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Road detection is a key component of Advanced Driving Assistance Systems, which provides valid space and candidate regions of objects for vehicles. Mainstream road detection methods have focused on extracting discriminative features. In this paper, we propose a robust feature fusion framework, called “Feature++”, which is combined with superpixel feature and 3D feature extracted from stereo images. Then a neural network classifier is been trained to decide whether a superpixel is road region or not. Finally, the classified results are further refined by conditional random field.

Road detection is a key component of Advanced Driving Assistance Systems, which provides valid space and candidate regions of objects for vehicles. Mainstream road detection methods have focused on extracting discriminative features. In this paper, we propose a robust feature fusion framework, called “Feature++”, which is combined with superpixel feature and 3D feature extracted from stereo images. Then a neural network classifier is been trained to decide whether a superpixel is road region or not. Finally, the classified results are further refined by conditional random field.

In this paper, we propose surrounding adaptive tone mapping in displayed images under ambient light. Under strong ambient light, the displayed images on the screen are darkly perceived by human eyes, especially in dark regions. We deal with the ambient light problem in mobile devices by brightness enhancement and adaptive tone mapping. First, we perform brightness compensation in dark regions using Bartleson-Breneman equation which represents lightness effect on the image under different surrounding illuminations.

This paper proposes a psychologically inspired convolutional neural network (PI-CNN) to achieve automatic facial beauty prediction. Different from the previous methods, the PI-CNN is a hierarchical model that facilitates both the facial beauty representation learning and predictor training. Inspired by the recent psychological studies, significant appearance features of facial detail, lighting and color were used to optimize the PI-CNN facial beauty predictor using a new cascaded fine-tuning method.

We propose a new variational method for the completion of moving shapes through binary video inpainting that works by smoothly recovering the objects into an inpainting hole. We solve it by a simple dynamic shape analysis algorithm based on threshold dynamics. The model takes into account the optical flow and motion occlusions. The resulting inpainting algorithm diffuses the available information along the space and the visible trajectories of the pixels in time. We show its performance with examples from the Sintel dataset, which contains complex object motion and occlusions.

In this paper, we propose retinex-based perceptual contrast enhancement in images using luminance adaptation. We use the retinex theory to decompose an image into illumination and reflectance layers, and adopt luminance adaptation to handle the illumination layer which causes detail loss. First, we obtain the illumination layer using adaptive Gaussian filtering to remove halo artifacts. Then, we adaptively remove illumination of the illumination layer in the multi-scale retinex (MSR) process based on luminance adaptation to preserve details.