This work focuses on cost reduction methods for forest species recognition systems. Current state-of-the-art shows that the accuracy of these systems have increased considerably in the past years, but the cost in time to perform the recognition of input samples has also increased proportionally. For this reason, in this work we focus on investigating methods for cost reduction locally (at either feature extraction or classification level individually) and globally (at both levels combined), and evaluate two main aspects: 1) the impact in cost reduction, given the proposed measures for it; and 2) the impact in recognition accuracy. The experimental evaluation conducted on two forest species datasets demonstrated that, with global cost reduction, the cost of the system can be reduced to less than 1/20 and recognition rates that are better than those of the original system can be achieved.
University of Padova（帕多瓦大学）；Fondazione Edmund Mach（埃德蒙·马赫基金会）；Provincia Autonoma di Trento（特伦托自治省）
This work intends to lay the foundations for identifying the prevailing forest types and the delineation of forest units within private forest inventories in the Autonomous Province of Trento (PAT), using currently available remote sensing solutions. In particular, data from LiDAR and hyperspectral surveys of 2014 made available by PAT were acquired and processed. Such studies are very important in the context of forest management scenarios. The method includes defining tree species ground-truth by outlining single tree crowns with polygons and labeling them. Successively two supervised machin…
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；IBM Research（IBM研究院）；McMaster University（麦克马斯特大学）；National Institute for Space Research (INPE)（巴西国家空间研究所）；Santa Catarina State University (UDESC)（圣卡塔琳娜州立大学）
This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoder…
Automatic Number Plate Recognition System (ANPRS) is a mass surveillance embedded system that recognizes the number plate of the vehicle. This system is generally used for traffic management applications. It should be very efficient in detecting the number plate in noisy as well as in low illumination and also within required time frame. This paper proposes a number plate recognition method by processing vehicle's rear or front image. After image is captured, processing is divided into four steps which are Pre-Processing, Number plate localization, Character segmentation and Character recognit…
Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE)（互动媒体、智能系统与新兴技术研究中心）；University of Twente（特温特大学）；Leibniz University Hannover（莱布尼茨汉诺威大学）
Natural forests are complex ecosystems whose tree species distribution and their ecosystem functions are still not well understood. Sustainable management of these forests is of high importance because of their significant role in climate regulation, biodiversity, soil erosion and disaster prevention among many other ecosystem services they provide. In Japan particularly, natural forests are mainly located in steep mountains, hence the use of aerial imagery in combination with computer vision are important modern tools that can be applied to forest research. Thus, this study constitutes a prel…
University of Cambridge（剑桥大学）；Royal Society for Protection of Birds（英国皇家鸟类保护协会）；Jambi University（占碑大学）；PT Restorasi Ekosistem Indonesia（印度尼西亚生态系统恢复公司）
Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crow…
Recently, there has been a growing interest in monitoring brain activity for individual recognition system. So far these works are mainly focussing on single channel data or fragment data collected by some advanced brain monitoring modalities. In this study we propose new individual recognition schemes based on spatio-temporal resting state Electroencephalography (EEG) data. Besides, instead of using features derived from artificially-designed procedures, modified deep learning architectures which aim to automatically extract an individual's unique features are developed to conduct classificat…
Traditionally, only experts who are equipped with professional knowledge and rich experience are able to recognize different species of wood. Applying image processing techniques for wood species recognition can not only reduce the expense to train qualified identifiers, but also increase the recognition accuracy. In this paper, a wood species recognition technique base on Scale Invariant Feature Transformation (SIFT) keypoint histogram is proposed. We use first the SIFT algorithm to extract keypoints from wood cross section images, and then k-means and k-means++ algorithms are used for cluste…
Label-Efficient 3D Forest Mapping: Self-Supervised and Transfer Learning for Instance Segmentation, Semantic Segmentation, and Species Classification
Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF)（德累斯顿-罗斯托克亥姆霍兹中心（HZDR）、弗里贝格资源技术亥姆霍兹研究所（HIF））；Remote Sensing and Geoinformatics, Freie Universität Berlin（柏林自由大学遥感与地理信息学系）；Institute of Geomatics, BOKU University（博科尼大学测绘学院）；Faculty of Electrical and Computer Engineering, University of Iceland（爱沙尼亚大学电气与计算机工程学院）
Sorbonne Universités（索邦大学）；UPMC Univ Paris 06（巴黎第六大学）；CNRS（法国国家科学研究中心）；ISIR UMR 7222（智能系统与机器人研究所）
Automatic facial expression classification (FER) from videos is a critical problem for the development of intelligent human-computer interaction systems. Still, it is a challenging problem that involves capturing high-dimensional spatio-temporal patterns describing the variation of one's appearance over time. Such representation undergoes great variability of the facial morphology and environmental factors as well as head pose variations. In this paper, we use Conditional Random Forests to capture low-level expression transition patterns. More specifically, heterogeneous derivative features (e…
A human action can be seen as transitions between one's body poses over time, where the transition depicts a temporal relation between two poses. Recognizing actions thus involves learning a classifier sensitive to these pose transitions as well as to static poses. In this paper, we introduce a novel method called transitions forests, an ensemble of decision trees that both learn to discriminate static poses and transitions between pairs of two independent frames. During training, node splitting is driven by alternating two criteria: the standard classification objective that maximizes the dis…
Center for Pattern Recognition and Machine Intelligence, PES University（PES大学模式识别与机器智能中心）
The paper posits a computationally-efficient algorithm for multi-class facial image classification in which images are constrained with translation, rotation, scale, color, illumination and affine distortion. The proposed method is divided into five main building blocks including Haar-Cascade for face detection, Bilateral Filter for image preprocessing to remove unwanted noise, Affine Speeded-Up Robust Features (ASURF) for keypoint detection and description, Vector of Locally Aggregated Descriptors (VLAD) for feature quantization and Cloud Forest for image classification. The proposed method a…
Visual Place Recognition in Forests with Depth-Aware Distillation
CSIRO Robotics, Brisbane, Australia（澳大利亚联邦科学与工业研究组织机器人实验室，布里斯班，澳大利亚）；University of Queensland, Brisbane, Australia（昆士兰大学，布里斯班，澳大利亚）；Queensland University of Technology, Brisbane, Australia（昆士兰科技大学，布里斯班，澳大利亚）
University of Sao Paulo（圣保罗大学）；University of Pennsylvania（宾夕法尼亚大学）；Treeswift
In this letter, we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest. We propose a framework that uses these descriptors to detect previously seen observations and landmark correspondences, even with partial overlap and noise. We run loop closure detection experiments in simulation and real-world data map-merging from different flights of an Unmanned Aerial Vehicle (UAV) in a pine tree forest and show that our method outperforms state-of-the-art approaches in accuracy and robustness.
Imperial College London（帝国理工学院）；OMRON Corporation（欧姆龙公司）
In this paper, we tackle the problem of 24 hours-monitoring patient actions in a ward such as "stretching an arm out of the bed", "falling out of the bed", where temporal movements are subtle or significant. In the concerned scenarios, the relations between scene layouts and body kinematics (skeletons) become important cues to recognize actions; however they are hard to be secured at a testing stage. To address this problem, we propose a kinematic-layout-aware random forest which takes into account the kinematic-layout (\ie layout and skeletons), to maximize the discriminative power of depth i…
Automatic snake species recognition is important because it has vast potential to help lower deaths and disabilities caused by snakebites. We introduce our solution in SnakeCLEF 2022 for fine-grained snake species recognition on a heavy long-tailed class distribution. First, a network architecture is designed to extract and fuse features from multiple modalities, i.e. photograph from visual modality and geographic locality information from language modality. Then, logit adjustment based methods are studied to relieve the impact caused by the severe class imbalance. Next, a combination of super…
This paper investigates the issue of real-world identification to fulfill better species protection. We focus on plant species identification as it is a classic and hot issue. In tradition plant species identification the samples are scanned specimen and the background is simple. However, real-world species recognition is more challenging. We first systematically investigate what is realistic species recognition and the difference from tradition plant species recognition. To deal with the challenging task, an interdisciplinary collaboration is presented based on the latest advances in computer…
Visual animal biometrics is rapidly gaining popularity as it enables a non-invasive and cost-effective approach for wildlife monitoring applications. Widespread usage of camera traps has led to large volumes of collected images, making manual processing of visual content hard to manage. In this work, we develop a framework for automatic detection and recognition of individuals in different patterned species like tigers, zebras and jaguars. Most existing systems primarily rely on manual input for localizing the animal, which does not scale well to large datasets. In order to automate the detect…
SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests
Northern Robotics Laboratory, Université Laval（北方机器人实验室，拉瓦尔大学）；Département des sciences du bois et de la forêt, Université Laval（林业与木材科学系，拉瓦尔大学）；Institut des Sciences de la Forêt tempérée, Université du Québec en Outaouais（温带森林科学研究所，魁北克outsouais大学）
How many labels do you need? A decision framework for cross-habitat marine species recognition
College of Science and Engineering, James Cook University（詹姆斯·库克大学科学与工程学院）；Centre for AI and Data Science Innovation, James Cook University（詹姆斯·库克大学人工智能与数据科学创新中心）
In this paper, we address the basic problem of recognizing moving objects in video images using SP Theory of Intelligence. The concept of SP Theory of Intelligence which is a framework of artificial intelligence, was first introduced by Gerard J Wolff, where S stands for Simplicity and P stands for Power. Using the concept of multiple alignment, we detect and recognize object of our interest in video frames with multilevel hierarchical parts and subparts, based on polythetic categories. We track the recognized objects using the species based Particle Swarm Optimization (PSO). First, we extract…
Camera traps are a method for monitoring wildlife and they collect a large number of pictures. The number of images collected of each species usually follows a long-tail distribution, i.e., a few classes have a large number of instances, while a lot of species have just a small percentage. Although in most cases these rare species are the ones of interest to ecologists, they are often neglected when using deep-learning models because these models require a large number of images for the training. In this work, a simple and effective framework called Square-Root Sampling Branch (SSB) is propose…
CUBE: Class-conditioned Unknown Boundary Evidence for Open-Set Insect Species Recognition
Guangdong University of Technology（广东工业大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong Polytechnic University（香港理工大学）；Zunyi Medical University（遵义医科大学）
Animal biometrics is an important requirement for monitoring and conservation tasks. The classical animal biometrics risk the animals' integrity, are expensive for numerous animals, and depend on expert criterion. The non-invasive biometrics techniques offer alternatives to manage the aforementioned problems. In this paper we propose an automatic segmentation and identification algorithm based on artificial vision algorithms to recognize Diploglossus millepunctatus. Diploglossus millepunctatus is an endangered lizard species. The algorithm is based on two stages: automatic segmentation to remo…
Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation
Mathematical Modeling Laboratory, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University（九州大学生物资源与生物环境科学研究生院数学建模实验室）；International Cooperation Center, Thai Nguyen University（Thai Nguyen大学国际合作中心）；Centrer of Crop Research for Adaptation to Climate Change, Thai Nguyen University of Agriculture and Forestry（Thai Nguyen农业与林业大学适应气候变化作物研究中心）
Recognition of Unseen Bird Species by Learning from Field Guides
EcoVision Lab - Photogrammetry and Remote Sensing, ETH Zurich（苏黎世联邦理工学院摄影测量与遥感生态视觉实验室）；Institute for Computational Science, University of Zurich（苏黎世大学计算科学研究所）
ORCA: Object Recognition and Comprehension for Archiving Marine Species
Hong Kong University of Science and Technology（香港科技大学）；University of Electronic Science and Technology of China（电子科学与技术大学）；Indo Ocean Foundation Project（印度洋基金会项目）
Bialystok University of Technology（比亚韦斯托克理工大学）；Mammal Research Institute, Polish Academy of Sciences（波兰科学院哺乳动物研究所）
Camera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Bialowieza Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open source application for managin…
Michigan State University（密歇根州立大学）；University of Chester（切斯特大学）
We present a new method of primate face recognition, and evaluate this method on several endangered primates, including golden monkeys, lemurs, and chimpanzees. The three datasets contain a total of 11,637 images of 280 individual primates from 14 species. Primate face recognition performance is evaluated using two existing state-of-the-art open-source systems, (i) FaceNet and (ii) SphereFace, (iii) a lemur face recognition system from literature, and (iv) our new convolutional neural network (CNN) architecture called PrimNet. Three recognition scenarios are considered: verification (1:1 compa…
Bilkent University（比尔肯特大学）；Middle East Technical University（中东技术大学）
Multisource image analysis that leverages complementary spectral, spatial, and structural information benefits fine-grained object recognition that aims to classify an object into one of many similar subcategories. However, for multisource tasks that involve relatively small objects, even the smallest registration errors can introduce high uncertainty in the classification process. We approach this problem from a weakly supervised learning perspective in which the input images correspond to larger neighborhoods around the expected object locations where an object with a given class label is pr…
School of Engineering, Griffith University（格里菲斯大学工程学院）；School of Computer Science and Technology, Wuhan University of Technology（武汉理工大学计算机科学与技术学院）；Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences（中国科学院东北地理与农业生态研究所）
Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether leaf patterns can provide sufficient information for cultivar recognition. This paper reports the first attempt on soybean cultivar recognition from plant leaves which is not only a challenging research problem but also important for soybean cultivar evaluation, selection and production in agriculture. In this paper, we propose a novel multiscale sliding chord matching (MSCM) approach to extract leaf patterns that are distinctive for soybean cultivar identificati…
In this paper, we address the basic problem of recognizing moving objects in video images using Visual Vocabulary model and Bag of Words and track our object of interest in the subsequent video frames using species inspired PSO. Initially, the shadow free images are obtained by background modelling followed by foreground modeling to extract the blobs of our object of interest. Subsequently, we train a cubic SVM with human body datasets in accordance with our domain of interest for recognition and tracking. During training, using the principle of Bag of Words we extract necessary features of ce…
Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring
McGill University（麦吉尔大学）；Mila - Quebec Artificial Intelligence Institute（魁北克人工智能研究所）；University of Copenhagen（哥本哈根大学）；Agriculture and Agri-food Canada（加拿大农业与农业食品部）；Smithsonian Tropical Research Institute（史密松尼热带研究所）；Biology Center, Czech Academy of Sciences（捷克科学院生物学中心）；Maestria de Entomologia, University of Panama（巴拿马大学昆虫学系）；Massachusetts Institute of Technology（麻省理工学院）；Montréal Insectarium（蒙特利尔昆虫馆）
