Toyota Jidosha Kabushiki Kaisha

Katholieke Universiteit Leuven

Mitsubishi Electric Corporation

Mitsubishi Electric Research Laboratories, Inc.

Eth Zurich

Wim Abbeloos

Gabriel Othmezouri

Luc Van Gool

Stamatios Georgoulis

Yuichi Taguchi

Yukiyasu Domae

Esra Cansizoglu

Marc Proesmans

Sergio Salvatore Caccamo

Liqian Ma

Simon Vandenhende

Wouter Van Gansbeke

A computer-implemented method for training a classifier (Φη), including: training a pretext model (ΦΘ) to learn a pretext task, so as to minimize a distance between an output of a source sample via the pretext model (ΦΘ) and an output of a corresponding transformed sample via the pretext model (ΦΘ), the transformed sample being a sample obtained by applying a transformation (T) to the source sample; S) determining a neighborhood (NXi) of samples (Xi) of a dataset (SD) in the embedding space; S) training the classifier (Φη) to predict respective estimated probabilities Φηj(Xi), j=1 . . . C, for a sample (Xi) to belong to respective clusters (Cj), by using a second training criterion which tends to: maximize a likelihood for a sample and its neighbors (Xj) of its neighborhood (Nxi) to belong to the same cluster; and force the samples to be distributed over several clusters.

Computer-implemented training method, classification method and system and computer-readable recording medium

A system for image completion is disclosed. The system comprises a coordinate generation module configured to receive past frames and a present frame having a first field-of-view and to generate a set of coordinate maps, one for each of the received past frames; and a frame aggregation module configured to receive as input the past frames, the present frame, and the coordinate maps and to synthesize, based on said input, a present frame having a second field-of-view.

System for image completion

The disclosure relates to system for processing an image of at least one camera. The camera has predetermined camera parameters including a lens distortion and a camera pose with respect to a predefined reference frame. The system comprises:

System for processing an image, method for processing the image, method for training a neural network for processing the image, and recording medium for executing the method

A method and system detects and localizes multiple instances of an object by first acquiring a frame of a three-dimensional (3D) scene with a sensor, and extracting features from the frame. The features are matched according to appearance similarity and triplets are formed among matching features. Based on 3D locations of the corresponding points in the matching triplets, a geometric transformation is computed. Matching triplets are clustered according to the computed geometric transformations. Since the set of features coining from two different object instances should have a single geometric transform, the output of clustering provides the features and poses of each object instance in the image.

Imaging system and method for object detection and localization

Growing community of inventors

Hove, Belgium

Wim Abbeloos