Localize car door handles with image segmentation and saliency detection

Importance of little objects in cars such as door handles is obvious, both in daily lives and in industrial manufacture. However, since the lack of the distinctive appearance and feature, obtaining the location of them is still remaining a challenge. This paper proposes an effective approach for the detection of the door handles of cars. The method innovatively combines frequency and spatial domains' algorithms to detect the location of little objects of cars in a relatively large image. To illustrate the method more concisely, our method localizes door handles through image segmentation and visual saliency detection. First, by segmenting the image we can remove the unnecessary area to improve the speed and accuracy of our approach. After finding the region of interest, our approach uses a visual saliency detection algorithm named Spectral Residual Approach which can get the location of door handles accurately. At last, the approach is tested by different kinds of images of vehicles. The results of the experiments show that our approach is obvious and practical.

[1]  Shiping Zhu,et al.  An Improved Inter-Frame Prediction Algorithm for Video Coding Based on Fractal and H.264 , 2017, IEEE Access.

[2]  Svetlana Lazebnik,et al.  Active Object Localization with Deep Reinforcement Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[3]  Yee Whye Teh,et al.  Searching for objects driven by context , 2012, NIPS.

[4]  Shiping Zhu,et al.  Local stereo matching algorithm with efficient matching cost and adaptive guided image filter , 2017, The Visual Computer.

[5]  Shuo Wang,et al.  Predicting human gaze beyond pixels. , 2014, Journal of vision.

[6]  Shiping Zhu,et al.  Spatiotemporal visual saliency guided perceptual high efficiency video coding with neural network , 2018, Neurocomputing.

[7]  D. Ruderman The statistics of natural images , 1994 .

[8]  Xiaochun Cao,et al.  Cluster-Based Co-Saliency Detection , 2013, IEEE Transactions on Image Processing.

[9]  Yang Gao,et al.  Noncontact 3-D Coordinate Measurement of Cross-Cutting Feature Points on the Surface of a Large-Scale Workpiece Based on the Machine Vision Method , 2010, IEEE Transactions on Instrumentation and Measurement.

[10]  Peter N. Belhumeur,et al.  Part-Pair Representation for Part Localization , 2014, ECCV.

[11]  C. V. Jawahar,et al.  Blocks That Shout: Distinctive Parts for Scene Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  H. Egeth,et al.  Searching for conjunctively defined targets. , 1984, Journal of experimental psychology. Human perception and performance.

[13]  Joshua Gluckman,et al.  Higher order whitening of natural images , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Jonathan Krause,et al.  3D Object Representations for Fine-Grained Categorization , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[15]  Jiancheng Fang,et al.  A New Noncontact Flatness Measuring System of Large 2-D Flat Workpiece , 2008, IEEE Transactions on Instrumentation and Measurement.

[16]  Lei Guo,et al.  Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[18]  Ben Taskar,et al.  Cascaded Models for Articulated Pose Estimation , 2010, ECCV.

[19]  David A. Forsyth,et al.  Learning to Localize Little Landmarks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[21]  Ali Borji,et al.  What stands out in a scene? A study of human explicit saliency judgment , 2013, Vision Research.

[22]  Xiaoyan Sun,et al.  Subjective-Driven Complexity Control Approach for HEVC , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Zaikuo Wang,et al.  Fractal video sequences coding with region-based functionality , 2012 .

[24]  Koray Kavukcuoglu,et al.  Visual Attention , 2020, Computational Models for Cognitive Vision.

[25]  Zheng Li,et al.  Stereo matching algorithm with guided filter and modified dynamic programming , 2015, Multimedia Tools and Applications.

[26]  Yuan Yan Tang,et al.  A Visual-Attention Model Using Earth Mover's Distance-Based Saliency Measurement and Nonlinear Feature Combination , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Yi Yang,et al.  Articulated Human Detection with Flexible Mixtures of Parts , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Alex Graves,et al.  Recurrent Models of Visual Attention , 2014, NIPS.

[30]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[31]  Chunyan Zhang,et al.  A fast algorithm of intra prediction modes pruning for HEVC based on decision trees and a new three-step search , 2017, Multimedia Tools and Applications.

[32]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..