furthermore, each decoder step is preceded by an attention gate, aiding in the extraction of the most salient features from the preceding layer, while skip connections correspond to the respective encoder steps. Lastly, the multi-scale feature aggregation ( MSFA ) block consolidates features from various decoder steps. Four benchamar datasets are used for evaluating our model: Krusir-SEG, CVC ClinicDB, 2018 Data Science Bowl, and ISIC-2018 skin lesion segmentation challenge dataset based on two metrics: the Mean Dice Similarity Coefficient (mDSC) and the Mean Intersection Over Union (mIOU). The results of the proposed PolyRes-Net outperformed the state-of-the-art segmentation methods. Specifically, PolyRes-Net achieves the highest mDSC scores of 91.02%, 91.80%, and 89.25% on CVC ClinicDB, 2018 Data Science Bowl, and ISIC-2018 skin lesion segmentation challenge dataset, respectively. Additionally, the highest mIOU scores are 85.60%, 85.32%, and 82.14% for the same datasets, further underscoring the efficacy of the proposed model.

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality. Precise diagnosis of CRC plays a crucial role in increasing patient survival rates and formulating effective treatment strategies. Deep learning algorithms have demonstrated remarkable proficiency in the precise categorization of histopathology images. In this article, we introduce a novel deep learning model, termed DeepCon which incorporates the divide-and-conquer principle into the classification task. DeepCon has been methodically conceived to scrutinize the influence of acquired composition on the learning process, with a specific application to the classification of histology images related to CRC. Our model harnesses pre-trained networks to extract features from both the source and target domains, employing a two-stage transfer learning approach encompassing multiple loss functions. Our transfer learning strategy exploits a learned composition of decomposed images to enhance the transferability of extracted features. The efficacy of the proposed model was assessed using a clinically valid dataset of 5000 CRC images. The experimental results reveal that DeepCon when coupled with the Xception network as the backbone model and subjected to extensive fine-tuning, achieved a remarkable accuracy rate of 98.4% and an F1 score of 98.4%.

Biometric recognition is an automated technique of recognising persons based on their traits. Because of their exceptional texture, the biometric features' ostensibly random nature makes them good candidates for recognition. These features are unique for each individual even for identical twins authentication. The latest developments in Deep Learning (DL) and computer vision has proved that Convolutional Neural Networks (CNNs) can extract generic descriptors that can represent complex image features. How to protect the biometric data and ensure user’s privacy is a main concern, nowadays. Hence, several cancelable biometric scenarios have been proposed. In this paper, we propose a novel cancelable biometric recognition system based on a CNN model with bio-convolution. The performance metrics are estimated on different face and iris datasets. In contrary to most conventional secure biometric recognition systems, the proposed system achieves superior accuracy results, while keeping the ability to cancel the biometric traits if compromised. The experimental findings on each database are shown and compared to those of the state-of-the-art systems that have been tested on the same database. Furthermore, the recognition rates reach 99.15%, 98.35%, 97.89, and 95.48% with the LFW, FERET, IITD, and CASIA-IrisV3 databases, respectively.

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is equipped with a high resolution 128 channel LiDAR, a 2MPix stereo camera, a lux meter and a GNSS/INS system. Ambient and reflectivity images are made available along with the LiDAR point clouds to facilitate multi-modal use of concurrent ambient and reflectivity scene information. Leveraging DurLAR, with a resolution exceeding that of prior benchmarks, we consider the task of monocular depth estimation and use this increased availability of higher resolution, yet sparse ground truth scene depth information to propose a novel joint supervised/self-supervised loss formulation. We compare performance over both our new DurLAR dataset, the established KITTI benchmark and the Cityscapes dataset. Our evaluation shows our joint use supervised and self-supervised loss terms, enabled via the superior ground truth resolution and availability within DurLAR improves the quantitative and qualitative performance of leading contemporary monocular depth estimation approaches (RMSE=3.639, Sq Rel=0.936).

BACKGROUND:The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work. OBJECTIVE:In this paper, we present a solution to AATR based on such X-ray CT baggage scan imagery. This aims to address the issues of rapidly evolving threat signatures within the screening requirements. Ideally, the detection algorithms deployed within the security scanners should be readily adaptable to different situations with varying requirements of threat characteristics (e.g., threat material, physical properties of objects). METHODS:We tackle this issue using a novel adaptive machine learning methodology with our solution consisting of a multi-scale 3D CT image segmentation algorithm, a multi-class support vector machine (SVM) classifier for object material recognition and a strategy to enable the adaptability of our approach. Experiments are conducted on both open and sequestered 3D CT baggage image datasets specifically collected for the AATR study. RESULTS:Our proposed approach performs well on both recognition and adaptation. Overall our approach can achieve the probability of detection around 90% with a probability of false alarm below 20%. CONCLUSIONS:Our AATR shows the capabilities of adapting to varying types of materials, even the unknown materials which are not available in the training data, adapting to varying required probability of detection and adapting to varying scales of the threat object.

Electrical Impedance Tomography (EIT) is a novel industrial imaging technique that can be applied to visualize runtime changes of impedance entire along a pipeline to implement measurements of the industrial continuous processes of flow velocity profile distribution. In this paper, we propose a full three-dimensional (3D) pipeline sensing strategy which considers the 3D nature of the EIT sensing field. The proposed strategy includes a new 3D sensing system and an implementation of the fast forward solver using Finite Element Modelling (FEM). Also, in this paper, the implementation of a one-step Gauss-Newton solver reconstruction inverse solution algorithm is introduced. An application of auto/cross-correlation function for the reconstructed centered images obtained by the EIT system for the box moves from the upstream sensing section to the downstream sensing section on the surface of the pipeline is also introduced. According to the correlation test results of our proposal, the best fit of the correlation coefficient to images was distinguished with a higher correlation coefficient between 0.8687 and 0.9995.

In this paper, we propose a cancelable multi-biometric face recognition method that uses multiple convolutional neural networks (CNNs) to extract deep features from different facial regions. We also propose a new CNN architecture that exploits batch normalization, depth concatenation and a residual learning framework. The proposed method adopts a region-based technique in which face, eyes, nose and mouth regions are detected from the original face images. Multiple CNNs are used to extract deep features from each region, and then, a fusion network combines these features. Moreover, to provide user’s privacy and increase the system resistance against spoof attacks, a cancelable biometric technique using bio-convolving encryption is performed on the final facial descriptor. Our experiments on the FERET, LFW and PaSC datasets show excellent and competitive results compared to state-of-the-art methods in terms of recognition accuracy, specificity, precision, recall and f score .

Biometric recognition refers to the automated process of recognizing individuals using their biometric patterns. Recent advancements in deep learning and computer vision indicate that generic descriptors which are extracted using convolutional neural networks (CNNs) could represent complex image characteristics. This paper presents a number of cancelable fusion-based face recognition (FR) methods; region-based, multi-biometric and hybrid-features. The former included methods incorporate the use of CNNs to extract deep features (DFs). A fusion network combines the DFs to obtain a discriminative facial descriptor. Cancelabilitiy is provided using bioconvolving as an encryption method. In the region-based method, the DFs are extracted from different face regions. The multi-biometric method uses different biometric traits to train multiple CNNs. The hybrid-features method merges the merits of deep-learned features and hand-crafted features to obtain a more representative output. Also, an efficient CNN model is proposed. Experimental results on various datasets prove that; (a) the proposed CNN model achieves remarkable results compared to other state-of-the-art CNNs, (b) region-based method is superior to multi-biometric and hybrid-features methods and (c) the utilization of bio-convolving method increases the system security with a slight degradation in the recognition accuracy.

Whilst real-time object detection has become an increasingly important task within urban scene understanding for autonomous driving, the majority of prior work concentrates on the detection of obstacles, dynamic scene objects (pedestrians, vehicles) and road sign-age within the scene. By contrast, for an autonomous vehicle to be truly able to interact with occupants and other road users using a common semantic understanding of the environment it is traversing it requires a considerably extended scene understanding capability. In this work, we consider the performance of extended "long-list" object detection, via an extended end-to-end Region-based Convolutional Neural Network (R-CNN) architecture, over a large-scale 31 class detection problem of urban scene objects with integrated object attribute estimation for appropriate colour and primary orientation. We examine the extended performance of this multiple class object detection and attribute estimation task operating in real-time with on-vehicle processing at 10 fps. Our work is evaluated under a range of real-world automotive conditions across multiple complex and cluttered urban environments.