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Reading text from scene images is a challenging problem that is receiving much attention, especially since the appearance of imaging devices in low-cost consumer products like mobile phones. This paper presents an easy and fast method to recognize individual characters in images of natural scenes that is applied after an algorithm that robustly locates text on such images. The recognition is based...
We propose a new Iterative-Midpoint-Method (IMM) for video character gap filling based on end pixels and neighbor pixels in the extracted contour of a character. The method obtains the Enhanced Gradient Image (EGI) for the given gray character image to sharpen text pixels. Max-Min clustering and K-means clustering algorithm with K=2 are applied on the EGI to obtain text candidates. To clean up the...
This paper addresses the problem of Car Make and Model recognition as an example of within-category object class recognition. In this problem, it is assumed that the general category of the object is given and the goal is to recognize the object class within the same category. As compared to general object recognition, this problem is more challenging because the variations among classes within the...
A new machine vision method is presented to detect micro tubes with colored solutions or undissolved compounds in solvents on screening plates used in drug discovery. The method presented herein takes an image of a 96 tubes micro tube rack as input. After applying edge detection on the input image, the circles that characterize the borders of the micro tubes are detected via shape matching. A gradient...
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...
Over the recent years, low-level visual descriptors, among which the most popular is the histogram of oriented gradients (HOG), have shown excellent performance in object detection and categorization. We form a hypothesis that the low-level image descriptors can be improved by learning the statistically relevant edge structures from natural images. We validate this hypothesis by introducing a new...
In this paper, we propose a novel active contour method for image segmentation, which utilizes the advantages of the GAC and the LRAC methods. We consider the smoothing force of the GAC method and local region-based force of the LRAC method. The advantages of our method are as follows. First the proposed method a new region-based signed pressure force function, which can efficiently stop the contours...
This paper proposes a robust detection method for circular objects in noisy and inhomogeneous contrast image. This method detects circular objects not by the difference in image intensities between the object interior and its surrounding, but by the separability and uniformity of the image intensity distributions as calculated by Bhattacharyya Coefficient. The proposed method can detect obscure and...
We present a method for recovering fast and robustly the 3D shape of inextensible and smooth surfaces from a monocular image. We propose a weighted iterative least squares approach to minimize the reprojection error between 2D-3D point correspondences preserving the 3D lengths. In addition, a local 3D smoothness constraint for each mesh vertex is proposed to increase the robustness to noisy correspondences...
Photometric stereo algorithms produce a map of normal directions from the input images. The 3D surface can be reconstructed from this normal map. Existing surface reconstruction works often assume the normal map is integrable but contaminated by small scale non-integrable noise. However, real surfaces often contain large discontinuities such as occlusion boundaries and sharp depth changes, which break...
In this paper, we address the problem of detecting occlusion boundaries from video sequences. We build a bi-directed graph whose nodes are line fragments extracted from superpixels's edges. Based on the graph, we compute a global occlusion saliency map by integrating motion, shape and topology cues into the framework of Saliency Network. Furthermore, with the structural information generated from...
We consider the problem of locating pupillary and limbic boundaries in iris images captured in non-cooperative environment. This work presents an efficient segment search algorithm, which takes advantage of shape information and learned iris boundary detectors, to enable exclusion of most noisy edges and extraction of genuine pupillary contour segments. Pupillary boundaries can then be accurately...
Shape context has been proven to be an effective method for both local feature matching and global context description. In this paper, we propose a method to build a glocal shape context descriptor in cluttered images. By using the proposed keypoint centered multiple scale edge detection (KMSED) method, glocal shape context encodes fine-scale edges in the keypoint center region while coarse-scale...
Due to intensity overlapping, blurred edges and complex backgrounds with clutter features, liver segmentation is still a challenging task. In this paper, we address it with a constrained convex variational model, which can definitely avoid leakage through anatomical knowledge from users. A novel heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation...
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