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Classification of large amount of images calls for diverse types of features, but employing all possible feature types will create unnecessary computation burden, and may result in reduced classification accuracy. Selecting feature vectors individually is not a feasible solution in this scenario due to the high amount of feature vectors needed for reasonable performance. Instead, this paper proposes...
While much work in the domain of traffic lights recognition is invested in the detection of traffic lights, classification of their exact state (including color phase and possible arrow pictogram) is often neglected. In this paper, we propose a robust approach for efficient video-based classification of said state with particular attention to the displayed pictogram and an additional ability to reject...
Object detection is one of the most interesting branches in computer vision. Accurate detection systems can be utilized to various areas. There are two steps in detection, feature extraction and classification. In this paper, new feature extraction method is proposed. Histogram Oriented Gradient (HOG) is famous, fast and accurate feature, but it is not rotation invariant. This paper proposes a new...
This paper presents a method to recover the pixel-wise illuminant colour for scenes lit by multiple lights. Here, we start from the image formation process and pose the illuminant recovery task in hand into an evidence combining setting. To do this, we construct a factor graph making use of the scale space of the input image and a set of illuminant prototypes. The computation of these prototypes is...
The time-consuming search for parking lots could be assisted by efficient routing systems. Still, the needed vacancy detection is either very hardware expensive, lacks detail or does not scale well for industrial application. This paper presents a video-based system for cost-effective detection of vacant parking lots, and an extensive evaluation with respect to the system's transferability to unseen...
Classification of roadside objects is very important task in identifying fire risk regions, analysing roadside conditions and improving roadside safety. This paper introduces a novel and effective way to detect soil, grass, road and tree from roadside images thus giving a better decision-making system for analysing roadside video data. A new feature extraction approach is proposed to detect and classify...
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