Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose Atomvision library that can be used to generate and curate microscopy i...
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Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose Atomvision library that can be used to generate and curate microscopy image (such as scanning tunneling microscopy and scanning transmission electron microscopy) data sets and apply a variety of machine learning techniques. To demonstrate the applicability of this library, we (1) establish an atomistic image data set of about 10 000 materials with large structural and chemical diversity, (2) develop and compare convolutional and atomistic line graph neural network models to classify the Bravais lattices, (3) demonstrate the application of fully convolutional neural networks using U-Net architecture to pixelwise classify atom versus background, (4) use a generative adversarial network for super resolution, (5) curate an image data set on the basis of natural language processing using an open-access arXiv data set, and (6) integrate the computational framework with experimental microscopy images for Rh, Fe3O4, and SnS systems. The Atomvision library is available at https://***/ usnistgov/atomvision.
Court line extraction is one of the important steps in the analysis of sport videos. The court extraction is the foundation of the analysis of badminton video, and an efficient method with horizontal line projection K...
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Court line extraction is one of the important steps in the analysis of sport videos. The court extraction is the foundation of the analysis of badminton video, and an efficient method with horizontal line projection K-means machine learning algorithm to extract court lines from different broadcast badminton tournament videos is proposed in this paper. The horizontal lines are projected into 1-D histogram signal;then the signal is trained to learn the intensity of the histogram signal for locating the positions of the horizontal court lines. After the equations of the horizontal court lines and the court lines in the vertical direction have been formulized, the intersection points of the court lines can be calculated and the court line can be extracted. The experimental results show that the proposed method can extract the court lines more efficiently than that done by the Hough transform-related algorithms, which are widely applied in computer vision and self-driving car applications.
Argus ii is the most advanced retina implants approved by the US FDA and almost 350 visually impaired people are using it. This implant uses 60 microelectrodes implanted in the retina. The goal of this implant is to i...
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Argus ii is the most advanced retina implants approved by the US FDA and almost 350 visually impaired people are using it. This implant uses 60 microelectrodes implanted in the retina. The goal of this implant is to improve mobility and quality of life of its users. However, users' satisfaction is not very high due to the very low resolution of the phosphene images and features created by this device. This article proposes a system to improve the artificial vision created by visual implants. The proposed method extracts information about the people around the visually impaired person by using imageprocessing and machinevision algorithms. This information includes the number of the people in the scene, whether they are known or unknown, their gender, estimated ages, facial emotions, and approximate distance from the visually impaired person. This information is extracted from the frames received by a camera mounted on the glasses of the user to generate signals that are fed into a visual stimulator. This information is shown to the user by a schematic vision created by some pre-trained patterns of phosphenes reflecting the information communicated to the user. The proposed system is validated with a simulated prosthetic vision comprising 150 microelectrodes that is compatible with the retina and visual cortex implants. A low-cost and energy efficient implementation of the proposed method executing on a Raspberry Pi 4 B at a frame rate of 4.5 frames/second shows the feasibility of using it in portable systems.
In-sensor-processing (ISP) paradigm has been exploited in state-of-the-art vision system designs to pave the way towards power-efficient sensing and processing. The redundant data transmission between sensors and proc...
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In-sensor-processing (ISP) paradigm has been exploited in state-of-the-art vision system designs to pave the way towards power-efficient sensing and processing. The redundant data transmission between sensors and processors is significantly minimized by local computation within each pixel. However, existing ISP designs suffer from limited frame rates and degraded fill factors. In this brief, we introduce a low-latency in-sensor-intelligence neuromorphic vision system using neuromorphic spiking neurons, namely SpikeSen. SpikeSen directly operates on the photocurrents and executes the computation in the frequency domain, reducing the long exposure time and speeding up the computation. Experiments show that SpikeSen can achieve more than 6.1x computation speedup compared to existing ISP designs with competitive energy consumption per pixel.
In the exploration of robot vision systems based on artificial neural networks, the research mainly focuses on their applications in 3D information recognition and processing. By simulating the processing of the human...
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A document layout can be more informative than merely a document’s visual and structural ***,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image ana...
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A document layout can be more informative than merely a document’s visual and structural ***,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different *** research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten *** proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative *** approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex *** experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten *** also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.
image segmentation is a crucial task in computer vision and imageprocessing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image an...
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image segmentation is a crucial task in computer vision and imageprocessing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compression, among others. In light of this, the widespread popularity of deep learning (DL) and machine learning has inspired the creation of fresh methods for segmenting images using DL and ML models respectively. We offer a thorough analysis of this recent literature, encompassing the range of ground-breaking initiatives in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid-based methods, recurrent networks, visual attention models, and generative models in adversarial settings. We study the connections, benefits, and importance of various DL- and ML-based segmentation models;look at the most popular datasets;and evaluate results in this Literature.
Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and va...
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Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources. machine learning is automating human assistance by training an algorithm on relevant data. Supervised, Unsupervised, and Reinforcement Learning are the three fundamental categories of machine learning techniques. In this paper, we have discussed the different learning styles used in the field of Computer vision, Deep Learning, Neural networks, and machine learning. Some of the most recent applications of machine learning in computer vision include object identification, object classification, and extracting usable information from images, graphic documents, and videos. Some machine learning techniques frequently include zero-shot learning, active learning, contrastive learning, self-supervised learning, life-long learning, semi-supervised learning, ensemble learning, sequential learning, and multi-view learning used in computer vision until now. There is a lack of systematic reviews about all learning styles. This paper presents literature analysis of how different machine learning styles evolved in the field of Artificial Intelligence (AI) for computer vision. This research examines and evaluates machine learning applications in computer vision and future forecasting. This paper will be helpful for researchers working with learning styles as it gives a deep insight into future directions.
We propose a new variational model in Sobolev-Orlicz spaces with non-standard growth conditions of the objective functional and discuss its applications to imageprocessing. The characteristic feature of the proposed ...
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We propose a new variational model in Sobolev-Orlicz spaces with non-standard growth conditions of the objective functional and discuss its applications to imageprocessing. The characteristic feature of the proposed model is that the variable exponent, which is associated with non-standard growth, is unknown a priori and it depends on a particular function that belongs to the domain of objective functional. So, we deal with a constrained minimization problem that lives in variable Sobolev-Orlicz spaces. In view of this, we discuss the consistency of the proposed model, give the scheme for its regularization, derive the corresponding optimality system, and propose an iterative algorithm for practical implementations.
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