24 papers · ranked by Valyu relevance
Tomochika Fujisawa, Víctor Noguerales, Emmanouil Meramveliotakis, Anna Papadopoulou + 1 more
Complex bulk samples of invertebrates from biodiversity surveys present a great challenge for taxonomic identification, especially if obtained from unexplored ecosystems. High-throughput imaging combined with machine learning for rapid classification could overcome this bottleneck. Developing such procedures requires…
Adam Stančić, Vedran Vyroubal, Vedran Slijepčević, Pier Luigi Mazzeo
This paper presents the evaluation of 36 convolutional neural network (CNN) models, which were trained on the same dataset (ImageNet). The aim of this research was to evaluate the performance of pre-trained models on the binary classification of images in a “real-world” application. The classification of wildlife…
Bekhzod Olimov, Barathi Subramanian, Rakhmonov Akhrorjon Akhmadjon Ugli, Jea-Soo Kim + 1 more
'Rakhmonov Akhrorjon Akhmadjon Ugli' 'Jea-Soo Kim' 'Jeonghong Kim'] Extracting useful features at multiple scales is a crucial task in computer vision. The emergence of deep-learning techniques and the advancements in convolutional neural networks (CNNs) have facilitated effective multiscale feature extraction that…
Aashish Dhawan, Mudgal, Divyanshu
— the major challenge in today's computer vision scenario is the availability of good quality labeled data. In a field of study like image classification, where data is of utmost importance, we need to find more reliable methods which can overcome the scarcity of data to produce results comparable to previous benchmark…
Michael J. Falato, Bradley T. Wolfe, Tali Natan, Xinhua Zhang + 4 more
'Ryan S. Marshall' 'Yi Zhou' 'Paul M. Bellan' '\u202aZhehui Wang'] Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei, and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma…
Maminiaina Alphonse Rafidison, Hajasoa Malalatiana Ramafiarisona, Paul Auguste Randriamitantsoa, Sabine Harisoa Jacques Rafanantenana + 3 more
'Paul Auguste Randriamitantsoa' 'Sabine Harisoa Jacques Rafanantenana' 'Faniriharisoa Maxime Rajaonarison Toky' 'Lovasoa Patrick Rakotondrazaka' 'Andry Harivony Rakotomihamina'] Recently, most image classification studies solicit the intervention of convolutional neural networks because these DL-based classification…
Kohulan Rajan, Henning Otto Brinkhaus, M. Isabel Agea, Achim Zielesny + 1 more
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that…
Hanna Böhner, Eivind Flittie Kleiven, Rolf Anker Ims, Eeva M. Soininen
Camera traps have become popular for monitoring biodiversity and animal populations. Artificial intelligence is increasingly used to automatically classify large image data sets produced by camera traps and many tools that incorporate machine-learning models for automatic image classification have been developed over…
Chloe A. Game, Nils Piechaud, Kerry L. Howell
Deep learning (DL) is a powerful tool to extract ecological information from large image datasets efficiently and consistently. However, applying these methods remains challenging, due in part to the complexity of DL workflows and the dynamic nature of available tools. To address this, we created a practical guide and…
Ying Bi, Bing Xue, Pablo Mesejo, Stefano Cagnoni + 1 more
—Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations…
Francisco Garibaldi-Márquez, Gerardo Flores, Diego A. Mercado-Ravell, Alfonso Ramírez-Pedraza + 2 more
Crop and weed discrimination in natural field environments is still challenging for implementing automatic agricultural practices, such as weed control. Some weed control methods have been proposed. However, these methods are still restricted as they are implemented under controlled conditions. The development of a…
Mauro Tropea, Giuseppe Fedele, Raffaella De Luca, Domenico Miriello + 2 more
'Floriano De Rango' 'Hsiao-Chun Wu'] This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Identificazione di Lapidei Per Immagini”)…
Nur Shazwani Kamarudin, Mokhairi Makhtar, Syadiah Nor Wan Shamsuddin, Syed Abdullah Fadzli
Classifiers Authors: ['Nur Shazwani Kamarudin' 'Mokhairi Makhtar' 'Syadiah Nor Wan Shamsuddin' 'Syed Abdullah Fadzli'] Abstract— Nowadays, more and more images are available. Annotation and retrieval of the images pose classification problems, where each class is defined as the group of database images labelled with a…
Yingzhou Lu, Kosaku Sato, Jialu Wang
With the rise of internet technology amidst increasing urbanization rates, sharing information has never been easier, thanks to globally-adopted platforms for digital communication. The resulting output of massive amounts of usergenerated data can be used to enhance our understanding of significant societal issues…
Asal Rouhafzay, Nadia Baaziz, Mohand Saïd Allili
features Authors: ['Asal Rouhafzay' 'Nadia Baaziz' 'Mohand Saïd Allili'] Abstract – In this paper, we propose a new framework for improving Content Based Image Retrieval (CBIR) for texture images. This is achieved by using a new image representation based on the RCT-Plus transform which is a novel variant of the…
Yi-Hong Lin, Chih-Ning Tsai, Po-Feng Chen, Yen-Tzu Lin + 5 more
Scanning electrochemical microscopy (SECM) is one of the scanning probe techniques that has attracted considerable attention because of its ability to interrogate surface morphology or electrochemical reactivity. However, the quality of SECM images generally depends on the sizes of the electrodes and many…
Somaieh Amraee, Maryam Chinipardaz, Mohammadali Charoosaei
This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws, nuts, keys, and coins: the histogram of oriented gradients (HOG) and local binary pattern (LBP). The desired features for the labeled images are first extracted and saved in the form of a feature…
Wen Han Chia, Ilia Jahanshahi, Le Yang Loh, Anqi Zheng + 4 more
Community science platforms like iNaturalist generate unprecedented volumes of biodiversity data, but their scientific utility depends critically on accurate species identification—a persistent challenge when contributors often lack taxonomic expertise. We developed “LizardLens”, a two-stage machine learning pipeline…
Rui Sun, Zhengyin Zhang, Yajun Liu, Xiaohang Niu + 1 more
Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information…
Authors not listed
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…
Sheekar Banerjee, Humayun Kabir
In the world of civilized medical scientific progression, cancer has become a very serious threat for the natural survival of human beings where breast cancer stays to be the second most dangerous type. Mostly women are embracing very pathetic death because of the delayed detection of the cancer cell in the certain…
Lutz Weber, Aleksei Krasnov, Shadrack Barnabas, Timo Böhme + 1 more
The extraction of chemical information from images, also known as Optical Chemical Structure Recognition (OCSR) has recently gained new attention. This new interest is ignited by various machine learning methods introduced over the last years and the new possibilities to train image models for specific tasks such as…
Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen + 3 more
In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly…
Hongyang Dong, Simon D.M. Jacques, Winfried Kockelmann, Stephen W. T. Price + 10 more
Hongyang Dong 3 , Simon D.M. Jacques 1 , Winfried Kockelmann 4 , Stephen W. T. Price 1 , Robert Emberson 5 , Dorota Matras 6,7 , Yaroslav Odarchenko 1 , Vesna Middelkoop 10 , Athanasios Giokaris 1 , Olof Gutowski 8 , Ann-Christin Dippel 8 , Martin v. Zimmermann 8 , Andrew M. Beale 3 , Keith T. Butler 9 , Antonis…