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A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB image

A2J-Transformer: Anchor-to-Joint Transformer Network for 3D ...

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conference on computer vision and pattern recognition (CVPR)

作者： Changlong Jiang Yang Xiao Cunlin Wu Mingyang Zhang Jinghong Zheng Zhiguo Cao Joey Tianyi Zhou Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Alibaba Group Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore

3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition. Our key idea is to equip A2J with strong local-global aware ability to well capture interacting hands' local fine details and global articulated clues among joints jointly. To this end, A2J is evolved under Transformer's non-local encoding-decoding framework to build A2J- Transformer. It holds 3 main advantages over A2J. First, self-attention across local anchor points is built to make them global spatial context aware to better capture joints' articulation clues for resisting occlusion. Secondly, each anchor point is regarded as learnable query with adaptive feature learning for facilitating pattern fitting capacity, instead of having the same local representation with the others. Last but not least, anchor point locates in 3D space instead of 2D as in A2J, to leverage 3D pose prediction. Experiments on challenging InterHand 2.6M demonstrate that, A2J-Transformer can achieve state-of-the-art model-free performance (3.38mm MPJPE advancement in 2-hand case) and can also be applied to depth domain with strong generalization. The code is avaliable at https://***/ChanglongJiangGit/A2J-Transformer.

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Medicinal Herbs Identification

Medicinal Herbs Identification

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International conference on Advanced Computing and Communication Systems (ICACCS)

作者： M. Preethi S. Jansi Rani K. S. Pradhiksha J. Ram Kumar T. Vishal Sri Ramakrishna Engineering College Coimbatore India

Many rural communities have a strong belief in plant diversity. They collect useful plants and herbs and use them with indigenous knowledge and customs. One such oldest system that results in the use of medicinal herbs is Ayurveda. Approximately 10,000 plants are used medicinally in India, but not all plants are included in the official Ayurvedic Pharmacopoeia. Before becoming part of Ayurvedic medicine, all plants need to be thoroughly studied. For this reason, identifying herbs is the most important step. Many of these identifications are fully supported by human perception, leaving room for error and misjudgement. Therefore, it is necessary to develop an efficient system using computer vision, pattern recognition, and image processing algorithms alongside the availability of various combinations of feature detection methods with different classifiers that are often utilized in building an automatic identification system for herbal leaves using leaf images and reveal its associated information to realize knowledge.

关键词： Training image segmentation Machine learning algorithms image color analysis Neural networks Machine learning pattern recognition

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Biometric Muzzle pattern recognition for Cattle Identification using Deep Learning

Biometric Muzzle Pattern Recognition for Cattle Identificati...

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International conference on Inventive Computation Technologies (ICICT)

作者： Nithya Jayakumar R. Nithish kumar A. Ruthrakumar K. Saikavya Department of Information Technology K. S. Rangasamy College of Technology Tiruchengode India

The ability to identify specific cows is a critical aspect of keeping cattle. Registration is essential for the production, distribution, and breeding of cows. It is dangerous to brand cows in the ear in the traditional manner. To solve this issue, we recommend using a biometric scanner for identification. Numerous studies have shown that the muzzle pattern can be used to distinguish animals and eliminate inconsistent patterns. This strategy might be effective in reducing spurious matching muzzle patterns. On the tip of their nose or muzzle, the majority of animals have a distinctive pattern. An animal's characteristic pattern is obvious to the unaided eye once it is born. Animals can be recognized by human muzzle patterns, just like with fingerprints. Systems for identifying animals are needed, as they would be useful for submitting loan and insurance applications. Based on a biometric muzzle scanner, the approach of machine learning, image processing, and computer vision has been assessed for identification purposes.

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A Novel Two-Stage Data-mining Model Combining Gait recognition and Temporal Sequence Mining 23

A Novel Two-Stage Data-mining Model Combining Gait Recogniti...

引用

Proceedings of the 2023 7th International conference on Video and image processing

作者： Pu-Tai Yang Tsu-Tang Liao Chih-Jui Ray Chen National Taipei University Taiwan LongGood Meditech Taiwan

ISBN: (纸本)9798400709388

In recent years, artificial intelligence applications have been on the rise. Many enterprises have embraced digital transformation and have established new business models based on artificial intelligence and the Internet of Things, such as the telerehabilitation industry. The companies may utilize sensors or cameras to collect user data, and data mining is applied to discover insights for doctors’ aids. This paper establishes a novel two-stage data mining model combining gait recognition and sequential pattern mining. In the first stage, a particular computer vision application, gait recognition, identifies possible diseases using the subject's walking postures. The gaits in a video can be converted to a temporal sequence according to user-defined events. For example, (normal gait, Parkinsonian gait, normal gait) is a temporal sequence in which the identified gaits are arranged by temporal orders in the sequence. In the second stage, after collecting a dataset of temporal sequences, the frequent patterns are discovered by sequential pattern mining. Our preliminary experiment collected 30 samples from the real world and demonstrated the model's feasibility.

关键词： Articulated Body Pose Estimation Artificial Intelligence Data Mining Gait recognition Sequential pattern Mining

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Pre-processing Of Resize And Region Of Interest (ROI) To Improve The Accuracy Of Batik Detection Based On VGG-16

Pre-Processing Of Resize And Region Of Interest (ROI) To Imp...

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International conference on Electrical Engineering, computer Science and Informatics (EECSI)

作者： Jani Kusanti Edi Noersasongko Moch Arief Soeleman Farrikh Alzami Purwanto Zainal Abidin Hasibuan Faculty of Computer Science Universitas Dian Nuswantoro Faculty of Electrical Information and Engineering Universitas Surakarta Faculty of Computer Science Universitas Dian Nuswantoro Indonesia

The patterns and designs used in batik are a traditional textile art form from Indonesia. With the increasing popularity of computer vision applications, the interest in automatic detection and recognition of batik patterns is increasing. Convolutional Neural Networks (CNNs) show very good performance in detecting objects. The problem addressed in this study is the variation in image sizes, which affects accuracy. The selection of pre-processing techniques will impact the accuracy of the results. For this reason, it is important in this study to compare tests on data that has been processed using resize and Region of Interest (ROI). This study aims to determine the impact of two pre-processing techniques, resize and ROI, on the accuracy of batik pattern recognition using the Visual Geometry Group (VGG)-16 model. The dataset consisted of 1,445 images, with 1,301 images used for training and 144 images used for testing. The classes used were Kawung, Parang, Satriomanah, Sawat, Sementrante, Sidomukti, Tambal, and Truntum. The experimental results demonstrate that the choice of pre-processing techniques significantly affects the accuracy of batik pattern detection. Resizing provides an efficient computational solution, while ROI achieves a detection accuracy of 0.96, which is superior to Resize's accuracy of 0.89. This study highlights the importance of preprocessing techniques in detecting batik patterns using the VGG-16 model. Resizing and ROI have advantages and disadvantages, ROI techniques generally result in higher accuracy.

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Estimation of Varietal Classification and Quality Analysis in Rice Seeds Using Digital image processing – A Survey

Estimation of Varietal Classification and Quality Analysis i...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International conference on

作者： M. Sandhiya S. Christy Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Agriculture plays a major role in today’s world. 70% of rice production is cultivated in south India. Thailand, the US, India, Vietnam, etc., are exporting rice all over the world. But they are facing one of the most difficult tasks: assuring the rice seed quality. Good seeds can increase production, so selecting seeds is very important. A maximum of the work was done physically or by using huge, expensive machines. For choosing good seeds, agronomists be conscious of two factors, such as the variety which is determined by some quality criteria, and next by the skilled one who is going to analyze the rice seed physically. Both tasks take a long time and the result of accuracy is also not as expected. Nowadays computer vision technology with Digital image processing plays a major role in many fields, namely agronomy, healthcare, machine vision, pattern recognition, remote sensing, Video processing, etc. Our research work mainly focuses on the agricultural arena, going to provide good quality seeds and identify dissimilar varieties of rice seeds by digital image processing techniques such as image capturing, segmenting the rice seeds, extracting the features from the rice seeds and finally feeding into the machine learning algorithm to find out the accuracy too.

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MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery

MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic...

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conference on computer vision and pattern recognition (CVPR)

作者： Duowen Chen Yunhao Bai Wei Shen Qingli Li Lequan Yu Yan Wang Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University The University of Hong Kong

We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In teacher-student model, data augmentation is usually adopted on unlabeled data to regularize the consistent training between teacher and student. We start from a key perspective that fixed relative locationsand variable sizes of different organs can provide distribution information where a multi-organ CT scan is drawn. Thus, we treat the prior anatomy as a strong tool to guide the data augmentation and reduce the mismatch between labeled and unlabeled images for semi-supervised learning. More specifically, we propose a data augmentation strategy based on partition-and-recovery N 3 cubes cross-and within-labeled and unlabeled images. Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch). For within-branch, we further propose to refine the quality of pseudo labels by blending the learned representations from small cubes to incorporate local attributes. Our method is termed as MagicNet, since it treats the CT volume as a magic-cube and N 3 -cube partition-and-recovery process matches with the rule of playing a magic-cube. Extensive experiments on two public CT multi-organ datasets demonstrate the effectiveness of MagicNet, and noticeably outperforms state-of-the-art semi-supervised medical image segmentation approaches, with + 7% DSC improvement on MACT dataset with 10% labeled images. Code is avaiable at https://***/DeepMed-Lab-ECNU/MagicNet.

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Implicit Temporal Modeling with Learnable Alignment for Video recognition

Implicit Temporal Modeling with Learnable Alignment for Vide...

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International conference on computer vision (ICCV)

作者： Shuyuan Tu Qi Dai Zuxuan Wu Zhi-Qi Cheng Han Hu Yu-Gang Jiang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft Research Asia Carnegie Mellon University

Contrastive language-image pretraining (CLIP) has demonstrated remarkable success in various image tasks. However, how to extend CLIP with effective temporal modeling is still an open and crucial problem. Existing factorized or joint spatial-temporal modeling trades off between the efficiency and performance. While modeling temporal information within straight through tube is widely adopted in literature, we find that simple frame alignment already provides enough essence without temporal attention. To this end, in this paper, we proposed a novel Implicit Learnable Alignment (ILA) method, which minimizes the temporal modeling effort while achieving incredibly high performance. Specifically, for a frame pair, an interactive point is predicted in each frame, serving as a mutual information rich region. By enhancing the features around the interactive point, two frames are implicitly aligned. The aligned features are then pooled into a single token, which is leveraged in the subsequent spatial self-attention. Our method allows eliminating the costly or insufficient temporal self-attention in video. Extensive experiments on benchmarks demonstrate the superiority and generality of our module. Particularly, the proposed ILA achieves a top-1 accuracy of 88.7% on Kinetics-400 with much fewer FLOPs compared with Swin-L and ViViT-H. Code is released at https://***/Francis-Rings/ILA.

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Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach

Road Condition Detection and Crowdsourced Data Collection fo...

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Md Saroar Jahan Mominul Islam Md Sanjid Hossain Jhuma Kabir Mim Mourad Oussalah Nasrin Akter Centre for Machine Vision and Signal Processing (CMVS) Faculty of ITEE University of Oulu Oulu Finland Dept. of CSE Daffodil International University Dhaka Bangladesh Computer Vision and Pattern Recognition LUT Univerity Lappeenranta Finland CMVS Faculty of ITEE University of Oulu Oulu Finland

Bangladesh is one of the countries struggling to prevent road accidents, which is a global cause for concern. An early warning system that indicates road conditions can contribute to the prevention task. For this purpose, a deep-learning based approach using a Convolutional Neural Network (CNN) to learn from random road images the safety factor is developed. This results in a three-class categorization: (i) Severely risky roads, (ii) Mildly risky roads, and (iii) Normal roads. The application of deep learning techniques in this study yields an accuracy of 95.5% in detecting problematic road conditions. Furthermore, based on the study’s findings, a mobile application has been developed. The app enables real-time crowdsourced data collection of road conditions and provides a platform for users to share this information in real-time with other drivers, thereby, contributing to prevent accidents and raise awareness among drivers and users by pinpointing the location of the risky road. Finally, crowdsourced data has been reused to update the trained model, which further improves the classifier accuracy.

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Low Resource Degraded Quality Document image Binarization – Domain Adaptation is the Way 22

Low Resource Degraded Quality Document Image Binarization –...

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Proceedings of the Thirteenth Indian conference on computer vision, Graphics and image processing

作者： Ahana Kundu Ujjwal Bhattacharya Computer Vision and Pattern Recognition Indian Statistical Institute IN Computer Vision and Pattern Recognition ISI Kolkata IN

ISBN: (纸本)9781450398220

Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy background and faded foreground pixels. The study presented here is particularly focused on binarization of images of low-resource degraded quality documents based on a set of recently collected image samples of several rare, ancient and severely degraded quality printed documents of Bangla, the 2nd and 5th most popular script of India and the world respectively. This new collection of degraded document image samples will henceforth be referred as ’ISIDDI2’ and it consists of 139 images of Bangla old document pages. Samples of ’ISIDDI’, another existing database of degraded Bangla document image samples, have also been used in the present study. A novel deep architecture based on attention UNET++ with dilated convolution operation is proposed for this binarization task. The model is optimized using human vision perceptible distance reciprocal distortion (DRD) loss. Since the binarization ground truth of samples of both ’ISIDDI2’ and ’ISIDDI’ are not available, the proposed network has been trained using samples of DIBCO and H-DIBCO datasets and an unsupervised domain adaptation (DA) module is employed for adaptation of the proposed architecture to the degradation patterns of ’ISIDDI2’ or ’ISIDDI’ samples. The proposed binarization strategy includes certain post-processing operation based on a modified k-neighbourhood based approach for recovery of broken characters. Results of our extensive experimentation show that the proposed binarization strategy has improved the binarization output of state-of-the-art methods on both ISIDDI2 and ISIDDI datasets. Also, its performance on well-known DIBCO samples is satisfactory.

关键词： UNET++ degraded document image domain adaptation image binarization attention

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