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Found 8 papers, 0 papers with code
Visual-Inertial-Semantic Scene Representation for 3D Object Detection
We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones.
Learning Adaptive Parameter Tuning for Image Processing
The non-stationary nature of image characteristics calls for adaptive processing, based on the local image content.
Visual-Inertial-Semantic Scene Representation for 3-D Object Detection
We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones.
Multi-View Feature Engineering and Learning
We frame the problem of local representation of imaging data as the computation of minimal sufficient statistics that are invariant to nuisance variability induced by viewpoint and illumination.
An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability
We conduct an empirical study to test the ability of Convolutional Neural Networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect ratio.
Domain-Size Pooling in Local Descriptors: DSP-SIFT
We introduce a simple modification of local image descriptors, such as SIFT, based on pooling gradient orientations across different domain sizes, in addition to spatial locations.
Visual Scene Representations: Contrast, Scaling and Occlusion
We study the structure of representations, defined as approximations of minimal sufficient statistics that are maximal invariants to nuisance factors, for visual data subject to scaling and occlusion of line-of-sight.
On the Design and Analysis of Multiple View Descriptors
We propose an extension of popular descriptors based on gradient orientation histograms (HOG, computed in a single image) to multiple views.
