scholarly journals Multi-scale keypoints in V1 and beyond: Object segregation, scale selection, saliency maps and face detection

Biosystems ◽  
2006 ◽  
Vol 86 (1-3) ◽  
pp. 75-90 ◽  
Author(s):  
João Rodrigues ◽  
J.M.H. du Buf
Keyword(s):  
Face Detection ◽  
Scale Selection ◽  
Multi Scale ◽  
Saliency Maps ◽  
Object Segregation

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

scholarly journals Optimal scale combination selection for inconsistent multi-scale decision tables

2021 ◽  
Author(s):  
Yingjie Zhu ◽  
Bin Yang
Keyword(s):  
Lattice Model ◽  
Selection Model ◽  
Decision Table ◽  
Scale Selection ◽  
Multi Scale ◽  
Optimal Scale ◽  
Model Based ◽  
Positive Region ◽  
Decision Tables ◽  
Combination Selection

Abstract Hierarchical structured data are very common for data mining and other tasks in real-life world. How to select the optimal scale combination from a multi-scale decision table is critical for subsequent tasks. At present, the models for calculating the optimal scale combination mainly include lattice model, complement model and stepwise optimal scale selection model, which are mainly based on consistent multi-scale decision tables. The optimal scale selection model for inconsistent multi-scale decision tables has not been given. Based on this, firstly, this paper introduces the concept of complement and lattice model proposed by Li and Hu. Secondly, based on the concept of positive region consistency of inconsistent multi-scale decision tables, the paper proposes complement model and lattice model based on positive region consistent and gives the algorithm. Finally, some numerical experiments are employed to verify that the model has the same properties in processing inconsistent multi-scale decision tables as the complement model and lattice model in processing consistent multi-scale decision tables. And for the consistent multi-scale decision table, the same results can be obtained by using the model based on positive region consistent. However, the lattice model based on positive region consistent is more time-consuming and costly. The model proposed in this paper provides a new theoretical method for the optimal scale combination selection of the inconsistent multi-scale decision table.

An improved multi-scale face detection using convolutional neural network

2020 ◽  
Vol 14 (7) ◽  
pp. 1345-1353 ◽  
Author(s):  
Hazar Mliki ◽  
Sahar Dammak ◽  
Emna Fendri
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Multi Scale

scholarly journals Research on an Infrared Multi-Target Saliency Detection Algorithm under Sky Background Conditions

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 459
Author(s):  
Shaosheng Dai ◽  
Dongyang Li
Keyword(s):  
Saliency Detection ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Phase Spectrum ◽  
Background Suppression ◽  
Suppression Effect ◽  
Multi Scale ◽  
Saliency Maps ◽  
Image Saliency

Aiming at solving the problem of incomplete saliency detection and unclear boundaries in infrared multi-target images with different target sizes and low signal-to-noise ratio under sky background conditions, this paper proposes a saliency detection method for multiple targets based on multi-saliency detection. The multiple target areas of the infrared image are mainly bright and the background areas are dark. Combining with the multi-scale top hat (Top-hat) transformation, the image is firstly corroded and expanded to extract the subtraction of light and shade parts and reconstruct the image to reduce the interference of sky blurred background noise. Then the image obtained by a multi-scale Top-hat transformation is transformed from the time domain to the frequency domain, and the spectral residuals and phase spectrum are extracted directly to obtain two kinds of image saliency maps by multi-scale Gauss filtering reconstruction, respectively. On the other hand, the quaternion features are extracted directly to transform the phase spectrum, and then the phase spectrum is reconstructed to obtain one kind of image saliency map by the Gauss filtering. Finally, the above three saliency maps are fused to complete the saliency detection of infrared images. The test results show that after the experimental analysis of infrared video photographs and the comparative analysis of Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) index, the infrared image saliency map generated by this method has clear target details and good background suppression effect, and the AUC index performance is good, reaching over 99%. It effectively improves the multi-target saliency detection effect of the infrared image under the sky background and is beneficial to subsequent detection and tracking of image targets.

scholarly journals Multi-Scale Fully Convolutional Network for Fast Face Detection

2016 ◽  
Author(s):  
Yancheng Bai ◽  
Wenjing Ma ◽  
Yucheng Li ◽  
Liangliang Cao ◽  
Wen Guo ◽  
...  
Keyword(s):  
Face Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale

Dense small face detection based on regional cascade multi-scale method

2019 ◽  
Vol 13 (14) ◽  
pp. 2796-2804 ◽  
Author(s):  
Xiao Ke ◽  
Jianping Li ◽  
Wenzhong Guo
Keyword(s):  
Face Detection ◽  
Multi Scale ◽  
Scale Method

Enhanced MSRCR optical frequency segmented filter algorithm for alow-light vehicle environment

2021 ◽  
pp. 095440702110518
Author(s):  
Xin Lai ◽  
Hang Chen
Keyword(s):  
Face Detection ◽  
Large Scale ◽  
Frequency Noise ◽  
Low Light ◽  
Multi Scale ◽  
Guided Filtering ◽  
The Face ◽  
Color Restoration ◽  
Light Vehicle ◽  
Face Detector

To solve the problem of difficult face detection in a low illumination vehicle environment, a novel multi-scale retinex color restoration (MSRCR) approach exploiting the RGB three-channel decomposition and guided filtering (MSRCR-3CGF) is proposed. The MSRCR algorithm is employed to remove the artifacts and interference of low-light in the image based on the face detector using a multi-task cascaded convolutional neural network (MTCNN). The enhanced face image is decomposed into RGB, and GF is applied to each channel. The proposed method is tested on three widely used datasets: Dark Face, large-scale CelebFaces attributes (CelebA) and WIDER FACE, and an actual low-light scene in vehicles. The experimental results show that the proposed method suppresses the high-frequency noise of MSRCR, whilst improving the image enhancement and accuracy in the face detection in a low-light vehicle environment.

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