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Rapid growth of visual data processing and analysis applications, such as content based image retrieval, augmented reality, automated inspection and defect detection, medical image understanding, and remote sensing has made the problem of developing accurate and efficient image representation and classification methods one of the key research areas. This research proposes new higher-level perceptual...
The identification of pond turtles is important to scientists who monitor local populations, as it allows them to track the growth and health of subjects over their lifetime. Traditional non-invasive methods for turtle recognition involve the visual inspection of distinctive coloured patterns on their plastron. This visual inspection is time consuming and difficult to scale with a potential growth...
In this paper we propose an automatic method for computing the bin boundaries of complex 3D LAB histograms in order to extract optimal color feature vectors from digital images. The size of the feature vectors can be adapted to particular application needs. We tested our approach with very good results on an iris recognition problem solved empirically before.
We present a novel approach for the online learning of hand gestures in swarm robotic (multi-robot) systems. We address the problem of online feature learning by proposing Convolutional Max-Pooling (CMP), a simple feed-forward two-layer network derived from the deep hierarchical Max-Pooling Convolutional Neural Network (MPCNN). To learn and classify gestures in an online and incremental fashion, we...
We propose a new approach to semantic parsing, which can seamlessly integrate evidence from multiple sensors with overlapping but possibly different fields of view (FOV), account for missing data and predict semantic labels over the spatial union of sensors coverages. The existing approaches typically carry out semantic segmentation using only one modality, incorrectly interpolate measurements of...
When a robot is deployed it needs to understand the nature of its surroundings. In this paper, we address the problem of semantic labeling 3D point clouds by object affordance (e.g., ‘pushable’, ‘liftable’). We propose a technique to extract geometric features from point cloud segments and build a classifier to predict associated object affordances. With the classifier, we have developed an algorithm...
In this paper, we propose a visual place recognition algorithm which uses only straight line features in challenging outdoor environments. Compared to point features used in most existing place recognition methods, line features are easily found in man-made environments and more robust to environmental changes such as illumination, viewing direction, or occlusion because they are more likely to be...
The Kleihauer-Betke test (KBT) is a widely used method for measuring fetal-maternal hemorrhage (FMH) in maternal care. In hospitals, KBT is performed by a certified technologist to count a minimum of 2,000 fetal and maternal red blood cells (RBCs) on a blood smear. Manual counting is inherently inconsistent and subjective. This paper presents a system for automated counting and distinguishing fetal...
Image segmentation is a key topic in computer vision, serving as a pre-step in a number of robotics tasks, including object recognition, obstacle avoidance and topological localization. In the literature, image segmentation has been employed as auxiliary information in order to improve optical flow performance. In this work, an alternative approach is proposed, in which optical flow information is...
We present a framework for handwritten Bangla digit recognition using Sparse Representation Classifier. The classifier assumes that a test sample can be represented as a linear combination of the train samples from its native class. Hence, a test sample can be represented using a dictionary constructed from the train samples. The most sparse linear representation of the test sample in terms of this...
We propose a Markov random field (MRF)-based method to segment photographic biomedical images into three image sub-regions, viz., tissue, photo, and background. Segmentation results are then used to extract local and global visual features to separate images with tissue, such as endoscopic images, from general photographs.
A vessel segmentation algorithm for pathological retina images is proposed. Firstly, the vessel centerlines are extracted by using the divergence of the normalized gradient vector field. Secondly, the main vessels are segmented by a sequence of bot-hat operators with different scales and directions. Thirdly, the skeleton lines of main vessels are generated after a skeletonization procedure. The distances...
Image segmentation is a fundamental problem in computer vision. Normalized Cut (Ncut) scheme uses second smallest eigenvector for solving this problem, while such eigenvectors may be sensitive to undesired changes in image. In this paper, firstly, we point out that optimization of Ncut is equivalent to optimization of Fisher-Rao criterion in classification. Then we look at the classification experience...
This paper presents a new method of producing a high-resolution image from a single low-resolution image without any external training image sets. We use a dictionary-based regression model for practical image super-resolution using local self-similar example patches within the image. Our method is inspired by the observation that image patches can be well represented as a sparse linear combination...
This note presents theoretical supplements for our proposed convex image segmentation model based on local and global intensity fitting energy. We emphasize several important results of our proposed model and supplement theoretical proofs for them.
Word segmentation from video text line is challenging because video poses several challenges, such as complex background, low resolution, arbitrary orientation, etc. Besides, word segmentation is essential for improving text recognition accuracy. Therefore, we propose a novel method for segmenting words by exploring zero crossing points for each sliding window over text line. The candidate zero crossing...
The most widely used in the field of visual object recognition descriptive features are shape based features. Identify objects in the image, contour and region shape descriptors based on two main topics to be examined. In order to describe objects with lesser number of descriptors, linear or cubic curves are fitted to the contours of the objects. The end points of these finite length curves are usually...
Key-Word Spotting (KWS) in handwritten documents is approached here by means of Word Graphs (WG) obtained using segmentation-free handwritten text recognition technology based on N-gram Language Models and Hidden Markov Models. Linguistic context significantly boost KWS performance with respect to methods which ignore word contexts and/or rely on image-matching with pre-segmented isolated words. On...
Driving space detection is one of the basic steps for autonomous guidance of vehicles. In this paper a real time road segmentation algorithm is proposed for identifying driving space. The presented algorithm uses the concept of appearance based object detection (ABOD) to detect traversable area for vehicles. The input image is partitioned as road and off road and each individual pixel is classified...
We propose a novel active contour for the analysis of filament-like structures and boundaries — features that traditional snakes based on closed curves have difficulties to delineate. Our method relies on a parametric representation of an open curve involving Hermite-spline basis functions. This allows us to impose constraints both on the contour and on its derivatives. The proposed parameterization...
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