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Image and vision computing
Elsevier Science
Image and vision computing

Elsevier Science

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0262-8856

Image and vision computing/Journal Image and vision computingSCI
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    Detection of fractional difference in inter vertebral disk MRI images for recognition of low back pain

    Singh M.Ansari M.S.A.Govil M.C.
    1.1-1.17页
    查看更多>>摘要：© 2024 Elsevier B。V。Low Back Pain (LBP) diagnosis through MR images of IVDs is a challenging task due to complex spinal anatomy and varying image quality。 These factors make it difficult to analyse and segment IVD images accurately。 Further， simple metrics are ineffective in interpreting nuanced features from IVD images for accurate diagnoses。 Overcoming these challenges is crucial to improving the precision and reliability of IVD-based LBP diagnosis。 Also， the existing systems have a very high false negative rate pushes the system towards less use。 This research study proposes a new framework for the detection of LBP symptoms using the Otsu Segmented Structural and Gray-Level Co-occurrence Matrix (GLCM) feature-based ML-model (OSSG-ML model) that eliminates manual intervention for low back pain detection。 The proposed framework uses Otsu segmentation's dynamic thresholding to differentiate spinal and backdrop pixel clusters。 The segmented image is then used by the feature extraction using GLCM and Wavelet-Fourier module to extract two types of features。 The first feature type analyzes the structural variation between normal and low back pain symptom patients。 The second feature type detects LBP using statistical measures in image analysis and texture recognition of the MRI IVD segmented image。 Various machine learning models are built for LBP detection， utilizing both features separately。 First， the model employs structural and geometric differences， while the second model analyzes statistical measurements。 On evaluating the model's performance， it accurately detects low back pain with a 98 to 100% accuracy rate and a very low false negative rate。
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    Infrared and visible image fusion using quantum computing induced edge preserving filter

    Parida P.Panda M.K.Rout D.K.Panda S.K....
    1.1-1.16页
    查看更多>>摘要：© 2024 Elsevier B。V。Information fusion by utilization of visible and thermal images provides a more comprehensive scene understanding in the resulting image rather than individual source images。 It applies to wide areas of applications such as navigation， surveillance， remote sensing， and military where significant information is obtained from diverse modalities making it quite challenging。 The challenges involved in integrating the various sources of data are due to the diverse modalities of imaging sensors along with the complementary information。 So， there is a need for precise information integration in terms of infrared (IR) and visible image fusion while retaining useful information from both sources。 Therefore， in this article， a unique image fusion methodology is presented that focuses on enhancing the prominent details of both images， preserving the textural information with reduced noise from either of the sources。 In this regard， we put forward a quantum computing-induced IR and visible image fusion technique which preserves the required information with highlighted details from the source images efficiently。 Initially， the proposed edge detail preserving strategy is capable of retaining the salient details accurately from the source images。 Further， the proposed quantum computing-induced weight map generation mechanism preserves the complementary details with fewer redundant details which produces quantum details。 Again the prominent features of the source images are retained using highly rich information。 Finally， the quantum and the prominent details are utilized to produce the fused image for the corresponding source image pair。 Both subjective and objective analyses are utilized to validate the effectiveness of the proposed algorithm。 The efficacy of the developed model is validated by comparing the outcomes attained by it against twenty-six existing fusion algorithms。 From various experiments， it is observed that the developed framework achieved higher accuracy in terms of visual demonstration as well as quantitative assessments compared to different deep-learning and non-deep learning-based state-of-the-art (SOTA) techniques。
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    A lightweight depth completion network with spatial efficient fusion

    Fu Z.Wu A.Zhuang Z.Wu X....
    1.1-1.12页
    查看更多>>摘要：© 2024 Elsevier B。V。Depth completion is a low-level task rebuilding the dense depth from a sparse set of measurements from LiDAR sensors and corresponding RGB images。 Current state-of-the-art depth completion methods used complicated network designs with much computational cost increase， which is incompatible with the realistic-scenario limited computational environment。 In this paper， we explore a lightweight and efficient depth completion model named Light-SEF。 Light-SEF is a two-stage framework that introduces local fusion and global fusion modules to extract and fuse local and global information in the sparse LiDAR data and RGB images。 We also propose a unit convolutional structure named spatial efficient block (SEB)， which has a lightweight design and extracts spatial features efficiently。 As the unit block of the whole network， SEB is much more cost-efficient compared to the baseline design。 Experimental results on the KITTI benchmark demonstrate that our Light-SEF achieves significant declines in computational cost (about 53% parameters， 50% FLOPs & MACs， and 36% running time) while showing competitive results compared to state-of-the-art methods。
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    Unified Volumetric Avatar: Enabling flexible editing and rendering of neural human representations

    Fan J.Lv X.Zeng X.Bao Z....
    1.1-1.12页
    查看更多>>摘要：© 2024 The AuthorsNeural Radiance Field (NeRF) has emerged as a leading method for reconstructing 3D human avatars with exceptional rendering capabilities， particularly for novel view and pose synthesis。 However， current approaches for editing these avatars are limited， typically allowing only global geometry adjustments or texture modifications via neural texture maps。 This paper introduces Unified Volumetric Avatar， a novel framework enabling independent and simultaneous global and local editing of both geometry and texture of 3D human avatars and user-friendly manipulation。 The proposed approach seamlessly integrates implicit neural fields with an explicit polygonal mesh， leveraging distinct geometry and appearance latent codes attached to the body mesh for precise local edits。 These trackable latent codes permeate through the 3D space via barycentric interpolation， mitigating spatial ambiguity with the aid of a local signed height indicator。 Furthermore， our method enhances surface illumination representation across different poses by incorporating a pose-dependent shading factor instead of relying on view-dependent radiance color。 Experimental results on multiple human avatars demonstrate its efficacy in achieving competitive results for novel view synthesis and novel pose rendering， showcasing its potential for versatile human representation。 The source code will be made publicly available。
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    Mobile-friendly and multi-feature aggregation via transformer for human pose estimation

    Li B.Tang S.Li W.
    1.1-1.12页
    查看更多>>摘要：© 2024Human pose estimation is pivotal for human-centric visual tasks， yet deploying such models on mobile devices remains challenging due to high parameter counts and computational demands。 In this paper， we study Mobile-Friendly and Multi-Feature Aggregation architectural designs for human pose estimation and propose a novel model called MobileMultiPose。 Specifically， a lightweight aggregation method， incorporating multi-scale and multi-feature， mitigates redundant shallow semantic extraction and local deep semantic constraints。 To efficiently aggregate diverse local and global features， a lightweight transformer module， constructed from a self-attention mechanism with linear complexity， is designed， achieving deep fusion of shallow and deep semantics。 Furthermore， a multi-scale loss supervision method is incorporated into the training process to enhance model performance， facilitating the effective fusion of edge information across various scales。 Extensive experiments show that the smallest variant of MobileMultiPose outperforms lightweight models (MobileNetv2， ShuffleNetv2， and Small HRNet) by 0。7， 5。4， and 10。1 points， respectively， on the COCO validation set， with fewer parameters and FLOPs。 In particular， the largest MobileMultiPose variant achieves an impressive AP score of 72。4 on the COCO test-dev set， notably， its parameters and FLOPs are only 16% and 18% of HRNet-W32， and 7% and 9% of DARK， respectively。 We aim to offer novel insights into designing lightweight and efficient feature extraction networks， supporting mobile-friendly model deployment。
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    IFE-Net: Integrated feature enhancement network for image manipulation localization

    Su L.Dai C.Yu H.Chen Y....
    1.1-1.11页
    查看更多>>摘要：© 2024 Elsevier B。V。Image tampering techniques can lead to distorted or misleading information， which in turn poses a threat in many areas， including social， legal and commercial。 Numerous image tampering detection algorithms lose important low-level detail information when extracting deep features， reducing the accuracy and robustness of detection。 In order to solve the problems of current methods， this paper proposes a new network called IFE-Net to detect three types of tampered images， namely copy-move， heterologous splicing and removal。 Firstly， this paper constructs the noise stream using the attention mechanism CBAM to extract and optimize the noise features。 The high-level features are extracted by the backbone network of RGB stream， and the FEASPP module is built for capturing and enhancing the features at different scales。 In addition， in this paper， the initial features of RGB stream are additionally supervised so as to limit the detection area and reduce the false alarm。 Finally， the final prediction results are obtained by fusing the noise features with the RGB features through the Dual Attention Mechanism (DAM) module。 Extensive experimental results on multiple standard datasets show that IFE-Net can accurately locate the tampering region and effectively reduce false alarms， demonstrating superior performance。
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    • Elsevier