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Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object ...
Programme doctoral en Informatique, Communications et Information2013
We consider the problem of planning paths on graphs with some edges whose traversability is uncertain; for each uncertain edge, we are given a probability of being traversable (e.g., by a learned classifier). We categorize different interpretations of the ...
Detection of fidgeting activities is a field which has not been much explored as of now. Studies have shown that fidgeting has a beneficial impact on people’s healthiness as it burns a significant amount of energy. Being able to detect when someone is fidg ...
We present a comparative study on sentence boundary prediction for German and English broadcast news that explores generalization across different languages. In the feature extraction stage, word pause duration is firstly extracted from word aligned speech ...
Optical flow estimation is one of the oldest and still most active research domains in computer vision. In 35 years, many methodological concepts have been introduced and have progressively improved performances, while opening the way to new challenges. In ...
A crucial feature of a good scene recognition algorithm is its ability to generalize. Scene categories, especially those related to human made indoor places or to human activities like sports, do present a high degree of intra-class variability, which in t ...
Automatically extracting linear structures from images is a fundamental low-level vision problem with numerous applications in different domains. Centerline detection and radial estimation are the first crucial steps in most Computer Vision pipelines aimin ...
Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits t ...
In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...
Binary keypoint descriptors provide an efficient alternative to their floating-point competitors as they enable faster processing while requiring less memory. In this paper, we propose a novel framework to learn an extremely compact binary descriptor we ca ...
