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49th IEEE International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hou, Jingchao He, Guanghui Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

This paper addresses two key limitations in existing image Signal processing (ISP) approaches: the suboptimal performance in low-light conditions and the lack of trainability in traditional ISP methods. To tackle these issues, we propose a novel, trainable ISP framework that incorporates both the strengths of traditional ISP techniques and advanced MultiScale Retinex (MSR) algorithms for night-time enhancement. Our method consists of three primary components: an ISP-based Luminance Harmonization layer to initially optimize luminance levels in RAW data, a deep learning-based MSR layer for nuanced decomposition of image components, and a specialized enhancement layer for both precise, regionspecific luminance enhancement and color denoising. The proposed approach is validated through rigorous experiments on machine vision benchmarks and objective visual quality indicators. Our results demonstrate not only a significant improvement over existing methods but also robust adaptability under diverse lighting conditions. This work offers a versatile ISP framework with promising applications beyond its immediate scope.

关键词： image Signal processing (ISP) Multi-Scale Retinex (MSR) Enhancement Reflection and Illumination Separation

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Design and Implementation of Adaptive Gaussian Denoising Algorithm Based on FPGA 7

Design and Implementation of Adaptive Gaussian Denoising Alg...

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7th International Conference on Automation Electronics and Electrical Engineering

作者： Chen, Xinghao Beihang Univ Sch Elect Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350377040;9798350377033

With the continuous progress of image processing and machine vision technology, the demand for efficient and real-time processing is becoming more and more prominent, especially in the field of high-noise image processing. In this study, an adaptive Gaussian filtering algorithm is proposed, which is implemented based on FPGA and aims to improve the computational efficiency and real-time performance of the image processing system. Compared with the traditional fixed-weight filter, this algorithm is able to dynamically adjust the filtering parameters according to different noise environments, effectively balancing noise suppression and image detail retention. We coded the algorithm using Verilog hardware description language and verified it on PYNQ-Z2 FPGA platform. The experimental results show that the adaptive algorithm outperforms the fixed-weight filtering method in terms of performance, especially in terms of noise suppression and detail preservation. Meanwhile, the FPGA hardware implements the reduction of filtering delay and optimization of resource consumption, making it well suited for real-time applications. This study demonstrates the promise of FPGA adaptive filtering for applications in medical imaging, remote sensing, and intelligent surveillance, which have stringent requirements for high-performance and high-efficiency processing. This research provides new hardware solutions for real-time, high-quality image processing in constrained environments.

关键词： FPGA image denoising gaussian filtering adaptive algorithm

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Design and Implementation of machine vision Experiment Platform for Virtual Production Line 9

Design and Implementation of Machine Vision Experiment Platf...

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9th International Conference on Virtual Reality, ICVR 2023

作者： Li, Jinfang He, Mingtong Su, Jiancong Wang, Boyang Li, Zhenxian Guangdong University of Technology School of Electromechanical Engineering Guangzhou China

ISBN: (纸本)9798350345810

To meet the needs of teaching and practical applications in machine vision technology, a virtual reality-based machine vision experimental platform has been designed and developed. Unity3D was utilized as the development engine, and image processing technology was integrated to achieve the construction of virtual production line scenes, simulation of vision component parameter adjustments, and image acquisition. The platform features a graphical programming interface for visualizing image processing algorithms, which can be used to perform visual debugging of vision stations with a virtual robot system driven by software PLC. This machine vision experimental platform ensures the consistency between simulation and actual engineering processes, and enables students to explore different vision schemes on an industrial production line, thereby avoiding constraints on location, time, and equipment in related experiments. © 2023 IEEE.

关键词： Computer vision

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Automated Classification of Indian Mango Varieties Using machine Learning and MobileNet-v2 Deep Features

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TRAITEMENT DU SIGNAL 2024年第2期41卷 669-678页

作者： Ratha, Ashoka Kumar Barpanda, Nalini Kanta Sethy, Prabira Kumar Behera, Santi Kumari Sambalpur Univ Dept Elect Engn Jyoti Vihar Sambalpur 768019 Odisha India Guru Ghasidas Vishwavidyalaya Dept Elect & Commun Engn Bilaspur 495009 Chhattisgarh India VSSUT Dept Comp Sci & Engn Sambalpur 768018 Odisha India

In agricultural applications, the utilization of image processing with machine learning, particularly for fruit classification, has become increasingly prevalent. This study focuses on the automated classification of various Indian mango varieties, employing the deep features of MobileNet-v2 and Shufflenet, integrated with diverse machine learning classifiers. The research is anchored on an extensive dataset, encompassing 15 distinct Indian mango varieties, meticulously collated from various vegetable markets across India. This dataset is accessible at "Sethy, Prabira Kumar;Behera, Santi;Pandey, Chanki (2023), 'Mango Variety', Mendeley Data, V2, doi: 10.17632/tk6d98f87d.2". A comprehensive comparison of various machine learning classifiers highlighted the dominance of the Cubic Support Vector machine (SVM) when integrated with deep features extracted from MobileNet-v2. This pairing resulted in an outstanding classification accuracy of 99.5% and an Area Under the Curve (AUC) of 1, demonstrating exceptional performance in identifying fruit varieties. The significance of this research lies in its potential to revolutionize fruit classification processes in supermarkets and related sectors. By demonstrating the feasibility of applying advanced computer vision technology for the accurate classification of fruits, this study lays the groundwork for future exploration into the scalability, robustness, and wider applicability of these methods, potentially extending beyond mangoes to other fruit varieties. Such advancements could substantially benefit the agricultural industry, enhancing efficiency in both production and retail sectors.

关键词： mango identification deep learning convolutional neural network (CNN) computer vision machine learning Support Vector machine (SVM)

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IMOFC: Identity-Level Metric Optimized Feature Compression for Identification Tasks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第2期35卷 1855-1869页

作者： Gao, Changsheng Jiang, Yiheng Wu, Siqi Ma, Yifan Li, Li Liu, Dong Univ Sci & Technol China Key Lab Brain Inspired Intelligent Percept & Cogni MOE Hefei 230037 Peoples R China Univ Missouri Dept Elect Engn Columbia MO 65211 USA

Feature compression has attracted much attention in recent years due to its promising applications in scenarios where features are transmitted and analyzed by machine vision. However, existing research mainly focuses on coarse-grained features extracted from recognition tasks such as classification and detection, neglecting fine-grained features extracted from identification tasks. In this paper, we make a pioneering attempt to study fine-grained feature compression in the context of identification tasks. Our main focus is on the distortion metric, given its critical importance in optimizing the performance of a compression network. We initiate our discussion by reviewing the instance-level metrics in existing literature, highlighting their oversight of the inter-feature relationships. The inter-feature relationships are especially important for identification tasks as they involve similarity comparison among different identities. To address this problem, we propose to consider inter-feature relationships from the perspective of identity information. Specifically, we propose an identity-level metric to incorporate both intra-identity similarity and inter-identity discriminability. The intra-identity similarity constraint aims to cluster features from the same identity, while the inter-identity discriminability constraint ensures that features from different identities deviate from each other. We implement the identity-level metric on four different feature compression networks designed based on feature characteristics. Experimental results show the effectiveness of the proposed identity-level metric on person re-identification and face verification tasks.

关键词： image coding Distortion Feature extraction Distortion measurement Semantics image reconstruction machine vision Feature compression identification tasks identity-level metric inter-feature relationships inter-identity discriminability

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On Comparing Multiple Approaches for Classifying Vessels in imagery 6

On Comparing Multiple Approaches for Classifying Vessels in ...

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Conference on Artificial Intelligence and machine Learning for Multi-Domain Operations applications VI

作者： Warner, John G. Patel, Vishal M. US Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781510674219;9781510674202

Deep learning-based approaches, such as Convolutional Neural Nets (CNNs), have shown high performance in classifying contents of images. CNNs, however, have the notable drawbacks of potentially high computing costs, poor explainability, and wide performance variance if the underlying imagery data deviates from the training baseline. As advanced image processing capabilities are matured, the on-board detection of objects in space-based imagery is increasingly proposed. On-board satellite processing applications, which may be resource-limited, can drive the need for simpler models that reduce the necessary computing burden for edge computing applications. This raises the question of how well classic computer vision techniques can compete with more modern approaches. This paper characterizes and compares the performance of multiple computer vision models for the application of distinguishing maritime vessels from typical clutter in commercial electrooptical (EO) satellite imagery. A Support Vector machine (SVM) model using manually curated features is compared to multiple DL-based models spanning a range of model sizes, with the goal of determining whether classical approaches can compete favorably with DL when computational resources are taken into consideration. Differences in performance and processing resources are characterized between the approaches. Findings include that the SVM-based model may approach the accuracy of some CNN-based models for classifying images of clouds in satellite EO imagery for smaller DL-based models. However, even the smallest DL-based models, which take about the same computational resources as the SVM-based model, generally out-perform the SVMbased model. This finding may have implications for the operational use of on-board processing techniques for satellite payloads.

关键词： CNN spacecraft onboard processing Automatic Target Recognition maritime

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Approximate MRAM: High-Performance and Power-Efficient Computing With MRAM Chips for Error-Tolerant applications

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IEEE TRANSACTIONS ON COMPUTERS 2023年第3期72卷 668-681页

作者： Ferdaus, Farah Talukder, B. M. S. Bahar Rahman, Md Tauhidur Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA

Approximate computing (AC) leverages the inherent error resilience and is used in many big-data applications from various domains such as multimedia, computer vision, signal processing, and machine learning to improve systems performance and power consumption. Like many other approximate circuits and algorithms, the memory subsystem can also be used to enhance performance and save power significantly. This paper proposes an efficient and effective systematic methodology to construct an approximate nonvolatile magneto-resistive RAM (MRAM) framework using consumer-off-the-shelf (COTS) MRAM chips. In the proposed scheme, an extensive experimental characterization of memory errors is performed by manipulating the write latency of MRAM chips which exploits the inherent (intrinsic/extrinsic process variation) stochastic switching behavior of magnetic tunnel junctions (MTJs). The experimental results, involving error-resilient image compression and machine learning applications, reveal that the proposed AC framework provides a significant performance improvement and demonstrates a reduction in MRAM write energy of similar to 47:5% on average with negligible or no loss in output quality.

关键词： Approximate computing MRAM cache write policy low power design approximate MRAM

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Scale-adaptive gesture computing: detection, tracking and recognition in controlled complex environments

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machine vision AND applications 2024年第4期35卷 1-24页

作者： Kirupakaran, Anish Monsley Laskar, Rabul Hussain Natl Inst Technol Silchar Dept ECE Cachar 788010 Assam India

Complexity intensifies when gesticulations span various scales. Traditional scale-invariant object recognition methods often falter when confronted with case-sensitive characters in the English alphabet. The literature underscores a notable gap, the absence of an open-source multi-scale un-instructional gesture database featuring a comprehensive dictionary. In response, we have created the NITS (gesture scale) database, which encompasses isolated mid-air gesticulations of ninety-five alphanumeric characters. In this research, we present a scale-centric framework that addresses three critical aspects: (1) detection of smaller gesture objects: our framework excels at detecting smaller gesture objects, such as a red color marker. (2) Removal of redundant self co-articulated strokes: we propose an effective approach to eliminate redundant self co-articulated strokes often present in gesture trajectories. (3) Scale-variant approach for recognition: to tackle the scale vs. size ambiguity in recognition, we introduce a novel scale-variant methodology. Our experimental results reveal a substantial improvement of approximately 16% compared to existing state-of-the-art recognition models for mid-air gesture recognition. These outcomes demonstrate that our proposed approach successfully emulates the perceptibility found in the human visual system, even when utilizing data from monophthalmic vision. Furthermore, our findings underscore the imperative need for comprehensive studies encompassing scale variations in gesture recognition.

关键词： Gesture computing Computer vision Deep learning Scale variance Human-computer interaction

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Discrete aggregation hashing for image set classification

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EXPERT SYSTEMS WITH applications 2024年 237卷

作者： Sun, Yuan Peng, Dezhong Ren, Zhenwen Sichuan Univ Coll Comp Sci Chengdu 610044 Peoples R China Chengdu Ruibei Yingte Informat Technol Co Ltd Chengdu 610054 Peoples R China Southwest Univ Sci & Technol Coll Natl Def Sci & Technol Mianyang 621010 Peoples R China

With the development of vision technology, image set classification (ISC) has flourished in the image processing field. Different from the one-shot image classification, ISC focuses on the set rather than a one-shot image. Hence, ISC can synthesize the abundant set information to alleviate various appearance variations. Despite the great success of the existing ISC methods, there are still some problems: (1) They usually face an expensive time complexity, which directly limits the practical application;(2) They largely ignore the intrinsic relationships between different sets. In light of this, we propose a novel Discrete Aggregation Hashing (DAH) for fast ISC. To be specific, to extract more semantic information from each set and each sample, we adopt the same projection standard to embed dual semantic labels (i.e., sample label and set label) into instance and set hash codes. Then we regard set hash codes as set-specific centers. A hashing aggregation strategy is proposed to learn compact discriminative instance hash codes via iteratively aggregating intrinsic neighborhood representations around each central node. Therefore, instance hash codes can obtain greater intra-set compactness and inter -set separability. Extensive experiments demonstrate that our DAH can obtain promising performance and outperform these state-of-the-art ISC methods on four image set datasets.

关键词： image set classification Aggregation hashing Binary representation

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Unveiling the power of Haar frequency domain: Advancing small target motion detection in dim light

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Chen, Hao Sun, Xuelong Hu, Cheng Wang, Hongxin Peng, Jigen Guangzhou Univ Machine Life & Intelligence Res Ctr Guangzhou Peoples R China Guangzhou Univ Sch Math & Informat Sci Guangzhou Peoples R China

Visual small target motion detection finds successful applications in varied scenarios. However, dim-light conditions, such as the tunnel scenes and nighttime environments, present significant challenges to existing detection methods which mainly operate within the spatiotemporal domain. This is because the transmission of small target motion information suffers from the inevitable interference of image noise caused by dim light in the spatiotemporal domain, resulting in the detriment of extracting essential spatiotemporal features of the target motion. Given the significant obstacles posed by dim-light imaging to small target motion detection within the spatiotemporal domain, the exploration of an alternative observation domain for small target motion, alongside the development of a corresponding detection method, emerges as a viable solution. To address this, in this paper, we discovered the remarkable potential of the Haar frequency domain in characterizing the small target motion in dim light. To investigate the advantages of integrating Haar frequency processing in small target motion detection, we introduce a Haar-windowed summation mechanism into an existing bio-inspired small target motion detection model. The proposed mechanism integrates visual information in spatiotemporal windows regulated by frequency parameters of Haar wavelets and effectively discriminates the small target motion from the disturbance of random noise caused by dim light. Theoretical analysis and numerical experiments confirm the superior performance of integrating the Haar frequency processing. This study provides a new vision of small target motion detection through the lens of the frequency domain and extends the limits of existing bio-inspired models for practical applications in dim light.

关键词： Frequency analysis Small target motion detection Dim light vision Haar wavelet transformation Visual neural system

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