Grouping explains contextual modulation in low level vision

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Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

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We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

Monocular and stereo visions are cost-effective solutions for 3D human localization in the context of self-driving cars or social robots. However, they are usually developed independently and have their respective strengths and limitations. We propose a no ...

Human visual system relies on both monocular focusness cues and binocular stereo cues to gain effective 3D perception. Correspondingly, depth from focus/defocus (DfF/DfD) and stereo matching are two most studied passive depth sensing schemes, which are tra ...

Human vision has evolved to make sense of a world in which elements almost never appear in isolation. Surprisingly, the recognition of an element in a visual scene is strongly limited by the presence of other nearby elements, a phenomenon known as visual c ...

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Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these ...

Despite well-established baselines, learning of scene depth and ego-motion from monocular video remains an ongoing challenge, specifically when handling scaling ambiguity issues and depth inconsistencies in image sequences. Much prior work uses either a su ...

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Deriving accurate three-dimensional (3-D) structural information of materials at the nanometre level is often crucial for understanding their properties. Tomography in transmission electron microscopy (TEM) is a powerful technique that provides such inform ...

Crowding is traditionally thought to occur by local interactions between the target and the neighboring flankers, for example, by pooling neural responses corresponding to both the target and flankers. Accordingly, crowding is thought to occur within a sma ...

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

To cope with the complexity of vision, most models in neuroscience and computer vision are of hierarchical and feedforward nature. Low-level vision, such as edge and motion detection, is explained by basic low-level neural circuits, whose outputs serve as ...

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Providing multiple meanings in a single piece of art has always been intriguing to both artists and observers. We present Purkinje images, which have different interpretations depending on the luminance adaptation of the observer. Finding such images is an ...

Grouping explains contextual modulation in low level vision

Chattez avec Graph Search

Aggregating Spatial and Photometric Context for Photometric Stereo

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

Detecting Anomalies and Obstacles in Road Scenes

MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization

Deep eyes: Joint depth inference using monocular and binocular cues

Crowding and the importance of grouping and segmentation processes in human vision

Beyond Cameras: Real-Time High-Resolution 3D and Panoramic MultiView Vision Systems with Their Applications

Learning stereo reconstruction with deep neural networks

SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion

Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing

A Real-Time Multi-Camera Depth Estimation ASIC with Custom On-Chip Embedded DRAM

Towards Modelling of Visual Saliency in Point Clouds for Immersive Applications

Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks

Stereo-vision three-dimensional reconstruction of curvilinear structures imaged with a TEM

How grouping determines crowding

Large-Scale Image Segmentation with Convolutional Networks

Putting low-level vision into global context: Why vision cannot be reduced to basic circuits

Child-Robot Spatial Arrangement in a Learning by Teaching Activity

Template-based Monocular 3-D Shape Reconstruction And Tracking Using Laplacian Meshes

Purkinje Images: Conveying Different Content for Different Luminance Adaptations in a Single Image