Search Results for author:
Found 23 papers, 5 papers with code
SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data
In conclusion, this study shows the feasibility of a sensor-agnostic model for diverse lidar data, surpassing sensor-specific approaches and setting new standards in tree segmentation, particularly in complex forests.
Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction
Creating accurate and fast models that predict such intentions from sequential images is challenging.
Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services.
FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees
In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.
Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification
The research sheds light on the significance of CNN-based IQA in computer-aided cytology diagnosis for animals, enhancing the accuracy of disease classification.
Using simulation to calibrate real data acquisition in veterinary medicine
This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs.
Assessing Dataset Quality Through Decision Tree Characteristics in Autoencoder-Processed Spaces
In this paper, we delve into the critical aspect of dataset quality assessment in machine learning classification tasks.
Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology
The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures.
Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis
This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine.
Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest
We trained a model based on Pointnet++ architecture which achieves 0. 92 F1-score in semantic segmentation.
CARLA-BSP: a simulated dataset with pedestrians
We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0. 9. 13).
Modern Cybersecurity Solution using Supervised Machine Learning
Cybersecurity is essential, and attacks are rapidly growing and getting more challenging to detect.
Retrain or not retrain? -- efficient pruning methods of deep CNN networks
Convolutional neural networks (CNN) play a major role in image processing tasks like image classification, object detection, semantic segmentation.
Falls Prediction in eldery people using Gated Recurrent Units
Falls prevention, especially in older people, becomes an increasingly important topic in the times of aging societies.
Convolutional neural network compression for natural language processing
The method responsible for mapping compressed network to FPGA and results of this implementation are presented.
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection.
Improving text classification with vectors of reduced precision
This paper presents the analysis of the impact of a floating-point number precision reduction on the quality of text classification.
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets.
The observer-assisted method for adjusting hyper-parameters in deep learning algorithms
An external agent-observer monitors a performance of a selected Deep Learning algorithm.
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance.
A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework
This article presents a development of web application for quench prediction in \gls{te-mpe-ee} at CERN.
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity
The authors conducted a series of experiments for various macro parameters of HTM SP, as well as for different levels of video reduction ratios.
OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory
The classification accuracy of the system was examined through a series of experiments and the performance was given in terms of F1 score as a function of the number of columns, synapses, $min\_overlap$ and $winners\_set\_size$.
