This paper presents a novel image encryption algorithm that leverages the chaotic properties of the Chen system, the cryptographic strength of OpenSSL, and the mathematical robustness of the Fibonacci Q-Matrix. The pr...
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Interactive information fault diagnosis technology is a new type of fault diagnosis technology which is integrated by information fusion, artificial intelligence, computer science and other disciplines. It can extract...
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imageprocessing pipelines are ubiquitous and we rely on them either directly, by filtering or adjusting an image post-capture, or indirectly, as image signal processing (ISP) pipelines on broadly deployed camera syst...
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imageprocessing pipelines are ubiquitous and we rely on them either directly, by filtering or adjusting an image post-capture, or indirectly, as image signal processing (ISP) pipelines on broadly deployed camera systems. Used by artists, photographers, system engineers, and for downstream vision tasks, traditional imageprocessing pipelines feature complex algorithmic branches developed over decades. Recently, image-to-image networks have made great strides in imageprocessing, style transfer, and semantic understanding. The differentiable nature of these networks allows them to fit a large corpus of data;however, they do not allow for intuitive, fine-grained controls that photographers find in modern photo-finishing tools. This work closes that gap and presents an approach to making complex photo-finishing pipelines differentiable, allowing legacy algorithms to be trained akin to neural networks using first-order optimization methods. By concatenating tailored network proxy models of individual processing steps (e.g. white-balance, tone-mapping, color tuning), we can model a non-differentiable reference image finishing pipeline more faithfully than existing proxy image-to-image network models. We validate the method for several diverse applications, including photo and video style transfer, slider regression for commercial camera ISPs, photography-driven neural demosaicking, and adversarial photo-editing.
The rapid expansion of urban areas has intensified the challenge of finding parking spaces for drivers. Intelligent parking systems emerge as a crucial solution by providing real-time detection of available spaces. Wh...
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Color Filter Arrays (CFA) are essential components of digital cameras and image sensors to capture the color information needed to produce full-color images from only a single image sensor per pixel. Many methods and ...
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ISBN:
(纸本)9798350388787;9798350388770
Color Filter Arrays (CFA) are essential components of digital cameras and image sensors to capture the color information needed to produce full-color images from only a single image sensor per pixel. Many methods and algorithms have been proposed to recover the missing color information of CFAs. In this work, we use a simplified version of the Theshold-based Variable Number of Gradients algorithm proposed by Chang et al. to estimate the full-color information from Bayer images. We also show that the slight modification to algorithm does not effect images quality while making it more compatible with hardware. We propose an efficient implementation of the algorithm that reduces the number of calculations per pixel at the cost of increased memory resources. Our implementation targets an imageprocessing pipeline in an FPGA platform which is short on LUTs and FF resources but has DSPs and BRAMs to spare. We buffer the absolute differences and average color components to be shared and re-used between neighboring pixels, on two levels: within the same row, and between different rows. The latter strategy reduces the number of absolute differences calculated every cycle from 32 to 4 and average color components from 32 to 6. However, the memory requirements are increased from storing 4 image rows to 18 image rows. We implement the solutions on an FPGA using high-level synthesis (HLS) and optimize it to further reduce resources.
Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machi...
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ISBN:
(纸本)9783031702587;9783031702594
Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional imageprocessing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed and utilization. With the existing operational constraints of our client, frequent re-training of the deep learning model for object detection is not feasible. Thus, we compared the generalizability of the two techniques across 'unseen' cutleries and found traditional imageprocessing to be generalizable across 'unseen' images. Our proposed final solution uses traditional imageprocessing for computation of utilization but a hybrid of traditional imageprocessing and deep learning model for speed computation as it is more reliable. Our client has implemented our proposed solution for one conveyor belt-based cutlery washing machine and will be planning to scale this to multiple conveyor belt-based cutlery washing machines.
Improved fuzzy c-means (FCM) clustering algorithms have been widely used for image recognition and localization. However, in industrial assembly systems, the unsatisfactory pixel merging and segmentation results betwe...
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Manufacturing process (MP) selection systems require a large amount of labelled data, typically not provided as design outputs. This issue is made more severe with the continuous development of Additive Manufacturing ...
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One of the interesting fields in video processing is motion detection and human action detection (HAR) in video. In some applications where both objects in the scene and the camera may be moving, camera movement cance...
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ISBN:
(纸本)9783031456503;9783031456510
One of the interesting fields in video processing is motion detection and human action detection (HAR) in video. In some applications where both objects in the scene and the camera may be moving, camera movement cancellation is very important to increase accuracy in extracting motion features. HAR systems usually use image matching/registration algorithms to remove the camera movement. In these methods, the source (fixed) image frame is compared with moved image frame, and the best match is determined geometrically. In video processing, due to the existence of a set of frames, one can correct errors using previous data, but at the same time, it is needed a fast frame registration algorithm. According to the above explanations, this article proposes a method to detect and minimize camera movement in video using phase information. In addition to having the acceptable speed and the ability to be implemented online, the proposed method, by combining texture and phase congruency (PC), can significantly increase the accuracy of detecting the objects in the scene. The proposed method was implemented on a HAR dataset, which includes camera movement, and its ability to compensate for camera motion and pre-serve object motion was verified. Finally, the speed and accuracy of the proposed method were compared with a number of the latest image registration methods, and its efficiency in terms of camera movement cancellation and execution time is discussed.
The increase in amount of vehicles in the past few years have made traffic management a difficult job. Technologies play an important role in these systems to regulate the traffic. Number plates are distinguished by t...
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