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Boundary Cue Guidance and Contextual Feature Mining for Glasss Segmentation

Boundary Cue Guidance and Contextual Feature Mining for Glas...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Qiquan Xiao Yuan Zhang Xuanya Li Kai Hu Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc. Beijing China

Glass is ubiquitous in the real world, and its perception has many applications, including robot navigation and drone tracking. However, due to the transparent property of glass, the interior of a glass area can be any surrounding scene or object, which brings challenges for computer vision. Inspired by the human senses, boundary cues are one of the crucial factors for people to judge the location of glass contours. Hence, we propose a boundary cue guidance and contextual feature mining network (BCNet) to accurately and efficiently segment glass. Specifically, we first design a multi-branch boundary extraction module (MBEM) for learning accurate boundary cues combined with multi-level encoded features. Second, we propose a boundary cue guidance module (BCGM), inject the boundary cues into the representation learning, and provide constraints with object structure semantics to guide feature extraction. Besides, we design a contextual feature mining module (CFMM) to dynamically capture the contextual information of different receptive fields for the detection of different sizes and shapes of the glass. Finally, extensive experiments on two benchmark glass datasets, GDD and GSD. The results demonstrate that our BCNet achieves state-of-the-art segmentation performance against existing methods.

关键词： Representation learning Shape Semantics Neural networks Glass Benchmark testing Signal processing

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Exploration of Deep Learning Based Underwater image processing Techniques

Exploration of Deep Learning Based Underwater Image Processi...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： Jayashree Rajesh Prasad Ayushi Parikh Hridya K Prasanth Dept of Computer Science MIT ADT University Pune India

Underwater image classification is a vast field that is being extensively researched upon. Having the ability to inspect and examine the ecosystem beneath the blue ocean layer is a power. Specific domains as marine life detection, coral reef detection, underwater trash detection and image quality enhancement are well documented and complex solutions and technologies are being developed for the same. With the increase in the use of marine resources, a significant corpus of research is emerging. Underwater image processing and analysis is not as easy as it seems, unlike typical photographs, marine environments are frequently subjected to challenging conditions. Through our paper we propose a system that helps accurately detect and classify various underwater life and objects. Through the means of this paper we plan to deliver an ideology that can aid many to filter the unclear underwater and detect objects with ease. Existing research aims to perfect the algorithms to precisely detect a particular type of underwater object. Our paper puts forward a novel idea to fabricate an algorithm that helps us accurately not only detect a single class however detect and classify multiple classes with ease to increase the applications and scope of the model.

关键词： Deep learning Statistical analysis Oceans Marine vegetation Object detection Predictive models Prediction algorithms

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Disentangling Ensemble Models on Adversarial Generalization in image Classification

Disentangling Ensemble Models on Adversarial Generalization ...

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international conference on Electronic Engineering and Informatics (EEI)

作者： Chenwei Li Mengyuan Pan Bo Yang Hengwei Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Key Laboratory of Information Security Zhengzhou China Guizhou University School of Chemistry and Chemical Engineering Guiyang China

Convolutional neural networks are widely used in computer vision and image processing. However, when the original input is added with manually imperceptible perturbations, these deep network models mostly tend to output incorrect predictions. The vulnerability of these models poses great threat to intelligent applications, and these manually imperceptible perturbations are called adversarial examples. Current baseline methods have achieved considerable white-box attack success rate, but black-box rate remains to be improved. To boost the adversarial generalization, ensemble models method is introduced to the process of generating adversarial examples. This paper proposes multiple ensemble strategies with baseline attack methods based on existing ensemble strategy used by former methods. Experiment on imageNet dataset empirically verifies the optimal ensemble strategy on boosting adversarial generalization.

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Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-based Vehicle Retrieval

Comprehensive Visual Features and Pseudo Labeling for Robust...

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ieee computer Society conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Bach Hoang Ngo Dat Thanh Nguyen Nhat-Tuong Do-Tran Phuc Pham Huy Thien Minh-Hung An Tuan-Ngoc Nguyen Loi Nguyen Hoang Vinh Dinh Nguyen Quang-Vinh Dinh AI Vietnam Research Vietnam FPT Telecom Vietnam Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology School of Computing and Information Technology Eastern International University Vietnam

Vehicle retrieval has become crucial for public transportation and intelligent transportation systems due to the exponential development of large-scale transportation videos. Vehicle re-identification and vehicle tracking are the main components of most vision-based vehicle recovery systems. Unfortunately, the limited amount of information provided by traffic video feeds limits the vision-based vehicle retrieval algorithm’s efficacy. Therefore, this article proposes a contrastive cross-modal vehicle retrieval approach to maximize the complementarity of natural language and visual representations. An efficient method to fuse multiple input image features is also proposed to extract comprehensive information from various vehicles along with pseudo labeling and efficient post-processing techniques to enhance retrieval accuracy. The proposed method achieved the 3rd ranking of Mean Reciprocal Rank (MRR) score of 0.4795 on the test set for the Challenge Track 2: Tracked-Vehicle Retrieval by Natural Language Descriptions 2023. Source code for the proposed approaches is openly accessible at https://***/anminhhung/AICity-2023-Track2.

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Mask R-CNN Transfer Learning Variants for Multi-Organ Medical image Segmentation

Mask R-CNN Transfer Learning Variants for Multi-Organ Medica...

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ieee international conference on Systems, Man and Cybernetics

作者： Hongjian Lem Li Zhang Department of Computer Science Royal Holloway University of London Surrey UK

Medical abdomen image segmentation is a challenging task owing to discernible characteristics of the tumour against other organs. As an effective image segmenter, Mask R-CNN has been employed in many medical imaging applications, e.g. for segmenting nucleus from cytoplasm for leukaemia diagnosis and skin lesion segmentation. Motivated by such existing studies, this research takes advantage of the strengths of Mask R-CNN in leveraging on pre-trained CNN architectures such as ResNet and proposes three variants of Mask R-CNN for multi-organ medical image segmentation. Specifically, we propose three variants of the Mask R-CNN transfer learning model successively, each with a set of configurations modified from the one preceding. To be specific, the three variants are (1) the traditional transfer learning with customized loss functions with comparatively more weightage on the segmentation performance, (2) transfer learning based on Mask R-CNN with deepened re-trained layers instead of only the last two/three layers as in traditional transfer learning, and (3) the fine-tuning of Mask R-CNN with expansion of the Region of Interest pooling sizes. Evaluating using Beyond-the-Cranial-Vault (BTCV) abdominal dataset, a well-established benchmark for multi-organ medical image segmentation, the three proposed variants of Mask R-CNN obtain promising performances. In particular, the empirical results indicate the effectiveness of the proposed adapted loss functions, the deepened transfer learning process, as well as the expansion of the RoI pooling sizes. Such variations account for the great efficiency of the proposed transfer learning variant schemes for undertaking multi-organ image segmentation tasks.

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An Enhanced and Efficient approach towards text detection and extracting text in multi-oriented images

An Enhanced and Efficient approach towards text detection an...

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Artificial Intelligence and Smart Communication (AISC), international conference on

作者： Ashutosh Shankhdhar Lakshya Daga Gargi Jadaun Department of Computer Science GLA University Mathura

Machine learning is a part of artificial intelligence. It allows a machine to learn on its own and earn knowledge and experience based on the input data set and training set. Text detection and extraction is one of the prominent applications of machine learning. It identifies text in an image with varying orientation, style, alignment, brightness, or contrast in images with complex backgrounds and extracts valuable information from them. Hence solving the problem of reading text in images with text having various variations. The goal here is to help people from different places read the text in languages that the person might not even know. This paper focuses on various challenges in text detection and the techniques used in extracting text in multi-oriented images such as image processing, data mining, or neural network techniques, and a comparative study on them. One such technique is OCR (optical character recognition). This technique helps us to detect and extract text data and convert it into data that can hence be edited and searched. This paper additionally focuses on performance comparison of various techniques that are currently in use. The results of the experimental outcome are judged on parameters like recall, precision, and accuracy.

关键词： Training image edge detection image processing Digital images Optical character recognition Neural networks Brightness

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TexFusion: Synthesizing 3D Textures with Text-Guided image Diffusion Models

TexFusion: Synthesizing 3D Textures with Text-Guided Image D...

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international conference on computer Vision (ICCV)

作者： Tianshi Cao Karsten Kreis Sanja Fidler Nicholas Sharp Kangxue Yin NVIDIA University of Toronto Vector Institute

We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D geometries, using large-scale text-guided image diffusion models. In contrast to recent works that leverage 2D text-to-image diffusion models to distill 3D objects using a slow and fragile optimization process, TexFusion introduces a new 3D-consistent generation technique specifically designed for texture synthesis that employs regular diffusion model sampling on different 2D rendered views. Specifically, we leverage latent diffusion models, apply the diffusion model’s denoiser on a set of 2D renders of the 3D object, and aggregate the different denoising predictions on a shared latent texture map. Final output RGB textures are produced by optimizing an intermediate neural color field on the decodings of 2D renders of the latent texture. We thoroughly validate TexFusion and show that we can efficiently generate diverse, high quality and globally coherent textures. We achieve state-of-the-art text-guided texture synthesis performance using only image diffusion models, while avoiding the pitfalls of previous distillation-based methods. The text-conditioning offers detailed control and we also do not rely on any ground truth 3D textures for training. This makes our method versatile and applicable to a broad range of geometry and texture types. We hope that TexFusion will advance AI-based texturing of 3D assets for applications in virtual reality, game design, simulation, and more.

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Efficient processing of Group Planning Queries Over Spatial-Social Networks (Extended Abstract)

Efficient Processing of Group Planning Queries Over Spatial-...

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international conference on Data Engineering

作者： Ahmed Al-Baghdadi Gokarna Sharma Xiang Lian Consumer and Community Banking JPMorgan Chase & Co Columbus USA Department of Computer Science Kent State University Kent USA

Recently, location-based social networks, that involve both social and spatial information, have received much attention in many real-world applications such as location-based services (LBS), map utilities, business planning, and so on. In this paper, we seamlessly integrate both social networks and spatial road networks, resulting in a so-called spatial-social network, and study an important and novel query type, named group planning query over spatial-social networks (GP-SSN), which is very useful for applications such as trip recommendations. In particular, a GP-SSN query retrieves a group of friends with common interests on social networks and a number of spatially close points of interest (POIs) on spatial road networks that best match group’s preferences and have the smallest traveling distances to the group. In order to tackle the GP-SSN problem, we design effective pruning methods, matching score pruning, user pruning, and distance pruning, to rule out false alarms of GP-SSN query answers and reduce the problem search space. We also propose effective indexing mechanisms to facilitate the GP-SSN query processing and develop efficient GP-SSN query answering algorithms via index traversals. Extensive experiments have been conducted to evaluate the efficiency and effectiveness of our proposed GP-SSN query processing approaches.

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Machine Learning Techniques For AFM-Based Imaging of Cells

Machine Learning Techniques For AFM-Based Imaging of Cells

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international conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

作者： Junxi Wang Fan Yang Mingyan Gao Bowei Wang Guicai Song Zuobin Wang International Research Centre for Nano Handling and Manufacturing of China Changchun University of Science and Technology Changchun China Centre for Opto/Bio-Nano Measurement and Manufacturing Zhongshan Research Institute of Changchun University of Science and Technology Zhongshan China College of Physics Changchun University of Science and Technology Changchun China JR3CN & IRAC University of Bedfordshire Luton UK

In recent years, advances in computer vision and machine learning have supported a series of algorithms, and their ability to decipher image contents is impressive. These algorithms are being applied to the study of cell images, and gradually changing the analysis methods of cell image data. Atomic Force Microscopy (AFM) is a powerful tool for imaging and analyzing cell structures. It has been used to study the morphology, structure and mechanical properties of cells. However, it is still a challenging topic to identify cells based on AFM images. This article summarizes the applications of machine learning methods in the cell image analysis by AFM. The research of the cell analysis method begins with the collection of cell data, and it emphasizes to identifying cell types, carcinogenesis or carcinogenesis levels, and its auxiliary work includes the cell segmentation, denoising, super-resolution, generation and simulation. Finally, the research experience in these analysis methods and their guiding significance for further study are discussed.

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RTUNet++: Assessment of Osteosarcoma MRI image Segmentation leveraging Hybrid CNN-Transformer Approach with Dense Skip Connection

RTUNet++: Assessment of Osteosarcoma MRI Image Segmentation ...

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ieee international conference on Signal and image processing (ICSIP)

作者： Binfeng Zou Yifei Chen Zhanghao Chen Yifei Sun Yifan Huang Feiwei Qin Changmiao Wang HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen China

Orthopedic osteosarcoma is a prevalent malignant bone tumor. Preoperative planning, efficacy evaluation, and metastasis detection of osteosarcoma necessitate the use of magnetic resonance imaging (MRI). Due to the varying 10-cations, structures, sizes, and shapes of osteosarcomas among patients, as well as the high degree of tumor heterogeneity, segmenting osteosarcoma images manually presents a significant challenge for clinicians. In the meantime, the grayscale and texture features within the tumor in MRI images are not homogeneous, resulting in small grayscale differences between the tumor tissue and the surrounding normal tissue, which also presents a significant challenge for deep learning-based image segmentation methods. This paper proposes RTUNet++, a novel method for segmenting osteosarcoma MRI images. This method integrates the ResNet residual module, Transformer attention mechanism, and UNet++ Dense Skip Connection structure. We employ a CNN-Transformer hybrid architecture to prevent the loss of spatial feature data. Features with different semantic scales are aggregated at the decoder using multi-fusion dense jump connections to achieve flexible feature fusion. Consequently, in comparison experiments with various classical models, our proposed RTUNet++ achieves superior segmentation results on an osteosarcoma MRI image dataset.

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