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Classification algorithm is the crucial technique for machine vision system. The Transfer Component Analysis has been introduced for workpiece classification system. The dataset with five classes of workpieces is constructed for experiments, and the data augmentation strategies have been employed to prevent class imbalance problems. The Histogram of the Oriented Gradient features are extracted over workpiece images, and they are transformed by the Transfer Component Analysis and the Principal Component Analysis separately. The Maximum Mean Discrepancy distances have been employed to measure the distribution distances between the training set and the testing set. And the results indicate that the Transfer Component Analysis has dramatically reduced the distribution differences in a dimensionality reduction subspace, better than the Principal Component Analysis. The Error Correction Output Code is combined with Support Vector Machine and K-Nearest Neighbor respectively as the classifiers. The macro-averaged F 1 measure is adopted to evaluate the classifiers performance. Experiment results show that, the Transfer Component Analysis is particularly beneficial for improving the classification performance, and the Support Vector Machine is more favorable than the K-Nearest Neighbor for workpiece classification. The method of the Transfer Component Analysis combined with the Support Vector Machine possesses the best classification performance among these methods in this paper. [ABSTRACT FROM AUTHOR]

In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications. Designing efficient 3D artificial neural networks chip remains a challenge. Here, the authors report a M3D-LIME chip with monolithic three-dimensional integration of hybrid memory architecture based on resistive random-access memory, which achieves a high classification accuracy of 96% in one-shot learning task while exhibiting 18.3× higher energy efficiency than GPU. [ABSTRACT FROM AUTHOR]

3D object recognition is a fundamental task in 3D computer vision. View-based methods have received considerable attention due to their high efficiency and superior performance. To better capture the long-range dependencies among multi-view images, Transformer has recently been introduced into view-based 3D object recognition and achieved excellent performance. However, the information among views on multiple scales is not utilized sufficiently in the existing Transformer-based methods. To address this limitation, we proposed a 3D object recognition method named iMVS to i ntegrate M ulti- V iew information on multiple S cales. Specifically, for the single-view image/features at each scale, we adopt a hybrid feature extraction module consisting of CNN and Transformer to jointly capture local and non-local information. For the extracted multi-view image features at each scale, we develop a feature transfer module including a view Transformer block to achieve the information transfer across views. Following a sequential process of the single-view feature extraction and multi-view feature transfer on multiple scales, the multi-view information is sufficiently interacted. Subsequently, the multi-scale features with multi-view information are fed into our designed feature aggregation module to generate a category-specific descriptor, where the adopted channel Transformer block facilitates the descriptor to be more expressive. Coupling with these designs, our method can fully exploit the information embedded within multi-view images. Experimental results on ModelNet40, ModelNet10 and a real-world dataset MVP-N demonstrate the superior performance of our method. • The view-based 3D object recognition method makes full use of multi-view information • The transfer module achieves multi-view feature interaction on multiple scales • The channel Transformer-based module effectively aggregates multi-scale features • Experimental results demonstrate the effectiveness of our method iMVS [ABSTRACT FROM AUTHOR]

This paper investigates the generation of large field-of-view (FOV) and high-resolution (HR) panorama images for smartphones. Existing techniques like image stitching struggle to produce satisfactory results due to geometry misalignment and inconsistent appearance. To circumvent the inherent challenges of image stitching methods and generate high-quality panoramas, we treat the image stitching problem as a multiple-reference-based super-resolution problem. Specifically, one large-FOV low-resolution (LR) image and several overlapped small-FOV HR images are taken as inputs, where the LR image acts as a base and multi-view references provide rich HR information. Building on this foundation, a novel multi-view references image super-resolution framework (MVRefSR) is proposed. Within this framework, to address the residual geometric misalignment between the LR-Ref image pairs after coarse alignment, a flow-based RefSR network (FlowSRNet) is proposed, which super-resolves LR patches with corresponding HR references. To facilitate adaptive feature fusion and minimize distortion in structured regions, a fusion weight estimation module and a gradient branch are introduced in FlowSRNet. Finally, the large-FOV HR image is generated by combining these SR patches together. Furthermore, the lack of real-world RefSR datasets for smartphones is addressed by designing an innovative dataset construction pipeline. Extensive experiments demonstrate the superior performance of FlowSRNet and MVRefSR over the compared SR methods and image stitching software, which proves the effectiveness of generating panoramas from a new perspective. • A novel multi-view references image super-resolution framework (MVRefSR) is proposed to generate the large field-of-view and high-resolution panoramas from a new perspective. • Two modules, a fusion weight estimation module and a gradient branch, are adopted to enhance the performance of our reference-based super-resolution (RefSR) methods within our MVRefSR framework. • A new RefSR dataset generation pipeline and a real-world RefSR dataset are presented to address the lack of datasets and facilitate the study of real-world RefSR. • Experimental results demonstrate the superiority of our RefSR network and our framework MVRefSR, which proves the effectiveness of generating panoramas from a new perspective. [ABSTRACT FROM AUTHOR]