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Illumination Invariant Three-Stage Approach for Face Alignment

Illumination Invariant Three-Stage Approach for Face Alignme...

引用

IEEE International conference on Image Processing

作者： Fatih Kahraman Muhittin Gokmen Informatics Institute Istanbul Technical University Istanbul Turkey Computer Engineering Department Istanbul Turkey Istanbul Technical University

Localization of facial components is very important for many pattern recognition and computer vision applications such as face recognition, tracking of face expressions. This paper addresses the problem of precisely finding facial components, such as the eyes, the mouth, the nose etc. We present an illumination invariant three-stage approach for face alignment. This paper combines the concept of component-based approach and face alignment and develops component-based active appearance model (AAM) method for fine face alignment. We propose a new image representation to improve AAM segmentation accuracy for illumination invariant face alignment.

关键词： Lighting Face detection Active appearance model pattern recognition computer vision Application software Face recognition Eyes Mouth Nose

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The 2nd 106-Point Lightweight Facial Landmark Localization Grand Challenge 25th

The 2nd 106-Point Lightweight Facial Landmark Localization G...

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25th International conference on pattern recognition workshops, ICPR 2020

作者： Liu, Yinglu Li, Peipei Tong, Xin Shi, Hailin Zhu, Xiangyu Sun, Zhenan Xu, Zhen Liu, Huaibo Su, Xuefeng Chen, Wei Huang, Han Wang, Duomin Tao, Xunqiang Guo, Yandong Tong, Ziye Lai, Shenqi Chai, Zhenhua JD AI Research Beijing China Institute of Automation Chinese Academy of Sciences Beijing China SogouAI Beijing China OPPO Research Institute Beijing China Vision Intelligence Center Meituan Beijing China

ISBN: (纸本)9783030687922

Facial landmark localization has been applied to numerous face related applications, such as face recognition and face image synthesis. It is a very crucial step for achieving high performance in these applications. We host the 2nd 106-point lightweight facial landmark localization grand challenge in conjunction with ICPR 2020. The purpose is to make effort towards benchmarking lightweight facial landmark localization, which enables efficient system deployment. Compared with the 1st grand challenge (https://***/ ), the JD-landmark-v2 dataset contains more than 24,000 images with larger variations in identity, pose, expression and occlusion. Besides, strict limits of model size (≤ 20M) and computational complexity (≤ 1G Flops) are employed for computational efficiency. The challenge has attracted attention from academia and industrial practitioners. More than 70 teams participate in the competition, and nine of them involve in the final evaluation. We give a detailed introduction of the competition and the solution by the winners in this paper. © 2021, Springer Nature Switzerland AG.

关键词： Face recognition

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Localised-NeRF: Specular Highlights and Colour Gradient Localising in NeRF

Localised-NeRF: Specular Highlights and Colour Gradient Loca...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dharmendra Selvaratnam Dena Bazazian Faculty of Science and Engineering School of Engineering Computing and Mathematics (SECaM) University of Plymouth

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Radiance Field (NeRF) based systems predominantly operate within the RGB (Red, Green, and Blue) space; however, the distinctive capability of the HSV (Hue, Saturation, and Value) space to discern between specular and diffuse regions is seldom utilised in the literature. We introduce Localised-NeRF, which projects the queried pixel point onto multiple training images to obtain a multi-view feature representation on HSV space and gradient space to obtain important features that can be used to synthesise novel view colour. This integration is pivotal in identifying specular highlights within scenes, thereby enriching the model’s understanding of specular changes as the viewing angle alters. Our proposed Localised-NeRF model uses an attention-driven approach that not only maintains local view direction consistency but also leverages image-based features namely the HSV colour space and colour gradients. These features serve as effective indirect priors for both the training and testing phases to predict the diffuse and specular colour. Our model exhibits competitive performance with prior NeRF-based models, as demonstrated on the Shiny Blender and Synthetic datasets. The code of Localised-NeRF is publicly available 1 .

关键词： Training computer vision Visualization Image color analysis Neural radiance field Rendering (computer graphics) Mathematical models

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Modeling Correspondences for Multi-Camera Tracking Using Nonlinear Manifold Learning and Target Dynamics

Modeling Correspondences for Multi-Camera Tracking Using Non...

引用

conference on computer vision and pattern recognition (CVPR)

作者： V.I. Morariu O.I. Camps Computer Vision Laboratory University of Maryland College Park MD USA Department of Electrical Engineering Pennsylvania State University University Park PA USA

Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D trajectories and fully take advantage of the additional information available from the multiple sensors. Previous approaches to the "correspondence across views" problem include matching features, using camera calibration information, and computing homographies between views under the assumption that the world is planar. However, it can be difficult to match features across significantly different views. Furthermore, calibration information is not always available and planar world hypothesis can be too restrictive. In this paper, a new approach is presented for matching correspondences based on the use of nonlinear manifold learning and system dynamics identification. The proposed approach does not require similar views, calibration nor geometric assumptions of the 3D environment, and is robust to noise and occlusion. Experimental results demonstrate the use of this approach to generate and predict views in cases where identity labels become ambiguous.

关键词： Target tracking Cameras Calibration Nonlinear dynamical systems Manifolds computer vision Trajectory Sensor systems Noise robustness Laboratories

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Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features

Crop Lodging Prediction from UAV-Acquired Images of Wheat an...

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IEEE/CVF conference on computer vision and pattern recognition workshops

作者： Sara Mardanisamani Farhad Maleki Sara Hosseinzadeh Kassani Sajith Rajapaksa Hema Duddu Menglu Wang Steve Shirtliffe Seungbum Ryu Anique Josuttes Ti Zhang Sally Vail Curtis Pozniak Isobel Parkin Ian Stavness Mark Eramian Department of Computer Science University of Saskatchewan Department of Plant Sciences University of Saskatchewan Agriculture and Agri-food Canada Saskatoon Research and Development Center

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes harvesting difficult. Plant breeders routinely evaluate several thousand breeding lines, and therefore, automatic lodging detection and prediction is of great value aid in selection. In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials. Also, using transfer learning, we trained 10 lodging detection models using well-established deep convolutional neural network architectures. Our proposed model outperforms the state-of-the-art lodging detection methods in the literature that use only handcrafted features. In comparison to 10 DCNN lodging detection models, our proposed model achieves comparable results while having a substantially lower number of parameters. This makes the proposed model suitable for applications such as real-time classification using inexpensive hardware for high-throughput phenotyping pipelines. The GitHub repository at . com/FarhadMaleki/LodgedNet contains code and models.

关键词： Domestic architecture lodging oilseed rape Wheat Transfer (Psychology) plant growth inspection methods Cereal Grain plant breeders

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Semantic Depth Map Fusion for Moving Vehicle Detection in Aerial Video

Semantic Depth Map Fusion for Moving Vehicle Detection in Ae...

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IEEE conference on computer vision and pattern recognition workshops

作者： Mahdieh Poostchi Hadi Aliakbarpour Raphael Viguier Filiz Bunyak Kannappan Palaniappan Guna Seetharaman Computer Science Department University of Missouri-Columbia Columbia MO USA US Naval Research Laboratory Washington D.C. USA

Wide area motion imagery from an aerial platform offers a compelling advantage in providing a global picture of traffic flows for transportation and urban planning that is complementary to the information from a network of ground-based sensors and instrumented vehicles. We propose an automatic moving vehicle detection system for wide area aerial video based on semantic fusion of motion information with projected building footprint information to significantly reduce the false alarm rate in urban scenes with many tall structures. Motion detections are obtained using the flux tensor and combined with a scene level depth mask to identify tall structures using height information derived from a dense 3D point cloud estimated using multi-view stereo from the same source imagery or a prior model. The trace of the flux tensor provides robust spatio-temporal information of moving edges including the motion of tall structures caused by parallax effects. The parallax induced motions are filtered out by incorporating building depth maps obtained from dense urban 3D point clouds. Using a level-set based geodesic active contours framework, the coarse thresholded tall structures depth masks evolved and stopped at the actual building boundaries. Experiments are carried out on a cropped 2k × 2k region of interest for 200 frames from Albuquerque urban aerial imagery. An average precision of 83% and recall of 76% have been reported using an object-level detection performance evaluation method.

关键词： Three-dimensional displays Tensile stress Vehicles Buildings Cameras Semantics Vehicle detection

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ALINA: Advanced Line Identification and Notation Algorithm

ALINA: Advanced Line Identification and Notation Algorithm

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Mohammed Abdul Hafeez Khan Parth Ganeriwala Siddhartha Bhattacharyya Natasha Neogi Raja Muthalagu Florida Institute of Technology Melbourne Florida NASA Langley Research Center Hampton Virginia BITS Pilani Dubai Campus Dubai UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labeling methods, such as crowd-sourcing, are prohibitive due to cost, data privacy, amount of time, and potential errors on large datasets. To address these issues, we propose a novel annotation framework, Advanced Line Identification and Notation Algorithm (ALINA), which can be used for labeling taxiway datasets that consist of different camera perspectives and variable weather attributes (sunny and cloudy). Additionally, the CIRCular threshoLd pixEl Discovery And Traversal (CIRCLEDAT) algorithm has been proposed, which is an integral step in determining the pixels corresponding to taxiway line markings. Once the pixels are identified, ALINA generates corresponding pixel coordinate annotations on the frame. Using this approach, 60,249 frames from the taxiway dataset, AssistTaxi have been labeled. To evaluate the performance, a context-based edge map (CBEM) set was generated manually based on edge features and connectivity. The detection rate after testing the annotated labels with the CBEM set was recorded as 98.45%, attesting its dependability and effectiveness.

关键词： Visualization Costs Annotations Image edge detection Roads Manuals pattern recognition

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Single View Reconstruction of Curved Surfaces

Single View Reconstruction of Curved Surfaces

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conference on computer vision and pattern recognition (CVPR)

作者： M. Prasad A. Fitzgibbon University of Oxford U.K.

Recent advances in single-view reconstruction (SVR) have been in modelling power (curved 2.5D surfaces) and automation (automatic photo pop-up). We extend SVR along both of these directions. We increase modelling power in several ways: (i) We represent general 3D surfaces, rather than 2.5D Monge patches; (ii) We describe a closed-form method to reconstruct a smooth surface from its image apparent contour, including multilocal singularities ("kidney-bean" self-occlusions); (iii) We show how to incorporate user-specified data such as surface normals, interpolation and approximation constraints; (iv) We show how this algorithm can be adapted to deal with surfaces of arbitrary genus. We also show how the modelling process can be automated for simple object shapes and views, using a-priori object class information. We demonstrate these advances on natural images drawn from a number of object classes.

关键词： Surface reconstruction Image reconstruction Layout Automation Shape Image segmentation computer vision Surface fitting Painting Topology

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VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer

VT-Former: An Exploratory Study on Vehicle Trajectory Predic...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Armin Danesh Pazho Ghazal Alinezhad Noghre Vinit Katariya Hamed Tabkhi University of North Carolina Charlotte

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Enhancing roadway safety has become an essential computer vision focus area for Intelligent Transportation Systems (ITS). As a part of ITS, Vehicle Trajectory Prediction (VTP) aims to forecast a vehicle’s future positions based on its past and current movements. VTP is a pivotal element for road safety, aiding in applications such as traffic management, accident prevention, work-zone safety, and energy optimization. While most works in this field focus on autonomous driving, with the growing number of surveillance cameras, another sub-field emerges for surveillance VTP with its own set of challenges. In this paper, we introduce VT-Former, a novel transformer-based VTP approach for highway safety and surveillance. In addition to utilizing transformers to capture long-range temporal patterns, a new Graph Attentive Tokenization (GAT) module has been proposed to capture intricate social interactions among vehicles. This study seeks to explore both the advantages and the limitations inherent in combining transformer architecture with graphs for VTP. Our investigation, conducted across three benchmark datasets from diverse surveillance viewpoints, showcases the State-of-the-Art (SotA) or comparable performance of VT-Former in predicting vehicle trajectories. This study underscores the potential of VT-Former and its architecture, opening new avenues for future research and exploration.

关键词： Road transportation computer vision Surveillance Computational modeling computer architecture Benchmark testing Transformers

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AnnoSearch: Image Auto-Annotation by Search

AnnoSearch: Image Auto-Annotation by Search

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conference on computer vision and pattern recognition (CVPR)

作者： Xin-Jing Wang Lei Zhang Feng Jing Wei-Ying Ma Microsoft Research Asia Beijing China

Although it has been studied for several years by computer vision and machine learning communities, image annotation is still far from practical. In this paper, we present AnnoSearch, a novel way to annotate images using search and data mining technologies. Leveraging the Web-scale images, we solve this problem in two-steps: 1) searching for semantically and visually similar images on the Web, 2) and mining annotations from them. Firstly, at least one accurate keyword is required to enable text-based search for a set of semantically similar images. Then content-based search is performed on this set to retrieve visually similar images. At last, annotations are mined from the descriptions (titles, URLs and surrounding texts) of these images. It worth highlighting that to ensure the efficiency, high dimensional visual features are mapped to hash codes which significantly speed up the content-based search process. Our proposed approach enables annotating with unlimited vocabulary, which is impossible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.

关键词： Image databases computer vision Asia Machine learning Image retrieval Data mining Content based retrieval Uniform resource locators Vocabulary Digital images

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