Convolutional networks for images, speech, and time series
The handbook of brain theory and neural networks
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Semantic object classes in video: A high-definition ground truth database
Pattern Recognition Letters
Segmentation and Recognition Using Structure from Motion Point Clouds
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
What, where and how many? combining object detectors and CRFs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Blocks world revisited: image understanding using qualitative geometry and mechanics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Semantic segmentation of urban scenes using dense depth maps
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand---designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process. Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state---of---the---art methods using other sources of information such as depth, motion or stereo.