Feature point detection on textureless surfaces remains a fundamental challenge in computer vision due to the absence of discernible color and brightness gradients. From the imaging mechanism perspective, micro-geometry structures of textureless surfaces provide physically stable cues for feature point extraction despite the absence of visual distinctiveness. Therefore, we propose a novel feature point detection method, which reconstructs surface micro-geometry structures from a single RGB image and leverages these micro-geometry structures for feature extraction, without relying on specialized equipment or complex deep learning models. Specifically, our method establishes a novel framework that models the light-surface interaction to analyze phase modulation in reflected light. Then it reconstructs underlying micro-geometry structures through Gabor Kernel-based spectral analysis, enabling accurate quantification of surface height variations from phase information. This information forms the foundation of our proposed Concave-Convex Index (CCI), a robust geometric descriptor that achieves stable feature characterization through geometry-aware measurements. Extensive evaluations on TUM, T-LESS, Shape2.5D datasets and self-collected images, demonstrate our method's superior capability in extracting stably distributed and highly repeatable feature points, even when visible texture or brightness gradients vanish. Our method offers a novel perspective for reliable feature point detection on challenging textureless surfaces across diverse materials and illumination conditions.

Since raining weather always degrades image quality and poses significant challenges to most computer vision-based intelligent systems, image de-raining has been a hot research topic in computer vision community. Fortunately, in a rainy Light Field (LF) image, background obscured by rain streaks in one sub-view may be visible in the other sub-views, and implicit depth information and recorded 4D structural information may benefit rain streak detection and removal. However, existing LF image rain removal methods either do not fully exploit the global correlations of 4D LF data or only utilize partial sub-views (i.e., under-utilization of the rich angular information), resulting in sub-optimal rain removal performance and no-equally good quality for all de-rained sub-views. In this article, we propose an efficient neural network, called MDeRainNet , for rain streak removal from LF images. The proposed network adopts a multi-scale encoder–decoder architecture, which directly works on Macro-pixel Images (MPIs) for improving the rain removal performance. To fully model the global correlation between the spatial information and the angular information, we propose an Extended Spatial-angular Interaction (ESAI) module to merge the two types of information, in which a simple and effective Transformer-based Spatial-angular Interaction Attention (SAIA) block is also proposed for modeling long-range geometric correlations and making full use of the angular information. Furthermore, to improve the generalization performance of our network on real-world rainy scenes, we propose a novel semi-supervised learning framework for our MDeRainNet , which utilizes multi-level KL loss to bridge the domain gap between features of synthetic and that of real-world rain streaks and introduces colored-residue image-guided contrastive regularization to reconstruct rain-free images. Extensive experiments conducted on both synthetic and real-world Light Field Images (LFIs) demonstrate that our method outperforms the state-of-the-art methods both quantitatively and qualitatively.

Surface normal estimation is a fundamental task in point cloud processing and plays a crucial role in downstream applications. Existing methods typically extract features from local neighborhoods or patches, followed by surface fitting or direct regression to predict normals. However, the scale ambiguity in determining the optimal neighborhood hinders effective extraction of geometric information, making normal estimation for unstructured point clouds with significant density variations particularly challenging. To address this challenge, we propose APN-Net, an adaptive perception network for point cloud normal estimation. Specifically, we design the Graphical Information Self-perception (GIS) module, which provides an implicit manner for region partitioning and expands the receptive field, enabling automatic extraction of both local geometric details and global structural information, while alleviating the scale ambiguity in determining the optimal neighborhood. Moreover, to capture complex geometric details, we introduce the Adaptive Graph Convolution (AGC) module, which employs adaptive kernels to model relationships among points across different semantic regions, thereby enabling richer feature representation. Extensive experiments on both synthetic and real-world scanned datasets demonstrate that APN-Net achieves superior performance in unoriented normal estimation, particularly for point clouds with significant density variations.

An image enhancement method, which is solving the issue of detail loss caused by the inability of existing image enhancement methods to balance brightness and saturation in Wireless Capsule Endoscope (WCE) low-light environment, is proposed. Firstly, we design a multi-scale fast guided filter to estimate the illumination component and utilize the OTSU method to determine the function parameters based on the grayscale information of the illumination component. Secondly, we construct a brightness enhancement function based on the Weber-Fechner law to achieve brightness enhancement of the V component image. At the same time, we designed the brightness enhancement coefficient and combined with Haar wavelet to operate the S component image to balance the brightness and saturation of the WCE enhanced image. Finally, the image enhancement result is obtained by merging the channels and converting to the RGB color space. Comprehensive experimental results show that compared with existing methods, our proposed method improves the mean, standard deviation and information entropy evaluation criteria by 18.2, 5.81 and 0.26 respectively. Furthermore, the feature point detection and matching numbers of the enhanced images increased by an average of 59.3% and 32.9% respectively. Moreover, the effectiveness of this method is further verified through the improvement of experimental results of single-image depth estimation accuracy.

Image deraining is a hot research topic, which aims to remove various rain streaks (raindrops) from rainy images and restore the backgrounds. Though image deraining has been extensively studied in recent years, few methods are able to effectively and efficiently derain real-world high-resolution rainy images. In general, existing image deraining methods are restricted by two main factors while processing high-resolution images. First, the computational complexity and memory usage of existing deep learning-based methods are high when it comes to derain high-resolution images. Second, as the image resolution increases, it is difficult to simultaneously extract and aggregate both global and local features for clean rain removal. In this paper, we propose a novel network, called Global-Local Grafting Fusion Network (GLGFN), for deraining real-world high-resolution images. Our GLGFN utilizes a staggered connection structure to achieve deeper sampling depth while maintaining low computational cost. It adopts the Transformer and CNN based encoders (backbones) to extract global and local features, respectively, and then grafts global features into local features to guide the extraction of rain streaks. In addition, for well fusing global and local features, we also propose a Grafting Fusion Module (GFM), which adopts Cross Sparse Attention (CSA) and Selective Kernel Fusion (SK Fusion) to efficiently aggregate global and local features. Extensive experiments conducted on several high-resolution real rainy datasets have demonstrated the effectiveness and efficiency of our proposed GLGFN. We will release our code and dataset.

Spatio-temporal action detection networks, which need to simultaneously extract and fuse spatial and temporal features, often result in existing models becoming bloated and difficult to run in real-time and deploy on edge devices. This paper introduces an efficient and real-time spatio-temporal action detection model, YOWOv3. This model uses efficient 3D and 2D backbone networks to separately extract spatial and spatial-temporal features from sequential information. A lightweight spatio-temporal feature fusion module, designed by deeply integrating convolution and self-attention mechanisms, further enhances the extraction of spatio-temporal features. We refer to this module as the CFACM (Channel Fusion & Attention Convolution Mix) module. Our approach not only outperforms the latest efficient spatio-temporal action detection models in terms of lightness, reducing the model size by 24% compared to the latter, but also improves the mAP accuracy on the UCF101-24 dataset by 1.35%, while maintaining excellent speed performance, thus achieving a balance between accuracy and speed. Furthermore, existing models often use 3D convolutions to extract temporal information, which may be limited on certain devices, such as Apple’s M series processors. To mitigate the potential issue of 3D convolution operators not being supported during edge deployment of spatio-temporal action detection models, we employ a spatio-temporal shift module containing only 2D convolutions. This enables the model to acquire temporal information and inject the obtained temporal features into multi-level spatio-temporal feature extraction models. This not only liberates the model from the constraints of 3D convolution operations but also enhances the model’s balance between accuracy and speed. This results in state-of-the-art performance in lightweight networks using only 2D convolutions.

Belief rule base (BRB) has attracted considerable interest due to its interpretability and exceptional modeling accuracy. Generally, BRB construction relies on prior knowledge or historical data. The limitations of knowledge constrain the knowledge-based BRB and are unsuitable for use in large-scale rule bases. Data-driven techniques excel at extracting model parameters from data, thus significantly improving the accuracy of BRB. However, the previous data-based BRBs neglected the study of interpretability, and some still depend on prior knowledge or introduce additional parameters. All these factors make the BRB highly problem-specific and limit its broad applicability. To address these problems, a model-agnostic statistical BRB (MAS-BRB) modeling approach is proposed in this article. It adopts an MAS methodology for parameter extraction, ensuring that the parameters both fulfill their intended roles within the BRB framework and accurately represent complex, nonlinear data relationships. A comprehensive interpretability analysis of MAS-BRB components further confirms their compliance with established BRB interpretability standards. Experiments conducted on multiple public datasets demonstrate that MAS-BRB not only achieves improved modeling performance but also shows greater effectiveness compared to existing rule-based and traditional machine learning models.

On behalf of the organizing committee, we are most delighted to welcome you to join us at 23rd IEEE/ACIS International Conference on Computer and Information Science (ICIS 2023), to be held on June 23-24, 2023 in Wuxi, China. The conference is sponsored by IEEE Computer Society and International Association for Computer and Information Science (ACIS), and in cooperation with Jiangnan University, China.

Balancing image local detail enhancement with brightness enhancement has been a challenge. The images captured by wireless capsule endoscopy (WCE) are low-light and unclear. To this end, we propose an adaptive enhancement method for WCE images. Firstly, we use the guided filter to filter and smooth the WCE images to approximate its illumination component, and then the reflection component is obtained by decomposing it based on the Retinex model. Secondly, an adaptive Sigmoid function is obtained according to the positive correlation between the just-noticeable difference (JND) threshold of the illumination component and the gain parameter of the Sigmoid function, to adaptively enhance the illumination component, and then based on the Retinex model fusion with the reflection component. Finally, we combine with Gamma correction algorithm to enhance the contrast of the above results. Experimental results show that the proposed method can adaptively enhance the overall effect and local details of WCE images; and the feature extraction and matching effects are better than the classical enhancement algorithms, with an average increase of 88.8% and 59.1% respectively.

3D convolution networks play an important role in extracting spatiotemporal features in video action recognition. However, it usually brings a large number of paramters, which results in deployment difficulty in edge devices with limited memory space. Although lightweight 3DCNNs can reduce the mode size significantly, it causes a serious loss of accuracy. This paper proposes a novel approach to reduce the model size while preserves accuracy by combining lightweight networks with structural reparameterization. To reduce the model size, we propose 3D-DBB module, based on 2D Diverse Branch Block(DBB). Furthermore, we propose three structures based on 3D-DBB: (1) 3D depthwise convolution (called 3D-DBB-DepthWise), (2) the 3D pointwise convolution (called 3D-DBB-PointWise), and (3) reparameterizable depthwise separable structure (called DP3DBB), which is the concatenation of the two previous structures. We design and compare the effect of two different replacements for replacing depthwise separable structures in lightweight networks. Our method achieves 93.33% with only 0.42% loss in accuracy when the model size is only 1/50 of that of 3D-ResNeXt101.

For static scenes with multiple depth layers, the existing defocused image deblurring methods have the problems of edge ringing artifacts or insufficient deblurring degree due to inaccurate estimation of blur amount, In addition, the prior knowledge in non blind deconvolution is not strong, which leads to image detail recovery challenge. To this end, this paper proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood, which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring, thus preventing boundary ringing artifacts. Then, the obtained blur map is used for blur detection to determine whether the image needs to be deblurred, thereby improving the efficiency of deblurring without manual intervention and judgment. Finally, a non blind deconvolution algorithm is designed to achieve image deblurring based on the blur amount selection strategy and sparse prior. Experimental results show that our method improves PSNR and SSIM by an average of 4.6% and 7.3%, respectively, compared to existing methods. Experimental results show that our method outperforms existing methods. Compared with existing methods, our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

A 3D model watermarking method robust to geometric attacks is proposed. The vertices of the model are classified into three groups. The vertices of the low‐resolution group are used to establish an invariant space in which to resist geometric attacks. In the medium‐resolution group, appropriate vertices in which to embed the watermarking information are selected. The selection of vertices that contain information about the watermark is based on the area of the local set of the vertex, the curvature of which determines the embedding strength of the watermark. The vertices of the high‐resolution group are reserved for resisting simplification and smoothing attacks. The choice of the embedding position and the embedding strength can provide a suitable trade‐off between good transparency and maximum robustness of the proposed method. The simulation results show that, compared to existing state‐of‐the‐art methods, the proposed method is robust against attacks such as noise, smoothing, simplification, cropping, rotation, translation, and scaling while ensuring high visual quality of the watermarked model.

Three‐dimensional (3D) facial data offer the potential to overcome the difficulties caused by the variation of head pose and illumination in 2D face recognition. In 3D face recognition, localisation of nose tip is essential to face normalisation, face registration and pose correction etc. Most of the existing methods of nose tip detection on 3D face deal mainly with frontal or near‐frontal poses or are rotation sensitive. Many of them are training‐based or model‐based. In this study, a novel method of nose tip detection is proposed. Using pose‐invariant differential surface features – high‐order and low‐order curvatures, it can detect nose tip on 3D faces under various poses automatically and accurately. Moreover, it does not require training and does not depend on any particular model. Experimental results on GavabDB verify the robustness and accuracy of the proposed method.

Terrestrial laser scanner (TLS) can be used to acquire 3D facade information of modern architectures, represented as point cloud data (PCD). Basic shape elements of an architecture, like windows and doors, should be recovered in reconstruction; and the model should be represented corresponding to the information of architectural design, such as lines and polygons. Most recent approaches could not reconstruct models automatically with designed shape details. Either user's interactions are needed [Zheng et al. 2010; Nan et al. 2010]; or the reconstructed model is coarse without information of shape details. Therefore, it is necessary to develop new algorithms to generate geometric models automatically, fitting well the design information of architectural PCD. A novel framework is proposed to generate explicitly an architectural model from scanned points of an existing architecture. An automatic, hierarchical and fast facade reconstruction framework is presented based on a novel combination of facade structures, detailed windows propagation, hierarchical model consolidation and contextual semantic representations. As a result, a high-quality geometric model of an architecture ia generated. Figure 1 shows the procedure of this work, from building detection, to planar region decomposition, to boundary point extraction, and to the consolidated hierarchal model.