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image processing: algorithms and systems v

ISBN: (纸本)0819466107

The proceedings contain 36 papers. The topics discussed include: stochastic resonance investigation of object detection in images;combining multiple similarity metrics for corner matching;detection of junction in images;color filter array interpolation based on spatial adaptivity;hierarchical texture motifs;dust and scratch removal in scanned images;cloud classification of satellite image performed in two stages;iris identification using counterlet transform;image-based motion stabilization for maritime surveillance;adaptive multi-histogram equalization using human vision thresholding;using block-based spectral angle measure for iris pattern matching;edge and corner preserving smoothing for artistic imaging;nonlinear mapping for dynamic range compression in digital images;wavelet-based texture image classification using vector quantization;adaptive color space transform using independent component analysis;and neural-network based multiscale image restoration approach.

关键词： Imaging systems

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Fλ/d as a predictor of image classification algorithm performance

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APPLIED OPTICS 2025年第4期64卷 845-854页

作者： Hixson, Jonathan g. Teaney, Brian Finch, Michael f. Nehmetallah, George Driggers, Ronald US Army Combat Capabil Dev Command DEVCOM C5ISR Ctr RTI Aberdeen Proving Ground NY 21005 USA Catholic Univ Amer EECS Dept 620 Michigan Ave NE Washington DC 20064 USA Univ Arizona Wyant Coll Opt Sci Infrared Syst Grp Tucson AZ 85721 USA

This research studied the effect of variations in a sensor's F lambda/d metric value (FLD) on the performance of machine learning algorithms such as the YOLO (You Only Look Once) algorithm for object classification. The YOLO_v3 and YOLO_v10 algorithms were trained using static imagery provided in the commonly available training dataset provided by Teledyne FLIR systems. image processing techniques were used to degrade image quality of the test dataset also provided by Teledyne FLIR systems, simulating detector-limited to optics-limited performance, which results in a variation of the FLD metric between 0.339 and 7.98. The degraded test set was used to evaluate the performance of YOLO_v3 and YOLO_v10 for object classification and relate the FLD metric to the probability of detection. Results of YOLO_v3 and YOLO_v10 are presented for the varying levels of image degradation. A summary of the results is discussed along with recommendations for evaluating an algorithm's performance using a sensor's FLD metric value. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： image quality image sensors Imaging systems Imaging techniques Machine learning Sensor performance

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LSHIM: Low-Power and Small-Area Inexact Multiplier for High-Speed Error-Resilient Applications

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND systems 2025年第1期15卷 94-104页

作者： Izadi, Azin Jamshidi, vahid Shahid Bahonar Univ Kerman Dept Comp Engn Kerman *** Iran

Numerical computations in various applications can often tolerate a small degree of error. In fields such as data mining, encoding algorithms, image processing, machine learning, and signal processing where error resilience is crucial approximate computing can effectively replace precise computing to minimize circuit delay and power consumption. In these contexts, a certain level of error is permissible. Multiplication, a fundamental arithmetic operation in computer systems, often leads to increased circuit delay, power usage, and area occupation when performed accurately by multipliers, which are key components in these applications. Thus, developing an optimal multiplier represents a significant advantage for inexact computing systems. In this paper, we introduce a novel approximate multiplier based on the Mitchell algorithm. The proposed design has been implemented using the Cadence software environment with the TSMC 45nm standard-cell library and a supply voltage of 1.1v. Simulation results demonstrate an average reduction of 31.7% in area, 46.8% in power consumption, and 36.1% in circuit delay compared to previous works. The mean relative error distance (MRED) for the proposed method is recorded at 2.6%.

关键词： Approximation algorithms Signal processing algorithms Circuits Accuracy Delays Performance evaluation Power demand Arithmetic Adders Energy consumption Approximate multiplier approximate computing Mitchell's multiplication algorithm energy-consumption area delay

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A Mode-Switchable Photonic Radar System for Aerial LSS Targets Detection and Classification

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC systems 2025年第2期61卷 3470-3482页

作者： Selvi, Amalorpava Babu, S. P. K. Raja, Arockia Bazil Periyar Maniammai Inst Sci & Technol Dept Elect & Commun Engn Thanjavur 613403 India Def Inst Adv Technol DIAT Dept Elect Engn RF Photon Lab Pune 411025 India

A mode-switchable continuous wave (CW) and frequency modulated CW (FMCW), photonic radar system with associated signal processing, and artificial intelligent (AI)-based classification algorithms are the need of the hour to accurately detect and classify today's low-altitude slow-speed and smaller size (LSS) targets. We have designed a mode-switchable X-band photonic radar system, whose capabilities are verified with the proposed range/body-Doppler/micro-Doppler imaging techniques and an AI-based classifier;operating six different LSS targets (front-propeller, small-top rotor, big-top rotor, bionic-bird, tiny-helicopter + bionic-bird, and quadcopter). The respective open-field experimental results are discussed/analyzed in terms of detection beat-frequency, range-time map, and range-velocity images in the FMCW mode while the frequency-time images and AI-based six-classes classifications in the CW mode. The obtained results proved that, with the support of designed X-band photonic radar system and proposed signal processing and classification techniques/algorithms, we could able to achieve the 0.1 m spatial resolution with the classification accuracy of 98.5%.

关键词： Radar Radar imaging Photonics Radar detection Radar signal processing Radar cross-sections Optical imaging Accuracy Quadrotors Optical signal processing Deep convolutional neural network (DCNN) low-altitude slow-speed and smaller size (LSS) aerial targets LSS targets accurate detection/classification mode [continuous wave (CW)/ frequency modulated CW (FMCW)] switchable operation photonic X -band radar range-/micro-Doppler (r-D/m-D) image range-time (R-T) image range-velocity (R-v) image

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Medical image Hybrid Watermark Algorithm Based on Frequency Domain processing and Inception v3

ADVANCED INTELLIGENT SYSTEMS

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ADvANCED INTELLIGENT systems 2025年第0期

作者： Fan, Yu Li, Jingbing Bhatti, Uzair Aslam Nawaz, Saqib Ali Chen, Yenwei Hainan Univ Sch Informat & Commun Engn Haikou 570100 Peoples R China Haikou Univ Econ ZX YZ Sch Network Sci Haikou 571127 Peoples R China Hainan Univ State Key Lab Marine Resource Utilizat South China Haikou 570100 Peoples R China Ritsumeikan Univ Grad Sch Informat Sci & Engn Kyoto 5258577 Japan

With the widespread use of digital medical images in recent years, the efficient and secure protection of sensitive data has become an urgent issue. Existing research has mostly focused on optimizing individual techniques, lacking comprehensive solutions that integrate the strengths of different methods. This article proposes a hybrid digital watermarking algorithm for medical images based on frequency domain transformation and deep learning convolutional neural networks. The algorithm combines the advantages of traditional frequency domain watermarking methods and deep learning techniques. Specifically, the algorithm first extracts feature vectors from medical images using discrete wavelet transform and discrete cosine transform, enhancing the robustness of the image watermark. It then integrates an improved Inception v3 network, optimizing the convolutional kernel design, thereby effectively enhancing the robustness of the watermarking process. To further improve the security of the watermark, the Logistic Map is used to scramble and encrypt the watermark information, and a hash function and XOR operation are employed for zero embedding of the watermark. The watermark extraction and decryption process relies on a key provided by a third party, enabling blind extraction of the watermark without requiring the original image. Through extensive experimental data analysis and comparisons with multiple existing algorithms, the results demonstrate that the proposed hybrid watermarking algorithm excels in terms of attack resistance and robustness, significantly improving the security and reliability of medical image watermarking.

关键词： blind extractions digital cosine transforms digital wavelet transforms hybrid algorithms Inception v3 watermark encryptions zero embedding

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LHTAM: Low-power and high-speed approximate multiplier for tiny inexact computing systems

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Izadi, Azin Jamshidi, vahid Shahid Bahonar Univ Kerman Dept Comp Engn Kerman Iran

The numerical computations related to certain applications can usually withstand a small amount of error. So in these types of applications, such as data mining, encoding algorithms, image processing, machine learning, signal processing, and other error-resilient applications, accurate computing can be replaced with approximate computing in order to reduce circuit delay and power consumption. In these applications, a certain degree of error is acceptable. Multiplication is a fundamental arithmetic operation in computer systems. However, performing it accurately using multipliers - key components in these systems - can result in increased circuit delay, higher power consumption, and greater use of area. Therefore, presenting an optimal multiplier would be considered as a significant advantage for inexact computing systems. In this paper, we propose a new Mitchell algorithm-based approximate multiplier that applied error-reduction factors can be used. The proposed design has been implemented in the Cadence software environment by using TSMC 45 nm standard-cell library and a supplied voltage of 1.1v. The simulation results indicate an average reduction of 31.7% in area, 64.7% in energy, and 36.1% in circuit delay relative to those achieved in previous works. The mean relative error distance (MRED) of the proposed method is 2.6%.

关键词： Approximate multiplier Approximate computing Mitchell's multiplication algorithm Energy-consumption Area Delay

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Gaussian mixture model clustering allows accurate semantic image segmentation of wheat kernels from near-infrared hyperspectral images

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CHEMOMETRICS AND INTELLIGENT LABORATORY systems 2025年 259卷

作者： Kartakoullis, Andreas Caporaso, Nicola Whitworth, Martin B. Fisk, Ian D. Univ Nottingham Sch Biosci Div Div Food Nutr & Dietet Sutton Bonington Campus Nottingham LE12 5RD England Campden BRI Chipping Campden GL55 6LD Glos England Buhler UK Ltd London E16 2LD England

In this study, an ad-hoc image processing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian Mixture Model (GMM), characterized as a soft clustering method, has been employed for this task, yielding noteworthy results in both kernel and germ segmentation. A comparative analysis was conducted, wherein GMM was compared with two hard clustering methods, hierarchical clustering and k-means, as well as other common clustering algorithms prevalent in food HSI applications. Notably, GMM exhibited the highest accuracy, with a Jaccard index of 0.745, surpassing hierarchical clustering at 0.698 and k-means at 0.652. Furthermore, the spectral variations observed in wheat kernel topology can be used for semantic image segmentation, especially in the context of selecting the germ portion within the wheat kernels. These findings carry practical significance for professionals in the fields of hyperspectral imaging (HSI) and machine vision, particularly for food product quality assessment and real-time inspection.

关键词： NIR hyperspectral imaging image segmentation Real time image processing Grain computer vision

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CECF: A DNN-Based Energy-Efficient Cloud-Edge Collaboration Framework for Intelligent Workload Scheduling in 6G-Enabled Transportation systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年

作者： Lu, Yao Liu, Lu Panneerselvam, John Gu, Jiayan Garraghan, Peter Min, Geyong Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF England Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230000 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England

The rapid growth of Internet of vehicle (Iov) devices and Artificial Intelligence (AI) applications has accelerated the adoption of Cloud and Edge Computing. The advent of sixth-generation mobile communication technology (6G) further facilitates the deployment of Cloud-Edge collaborative computing in large-scale Intelligent Transportation systems (ITS). Effective ITS must efficiently handle both latency-sensitive tasks (e.g., obstacle detection, traffic signal recognition) and computationally intensive tasks (e.g., path optimization, traffic flow prediction). However, existing Cloud-Edge collaborative frameworks struggle to accurately classify diverse workloads and provide efficient low-latency processing, leading to energy inefficiencies and task failures. To address these challenges, this paper introduces a Deep Learning-based Cloud-Edge Collaboration Framework (CECF) designed to optimize energy conservation in Cloud and Edge environments. CECF employs a DNN-based classifier to categorize workloads for processing in the Cloud or Edge. The classified tasks are managed by a dedicated Cloud scheduler (DSGA) and an Edge scheduler (EA-DFPSO), respectively. To enhance scheduling efficiency for highly variable Cloud tasks, DSGA incorporates a novel self-adaptive mutation algorithm and a random point fixed distance crossover method. Extensive evaluations using real-world workload traces demonstrate that CECF achieves up to a 8.5% improvement in system reliability and reduces energy consumption by 35.88% compared to baseline approaches.

关键词： Cloud computing Servers Energy consumption Collaboration Energy efficiency image edge detection Heuristic algorithms Scheduling algorithms Real-time systems virtual machines Cloud-edge collaboration computing Internet of vehicles (Iov) deep learning intelligent transportation systems

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Emergence Model of Perception With Global-Contour Precedence Based on Gestalt Theory and Primary visual Cortex

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IEEE TRANSACTIONS ON image processing 2025年 34卷 2721-2736页

作者： Li, Jingmeng Wei, Hui Fudan Univ Sch Comp Sci Lab Algorithms Cognit Models Shanghai 200438 Peoples R China Fudan Univ Innovat Ctr Callig & Painting Creat Technol Sch Comp Sci Lab Algorithms Cognit ModelsMCT Shanghai 200438 Peoples R China

Perceptual edge grouping is a technique for organizing the cluttered edge pixels into meaningful structures and further serves high-level vision tasks, which has long been a basic and critical task in computer vision. Existing methods usually have a poor performance when coping with the junctions caused by occlusion and noise in natural images. In this paper, we present GPGrouper, a perceptual edge grouping model based on gestalt theory and the primary visual cortex (v1). Different from the existing methods, GPGrouper leverages the edge representation and grouping matrix (ERGM), a functional structure inspired by v1 mechanisms, to represent edges in a way that can effectively reduce grouping errors caused by occlusion between objects. ERGM is trained with natural image contours and further provides a priori guidance for the construction of the edge connection graph (ECG) that is useful to minimize the impact of noise on grouping. In the experiment, we compared GPGrouper and the state-of-the-art (SOTA) method of perceptual grouping on the visual psychology pathfinder challenge. The results demonstrate that GPGrouper outperforms the SOTA method in grouping performance. Furthermore, in the grouping experiments involving line segments with varying lengths detected by the Line Segment Detector (LSD), as well as those involving superpixel segmentation results with significant levels of interfering noise using the SLIC algorithm, GPGrouper was superior to the existing methods in terms of grouping effect and robustness. Moreover, the results of applying the grouping results to the vision tasks objectness demonstrate that GPGrouper can contribute significantly to high-level visual tasks.

关键词： image edge detection visualization Computational modeling Noise image segmentation visual systems Psychology Organizations vectors Object detection Perceptual organization global precedence effect Gestalt theory primary visual cortex perceptual edge grouping

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An adaptive radial object recognition algorithm for lightweight drones in different environments

COMPUTER OPTICS

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COMPUTER OPTICS 2025年第3期49卷 480-492页

作者： Song, S. Liu, J. Shleimovich, M. P. Shakirzyanov, R. M. Novikova, S. v. Yang Zhou Marine Elect Instruments Inst Yangzhou 225101 Jangsu Peoples R China Kazan Natl Res Tech Univ st K Marksa 10 Kazan 420111 Russia

The paper proposes a group of radial shape object recognition methods capable of finding many different-sized circular objects in an image with high accuracy in minimum time and conditions of uneven brightness of frame areas. The methods are not computationally demanding, making them suitable for use in computer vision systems of light unmanned vehicles, which cannot carry powerful computing devices on board. The methods are also suitable for unmanned vehicles traveling at high speed, where image processing must be performed in real-time. The proposed algorithms are robust to noise. When combined into a single group, the developed algorithms constitute a customizable set capable of adapting to different imaging conditions and computing power. This property allows the method to be used for detecting objects of interest in different environments: from the air, from the ground, underwater, and when moving the vehicle between these environments. We proposed three methods: a hybrid FRODAS method combines the FRST and Hough methods to increase accuracy and reduce the time to search for circles in the image;a PaRCIS method based on sequential image compression and reconstruction to increase the speed of searching for multiple circles of different radii and removing noise;an additional modification of LIPIS is used with any of the primary or developed methods to reduce the sensitivity to sharp variations in the frame's brightness. The paper presents comparative experiments demonstrating the advantages of the developed methods over classical circle recognition methods regarding accuracy and speed. It shows the advantage of recognizing circles of different brightness. Experiments on recognizing multiple real-world objects in photographs taken on the ground, in the air, and underwater, with complex scenes under distortion and blurring with different degrees of illumination, demonstrate the effectiveness of the set of methods.

关键词： degrees computer vision multiple object recognition image compression recognition within a sliding window non-uniform image brightness changing shooting conditions

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