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Machine learning approaches have become the de-facto standard for creating object detectors (such as face and pedestrian detectors) which are robust to lighting, viewpoint, and pose. Generating sufficiently large labeled data sets to support accurate training is often the most challenging problem. To address this, the active learning paradigm suggests interactive user input, creating an initial classifier...
The objective of this paper is to use computer vision to detect and localize multiple object within an image in the presence of a cluttered background, substantial occlusion and significant scale changes. Our approach consists of first generating a set of hypotheses for each object using a generative model (pLSA) with a bag of visual words representing each image. Then, the discriminative part verifies...
We present a new similarity measure between a single shape and a shape group as a basis for shape clustering following the paradigm of context dependent shape comparison: clusters are generated in the context of a reference shape, defined by the query shape it is compared to. Tightly coupled, the distance measure is the basis for a soft k-means like framework to achieve robust clustering. Successful...
An algorithm is presented which uses evidence accumulation to perform shape recognition. Because it uses accumulators, noise and isotropic measurement errors tend to average out. Furthermore, such methods are intrinsically parallel. It is demonstrated to perform better than any competing technique, and is particularly robust under partial occlusion. Its performance is demonstrated in applications...
In this paper we present a multi-dimensional version of the Kadir and Brady scale saliency feature extractor, based on Entropic Graphs and Rényi alpha-entropy estimation. The original Kadir and Brady algorithm is conditioned by the curse of dimensionality when estimating entropy from multi-dimensional data like RGB intensity values. Our approach naturally allows to increase dimensionality, being its...
In current active contour image segmentation methods, the number of regions is assumed to be known beforehand. It is related directly to a fixed number of active curves. How to allow it to vary is an important question which has been generally avoided. This study investigates a segmentation prior related to regions area to allow the number of regions to vary automatically during curve evolution, thereby...
This paper describes a novel approach to extract object region from an image by tracking the enclosing contour. We assume that the image is not complex, and it can be roughly partitioned into two parts with an intensity threshold. A lot of images (for example medical images) are in accord with this assumption. Global constraint (threshold) and local constraint (gradient) are integrated in a particle...
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