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Full-Text Articles in Engineering
Measurement Error Correction Model Of Tof Depth Camera, Le Wang, Luo Yu, Haikuan Wang, Minru Fei
Measurement Error Correction Model Of Tof Depth Camera, Le Wang, Luo Yu, Haikuan Wang, Minru Fei
Journal of System Simulation
Abstract: 3D data intuitively reflects the full view of the target or scene. Time of Flight (ToF) camera is a range imaging sensor that can provide 3D geometric information of targets immediately, thus it is widely applied in the robot positioning and navigation, 3D reconstruction and other aspects etc. Due to the operational principle of the camera itself, there are variety measurement errors of the source data obtained by ToF, resulting in image distortion. The measurement errors in the imaging process of ToF camera were analyzed and summarized, and the cubic spline interpolation method combined with look-up table was proposed …
Vehicle Logo Recognition Based On Sparse Sampling And Gradient Distribution Features, Binbin Zhou, Shangbing Gao, Zhigeng Pan, Liangliang Wang, Hongyang Wang
Vehicle Logo Recognition Based On Sparse Sampling And Gradient Distribution Features, Binbin Zhou, Shangbing Gao, Zhigeng Pan, Liangliang Wang, Hongyang Wang
Journal of System Simulation
Abstract: The vehicle logo location and recognition are separated in the traditional method, the location errors will affect the subsequent recognition, at the same time the vehicle logo images are with low resolution and poor quality. Thus, a novel method was proposed which integrated the vehicle logo location and recognition organically. The sample images were sampled by sparse sampling, and then the point set was divided into adjacent point set and non adjacent point set, and the gradient feature and light and dark feature were extracted respectively, constructing the feature library. The logo coarse location area was multi-scale scanned. The …
Image Denoising Using Deep Convolutional Autoencoder With Feature Pyramids, Ekrem Çeti̇nkaya, Mustafa Furkan Kiraç
Image Denoising Using Deep Convolutional Autoencoder With Feature Pyramids, Ekrem Çeti̇nkaya, Mustafa Furkan Kiraç
Turkish Journal of Electrical Engineering and Computer Sciences
Image denoising is 1 of the fundamental problems in the image processing field since it is the preliminary step for many computer vision applications. Various approaches have been used for image denoising throughout the years from spatial filtering to model-based approaches. Having outperformed all traditional methods, neural-network-based discriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieve flexibility against various noise levels and types. In this paper, a deep convolutional autoencoder combined with a variant of feature pyramid network is proposed for image denoising. Simulated data generated by Blender software along with corrupted …