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Detecting and identifying Regions of Interest (ROIs) is an important task for navigation and retrieval services. In this paper, we focus on indoor scene images and detect object regions such as shop signs and merchandise. Our method is based on two approaches; 1) Indoor structure analysis from a single image by learning the types of scenes. 2) Detect ROIs by taking advantage of the relationship of...
Navigating safely in outdoor environments is a challenging activity for vision-impaired people. This paper is a step towards developing an assistive navigation system for the blind. We propose a robust method for detecting the pedestrian marked lanes at traffic junctions. The proposed method includes two stages: regions of interest (ROI) extraction and lane marker verification. The ROI extraction...
In reconstructing 3-D shape from images based on feature points, we usually define a triangular mesh that has those feature points as vertices, and display the object as a polyhedron. If the object itself is a polyhedron, however, some of the displayed edges may be inconsistent with the true shape. For this problem, Nakatsuji et al. proposed a method that automatically eliminates such inconsistencies...
In this article, we present a novel set of features for detection of text in images of natural scenes using a multi-layer perceptron (MLP) classifier. An estimate of the uniformity in stroke thickness is one of our features and we obtain the same using only a subset of the distance transform values of the concerned region. Estimation of the uniformity in stroke thickness on the basis of sparse sampling...
Unsupervised image segmentation is an important and difficult technique in pattern recognition. In this paper, we propose an interesting region merging algorithm for segmentation of natural images. It consists of two steps: first forming initial over-segmentation by the Connected Coherence Tree Algorithm (CCTA), and then merging the primitive regions in terms of their similarity and feature in the...
A general method for image contrast enhancement and noise reduction is proposed in this paper. The method is developed especially for enhancing images acquired under very low light conditions where the features of images are nearly invisible and the noise is serious. By applying an improved and effective image de-haze algorithm to the inverted input image, the intensity can be amplified so that the...
To efficiently detect all the possible linear features, a multi scale multi structuring element top-hat transform based algorithm is proposed in this paper. The algorithm is divided into two parts: the multi scale multi structuring element top-hat transform and postprocessing. In the multi scale multi structuring element top-hat transform, multi scales of multi structuring elements with increasing...
This paper proposes a new feature extraction technique for indexing and matching historical images. The complexity of these historical images troubles the existing indexing approaches due to their line patterns. Our indexing method relies on the global segmentation integrated with the knowledge of edge density to deal with the line patterns and eliminate over-segmented regions. Then the historical...
In this paper a new connectivity model is introduced which allows combined clustering and partitioning of structures without distortion, in contrast to mask connectivity. An algorithm to compute morphological attribute filters based on Max-Trees for this new form of connectivity is presented. It is shown that the new form of connectivity is effective in clustering diacritics together with the appropriate...
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