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Generative Representation and Discriminative Classification for Few-shot Open-set Object Detection

Generative Representation and Discriminative Classification ...

引用

IEEE visual communications and image processing (VCIP)

作者： Peixue Shen Ruoqi Li Yan Luo Yiru Zhao Chao Gao Chongyang Zhang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China China Pacific Insurance(Group) Co. Ltd. Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Open-Set Object Detection (OSOD) aims to train detectors on closed-set datasets to detect known objects and identify unknown objects in open-set conditions. Traditional discriminative classifier-based OSOD methods struggle to accurately learn the decision boundary between known and unknown classes, often resulting in the misclassification of unknown samples. In this work, we aim to combine generative representation with discriminative classification to alleviate the issue of misclassification by transforming known-unknown recognition into a binary classification problem. The proposed two-stage OSOD approach proceeds as follows: during the generative representation stage, we employ Class-Conditioned Normalizing Flow (CCNF) to establish distribution mapping for each known category; In the discriminative classification stage, by utilizing a small number of unknown class samples, semi-push-pull supervised learning and entropy contrast learning are used to separate known and unknown classes. Extensive experiments demonstrate that our method significantly enhances OSOD performance, evidenced by a 25.8%-28.6% reduction in the Wilderness Index and a decrease of 4391-8870 units in Absolute Open-Set Errors on the test set VOC-COCO-T1.

关键词： Accuracy visual communication image processing Supervised learning Object detection Detectors Contrastive learning Entropy Object recognition Indexes

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SketchAnimator: Animate Sketch via Motion Customization of Text-to-Video Diffusion Models

SketchAnimator: Animate Sketch via Motion Customization of T...

引用

IEEE visual communications and image processing (VCIP)

作者： Ruolin Yang Da Li Honggang Zhang Yi-Zhe Song PRIS School of Artificial Intelligence Beijing University of Posts and Telecommunications China SketchX CVSSP University of Surrey United Kingdom

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Sketching is a uniquely human tool for expressing ideas and creativity. The animation of sketches infuses life into these static drawings, opening a new dimension for designers. Animating sketches is a time-consuming process that demands professional skills and extensive experience, often proving daunting for amateurs. In this paper, we propose a novel sketch animation model SketchAnimator, which enables adding creative motion to a given sketch, like "a jumping car". Namely, given an input sketch and a reference video, we divide the sketch animation into three stages: Appearance Learning, Motion Learning and Video Prior Distillation. In stages 1 and 2, we utilize LoRA to integrate sketch appearance information and motion dynamics from the reference video into the pre-trained T2V model. In the third stage, we utilize Score Distillation Sampling (SDS) to update the parameters of the Bézier curves in each sketch frame according to the acquired motion information. Consequently, our model produces a sketch video that not only retains the original appearance of the sketch but also mirrors the dynamic movements of the reference video. We compare our method with alternative approaches and demonstrate that it generates the desired sketch video under the challenge of one-shot motion customization.

关键词： visual communication image processing Dynamics Animation Diffusion models Mirrors Automobiles Text to video Creativity

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ACIQA: A Dataset and Method for Assessing the Imaging Quality of Automotive Cameras

ACIQA: A Dataset and Method for Assessing the Imaging Qualit...

引用

IEEE visual communications and image processing (VCIP)

作者： Yijie Huang Haoyang Ni Kaiwei Zhang Ziheng Jia Fangfang Lu Xiongkuo Min Guangtao Zhai Shanghai University of Electric Power China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The imaging quality of automotive cameras is crucial in complex driving environments. Therefore, it is essential to conduct subjective experiments that can realistically reflect drivers’ evaluation of the imaging quality of automotive cameras in real traffic scenarios. To accurately assess the imaging quality of automotive cameras, this paper proposes a no-reference quality assessment method with quality scores that are highly consistent with human subjective perception. Initially, this study constructs a new image quality assessment dataset and then obtains the subjective scores of image quality through subjective experiments. The dataset is constructed by using a variety of realistic props to simulate scene elements that might be captured by an automotive camera and are captured using a wide range of cameras with different sensor types, lens focus, and viewing angles, resulting in a dataset of diverse images. The objective quality assessment method proposed in this paper consists of an object detection network and a multi-branch quality evaluation network. The object detection network is responsible for identifying and classifying scene elements, while the multi-branch quality evaluation network performs feature extraction and score regression on various types of elements to effectively evaluate the imaging quality of the automotive cameras. In the experiments, this no-reference quality assessment method is tested on our built dataset, and the results show that the proposed method exhibits the best performance compared with the state-of-the-art image quality assessment methods.

关键词： image quality visual communication Object detection Cameras Feature extraction Quality assessment Object recognition Automotive engineering Lenses

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Development of image processing Techniques in Crack Detection and Analysis

Development of Image Processing Techniques in Crack Detectio...

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Electrical, Computer, communications and Mechatronics Engineering (ICECCME), International conference on

作者： Luqman Sulaiman Al Dhafari Aftab Afzal Raid Ibrahim Al Bahrani Yahyah Al Busaidi Akbar Sheikh-Akbari Mohammad Sayeed Hossain Dept. Aeronautical Engineering Military Technological College Muscat Sultanate of Oman School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom

Inspection of aircraft skin is required as per the Corrosion Prevention and Control Program (CPCP) to ensure aircraft structural integrity. Human visual inspection is the most widely used technique in aircraft surface inspection, according to the CPCP. Scheduled inspections and regular maintenance of an aircraft through conventional methods constitute tedious and lengthy procedures. Often the visual inspections lead to subjective judgement and do not constitute repeatability. Many automated vision-based aircraft skin inspection systems have been designed to provide a safe, quick, and accurate visual assessment over the past years. This paper presents a section of research investigating defect detecting and accurately locating the outer body of an aircraft using an Unmanned Aerial Vehicle (UAV) to capture images and digital image processing techniques to locate possible cracks. The inspection system is used to initially detect locations of cracks (defects) on an aircraft’s outer skin and the detected crack is further investigated using thermal and ultrasound imaging methods. The scope of this paper includes a review of the design and development of a series of advanced dedicated image processing algorithms suitable for applying digital image processing on images captured from the outer surface of a typical aircraft fuselage.

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Micro-Spatial Attention with Sparse Constraint for Self-Supervised Learning for Oleaginous Yeast image Representation

Micro-Spatial Attention with Sparse Constraint for Self-Supe...

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IEEE visual communications and image processing (VCIP)

作者： Kazuma Ohtomo Yukina Kitahara Ryosuke Harakawa Akihiro Nakamura Yosuke Shida Wataru Ogasawara Masahiro Iwahashi Department of Information Science and Control Engineering Nagaoka University of Technology Department of Science of Technology Innovation Nagaoka University of Technology Department of Electrical Electronics and Information Engineering Nagaoka University of Technology Department of Bioengineering Nagaoka University of Technology

Feature representation of oleaginous yeast images is essential for non-invasively observing their cultivation status. However, conventional methods are difficult to capture the visual characteristics of oleaginous yeasts at the extremely local spatial regions. To tackle this problem, this paper proposes micro-spatial attention with sparse constraints for self-supervised learning. Specifically, we newly design a micro-spatial attention module to focus on the extremely local spatial regions for feature representation. Unlike the existing spatial attention module, which requires the guide information (e.g., labels and textual queries) to focus on specific regions, our proposed module employs a sparse constraint. By combining the sparse constraint with self-supervised learning, we can automatically focus on essential regions, which appear in extremely local regions. This improves the feature representation power for oleaginous yeast images with micro-spatial contexts. Experiments on real Lipomyces starkeyi datasets, a specific type of oleaginous yeasts, confirm the superiority of our method. Moreover, our method realized clustering of oleaginous yeast images, enabling us to non-invasively observe the cultivation trends over time.

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Multi-Exposure Fusion Light Field image Quality Assessment Based on Object Detection

Multi-Exposure Fusion Light Field Image Quality Assessment B...

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IEEE International conference on Signal processing, communications and Computing (ICSPCC)

作者： Keke Yao Guanglong Liao Gangyi Jiang Yeyao Chen Mei Yu Faculty of Information Science and Engineering Ningbo University Ningbo China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

The multi-exposure fusion algorithms can effectively improve the dynamic range of light field imaging, but distortion is inevitably introduced during the process. Therefore, it is crucial to construct an effective quality assessment method for multi-exposure fusion light field images (MEF-LFIs). Aiming at the significant artifact distortion phenomenon of MEF-LFIs and the limitations of the traditional dynamic region extraction methods, the MEF-LFI quality assessment method combined with object detection is proposed. Firstly, an object detection model is utilized to extract dynamic region masks of the original LFI sequences, and the artifact feature fusion module is constructed for artifact distortion. Subsequently, the MEF-LFIs are converted from the RGB color space to the HSV color space to measure color distortion. Then, the multi-stream feature extraction networks with attention mechanism are constructed to extract artifact, spatial and angular distortion features, respectively. Finally, a regression model is constructed to measure the visual quality of MEF-LFIs. Experimental results show that the proposed method outperforms many representative image quality assessment methods and maintains well consistency with the human visual perception.

关键词： image quality Attention mechanisms image color analysis Heuristic algorithms Object detection Feature extraction Distortion Light fields Quality assessment Distortion measurement

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722837

The proceedings contain 46 papers. The special focus in this conference is on Science, Engineering Management and Information Technology. The topics include: Verification and Validation of Knowledge Engineering Systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making Methods;advancing Anemia Diagnosis: Harnessing Machine Learning Methods for Accurate Detection;monkeypox Detection with K-mer Using Machine Learning Algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing Advanced AI Techniques: An In-Depth Analysis of Machine Learning Models for Improved Diabetes Prediction;image processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest Software;a Diagnosis Model Based on Federated Learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces images Using Transfer Learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep Learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of Signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time Based on the Digital Twin;role of Top Management Commitment and Information Technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722868

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A Novel Learnable Orthogonal Wavelet Unit Neural Network with Perfection Reconstruction Constraint Relaxation for image Classification

A Novel Learnable Orthogonal Wavelet Unit Neural Network wit...

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IEEE visual communications and image processing (VCIP)

作者： An D Le Shiwei Jin You Suk Bae Truong Nguyen Jacobs School of Engineering University of California San Diego California USA Department of Computer Engineering Technology University of Korea Gyeonggi-do Korea

CNNs utilize lowpass frequency features, evidenced by max pooling operations that preserve dominant features. Addressing this, we used a one-layer FCN using both coarse and detailed components via DWT. We then developed UwU, a learnable wavelet-based unit that integrates the PR constraint relaxation, allowing feature map component fine-tuning. Distinctively, UwU’s coefficients are trainable, unlike prior studies. This is the first work that utilizes PR constraint relaxation to enhance CNNs. Our innovative techniques serve to enhance stride-convolution, pooling, and downsampling units in CNNs. We tested these improved units using the ResNet family architectures against traditional frequency and wavelet-based units. Performance metrics from CIFAR10, imageNet1K, and the DTD show promising results. Particularly, while CIFAR10 results are on par with other methods, there’s a marked performance improvement on imageNet1K and DTD. Further, integrating UwU into the ResNet18-based encoder of the CFLOW-AD system yields competitive anomaly detection results, particularly for hazelnut images in the MVTecAD dataset.

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Retinal image Analysis Approach for Diabetic Retinopathy Grading 1

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15th International conference on Pattern Recognition and Information processing, PRIP 2021

作者： Golub, Yuliya Lukashevich, Marina Starovoitov, Valery Joint Institute of Informatics Problems The National Academy of Science of Belarus Surganova Street 6 Minsk Belarus Belarusian State University of Informatics and Radioelectronics Platonova Street 39 Minsk Belarus

ISBN: (数字)9783030988838

ISBN: (纸本)9783030988821

The eye is a complex organ that performs many functions. One of its components is the retina. One of the important properties of the retina is the reflection of the visible signs of a number of diseases. Diabetic retinopathy is the most common complication and the main cause of vision loss in diabetic patients. The retina also shows signs of other diseases that strongly affect visual impairment, such as cataracts, glaucoma, and macular degeneration. All of these diseases begin asymptomatically. Therefore, it is important to detect them at the very beginning of the disease and prescribe treatment. Advances in artificial intelligence (AI), especially in deep learning, are improving pathological image analysis in daily clinical practice. We have developed an approach for identifying and classifying diabetic retinopathy. It includes a universal method for segmentation of the retinal region for arbitrary fundus images and an assessment of the quality of the original image. Low quality images are excluded from further analysis. © 2022, Springer Nature Switzerland AG.

关键词： image analysis

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