27 papers · ranked by Valyu relevance
Murat ISIK, Bilal Alatas
Feature detection and matching are fundamental components in computer vision, underpinning a broad spectrum of applications. This study offers a comprehensive evaluation of traditional feature detections and descriptors, analyzing methods such as Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features…
Fintan O'Sullivan, Kirita-Rose Escott, Rachael C. Shaw, Andrew Lensen
This report investigates an unsupervised, feature-based image matching pipeline for the novel application of identifying individual kak¯ a. Applied with a similar- ¯ ity network for clustering, this addresses a weakness of current supervised approaches to identifying individual birds which struggle to handle the…
Alysson Ribeiro da Silva, Camila Guedes Silveira
—The accuracy of a classifier, when performing Pattern recognition, is mostly tied to the quality and representativeness of the input feature vector. Feature Selection is a process that allows for representing information properly and may increase the accuracy of a classifier. This process is responsible for finding…
Thales W. Cabral, Fernando B. Neto, Eduardo R. de Lima, Gustavo Fraidenraich + 2 more
Efficient energy management in residential environments is a constant challenge, in which Home Energy Management Systems (HEMS) play an essential role in optimizing consumption. Load recognition allows the identification of active appliances, providing robustness to the HEMS. The precise identification of household…
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…
Izabella Antoniuk, Jarosław Kurek, Artur Krupa, Grzegorz Wieczorek + 4 more
'Michał Bukowski' 'Michał Kruk' 'Albina Jegorowa' 'Jiayi Ma'] In this paper, a novel approach to evaluation of feature extraction methodologies is presented. In the case of machine learning algorithms, extracting and using the most efficient features is one of the key problems that can significantly influence overall…
Ming Xie, Tingfeng Lai, Yuhui Fang, Junzhi Yu
Visual signals are the upmost important source for robots, vehicles or machines to achieve human-like intelligence. Human beings heavily depend on binocular vision to understand the dynamically changing world. Similarly, intelligent robots or machines must also have the innate capabilities of perceiving knowledge from…
Oleg V. Favorov, Olcay Kursun
Neurons throughout the neocortex exhibit selective sensitivity to particular features of sensory input patterns. According to the prevailing views, cortical strategy is to choose features that exhibit predictable relationship to their spatial and/or temporal context. Such contextually predictable features likely make…
Nadia Amrouni, Amir Benzaoui, Rafik Bouaouina, Yacine Khaldi + 3 more
'Insaf Adjabi' 'Ouahiba Bouglimina' 'Zhe-Ming Lu'] In recent years, palmprint recognition has gained increased interest and has been a focus of significant research as a trustworthy personal identification method. The performance of any palmprint recognition system mainly depends on the effectiveness of the utilized…
Yiwei Zhang, Jiawei Han, Tengjun Liu, Zelan Yang + 2 more
Spike sorting is a fundamental step in extracting single-unit activity from neural ensemble recordings, which play an important role in basic neuroscience and neurotechnologies. A few algorithms have been applied in spike sorting. However, when noise level or waveform similarity becomes relatively high, their…
Zhigang Ren, Guoquan Ren, Dinhai Wu, Juvenal Rodriguez-Resendiz
Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature…
Zhengyu Zhao, Yuanyuan Lu, Yijie Tong, Xin Chen + 1 more
Discriminative traits are important in biodiversity and macroevolution, but extracting and representing these features from huge natural history collections using traditional methods can be challenging and time-consuming. To fully utilize the collections and their associated metadata, it is urgent now to increase the…
Tümay Capraz, Wolfgang Huber
A fundamental step in many analyses of high-dimensional data is dimension reduction. Two basic approaches are introduction of new, synthetic coordinates, and selection of extant features. Advantages of the latter include interpretability, simplicity, transferability and modularity. A common criterion for unsupervised…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Jeonghwan Park, Kang Li, Huiyu Zhou
We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid feature selection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimal feature vector that well represents the shapes of the subjects in the images. In detail, the…
Suvo Banik, Karthik Balasubramanian, Sukriti Manna, Sybil Derrible + 1 more
Identifying key descriptors and understanding important features across different classes of materials are crucial for machine learning (ML) tools to both predict material properties and reveal the physics underlying any process of interest. Traditionally, the predictive modeling of elastic properties of materials is…
Seyed Muhammad Hossein Mousavi, Atiye Ilanloo
Feature selection could be defined as an optimization problem and solved by bio-inspired algorithms. Bees Algorithm (BA) shows decent performance in feature selection optimization task. On the other hand, Local Phase Quantization (LPQ) is a frequency domain feature which has excellent performance on Depth images. Here…
Mohamed Alimoussa, Alice Porebski, Nicolas Vandenbroucke, Sanaa El Fkihi + 3 more
'Sanaa El Fkihi' 'Rachid Oulad Haj Thami' 'Raimondo Schettini' 'Edoardo Provenzi'] Color texture classification aims to recognize patterns by the analysis of their colors and their textures. This process requires using descriptors to represent and discriminate the different texture classes. In most traditional…
Jan Kevin Schluesener, Malav Shah, Florian Sandhaeger, Markus Siegel
Neural activity is selective for the identity and semantic category of natural visual stimuli. In human neuroimaging, this selectivity can be investigated using multivariate pattern analysis. The resulting neural information is thought to reflect neural processes underlying object recognition. However, due to the…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
Asish Bera, Debotosh Bhattacharjee, Mita Nasipuri
Biometrics is indispensable in this modern digital era for secure automated human authentication in various fields of machine learning and pattern recognition. Hand geometry is a promising physiological biometric trait with ample deployed application areas for identity verification. Due to the intricate anatomic…
Alexander S. Shved, Blake E. Ocampo, Elena S. Burlova, Casey L. Olen + 2 more
The construction, management and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, "molli", which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries.…
Rahi Jain, Wei Xu
Feature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper approaches primarily focus on shortening the path to find an…
Hasan M. Sayeed, Sterling G. Baird, Taylor D. Sparks
Capturing structure-property relationships of materials for property prediction using machine learning requires the representation or featurization of the structural aspects of materials at different levels, including atomic, crystal, and microscales. While crystal structure-based modeling techniques are effective for…
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
Authors not listed
Caenorhabditis elegans (C. elegans) is a well-established nematode model for studying metabolism and neurodegenerative disorders, such as Alzheimer’s (AD) and Parkinson’s disease (PD). Non-targeted metabolomics via liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has proven useful for uncovering…
Davide Ciccarelli, Tim Marczylo, Paolo Vineis, Leon Barron
A novel and generalisable analytical workflow for non-target analysis (NTA) and suspect screening (SS) of organic acids in drinking water is presented, featuring selective extraction by silica-based strong anion exchange solid-phase extraction (SAX-SPE), mixed-mode liquid chromatography-high resolution mass…