When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints. With the current tr...
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When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints. With the current trend towards using more and more data, our aim is not to fit the motion capture markers with a parameterized (blendshape) model or to smoothly interpolate a surface through the marker positions, but rather to find an instance in the high resolution dataset that contains local geometry to fit each marker. Just as is true for typical machine learning applications, this approach benefits from a plethora of data, and thus we also consider augmenting the dataset via specially designed physical simulations that target the high resolution dataset such that the simulation output lies on the same so-called manifold as the data targeted.
Neural Radiance Fields (NeRF) rendering is a promising Artificial intelligence (AI) technology for generating photorealistic views, with significant potential for automotive applications. However, traditional metrics ...
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machinevisionapplications for intelligent vision systems in manufacturing industries were reported based on imageprocessing and artificial intelligence technology. We propose the imaging and vision development plat...
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In this study, we investigate the Deep image Prior (DIP) in enhancing image smoothing, a crucial component in numerous computer vision and graphics applications. Although deep learning has demonstrated remarkable achi...
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ISBN:
(纸本)9798350351439;9798350351422
In this study, we investigate the Deep image Prior (DIP) in enhancing image smoothing, a crucial component in numerous computer vision and graphics applications. Although deep learning has demonstrated remarkable achievements in these domains, it often falls short in flexibility and controllability, in contrast to traditional methods, which are more adaptable and typically exhibit subpar performance. Notably, some end-to-end deep learning models offer control over edge preservation, yet their performance remains marginally suboptimal. To address this shortcoming, we introduce an innovative network architecture that diverges from the traditional U-Net model, featuring a Laplacian pyramid as the encoder and a deep decoder as the decoding component, integrated with a bilateral filter loss to improve DIP. This design aids the network in rapidly assimilating essential low-frequency information. Our approach excels in retaining texture details, significantly improving image smoothing and related tasks beyond the capabilities of standard DIP methods. Moreover, our technique outperforms the leading unsupervised method, pyramid texture filtering, in texture filtering tasks and other applications.
image captured under poor-illumination conditions often display attributes of having poor contrasts, low brightness, a narrow gray range, colour distortions and considerable interference, which seriously affect the qu...
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image captured under poor-illumination conditions often display attributes of having poor contrasts, low brightness, a narrow gray range, colour distortions and considerable interference, which seriously affect the qualitative visual effects on human eyes and severely restrict the efficiency of several machinevision systems. In addition, underwater images often suffer from colour shift and contrast degradation because of an absorption and scattering of light while travelling in water. These unpleasant effects limits visibility, reduce contrast and even generate colour casts that limits the use of underwater images and videos in marine archaeology and biology. In medical imaging applications, medical images are important tools for detecting and diagnosing several medical conditions and ailments. However, the quality of medical images can often be degraded during image acquisition due to factors such as noise interference, artefacts, and poor illumination. This may lead to the misdiagnosis of medical conditions, which can further aggravate life threatening situations. image enhancement is one of the most important technologies in the field of imageprocessing, and its purpose is to improve the quality of images for specific applications. In general, the basic principle of image enhancement is to improve the quality and visual interpretability of an image so that it is more suitable for the specific applications and the observers. Over the last few decades, numerous image enhancement techniques have been proposed in the literature This study covers a systematic survey on existing state-of-the-art image enhancement techniques into broad classification of their algorithms. In addition, this paper summarises the datasets utilised in the literature for performing the experiments. Furthermore, an attention has been drawn towards several evaluation parameters for quantitative evaluation and compared different state-of-the-art algorithms for performance analysis on benchmark
Bank Cheques are used mainly for financial transactions due to which they are processed in enormous amounts on daily basis around the globe. Often, Cheque execution time and expenses can be saved if the whole method o...
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Bank Cheques are used mainly for financial transactions due to which they are processed in enormous amounts on daily basis around the globe. Often, Cheque execution time and expenses can be saved if the whole method of recognition and verification of the Cheque becomes automatic. Automatic bank Cheque processing system is an emerging research field in the area of computer vision, imageprocessing, pattern recognition, machine learning, and deep learning. The article emphasizes the stages of the proceedings of image acquisition, pre-processing, and extraction and recognition in the automatic bank Cheque processing system. This paper describes the various steps involved in the system of automatic data extraction. It further classifies and examines existing challenges in different stages of automated processing of bank Cheques. An attempt is made in this paper to present state-of-the-art techniques for the automatic processing of bank Cheque images. The categories and sub-categories of various fields related to bank Cheque images are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. Moreover, it also contains some information about the products available in the market for automatic Cheque processing. This review provides a fundamental comparison and analysis of the remaining problems in the field. It is found that multilayer feed-forward neural network gave an accuracy of 97.31% for payee's name recognition systems;HMM-MLP gave an accuracy of 95.5% for date recognition system. In the courtesy and legal amount system, DNN gave an accuracy of 98.5% for digit recognition, MLP gave an accuracy of 93.2% for courtesy amount, MQDF gave an accuracy of 97.04% for the legal amount. Further, the SvM classifier gave an accuracy of 99.13% for signature recognition, and deep learning-based Convolutional Neural Networks (CNN) gave an accuracy of 99.14% for handwritten numeric character recognition. This survey paper
We propose a complex-amplitude diffractive processor based on diffractive deep neural networks (D2NNs). By precisely controlling the propagation of an optical field, it can effectively remove the motion blur in numera...
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We propose a complex-amplitude diffractive processor based on diffractive deep neural networks (D2NNs). By precisely controlling the propagation of an optical field, it can effectively remove the motion blur in numeral images and realize the restoration. Comparative analysis of phase-only, amplitude-only, and complex-amplitude diffractive processor reveals that the complex-amplitude network significantly enhances the performance of the processor and improves the peak signal-to-noise ratio (PSNR) of the images. Appropriate use of complex-amplitude networks contributes to reduce the number of network layers and alleviates alignment difficulties. Due to its fast processing speed and low power consumption, complex-amplitude diffractive processors hold potential applications in various fields including road monitoring, sports photography, satellite imaging, and medical diagnostics. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
In the field of computer vision, the task of facial super-resolution (FSR) is crucial for applications such as surveillance and photo restoration. However, factors such as noise and artifacts in real-world scenarios s...
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In the advanced field of imageprocessing and Computer vision (IP/Cv), there is a trend toward utilising parallel processing in computer architectures for enhanced efficiency, striking a balance between general-purpos...
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ISBN:
(纸本)9798350355291;9798350355284
In the advanced field of imageprocessing and Computer vision (IP/Cv), there is a trend toward utilising parallel processing in computer architectures for enhanced efficiency, striking a balance between general-purpose capabilities and hardware-specific processes. The RISC-v standard, now backed by a wide array of compilers, frameworks, and operating systems, is paving the way for innovative cores. Our introduction of a Multi-Processor Systems on Chip (MPSoC), MPRISC-v, is a testament to this evolution. This system incorporates a Network on Chip (NoC) for robust intra-chip communication. The processing System (PS) seamlessly integrates and manages it through a user-friendly API crafted to simplify the development cycle. To ascertain its effectiveness, we tested it on a Zynq Ultrascale+ MPSoC device, deploying a Sobel-based application benchmark. By evaluating its efficiency in terms of cycles/pixels, our findings underscore its potential and spotlight areas ripe for further enhancement.
Facial expression generation in computer vision is essential for improving human-computer interaction by enabling machines to interpret and respond to human emotions effectively. This area has attracted considerable r...
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ISBN:
(纸本)9798331541859;9798331541842
Facial expression generation in computer vision is essential for improving human-computer interaction by enabling machines to interpret and respond to human emotions effectively. This area has attracted considerable research interest. In this context, we introduce a new approach for generating facial expressions from a single neutral image and a target expression label. Our method, referred to as Motion-Oriented Diffusion Model (MODM), leverages latent diffusion techniques, which are known for their ability to learn complex latent spaces and integrate controlled stochasticity to diversify generated content. The main idea of MODM is separating the embedding space into identity and motion domains, and applying diffusion to the motion latent space only. This strategy enhances our model capability to generate various facial expressions while ensuring that the identity details remain consistent across different expressions. To assess the effectiveness of MODM, we perform qualitative and quantitative evaluations using the MUG facial expression database. The preliminary results demonstrate that MODM can generate realistic videos of the six basic facial expressions, preserving the identity of the input subject while accurately representing different emotional states. Additionally, our study highlights promising directions for potential future research and improvements.
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