In Computed tomography (CT) usage of common reconstruction algorithms to the projection data acquired with polychromatic probing radiation leads to the appearance of a cup-like distortion. CT image quality can be improved by adjusting the CT scanner or the reconstruction algorithm, but for this purpose assessment of cupping artifacts evaluation needs to be done. Existing assessment methods either rely on expert opinion or require an object binary mask, which can be unavailable. In this paper, we propose a method for blind assessment of cupping artifacts that do not require any prior information. The main idea of the proposed method is to evaluate the degree of change in intensity near automatically found edges of optically dense objects. We prove the applicability of the method on the collected dataset with cupping artifacts. The results show a monotonic dependency between the severity of cupping artifacts and the calculated with the proposed method value.
In this work, we consider a problem of quadrilateral document borders detection in images captured by a mobile device’s camera. State-of-the-art algorithms for the quadrilateral document borders detection are not designed for cases when one of the document borders is either completely out of the frame, obscured, or of low contrast. We propose the algorithm which correctly processes the image in such cases. It is built on the classical contour-based algorithm. We modify the latter using the document’s aspect ratio which is known a priori. We demonstrate that this modification reduces the number of incorrect detections by 34% on an open dataset MIDV-500.
This paper presents a method for metric rectification of planar objects that preserves angles and length ratios. An inner structure of an object is assumed to follow the laws of Manhattan World i.e. the majority of line segments are aligned with two orthogonal directions of the object. For that purpose we introduce the method that estimates the position of two vanishing points corresponding to the main object directions. It is based on an original optimization function of segments that estimates a vanishing point position. For calculation of the rectification homography with two vanishing points we propose a new method based on estimation of the camera rotation so that the camera axis is perpendicular to the object plane. The proposed method can be applied for rectification of various objects such as documents or building facades. Also since the camera rotation is estimated the method can be employed for estimation of object orientation (for example, during a surgery with radiograph of osteosynthesis implants). The method was evaluated on the MIDV-500 dataset containing projectively distorted images of documents with complex background. According to the experimental results an accuracy of the proposed method is better or equal to the-state-of-the-art if the background occupies no more than half of the image. Runtime of the method is around 3ms on core i7 3610qm CPU.
In this paper the method of image alignment based on average image sharpness maximization is proposed. The algorithm for global-shift model is investigated, its efficiency by applying FFT is shown. For projective model, an approach for image alignment using local shifts and RANSAC to obtain the final transform is considered. Experimental results for the system of document's reconstruction in a video stream increasing quality of output image are demonstrated.
KEYWORDS: Digital signal processing, Image segmentation, Optical character recognition, Computer programming, Detection and tracking algorithms, Image processing algorithms and systems, Optical inspection, Animal model studies, Systems modeling, Optimization (mathematics), Object recognition
In this paper we propose a dynamic programming solution to the template-based recognition task in OCR case. We formulate a problem of optimal position search for complex objects consisting of parts forming a sequence. We limit the distance between every two adjacent elements with predefined upper and lower thresholds. We choose the sum of penalties for each part in given position as a function to be minimized. We show that such a choice of restrictions allows a faster algorithm to be used than the one for the general form of deformation penalties. We named this algorithm Dynamic Squeezeboxes Packing (DSP) and applied it to solve the two OCR problems: text fields extraction from an image of document Visual Inspection Zone (VIZ) and license plate segmentation. The quality and the performance of resulting solutions were experimentally proved to meet the requirements of the state-of-the-art industrial recognition systems.
In this paper we describe stitching protocol, which allows to obtain high resolution images of long length monochromatic objects with periodic structure. This protocol can be used for long length documents or human-induced objects in satellite images of uninhabitable regions like Arctic regions. The length of such objects can reach notable values, while modern camera sensors have limited resolution and are not able to provide good enough image of the whole object for further processing, e.g. using in OCR system. The idea of the proposed method is to acquire a video stream containing full object in high resolution and use image stitching. We expect the scanned object to have straight boundaries and periodic structure, which allow us to introduce regularization to the stitching problem and adapt algorithm for limited computational power of mobile and embedded CPUs. With the help of detected boundaries and structure we estimate homography between frames and use this information to reduce complexity of stitching. We demonstrate our algorithm on mobile device and show image processing speed of 2 fps on Samsung Exynos 5422 processor
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