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A multi-exposure and multi-focus image fusion algorithm is proposed. The algorithm is developed for color images and is based on blending the gradients of the luminance components of the input images using the maximum gradient magnitude at each pixel location and then obtaining the fused luminance using a Haar wavelet-based image reconstruction technique.(More)
Visceral artery aneurysms (VAAs) include aneurysms of the splanchnic circulation and those of the renal artery. Their diagnosis is clinically important because of the associated high mortality and potential complications. Splenic, superior mesenteric, gastroduodenal, hepatic and renal arteries are some of the common arteries affected by VAAs. Though(More)
Keywords: Feature selection Feature weighting Evolutionary multi-objective optimization MOEA/D Inter-and intra-class distances a b s t r a c t Selection of feature subset is a preprocessing step in computational learning, and it serves several purposes like reducing the dimensionality of a dataset, decreasing the computational time required for(More)
Recent efforts in computer vision consider joint scene and object classification by exploiting mutual relationships (often termed as context) between them to achieve higher accuracy. On the other hand, there is also a lot of interest in online adaptation of recognition models as new data becomes available. In this paper, we address the problem of how models(More)
The huge amount of time required to construct a set of labeled images to train a classifier has led researchers to develop algorithms which can identify the most informative training images, such that labelling those will be sufficient to achieve a considerable classification accuracy. In this paper we focus on choosing a subset of the most informative and(More)
Several works have shown that relationships between data points (i.e., context) in structured data can be exploited to obtain better recognition performance. In this paper, we explore a different, but related, problem: how can these interrelationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling(More)
In computer vision, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem. Furthermore, it is also useful to reduce the annotation cost, as it is time consuming to annotate unlabeled samples. In this paper, motivated by the theories in data compression, we propose(More)