Raluca Brehar

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Object recognition from images is one of the essential problems in automatic image processing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not necessarily related to image data. It has recently come to attention that high dimensional data also exhibit high hubness, which essentially(More)
Accurate pedestrian detection in urban environment is a highly explored research field. We propose a new approach in pedestrian detection that combines the popular Local Binary Patterns and Histogram of Oriented Gradient features. The novelty of our work resides in the combination of a reduced HOG feature vector with uniform LBP patterns for the pedestrian(More)
One central task to the idea of Semantic Web is reasoning over semantic descriptions of web pages and information items available on the Web. A flagship project that is advancing the state of the art in reasoning with Web scale data is the Large Knowledge Collider (LarKC). Having a plug gable architecture, LarKC enables the interested users to test their(More)
We propose a method for detecting pedestrians in infrared images. The method combines a fast region of interest generator with fast feature pyramid object detection. Knowing the appearance model of pedestrians in infrared images we infer some edge and intensity based filters that generate the regions in which pedestrian hypotheses may appear. On those(More)
The bag of words model has been actively adopted by content based image retrieval and image annotation techniques. We employ this model for the particular task of pedestrian detection in two dimensional images, producing this way a novel approach to pedestrian detection. The experiments we have done in this paper compare the behavior of discriminative(More)
Most of computation time when dealing with a pedestrian detector is spent in the feature computation and then in the multi-scale classification. This second step consists of applying scanning windows at multiple scales. Depending on the number of scales and on the image dimension, this step is slow because a large number of windows is generated. An(More)
Object recognition is an essential task in content-based image retrieval and classification. This paper deals with object recognition in WIKImage data, a collection of publicly available annotated Wikipedia images. WIKImage comprises a set of 14 binary classification problems with significant class imbalance. Our approach is based on using the local(More)
This paper describes a new approach for pedestrian detection in traffic scenes. The originality of the method resides in the combination of the benefits of the symmetry characteristic for pedestrians in intensity images and the benefits of deformable part-based models for recognizing pedestrians in multiple object hypotheses generated by a stereo vision(More)
In this paper we introduce a system for semantic understanding of traffic scenes. The system detects objects in video images captured in real vehicular traffic situations, classifies them, maps them to the OpenCyc<sup>1</sup> ontology and finally generates descriptions of the traffic scene in CycL or cvasi-natural language. We employ meta-classification(More)
Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible. A popular and successful approach for monocular intensity pedestrian detection is based on the approximation (instead of computation) of image features for multiple scales based on the features computed on set of predefined(More)