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Local Feature Enhanced Adversarial Network for the Blind Image Quality Assessment 48

Local Feature Enhanced Adversarial Network for the Blind Ima...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Shi, Xiaomei Zhang, Min Xia, Shouhai Zhang, Ruxue Feng, Jun Northwest University School of Mathematics China Northwest University School of Information Science and Technology China

ISBN: (纸本)9781728163277

As a hot research topic in the field of computer vision, blind image quality assessment (BIQA) can provide high-quality images for end-users and promote the development of other fields of computer vision. Although the existing BIQA based on convolution neural networks has made significant progress in synthetic distortion evaluation, it still cannot be well extended to authentic distortion and algorithm-related distortion. Therefore, this paper proposes a BIQA adversarial network with local feature enhancement to deal with this challenge. First, the ResNeSt50 network with local feature enhancement is used to extract the features of images, which effectively combines the overall semantic information and the local features of the images. Then, the mapping of distorted images to their quality scores is learned by the adversarial network. Extensive experiments demonstrate that the proposed method performs best on three categories of distorted scenario databases (nine databases) compared with state-of-the-art BIQA methods. © 2023 IEEE.

关键词： Computer vision

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TP-VIT: A TWO-PATHWAY vision TRANSFORMER FOR VIDEO ACTION RECOGNITION 47

TP-VIT: A TWO-PATHWAY VISION TRANSFORMER FOR VIDEO ACTION RE...

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47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Jing, Yanhao Wang, Feng East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781665405409

Recently, inspired by the success of Transformer in natural language processing tasks, a number of works have attempted to apply Transformer-based models to video action recognition. Existing works only use one RGB stream as the input for Transformer. How to use multiple pathways and multiple streams with Transformer for action recognition has not been studied. To address this issue, we present a novel structure namely Two-Pathway vision Transformer (TP-ViT). Two parallel spatial Transformer encoders are used as two pathways with different framerates and resolutions of the input video. The high-resolution pathway contains more spatial information, while the high-framerate pathway contains more temporal information. The two outputs are fused and fed into a temporal Transformer encoder for action recognition. Furthermore, we also fuse skeleton features into our model to get better results. Our experiments demonstrate that our proposed models achieve the state-of-the-art results on both the coarse-grained dataset Kinetics and the fine-grained dataset FineGym.

关键词： vision transformer video action recognition two-pathway transformer

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Computer vision and Deep Learning Enabled Real-Time Liquid Level Detection and Measurement in Transparent Containers

Computer Vision and Deep Learning Enabled Real-Time Liquid L...

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2024 international conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024

作者： Syed, J.A. Takriti, A.A. Damindarov, R.R. Innopolis University Dept. Of Robotics & Computer Vision Innopolis Russia

ISBN: (纸本)9798350395013

With the advent of the Industry 4.0, the capability to automatically detect and measure the liquid levels and volumes in the transparent containers (i.e. test tubes in a laboratory) in real-time is a crucial part in the whole perception system of an autonomous robot/system. And by the use of the state-of-the-art Computer vision and deep learning technology we can easily help solve this problem. This task of liquid level detection and measurement in transparent containers is a crucial task in various industries, including pharmaceuticals, manufacturing, and chemical processing especially in automated oil testing laboratories, where autonomous systems require precise perception capabilities. This paper explores the potential of computer vision and deep learning to accurately measure the liquid level and volume in transparent containers. Our approach is implemented by training a deep learning model, especially YOLOv8 (You Only Look Once version 8) model trained to recognize features like container type, liquid surface and height. After identifying these key features, our proposed algorithm uses them to correct the errors in the liquid height due to perspective distortion and get the liquid level followed by liquid volume accurately in real-time in a given transparent container. Our approach achieved an accuracy of 98.57 percent for transparent liquid and 98.12 percent for colored liquid, demonstrating the system's potential for practical applications particularly in various automated laboratory testing industries. © 2024 IEEE.

关键词： Computer vision

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Look and Think: Intrinsic Unification of Self-Attention and Convolution for Spatial-Channel Specificity 48

Look and Think: Intrinsic Unification of Self-Attention and ...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Gao, Xiang Lin, Honghui Li, Yu Fang, Ruiyan Zhang, Xin South China University of Technology School of Electronic and Information Engineering China

ISBN: (纸本)9781728163277

Convolution and self-attention are popular paradigms and many works take them as two separate components to explore their potential combination. In this work, we consider their intrinsic properties in spatial and channel domains for vision representation. Convolution has the great property of channel-specificity to "think"to refine diverse features, and it has the weaknesses of spatial-independent to perceive different regions. Self-attention has the great property of spatial-specificity to "look"to perceive various regions, and it has the weaknesses of channel-insensible to summarize different features. With the intrinsic insight of this, we combine the spatial-specificity of self-attention and channel-specificity of convolution to effectively compensate their respective weakness. We propose a unified module, termed as SCS module, to achieve the combinative advantage of Spatial-Channel Specificity. Specifically, SCS module calculates dynamic attention weight with self-attention mechanism, followed by a weighted sum of input features similar to convolution. Extensive experiments show that SCS module improves both of the CNN and Transformer models on image classification and downstream tasks. The visualizations show the outstanding ability of SCS module for vision representation. © 2023 IEEE.

关键词： Convolution Self-attention Spatial-Channel Specificity Unification vision Representation

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DIRECTIONAL ANTENNA SYSTEMS FOR LONG-RANGE THROUGH-WALL HUMAN ACTIVITY RECOGNITION 31

DIRECTIONAL ANTENNA SYSTEMS FOR LONG-RANGE THROUGH-WALL HUMA...

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2024 international conference on Image processing

作者： Strohmayer, Julian Kampel, Martin TU Wien Comp Vision Lab Favoritenstr 9-193-1 A-1040 Vienna Austria

ISBN: (纸本)9798350349405;9798350349399

WiFi Channel State Information (CSI)-based Human Activity Recognition (HAR) enables contactless, long-range sensing in spatially constrained environments while preserving visual privacy. However, despite the ubiquity ofWiFi-enabled devices, few expose CSI, limiting sensing hardware options. Variants of the Espressif ESP32 have emerged as potential compact, low-cost, and easy-to-deploy solutions for WiFi CSI-based HAR. In this work, four ESP32-S3-based 2.4 GHz directional antenna systems are evaluated for their ability to facilitate long-range through-wall HAR. Two promising systems are identified: one combines ESP32-S3 with a directional biquad antenna, and the second uses the built-in printed inverted-F antenna (PIFA) achieving directionality through a plane reflector. In a comprehensive evaluation of line-of-sight (LOS) and non-line-of-sight (NLOS) HAR performance, both systems are deployed in an office environment spanning a distance of 18 meters across five rooms. In this experimental setup, theWallhack1.8k dataset, comprising 1,806 CSI amplitude spectrograms of human activities, is collected and made publicly available. Based onWallhack1.8k, activity recognition models using the EfficientNetV2 architecture are trained to assess system performance in LOS and NLOS scenarios. For the core NLOS activity recognition problem, the biquad antenna and PIFA-based systems achieve accuracies of 92.0 +/- 3.5 and 86.8 +/- 4.7, respectively, demonstrating the feasibility of long-range through-wall HAR.

关键词： Human Activity Recognition WiFi Channel State Information Through-Wall Sensing ESP32

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Real-Time robot 3D Pose Computation from NIR Imagery and ToF Depth Maps for Space Applications 1

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3rd international conference on Applied Science and Advanced Technology, ICASAT 2023

作者： Martinez, Geovanni Escuela de Ingeniería Eléctrica Universidad de Costa Rica San José11501-2060 Costa Rica

ISBN: (数字)9783031576201

ISBN: (纸本)9783031576195

This paper presents an algorithm capable of determining the three-dimensional position and orientation (3D pose) of an exploration robot from the processing of two multidimensional signals, a monocular near-infrared (NIR) video signal and a time-of-flight (ToF) depth signal, both provided by a monocular NIR ToF camera rigidly attached to the side of a robot facing the terrain. It is shown that if the depth signal is also considered during processing, it is possible to accurately determine the 3D pose of the robot, even on irregular terrain. The 3D pose is calculated by integrating the frame-to-frame robot 3D motion over time using composition rules, where the frame-to-frame robot 3D motion is estimated by minimizing a linear photometric error by applying an iterative maximum likelihood estimator. Hundreds of experiments have been conducted over rough terrain, obtaining excellent absolute position and orientation errors of less than 1 percent of the distance and angle traveled, respectively. This good performance is mainly due to the algorithm’s more accurate knowledge of the depth provided by the monocular NIR ToF camera. The algorithm runs in real time and can process up to 50 fps at VGA resolution on a conventional laptop computer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cameras

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7th international conference on Cognitive Systems and Information processing, ICCSIP 2022

7th International Conference on Cognitive Systems and Inform...

引用

7th international conference on Cognitive Systems and Information processing, ICCSIP 2022

ISBN: (纸本)9789819906161

The proceedings contain 47 papers. The special focus in this conference is on Cognitive Systems and Information processing. The topics include: Mastering "Gongzhu" with Self-play Deep Reinforcement Learning;improved Vanishing Gradient Problem for Deep Multi-layer Neural Networks;incremental Quaternion Random Neural Networks;question Answering on Agricultural Knowledge Graph Based on Multi-label Text Classification;dairy Cow Individual Identification System Based on Deep Learning;automatic Packaging System Based on Machine vision;meteorological and Hydrological Monitoring Technology Based on Wireless Sensor Network Model and Its Application;a Review of Deep Reinforcement Learning Exploration Methods: Prospects and Challenges for Application to robot Attitude Control Tasks;aerobotSim: A High-Photo-Fidelity Simulator for Heterogeneous Aerial Systems Under Physical Interaction;T3SFNet: A Tuned Topological Temporal-Spatial Fusion Network for Motor Imagery with Rehabilitation Exoskeleton;trailer Tag Hitch: An Automatic Reverse Hanging System Using Fiducial Markers;anatomical and vision-Guided Path Generation Method for Nasopharyngeal Swabs Sampling;a Hierarchical Model for Dynamic Simulation of the Fault in Satellite Operations;design and Implementation of Autonomous Navigation System Based on Tracked Mobile robot;towards Flying Carpet: Dynamics Modeling, and Differential-Flatness-Based Control and Planning;region Clustering for Mobile robot Autonomous Exploration in Unknown Environment;human Intention Understanding and Trajectory Planning Based on Multi-modal Data;robotic Arm Movement Primitives Assembly Planning Method Based on BT and DMP;center-of-Mass-Based Regrasping of Unknown Objects Using Reinforcement Learning and Tactile Sensing;alongshore Circumnavigating Control of a Manta robot Based on Fuzzy Control and an Obstacle Avoidance Strategy;joint Trajectory Generation of Obstacle Avoidance in Tight Space for robot Manipulator;robot Calligraphy Based on Footprint Mo

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Design of a Smart Embedded vision System Based on FPGA for Medical Applications 4

Design of a Smart Embedded Vision System Based on FPGA for M...

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4th international conference on signal processing and Communication, ICSPC 2023

作者： Yandrapati, Amar Babu Singam, Venkata Krishna Pratap Reddy Namburu, Banbhushan Paladugu, Venkatesh Karakula, Tirumala Krishna Lakireddy Bali Reddy College Dept. of Electronics and Communication Mylavaram India

ISBN: (纸本)9798350300772

Applications in fields like medical, robots, and automobiles must have embedded vision with the optimized design metrics in today's smart environment. A tiny computer and camera can be used to create a tiny vision system for the specific purpose;this kind of system is called an embedded vision system. For better design metrics, this project involves developing an embedded vision system and putting it to use in real-time applications. Design criteria that are optimized include low price, rapid development, compact size, and swift speed. The proposed embedded vision system is implemented with the ZYNQSOC. The ZYNQ SOC combines the software programmability of a CPU with the hardware programming of such an FPGA to give high adaptability, excellent performance, scalability, and low power. The embedded vision system was tested by attaching the OV7670 camera to the zedboard. The Zedboard's client-server architecture enables real-time continuous data streaming to the Computer via Ethernet. The hardware platform for the suggested embedded vision system is developed with built-in IP cores in VHDL in Vivado, C-language application software written in SDK, and Petalinux on Ubuntu. This platform is required for applications based on embedded vision systems. Applications for the proposed biomedical clever integrated vision system are available. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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CODAMAL: CONTRASTIVE DOMAIN ADAPTATION FOR MALARIA DETECTION IN LOW-COST MICROSCOPES 31

CODAMAL: CONTRASTIVE DOMAIN ADAPTATION FOR MALARIA DETECTION...

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2024 international conference on Image processing

作者： Dave, Islum Rajenclrakumar de Blegiers, Tristan Chen, Chen Shah, Mubarak Univ Cent Florida Ctr Res Comp Vision Orlando FL 32816 USA

ISBN: (纸本)9798350349405;9798350349399

Malaria is a major health issue worldwide, and its diagnosis requires scalable solutions that can work effectively with low-cost microscopes (LCM). Deep learning-based methods have shown success in computer-aided diagnosis from microscopic images. However, these methods need annotated images that show cells affected by malaria parasites and their life stages. Annotating images from LCM significantly increases the burden on medical experts compared to annotating images from high-cost microscopes (HCM). For this reason, a practical solution would be trained on HCM images which should generalize well on LCM images during testing. While earlier methods adopted a multi-stage learning process, they did not offer an end-to-end approach. In this work, we present an end-to-end learning framework, named CodaMal (COntrastive Domain Adpation for MALaria). In order to bridge the gap between HCM (training) and LCM (testing), we propose a domain adaptive contrastive loss. It reduces the domain shift by promoting similarity between the representations of HCM and its corresponding LCM image, without imposing an additional annotation burden. In addition, the training objective includes object detection objectives with carefully designed augmentations, ensuring the accurate detection of malaria parasites. On the publicly available large-scale M5-dataset, our proposed method shows a significant improvement of 16% over the state-of-the-art methods in terms of the mean average precision metric (mAP), provides 21 x speed improvement during inference and requires only half of the learnable parameters used in prior methods. Our code is publicly available: https://***/codamal-webpage/.

关键词： Low-Cost Computer-Aided Diagnosis Contrastive Domain Adaptation End-to-End Learning

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Finding Optimal Numerical Format for Sub-8-Bit Post-Training Quantization of vision Transformers 48

Finding Optimal Numerical Format for Sub-8-Bit Post-Training...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Lee, Janghwan Hwang, Youngdeok Choi, Jungwook Hanyang University Department of Electronic Engineering Korea Republic of Cuny Baruch College Department of Information Systems and Statistics United States

ISBN: (纸本)9781728163277

vision Transformers (ViTs) have gained significant attention for their exceptional model accuracies on computer vision applications, but their demanding memory requirements and computational complexity have hindered active deployment. Post-training quantization (PTQ) is a practical method to tackle this challenge by directly reducing ViT's bit-precision. However, diverse data characteristics across different operations of ViT cannot be well captured solely by a single numerical format (fixed or floating-point). This work proposes an analytical framework that optimizes the numerical format of each matrix multiplication of ViTs for mixed-format sub-8bit quantization. The extensive evaluation demonstrates that the proposed method can reduce the PTQ error and achieve state-of-the-art accuracy for popular ViT models. © 2023 IEEE.

关键词： Digital arithmetic

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