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Probabilistic integration of audiovisual information to localize sound source in human-robot interaction

Probabilistic integration of audiovisual information to loca...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： B. Chen M. Meguro M. Kaneko Department of Electronic Engineering University of Electro-Communications Chofu Tokyo Japan

This paper proposes a method to estimate a sound source position by fusing the auditory and visual information with Bayesian network in human-robot interaction. We firstly integrate multi-channel audio signals and a depth image about the environment to generate a likelihood map for sound source localization. However, this integration, denoted by "MICs", does not always lead to locate a sound source correctly. For correcting the failure in localization, we integrate the likelihood values generated from "MICs" and the skin-color distribution in an image according to the result of classifying audio signal into speech/non-speech categories. The audio classifier is based on the support vector machine(SVM) and the skin-color distribution is modeled with GMM. With the evidences given by MICs, SVMs and GMM, we infer whether pixels in images correspond to sound source or not according to the trained Bayesian network. Finally, experimental results are presented to show the effectiveness of the proposed method.

关键词： Microphone arrays Bayesian methods Loudspeakers Color Intelligent robots Robot vision systems Cameras Face detection Signal processing Acoustic noise

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Lightweight image Classification Algorithm Based on Dual Knowledge Distillation 23

Lightweight Image Classification Algorithm Based on Dual Kno...

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Proceedings of the 2023 6th International conference on Artificial Intelligence and Pattern Recognition

作者： Peng Xiao Ying Liu Yu Hao Center for Image and Information Processing School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Deep learning models perform exceptionally well in image processing; however, the increasing number of parameters and computational burdens make it difficult to apply these models on mobile devices. To address this issue, a widely adopted approach is using knowledge distillation to compress and optimize deep learning models. Traditional knowledge distillation trains a student network with soft targets from a teacher network. This paper proposes a Novel Dual Knowledge Distillation (NDKD) network by integrating Convolutional Block Attention Module (CBAM) distillation and feature distillation. Specifically, a new CBAM module and a hash layer are first added to improve the teacher-student network. Next, attention distillation loss is computed to transfer CBAM knowledge from the teacher module to the student network to enhance the student network's ability to capture low-level visual features. Finally, feature distillation loss is constructed by hashing layers to transfer semantic knowledge from the teacher network to the student network to enhance the student network's high-level semantic ability. Our method is compared with other knowledge distillation methods on benchmarking datasets including the CIFAR-10, SVNM, EMNIST, and Tire Pattern image dataset CIIP-TPID. Experimental results show that our method outperforms other methods on adapted datasets.

关键词： Convolutional Block Attention Module Deep learning image processing knowledge distillation

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Accurate Recognition of Kiwifruit Based on Improved YOLOv5

Accurate Recognition of Kiwifruit Based on Improved YOLOv5

引用

Natural Language processing (ICNLP), International conference on

作者： Sun Wei Sun Yi Jun Li Zhao Chen Guo Jing School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

In order to meet the urgent needs of automation and intelligent picking of kiwifruit, aiming at the problems of unreasonable construction of kiwifruit data set, low fruit recognition accuracy and poor spatial positioning in the natural environment of orchard, a precise recognition and visual positioning method of kiwifruit based on improved Yolov5s was proposed. In view of the growth characteristics of kiwifruit in trellis orchards, a multi-type kiwifruit data set was first constructed. Furthermore, the attention mechanism and multi-scale module are combined to improve the Yolov5s network structure, identify kiwifruit and extract the center coordinates of the prediction box. The experimental results show that the average accuracy of the model for six kiwifruit types under different weather and light conditions is 98 %. The single image recognition time of $1280\times 720$ pixel is about 13.8 ms, and the weight is only 15.21 Mb. It can be seen that this study can provide technical support for the vision system of kiwifruit automatic picking robot, and provide reference for the intelligent recognition and positioning of other fruits (such as apples, mangoes and oranges).

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Defect detection in pattern texture analysis

Defect detection in pattern texture analysis

引用

International conference on communications and Signal processing

作者： Manimozhi Iyer S. Janakiraman subbaih Manonmaniam Sundaranar at Tirunelveli India Dept of Banking Technology in Pondicherry University Pondicherry

The detection of abnormalities is a very challenging problem in computer vision, especially if these abnormalities must be detected in images of textured surfaces. The cast extrusion manufacturing process is the initial step which enables the creation of the raw materials, such as clear polypropylene film, needed for the flexible packaging manufacturing process. The current methodology of controlling extrusion related defects occurrences is attempted by a combination of statistical Sampling and human inspection. However, the defects are small in size and hard to visualize in a clear thin film 3 m in width moving at a speed of 50m/min/.This resulted in poor product quality and high return ratio from customers. This problem becomes even more complicated in case when testing is possible only with one surface of the part. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. This automated classification method helps us to acquire knowledge about the pattern of defect within a very short period of time. This research investigates possible defect detection methodologies and has subsequently proposed a system that is capable of real time monitoring of defects on the cast extrusion manufacturing process.

关键词： Monitoring image edge detection Random access memory Performance evaluation

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Deep learning-based Object Detection in Underwater communications System 2

Deep learning-based Object Detection in Underwater Communica...

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2nd International conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Sangari, M. Siva Thangaraj, K. Vanitha, U. Srikanth, N. Sathyamoorthy, J. Renu, K. KPR Institute of Engineering and Technology Department of CSE Coimbatore India Sona College of Technology Department of Information Technology Salem India Sri Krishna College of Engineering and Technology Department of ECE Coimbatore India SASI Institute of Technology and Engineering Department of Electronics and Communication Technology Andhra Pradesh Tadepalligudem India Bannari Amman Institute of Technology Department of CSE Erode India GST Department of EECE GITAM Deemed to Be University A.P Visakhapatnam -45 India

ISBN: (纸本)9798350397635

Being at the nexus of robotics and ocean engineering, underwater robots have been a developing research area. They can be used for deep sea infrastructure inspections, oceanographic mapping, and environmental monitoring. Autonomous navigation skills are essential for doing these activities successfully, especially given the poor communication conditions in underwater locations. Autonomous navigation technologies, such as path planning and tracking, have been one of the fascinating but difficult issues in the field of study due to the extremely dynamic and three-dimensional settings. Due to their short detection ranges and poor visibility, cameras have not received much attention as an underwater sensor. However, using visual data from cameras is still a popular technique for underwater sensing, and it works particularly well for close-range detections. In this study, the enhancement of underwater vision is achieved by combining the max-RGB and shades of grey methods. Then, to solve the problem of poorly illuminated underwater images, a technique known as RCNN (Region-based Convolutional Neural Network) is proposed. This procedure tells the mapping relationship how to create the illumination map. Following image processing, an RCNN strategy for underwater detection and classification is recommended. Two improved strategies are then used to change the RCNN structure in accordance with the properties of underwater vision. In order to deal with the challenges of object tracking and detection in underwater communication, a correlation filter tracking algorithm (CFTA) method was created. The properties of the invariant moment and area were looked at after the object's region had been extracted using a threshold segment and morphological technique. The findings show that the suggested method is effective for underwater target tracking based on RCNN-CFTA in the aquatic environment. Simulated evaluation of these methods' performance demonstrates the potency of the suggested

关键词： Cameras

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Sequential RBF function estimator: memory regression network

Sequential RBF function estimator: memory regression network

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IEEE International conference on Systems, Man and Cybernetics

作者： Chi-kin Chow Hung-tat Tsui Visual Signal Processing and Communications Laboratory Departmenl of Electronic Engineering Chinese University of Hong Kong Hong Kong China

The neural-network training algorithm can be divided into 2 categories: (1) batch mode and (2) sequential mode. In this paper, a novel online RBF network called "memory regression network (MRN)" is proposed. Different from the previous approaches (de Freitas, N, et al., Aug. 1999), (Schiffman, W, et al., 1993), MRN involves two types of memories: experience and neuron, which handle short and long term memories respectively. By simulating human's learning behavior, a given function can be estimated without memorizing the whole training set. Two sets of function estimation experiments are examined in order to illustrate the performance of the proposed algorithm. The results show that MRN can effectively approximate the given function within a reasonable time and acceptable mean square error

关键词： Neurons Neural networks Signal processing algorithms Radial basis function networks Radio access networks Robots Laboratories Humans Interpolation image processing

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JADD-GAN: A Joint Attention Generative Adversarial Data Fusion Network for Object Detection and Tracking

JADD-GAN: A Joint Attention Generative Adversarial Data Fusi...

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IEEE International conference on High Performance Computing and communications (HPCC)

作者： Guoxia Xu Hao Wang Meng Zhao Hu Zhu Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

image fusion is the fusion of images captured by different sensors to generate a single image with enhanced information, and fusion technology, as one of the important branches in the field of information fusion, mainly realizes the processing of multi-source image information. However, many commonly used fusion methods usually ignore the visual naturalness and information fidelity of the fused images and lack emphasis on the salient information, which makes the fused images unsuitable for human visual perception. To address these shortcomings of existing methods, in this paper, we propose the Joint Attention and Dual Discriminator Generative Adversarial Data Fusion Network JADD-GAN. In the generator module, to increase the extraction of multi-level information by the network, we firstly adopt a dual encoder structure and give information fusion in the decoder part. Secondly, different discriminators are used for infrared and visible images in order to highlight the thermal radiation information and key textures. The effectiveness of the method is verified by experiments on four datasets, and the results show that the method can effectively highlight the thermal radiation information and key texture details of the fused images, fully demonstrating its great potential and performance in solving the infrared and visible images fusion (IVF) problem.

关键词： visualization Data integration Object detection Sensor fusion Feature extraction Generators Sensors

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Dual-modal Fusion Method based on Hierarchical Frequency Domain Neural Network

Dual-modal Fusion Method based on Hierarchical Frequency Dom...

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Information Systems and Computing Technology (ISCTech), International conference on

作者： Guoxia Xu Yue He Lizhen Deng Hu Zhu School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

To address the challenges of image degradation and the limitations of single-domain information, this paper proposes a fusion network that integrates both frequency-domain and spatial-domain information for infrared and visible image fusion. We begin by designing an image fusion network that combines the phase components of the visible image with its amplitude spectrum, effectively processing the high, low and mid-frequency. This method achieves the fusion of salient targets and the preservation of visual quality in the frequency domain. Additionally, an information complementarity network is introduced to address spatial domain differences. This network features an innovative compensation mechanism that addresses the texture disparities between the fused image. Furthermore, the network utilizes implicit degradation estimation during feature fusion. This approach helps the fusion model identify and optimize various potential degradation factors, thereby improving the extraction and aggregation of important content in degraded scenes. The experiment demonstrates our proposed method achieves the superior performance against other comparison methods.

关键词： Degradation Computers visualization Frequency-domain analysis Neural networks Estimation Feature extraction Sensors image fusion Information systems

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Self-Supervised Dense visual Representation Learning

Self-Supervised Dense Visual Representation Learning

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IEEE Signal processing and communications Applications (SIU)

作者： Timoteos Onur Özçelİk Berk Gökberk Lale Akarun Bilgisayar Mühendisliği Boğaziçi Üniversitesi ˙Istanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Self-supervised representation learning has shown promising results in recent years. However, most of the proposed methods are pre-trained on object-centric datasets with image-level pretext tasks. In this study, we follow DenseCL, which is pre-trained on pixel-level scene-centric datasets with contrastive learning. Our goal is to alleviate the false negative pairing problem in contrastive learning by consistency regularization. Our method outperforms DenseCL and PixContrast models in most of the scenarios. In PASCAL VOC object detection, we see 0.2% AP50 and 0.3% AP improvements. In COCO object detection, we get 0.3% AP and 0.7% AP boosts. We also improve by 0.4% AP and 0.6% AP in COCO instance segmentation, and 0.1% mAP and 0.9% mAP in PASCAL VOC semantic segmentation. Moreover, attention map visualization and k-nearest neighbour retrieval indicate qualitative improvement from the proposed method.

关键词： Representation learning Instance segmentation visualization Semantic segmentation Object detection Contrastive learning Signal processing

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Retina-like processing and coding platform for cortical neuro-stimulation

Retina-like processing and coding platform for cortical neur...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： F. Pelayo A. Martinez C.A. Morillas S. Romero L. Sousa P. Tomas Department of Computer Architecture and Technolog University of Granada Spain Department of Electrical and Computer Engineering IST INESC-ID Lisboa Portugal

This paper describes a bio-inspired preprocessing and coding system devised for proper multielectrode stimulation at the primary visual cortex. After an introduction of the software/hardware (Sw/Hw) retina-like preprocessor as well as the system that produces a continuously varying stream of spike patterns, the paper presents a comparison of the system responses when only spatial or a combined spatio-temporal visual processing are modeled. The system is implemented both in software and in reconfigurable hardware. The main target of this work is to build, in a near future, a portable system for a neuro-prosthesis able to stimulate efficiently an array of intra-cortical implanted electrodes. The Sw/Hw platform is highly programmable and reconfigurable as required for experimentation, providing real-time neuromorphic stimulation able to be particularized for each implanted blind individual.

关键词： Retina image coding Prosthetics Microelectrodes Filters Brain modeling Electrodes Neuromorphics visual system Biomedical optical imaging

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