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7th Chinese Conference on Pattern Recognition and Computer vision

作者： Wu, Enyi Shi, Qingxuan Wang, Kanglin Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Peoples R China Hebei Univ Hebei Machine Vis Engn Res Ctr Baoding 071002 Peoples R China

ISBN: (纸本)9789819784899;9789819784905

Despite the advancements in neural network technologies driving interactive image segmentation forward, challenges persist, especially concerning segmentation ambiguities caused by overlapping or visually similar objects against complex backgrounds, as well as intricate object boundaries. Addressing these challenges, we introduce FusionNet, focusing on effective feature fusion. Firstly, the Hierarchical Context Fusion Module aids in grasping holistic structures and multi-scale contextual information of target objects. Secondly, the Attention Feature Fusion Module captures more representative feature expressions. This design empowers FusionNet to capture details and contextual relationships better, thereby enhancing segmentation accuracy. For fine-grained boundary details, we propose the Local Correction Module, refining local mask details meticulously. This module initially focuses on information around newly clicked areas, employing discriminative correction feedback for enhanced detail processing accuracy. Rigorous experimentations on datasets like SBD, DAVIS, GrabCut, and Berkeley validate our model's effectiveness, with segmentation results strongly supporting the superiority of our approach.

关键词： Interactive image segmentation Feature fusion Attention mechanism

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach 6

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

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6th IEEE International Conference on image processing, applications and Systems, IPAS 2025

作者： Alsubaie, Norah A. Almalki, Ghayda A. Almutairi, Ghada N. Alrumaih, Sarah A. Princess Nourah Bint Abdulrahman University Department of Computer Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798331506520

This research introduces "Jaddah,"an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. The system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. The YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. These results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency. © 2025 IEEE.

关键词： image enhancement

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot Learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Xiao Jing, Haodong Chen, Hui Ma, Yongqiang Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi China

ISBN: (纸本)9798350368741

Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings. © 2025 IEEE.

关键词： Compositional Zero-shot Learning Cross-Modal Fusion Fine-Grained Refinement Multimodal Models

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A Current-Mode Multiply-Accumulate Macro in Sensing-Computing Fusion System for Feature Extraction and Redundancy Reduction

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS ii-EXPRESS BRIEFS 2025年第5期72卷 798-802页

作者： Ren, Xu Li, Xinpeng Xue, Chang He, Yandong Du, Gang Peking Univ Sch Integrated Circuits Beijing 100871 Peoples R China Peking Univ Beijing Adv Innovat Ctr Integrated Circuits Beijing 100871 Peoples R China

This brief presents a current-mode sensing-computing fusion system for advanced Internet of Things (IoT) machine vision. The key contributions of our work are: (a) a low-voltage of 0.9V multiply-accumulate (MAC) computing macro with reconfigurable weights is proposed, enabling efficient on-sensor feature extraction;(b) a weight-flipping method for processing negative signals is employed, reducing both power consumption and circuit complexity;(c) a convolutional horizontal shifting technique with fixed weights is equipped, eliminating power consumption associated with weight updates. A 16 x 16 CCIS prototype, fabricated using a 0.18 mu m CMOS process, achieves a power efficiency of 27.7pJ/frame-pixel at 2000fps. Experimental evaluations in edge feature extraction demonstrate a 32% reduction in power consumption, highlighting the efficiency gains of our approach.

关键词： Feature extraction Termination of employment Computational efficiency Switches Power demand Real-time systems Internet of Things image edge detection IP networks Convolution Sensing-computing fusion computing in/near sensor current mode computing edge detection feature extraction multiply-accumulate (MAC) macro low power design

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A Comparative Study of Deep Learning Algorithms for Glaucoma Classification Using Retinal images 5th

A Comparative Study of Deep Learning Algorithms for Glaucoma...

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5th International Conference on Data Science, machine Learning and applications

作者： Swapna, T. Varshitha, Y. Sai Raja Sudeepthi, K. L. Manavika, B. Saishree, T. G Narayanamma Inst Technol & Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

A group of eye conditions known as glaucoma impair the optic nerve, which is in charge of sending visual data from the eye to the brain. Glaucoma impacts 3.54% of adults aged 40 to 80 around the world. Early detection of glaucoma is crucial as it can prevent total optic nerve damage, which would cause irreversible vision loss. It is possible for specialists to diagnose glaucoma medically, but treatment options are either expensive or time-consuming and requires ongoing care from medical professionals. There have been numerous initiatives at streamlining all components of the glaucoma categorization process, however these models are challenging for users to comprehend the key predictors, resulting in them being unreliable for use by medical experts. The study uses eye fundus images to classify glaucoma patients using three distinct Deep Learning techniques: Convolutional neural network, Visual Geometry Group 16 (VGG16), and Global Context Network (GC-Net). In addition, several data pre-processing techniques are used to avoid overfitting and achieve high accuracy. This research compares and analyses the performance of various architectures using the aforementioned techniques. The CNN model had the best accuracy of 83% when in contrast to the other deep learning models.

关键词： Biomedical image processing Glaucoma Blind ness machine learning Visual geometry group 16 Convolutional neural network Global context network

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Reconfigurable parallel photonic matrix-vector multiplication processor based on multi-dimensional multiplexing

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OPTICS EXPRESS 2025年第9期33卷 19837-19850页

作者： Bi, Yanfeng Wu, Xingyu Fan, Chenrui Zhang, Lufan Wang, Chuan Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Beijing Normal Univ Appl Opt Beijing Area Major Lab Beijing 100875 Peoples R China

Matrix-vector multiplication (MVM) operations play an important role in applications such as data processing and artificial neural networks. To meet the growing demand for computing power, the photonic MVM processor provides what we believe to be a new computing architecture. In this paper, we propose a reconfigurable parallel MVM (RP-MVM) processor. To further improve the parallel computing dimension, wavelength division multiplexing (WDM) and digital subcarrier multiplexing (DSM) technologies were first incorporated into the photonic MVM. Compared with the traditional WDM-MVM architecture, the parallelism of RP-MVM scheme is increased by N times, where N is the carrier number of DSM signal. Moreover, the input data channel can be dynamically adjusted without changing the hardware scale, which improves the flexibility of computing system. The simulation results show that the RP-MVM scheme can achieve parallel computing operations of eight MVMs, with a computing speed of 128 GOPs. For a random 6-bit resolution data sequence, the root mean square error (RMSE) of calculation results is on the order of 1E-3. In addition, for the image edge extraction task based on Roberts operator, this scheme can realize the parallel processing of four grayscale images. Therefore, the proposed scheme provides an alternative approach for realizing a highly parallel and reconfigurable large-scale photonic MVM architecture.

关键词： machine vision Mode division multiplexing Neural networks Optical computing Variable optical attenuators Wavelength division multiplexing

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Efficient Facial Expression Recognition Through Lightweight CNN Technique on Public Datasets

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SN Computer Science 2025年第1期6卷 1-13页

作者： Grover, Richa Bansal, Sandhya Department of Computer Science and Engineering Maharishi Markandeshwar Engineering College Maharishi Markandeshwar Deemed to be University Ambala Haryana Mullana India

The exploration of sentiments through facial expressions is a captivating domain with applications across security, healthcare, and human–computer interaction, where understanding sentiments is primarily about interpreting an individual's stance from a piece of text. However, in the context of non-verbal communication, it extends to the interpretation of emotions conveyed through facial expressions. This research endeavor aims to push the boundaries of machine-assisted sentiment predictions by refining models and methods for more accurate emotion recognition. A major obstacle in this pursuit is accurately identifying emotions from images not constrained by controlled conditions, often marred by poor visibility, shadows, or inconsistent lighting. Our study presents an innovative approach employing a lightweight convolutional neural network technique. This technique addresses various challenges to enhance the accuracy and reliability of sentiment analysis after incorporating pre-processing techniques such as sharpening to deal with image inconsistencies and histogram equalization to manage contrast variations, which improve image quality but include some artifacts in the image that are further resolved through the application of contrast limited adaptive histogram equalization. The technique’s effectiveness is underscored by its performance, achieving a 52% accuracy rate on raw, unconstrained images of FER-2013 and a 70% accuracy rate following the application of our pre-processing technique to the same dataset. The demonstrated proposed lightweight model not only performed exceptionally well over FER-2013 but also over CK+, RAF-DB, KDFE with an accuracy of 99.2%, 84.4%, and 94%. Furthermore, the proposed technique shows strong performance on real-time images captured via webcam. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision Facial expression recognition image pre-processing Lightweight CNN Un-controlled datasets

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5th International Conference on Data Science, machine Learning and applications, ICDSMLA 2023

5th International Conference on Data Science, Machine Learni...

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5th International Conference on Data Science, machine Learning and applications, ICDSMLA 2023

ISBN: (纸本)9789819780426

The proceedings contain 128 papers. The special focus in this conference is on Data Science, machine Learning and applications. The topics include: Digitization of Monuments – An Impact on the Tourist Experience with Special Reference to Hampi;resume Parser Using machine Learning;IOT Based Smart Hydroponics System;comparative Study of machine Learning and Deep Learning Techniques for Cancer Disease Detection;High Thruput Modulation Approaches Used in Next Generation WiF’s Under Multi-impairments Environments with MATLAB Codes;skin Disease Detection;root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors;deep Learning Model Development for an Automatic Healthcare Edge Computing Application;Empathetic Conversations in Mental Health: Fine-Tuning LLMs for Supportive AI Interactions;exploring Block Chain Technology with applications, and Future Prospects;a Comprehensive Review of Soft Computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead;Performance Analysis of machine Learning Algorithms on Imbalanced Datasets Using SMOTE Technique;An AI Based Nutrient Tracking and Analysis System;power Saving Mechanism for Street Lights System Using IoT;Automatic Login System Using ATTINY85 IC;forecasting Stock Prices: A Comparative Analysis of machine Learning, Deep Learning, and Statistical Approaches;smart vision Bot;robots in Logistics: Apprehension of Current Status and Future Trends in Indian Warehouses;smart Healthcare: Enhancing Patient Well-Being with IoT;Detection of B-ALL Using CNN Model and Deep Learning;a Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image processing;a Comprehensive Survey on Enhancing Patient Care Through Deep Learning and IoT-Enabled Healthcare Innovations;attention-Based image Caption Generation.

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5th International Conference on Data Science, machine Learning and applications, ICDSMLA 2023

5th International Conference on Data Science, Machine Learni...

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5th International Conference on Data Science, machine Learning and applications, ICDSMLA 2023

ISBN: (纸本)9789819780303

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A New Video Summarization Approach Using Object Density Based image Similarity for Smart City applications 29

A New Video Summarization Approach Using Object Density Base...

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29th International Conference on Information Technology, IT 2025

作者： Altundogan, Turan Goktug Karakose, Mehmet Mert, Fatih Manisa Celal Bayar University Computer Engineering Department Manisa Turkey Firat University Computer Engineering Department Elazig Turkey Huawei Telecommunication Foreign Trade Co. Ltd. Istanbul Turkey

ISBN: (纸本)9798331517649

Summarization approaches are currently proposed solutions that focus on meaningfully reducing different types of data such as text, audio, and video. Many techniques such as machine learning, signal processing, image processing, computer vision, and deep learning can be used to develop summarization approaches. In this study, we performed object detection on videos that can be used in smart city applications using a pretrained YOLOv8 model. As a result of the object detection, we created a feature vector for each image frame by using the location information covered by the classes used in the object detection process. Then, we used several different approaches to determine the reference feature vector for the video. Finally, we calculated the cosine similarities of the feature vector for each frame to this reference feature vector using different methods. With the method we developed, we presented a similarity-focused summary created by selecting the video frames expressed with maximum similarity. We also developed an evaluation approach to evaluate the summaries we presented, comparing the overall heat maps of the video with the heat maps of the summary videos. Experimental results demonstrate the efficiency of our summarization approaches. © 2025 IEEE.

关键词： image Similarity Object Detection Smart City Video Summarization

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