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We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and natur ...
Deep learning-based algorithms have become increasingly efficient in recognition and detection tasks, especially when they are trained on large-scale datasets. Such recent success has led to a speculation that deep learning methods are comparable to or eve ...
In crowding, the perception of a target deteriorates in the presence of clutter. Crowding is usually explained within the framework of object recognition, where processing proceeds in a hierarchical and feedforward fashion from the analysis of low level fe ...
We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of ...
Depth information is used in a variety of 3D based signal processing applications such as autonomous navigation of robots and driving systems, object detection and tracking, computer games, 3D television, and free view-point synthesis. These applications r ...
The dyadic scaling in the discrete wavelet transform can lead to a loss of precision, in comparison to the computationally unrealistic continuous wavelet transform. To overcome this obstacle, we propose a novel method to locally scale wavelets between dyad ...
Blindsight denotes unconscious residual visual capacities in the context of an inability to consciously recollect or identify visual information. It has been described for color and shape discrimination, movement or facial emotion recognition. The present ...
Object detection is a significant challenge in Computer Vision and has received a lot of attention in the field. One such challenge addressed in this thesis is the detection of polygonal objects, which are prevalent in man-made environments. Shape analysis ...
Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier. Such approaches have been shown they can be f ...
Object tracking in image sequences is a key challenge in computer vision. Its goal is to follow objects that move or evolve over time while preserving the identity of each object. However, most existing approaches focus on one class of objects and model on ...