- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Curvature Augmented Deep Learning for 3D Object Recognition
摘要： This paper presents a new method to incorporate shape information into convolutional neural network (CNN)s for 3D object recognition. Voxel CNNs have been very successful with the task of 3D object recognition. However, continuous shape information that is useful for recognition is often lost in their conversion to a voxel representation. We propose a single dimensional feature that can be applied to voxel CNNs. This paper presents a novel rotation-invariant feature based on mean curvature that improves shape recognition for voxel CNNs. We augment the recent voxel CNN Octnet architecture with our feature and demonstrate a 1% overall accuracy increase on the ModelNet10 dataset.
关键词： 3D Object Recognition,Convolutional Neural Networks,Computational Geometry,Deep Learning
更新于2025-09-19 17:15:36
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Bi-Directional Vectors from Apex in CNN for Micro-Expression Recognition
摘要： The impressive performance of utilizing deep learning or neural network has attracted much attention in both the industry and research communities, especially towards computer vision aspect related applications. Despite its superior capability of learning, generalization and interpretation on various form of input, micro-expression analysis field is yet remains new in applying this kind of computing system in automated expression recognition system. A new feature extractor, BiVACNN is presented in this paper, where it first estimates the optical flow fields from the apex frame, then encode the flow fields features using CNN. Concretely, the proposed method consists of three stages: apex frame acquisition, multivariate features formation and feature learning using CNN. In the multivariate features formation stage, we attempt to derive six distinct features from the apex details, which include: the apex itself, difference between the apex and onset frames, horizontal optical flow, vertical optical flow, magnitude and orientation. It is demonstrated that utilizing the horizontal and vertical optical flow capable to achieve 80% recognition accuracy in CASME II and SMIC-HS databases.
关键词： CNN,micro-expression,apex,optical flow,recognition
更新于2025-09-19 17:15:36
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[IEEE 2018 International Conference on Microwave and Millimeter Wave Technology (ICMMT) - Chengdu, China (2018.5.7-2018.5.11)] 2018 International Conference on Microwave and Millimeter Wave Technology (ICMMT) - A 4-Way Broadband Power Divider Based on the Suspended Microstrip Line
摘要： Facial expression recognition (FER) is an important means of detecting human emotions and is widely applied in many ?elds, such as affective computing and human-computer interaction. Currently, several methods for FER heavily rely on large amounts of manually labeled data, which are costly and not available in real-world applications. To address this problem, this paper proposes a semi-supervised method based on the deep difference features. First, a cascaded structure is introduced to the original safe semi-supervised SVM (S4VM) to solve the multi-classi?cation task. Then, multiple deep different features are fed to the cascaded S4VM to train the six basic facial expressions using the information of the unlabeled data safely. Extensive experiments show that the proposed method achieved encouraging results on public databases even when using a small labeled sample set.
关键词： Deep learning,Semi-supervised method,Cascaded S4VM,Facial expression recognition
更新于2025-09-19 17:15:36
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Human-Computer Interaction using Finger Signing Recognition with Hand Palm Centroid PSO Search and Skin-Color Classification and Segmentation
摘要： This paper presents a novel image processing technique for recognizing finger signs language alphabet. A human-computer interaction system is built based on the recognition of sign language which constitutes an interface between the computer and hearing-impaired persons, or as an assistive technology in industrial robotics. The sign language recognition is articulated on the extraction of the contours of the sign language alphabets, therefore, converting into one dimensional signal processing, which improves the recognition efficiency and significantly reduces the processing time. The pre-processing of images is performed by a novel skin-color region segmentation defined inside the standard RGB (sRGB) color space, then a morphological filtering is used for non-skin residuals removal. Afterwards, a circular correlation achieves the identification of the sign language after extracting the sign closed contour vector and performing matching between extracted vector and target alphabets vectors. The closed contour vector is generated around the hand palm centroid with position optimized by a particle swarm optimization algorithm search. Finally, a multi-objective function is used for computing the recognition score. The results presented in this paper for skin color segmentation, centroid search and pattern recognition show high effectiveness of the novel artificial vision engine.
关键词： Skin-color,Pattern recognition,Sign language,Segmentation,Particle Swarm Optimization,Human-Machine Interaction
更新于2025-09-19 17:15:36
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[ACM Press the 2018 International Conference - Prague, Czech Republic (2018.10.12-2018.10.14)] Proceedings of the 2018 International Conference on Sensors, Signal and Image Processing - SSIP 2018 - SAR Target Recognition Based on Joint Sparse Representation of Complementary Features
摘要： This paper proposed a Synthetic Aperture Radar (SAR) target recognition method based on joint sparse representation of three complementary features. The Elliptical Fourier descriptors (EFDs) of the target outline and PCA features were extracted to depict the geometrical shape and intensity distribution of original SAR image. The azimuthal sensitivity image was constructed to describe the electromagnetic scattering characteristics of the target. The joint sparse representation was used to jointly classify the three features to exploit their complementary advantages. Finally, the target label of the test sample was decided based on the reconstruction errors. To validate the effeteness of the proposed method, experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under various operating conditions.
关键词： joint sparse representation,target recognition,Synthetic Aperture Radar (SAR),complementary features
更新于2025-09-19 17:15:36
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The Rotating Glass Illusion: Material Appearance Is Bound to Perceived Shape and Motion
摘要： We report a novel illusion in which a rotating transparent and refractive triangular prism (glass object) is perceived as being made of a specular reflective material (mirror), and simultaneously, its direction of rotation (clockwise or anticlockwise) is also misperceived. Our findings suggest that physical motion strongly influences viewers’ judgements of material in some situations.
关键词： shapes/objects,surfaces/materials,motion,object recognition
更新于2025-09-19 17:15:36
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Research on path guidance of logistics transport vehicle based on image recognition and image processing in port area
摘要： Due to the messy logistics goods in the port area, some automatic transport vehicles often have errors in cargo transportation due to large path identification errors. Based on this, this study is based on image recognition technology, taking the most common logistics transport vehicles in the port area as the research object and using video image recognition technology as a guiding technology to perform image recognition processing on the ground guidance path. Simultaneously, this study determined the image preprocessing method which is more favorable for visual navigation, used the morphological knowledge of the image to detect the edge of the path image, then determined the position of the path center line, and carried out simulation analysis. The research shows that the results of this study have certain practicality and can provide theoretical references for subsequent related research.
关键词： Image recognition,Logistics transport vehicle,Image processing,Port area
更新于2025-09-19 17:15:36
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New Evolutionary-Based Techniques for Image Registration
摘要： The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.
关键词： hybrid techniques,image recognition,image registration,firefly technique,evolutionary computing,affine perturbation,evolution strategies,mutual information
更新于2025-09-19 17:15:36
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Indoor Scene and Position Recognition Based on Visual Landmarks Obtained from Visual Saliency without Human Effect
摘要： Numerous autonomous robots are used not only for factory automation as labor saving devices, but also for interaction and communication with humans in our daily life. Although superior compatibility for semantic recognition of generic objects provides wide applications in a practical use, it is still a challenging task to create an extraction method that includes robustness and stability against environmental changes. This paper proposes a novel method of scene and position recognition based on visual landmarks (VLs) used for an autonomous mobile robot in an environment living with humans. The proposed method provides a mask image of human regions using histograms of oriented gradients (HOG). The VL features are described with accelerated KAZE (AKAZE) after extracting conspicuous regions obtained using saliency maps (SMs). The experimentally obtained results using leave-one-out cross validation (LOOCV) revealed that recognition accuracy of high-saliency feature points was higher than that of low-saliency feature points. We created our original benchmark datasets using a mobile robot. The recognition accuracy evaluated using LOOCV reveals 49.9% for our method, which is 3.2 percentage points higher than the accuracy of the comparison method without HOG detectors. The analysis of false recognition using a confusion matrix examines false recognition occurring in neighboring zones. This trend is reduced according to zone separations.
关键词： visual landmark,machine learning,saliency maps,semantic position recognition,histograms of oriented gradients
更新于2025-09-19 17:15:36
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Multi-task image set classification via joint representation with class-level sparsity and intra-task low-rankness
摘要： Image set classification has recently attracted great attention due to its widespread applications in computer vision and pattern recognition. The great challenges lie in effectively and efficiently measuring the similarity among image sets with high inter-class ambiguity and large intra-class variability. In this paper, we propose a joint representation based approach to image set classification, in which class-level sparse and globally low rank constraints are imposed on the representation coefficients to embody inter-set discrimination and intra-set commonality respectively. Furthermore, sometimes the small size of image sets or improper usage of a single kind of features causes useful information limited and lacking in discriminability. To address this problem, we extend the traditional image set classification to a multi-task version, i.e., modify the proposed model to incorporate multiple kinds of features. Fortunately, on the total multi-task representation coefficients, both the total class-level sparsity and the intra-task low-rankness constraints still apply. The proposed method is optimized as a non-smooth convex optimization problem by employing an alternating optimization technique. Experiments on five public datasets demonstrate that the proposed method surpasses existing joint representation models with various regularizations for image set classification and compares favorably with other state-of-the-art methods.
关键词： Class-level sparsity,Multi-task recognition,Image set classification,Low-rankness
更新于2025-09-19 17:15:36