Search results for "hahmontunnistus"

Neural specialization to human faces at the age of 7 months.

2021

AbstractSensitivity to human faces has been suggested to be an early emerging capacity that promotes social interaction. However, the developmental processes that lead to cortical specialization to faces has remained unclear. The current study investigated both cortical sensitivity and categorical specificity through event-related potentials (ERPs) previously implicated in face processing in 7-month-old infants (N290) and adults (N170). Using a category-specific repetition/adaptation paradigm, cortical specificity to human faces, or control stimuli (cat faces), was operationalized as changes in ERP amplitude between conditions where a face probe was alternated with categorically similar or …

BRIMA : Low-Overhead Browser-Only Image Annotation Tool

2021

Image annotation and large annotated datasets are crucial parts within the Computer Vision and Artificial Intelligence fields. At the same time, it is well-known and acknowledged by the research community that the image annotation process is challenging, time-consuming and hard to scale. Therefore, the researchers and practitioners are always seeking ways to perform the annotations easier, faster, and at higher quality. Even though several widely used tools exist and the tools’ landscape evolved considerably, most of the tools still require intricate technical setups and high levels of technical savviness from its operators and crowdsource contributors.In order to address such challenges, w…

Automatic image-based identification and biomass estimation of invertebrates

2020

1. Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and expert-based identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map and monitor invertebrate diversity altogether. Given recent advances in computer vision, we propose to enhance the standard human expert-based identification approach involving manual sorting and identification with an automatic image-based technology. 2. We describe a robot-enabled image-based ident…

Human experts vs. machines in taxa recognition

2020

The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets with the transfer learning paradigm and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hier…

Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems.

2022

ObjectiveA majority of BCI systems, enabling communication with patients with locked-in syndrome, are based on electroencephalogram (EEG) frequency analysis (e.g., linked to motor imagery) or P300 detection. Only recently, the use of event-related brain potentials (ERPs) has received much attention, especially for face or music recognition, but neuro-engineering research into this new approach has not been carried out yet. The aim of this study was to provide a variety of reliable ERP markers of visual and auditory perception for the development of new and more complex mind-reading systems for reconstructing the mental content from brain activity.MethodsA total of 30 participants were shown…

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm

2022

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitabl…

Zero-shot Semantic Segmentation using Relation Network

2021

Zero-shot learning (ZSL) is widely studied in recent years to solve the problem of lacking annotations. Currently, most studies on ZSL are for image classification and object detection. But, zero-shot semantic segmentation, pixel level classification, is still at its early stage. Therefore, this work proposes to extend a zero-shot image classification model, Relation Network (RN), to semantic segmentation tasks. We modified the structure of RN based on other state-of-the-arts semantic segmentation models (i.e. U-Net and DeepLab) and utilizes word embeddings from Caltech-UCSD Birds 200-2011 attributes and natural language processing models (i.e. word2vec and fastText). Because meta-learning …

Hyperspektrisen kaukokartoitusdatan analysointialgoritmeja

2003

Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network

2018

In this study we apply 3D convolutional neural network (CNN) for tree species identification. Study includes the three most common Finnish tree species. Study uses a relatively large high-resolution spectral data set, which contains also a digital surface model for the trees. Data has been gathered using an unmanned aerial vehicle, a framing hyperspectral imager and a regular RGB camera. Achieved classification results are promising by with overall accuracy of 96.2 % for the classification of the validation data set. nonPeerReviewed