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Found 10 papers, 1 papers with code
A novel spatial-frequency domain network for zero-shot incremental learning
The proposed SFDNet has the ability to effectively extract spatial-frequency feature representation from input images, improve the accuracy of image classification, and fundamentally alleviate catastrophic forgetting.
Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detection
This paper presents a track-before-detect (TBD) algorithm based on an improved particle filter, i. e. cost-reference particle filter bank (CRPFB), which turns the problem of target detection to the problem of two-layer hypothesis testing.
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification
However, all the CAM-based methods (e. g., CAM, Grad-CAM, and Relevance-CAM) can only be used for interpreting CNN models with fully-connected (FC) layers as a classifier.
Automotive Radar Mutual Interference Mitigation Based on Hough Transform in Time-Frequency Domain
It is worthwhile to note that more and more vehicles are equipped with automotive radars, resulting in mutual interference between radars.
TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation
The hybrid architecture of convolution neural networks (CNN) and Transformer has been the most popular method for medical image segmentation.
Second-order Anisotropic Gaussian Directional Derivative Filters for Blob Detection
Interest point detection methods have received increasing attention and are widely used in computer vision tasks such as image retrieval and 3D reconstruction.
Corner Detection Based on Multi-directional Gabor Filters with Multi-scales
In this work, the capability of the Gabor function to discriminate the intensity changes of step edges, L-shaped corners, Y-shaped or T-shaped corners, X-shaped corners, and star-shaped corners are investigated.
Color Image Edge Detection using Multi-scale and Multi-directional Gabor filter
Second, a set of Gabor filters are used to smooth the input images and the color edge strength maps are extracted, which are fused into a new ESM with the noise robustness and accurate edge extraction.
Image Feature Information Extraction for Interest Point Detection: A Review
The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed.
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification
Metric-based few-shot fine-grained image classification (FSFGIC) aims to learn a transferable feature embedding network by estimating the similarities between query images and support classes from very few examples.
