Feature extraction
Related papers: 20
About
Feature extraction is the process of automatically identifying and isolating meaningful patterns, structures, or measurements from raw data — such as images, point clouds, audio signals, or sensor readings — and transforming them into compact, informative representations that machine learning or computer vision algorithms can effectively process. In robotics and AI, feature extraction underpins nearly every perception task: convolutional neural networks extract hierarchical visual features for object detection and semantic segmentation, classical algorithms like SIFT and ORB identify keypoints for localization and mapping, and signal-processing methods derive relevant attributes from IMU or audio data for gesture and emotion recognition. These extracted features enable robots to recognize objects, navigate environments, estimate poses, and interpret sensor data without processing raw inputs in their entirety. Feature extraction matters because the quality and relevance of extracted representations directly determine downstream task performance — well-chosen features make models more accurate, data-efficient, and generalizable, while poor representations lead to brittle systems that fail in real-world conditions.
Top Researchers
Top Institutes
Top Cited Papers
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou, Oncel Tuzel
Citations: 4542 • 2018
Convolutional networks and applications in vision
Yann LeCun, Koray Kavukcuoglu, Clément Farabet
Citations: 2163 • 2010
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, Ali Farhadi
Citations: 1507 • 2017
Visual Place Recognition: A Survey
Stephanie Lowry, Niko Sünderhauf, Paul Newman, John J. Leonard, David Cox, Peter Corke, Michael Milford
Citations: 1071 • 2015
Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks
Stephen Se, David Lowe, Jim Little
Citations: 779 • 2002
Max-pooling convolutional neural networks for vision-based hand gesture recognition
Jawad Nagi, Frederick Ducatelle, Gianni A. Di, Dan Cireşan, Ueli Meier, Alessandro Giusti, Farrukh Nagi, Jürgen Schmidhuber, Luca Maria Gambardella
Citations: 648 • 2011
Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN
Yu Yang, Kailiang Zhang, Yang Li, Dongxing Zhang
Citations: 626 • 2019
Model-based recognition in robot vision
R.T. Chin, Charles R. Dyer
Citations: 565 • 1986
YOLO v3-Tiny: Object Detection and Recognition using one stage improved model
Pranav Adarsh, Pratibha Rathi, Manoj Kumar
Citations: 532 • 2020
Disease detection on the leaves of the tomato plants by using deep learning
Halil Durmuş, Ece Olcay Güneş, Mürvet Kırcı
Citations: 526 • 2017
HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
Nicholas Ayache, Olivier D. Faugeras
Citations: 492 • 1986
The MOPED framework: Object recognition and pose estimation for manipulation
Alvaro Collet, Manuel Martínez, Siddhartha S Srinivasa
Citations: 443 • 2011
Multiscale Feature Extraction and Fusion of Image and Text in VQA
Siyu Lu, Yueming Ding, Mingzhe Liu, Zhengtong Yin, Lirong Yin, Wenfeng Zheng
Citations: 414 • 2023
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments
Muhammad Hammad Saleem, Johan Potgieter, Khalid Mahmood Arif
Citations: 404 • 2021
Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM
Mustaqeem Mustaqeem, Muhammad Sajjad, Soonil Kwon
Citations: 396 • 2020
UAV-based crop and weed classification for smart farming
Philipp Lottes, Raghav Khanna, Johannes Pfeifer, Roland Siegwart, Cyrill Stachniss
Citations: 384 • 2017
Design and application of industrial machine vision systems
H. Golnabi, A. Asadpour
Citations: 376 • 2007
RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features
Max Schwarz, Hannes Schulz, Sven Behnke
Citations: 321 • 2015
GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation
Wujie Zhou, Jinfu Liu, Jingsheng Lei, Lu Yu, Jenq–Neng Hwang
Citations: 318 • 2021
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou, Oncel Tuzel
Citations: 317 • 2017