Face localization by closed loop discriminator estimation and improved detection using contemporary feature extraction techniques

Mallik, Sruti; Mallik, Rahul; Roy, Niladri; Chatterjee, Amitava

doi:10.1109/cgvis.2015.7449888

Cited by 1 publication

(1 citation statement)

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“…The collected sequences are RGB images. All RGB images were converted into gray images according to [42] = 0.2989 + 0.5870 + 0.1140 (1) This conversion reduces the computational burden by three times and contributes to real-time image processing.…”

Section: Image Preprocessingmentioning

confidence: 99%

Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder

Wang

Zhao

et al. 2018

Mathematical Problems in Engineering

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This paper presents a lane departure detection approach that utilizes a stacked sparse autoencoder (SSAE) for vehicles driving on motorways or similar roads. Image preprocessing techniques are successfully executed in the initialization procedure to obtain robust region-of-interest extraction parts. Lane detection operations based on Hough transform with a polar angle constraint and a matching algorithm are then implemented for two-lane boundary extraction. The slopes and intercepts of lines are obtained by converting the two lanes from polar to Cartesian space. Lateral offsets are also computed as an important step of feature extraction in the image pixel coordinate without any intrinsic or extrinsic camera parameter. Subsequently, a softmax classifier is designed with the proposed SSAE. The slopes and intercepts of lines and lateral offsets are the feature inputs. A greedy, layer-wise method is employed based on the inputs to pretrain the weights of the entire deep network. Fine-tuning is conducted to determine the global optimal parameters by simultaneously altering all layer parameters. The outputs are three detection labels. Experimental results indicate that the proposed approach can detect lane departure robustly with a high detection rate. The efficiency of the proposed method is demonstrated on several real images.

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Section: Image Preprocessingmentioning

confidence: 99%