Image Denoising Using Sure-Based Adaptive Thresholding In Directionlet Domain

The standard separable two dimensional wavelet transform has achieved a great success in image denoising applications due to its sparse representation of images. However it fails to capture efficiently the anisotropic geometric structures like edges and contours in images as they intersect too many wavelet basis functions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multi directional and anisotropic wavelet transform called directionlet is presented. The image denoising in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. The image is first segmented and the dominant direction of each segment is identified to make a directional map. Then according to the directional map, the directionlet transform is taken along the dominant direction of the selected segment. The decomposed images with directional energy are used for scale dependent subband adaptive optimal threshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLL subband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL) are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of numeric and visual quality

Postcode segmentation and recognition using projections and bispectral features

A system to segment and recognize Australian 4-digit postcodes from address labels on parcels is described. Images of address labels are preprocessed and adaptively thresholded to reduce noise. Projections are used to segment the line and then the characters comprising the postcode. Individual digits are recognized using bispectral features extracted from their parallel beam projections. These features are insensitive to translation, scaling and rotation, and robust to noise. Results on scanned images are presented. The system is currently being improved and implemented to work on-line.

Metal artifacts from titanium and steel screws in CT, 1.5T and 3T MR images of the tibial Pilon: A quantitative assessment in 3D

Radiographs are commonly used to assess articular reduction of the distal tibia (pilon) fractures postoperatively, but may reveal malreductions inaccurately. While Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are potential 3D alternatives they generate metal-related artifacts. This study aims to quantify the artifact size from orthopaedic screws using CT, 1.5T and 3T MRI data. Three screws were inserted into one intact human cadaver ankle specimen proximal to and along the distal articular surface, then CT, 1.5T and 3T MRI scanned. Four types of screws were investigated: titanium alloy (TA), stainless steel (SS) (Ø = 3.5 mm), cannulated TA (CTA) and cannulated SS (CSS)(Ø = 4.0 mm, Ø empty core = 2.6 mm). 3D artifact models were reconstructed using adaptive thresholding. The artifact size was measured by calculating the perpendicular distance from the central screw axis to the boundary of the artifact in four anatomical directions with respect to the distal tibia. The artifact sizes (in the order of TA, SS, CTA and CSS) from CT were 2.0 mm, 2.6 mm, 1.6 mm and 2.0 mm; from 1.5T MRI they were 3.7 mm, 10.9 mm, 2.9 mm, and 9 mm; and 3T MRI they were 4.4 mm, 15.3 mm, 3.8 mm, and 11.6 mm respectively. Therefore, CT can be used as long as the screws are at a safe distance of about 2 mm from the articular surface. MRI can be used if the screws are at least 3 mm away from the articular surface except SS and CSS. Artifacts from steel screws were too large thus obstructed the pilon from being visualised in MRI. Significant differences (P < 0.05) were found in the size of artifacts between all imaging modalities, screw types and material types, except 1.5T versus 3T MRI for the SS screws (P = 0.063). CTA screws near the joint surface can improve postoperative assessment in CT and MRI. MRI presents a favourable non-ionising alternative when using titanium hardware. Since these factors may influence the quality of postoperative assessment, potential improvements in operative techniques should be considered.

Automated Detection of Exposed Reinforcement in Post-Earthquake Safety and Structural Evaluations

Post-earthquake structural safety evaluations are currently performed manually by a team of certified inspectors and/or structural engineers. This process is time-consuming and costly, keeping owners and occupants from returning to their businesses and homes. Automating these evaluations would enable faster, and potentially more consistent, relief and response processes. In order to do this, the detection of exposed reinforcing steel is of utmost significance. This paper presents a novel method of detecting exposed reinforcement in concrete columns for the purpose of advancing practices of structural and safety evaluation of buildings after earthquakes. Under this method, the binary image of the reinforcing area is first isolated using a state-of-the-art adaptive thresholding technique. Next, the ribbed regions of the reinforcement are detected by way of binary template matching. Finally, vertical and horizontal profiling are applied to the processed image in order to filter out any superfluous pixels and take into consideration the size of reinforcement bars in relation to that of the structural element within which they reside. The final result is the combined binary image disclosing only the regions containing rebar overlaid on top of the original image. The method is tested on a set of images from the January 2010 earthquake in Haiti. Preliminary test results convey that most exposed reinforcement could be properly detected in images of moderately-to-severely damaged concrete columns.

Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments

The current procedures in post-earthquake safety and structural assessment are performed manually by a skilled triage team of structural engineers/certified inspectors. These procedures, and particularly the physical measurement of the damage properties, are time-consuming and qualitative in nature. This paper proposes a novel method that automatically detects spalled regions on the surface of reinforced concrete columns and measures their properties in image data. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the exposure of longitudinal reinforcement (depth of spalling into the column) and length of spalling along the column are measured using a novel global adaptive thresholding algorithm in conjunction with image processing methods in template matching and morphological operations. The method was tested on a database of damaged RC column images collected after the 2010 Haiti earthquake, and comparison of the results with manual measurements indicate the validity of the method.

改进的基于区域的运动目标分割方法

针对视频监控系统,提出了一种改进的基于区域的运动目标分割方法。与传统方法相比,在运动检测阶段,结合时域差分和背景差分进行运动检测,并通过自适应方法进行背景更新;在差分图像二值化时,采用自适应阈值方法来代替传统的手工确定阈值法;对于区域分割,使用基于加权平方欧式距离的均值聚类算法代替传统的均值聚类算法。实验结果表明该改进方法比传统方法具有更好的实时性、鲁棒性和有效性。

A camera based digit location and recognition system for garment tracking

Garment information tracking is required for clean room garment management. In this paper, we present a camera-based robust system with implementation of Optical Character Reconition (OCR) techniques to fulfill garment label recognition. In the system, a camera is used for image capturing; an adaptive thresholding algorithm is employed to generate binary images; Connected Component Labelling (CCL) is then adopted for object detection in the binary image as a part of finding the ROI (Region of Interest); Artificial Neural Networks (ANNs) with the BP (Back Propagation) learning algorithm are used for digit recognition; and finally the system is verified by a system database. The system has been tested. The results show that it is capable of coping with variance of lighting, digit twisting, background complexity, and font orientations. The system performance with association to the digit recognition rate has met the design requirement. It has achieved real-time and error-free garment information tracking during the testing.

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

Abstract: Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics and oxygenation in tissue. Changes in light-coupling due to movement of the subject can cause movement artifacts (MAs) in the recorded signals. Several methods have been developed so far that facilitate the detection and reduction of MAs in the data. However, due to fixed parameter values (e.g., global threshold) none of these methods are perfectly suitable for long-term (i.e., hours) recordings or were not time-effective when applied to large datasets. We aimed to overcome these limitations by automation, i.e., data adaptive thresholding specifically designed for long-term measurements, and by introducing a stable long-term signal reconstruction. Our new technique (“acceleration-based movement artifact reduction algorithm”, AMARA) is based on combining two methods: the “movement artifact reduction algorithm” (MARA, Scholkmann et al. Phys. Meas. 2010, 31, 649–662), and the “accelerometer-based motion artifact removal” (ABAMAR, Virtanen et al. J. Biomed. Opt. 2011, 16, 087005). We describe AMARA in detail and report about successful validation of the algorithm using empirical NIRS data, measured over the prefrontal cortex in adolescents during sleep. In addition, we compared the performance of AMARA to that of MARA and ABAMAR based on validation data.

Classificação das amostras do ensaio de Baumann através do processamento digital de imagens.

O presente trabalho apresenta uma alternativa ao processo de classificação do defeito da segregação central em amostras de aço, utilizando as imagens digitais que são geradas durante o ensaio de Baumann. O algoritmo proposto tem como objetivo agregar as técnicas de processamento digital de imagens e o conhecimento dos especialistas sobre o defeito da segregação central, visando a classificação do defeito de referência. O algoritmo implementado inclui a identificação e a segmentação da linha segregada por meio da aplicação da transformada de Hough e limiar adaptativo. Adicionalmente, o algoritmo apresenta uma proposta para o mapeamento dos atributos da segregação central nos diferentes graus de severidade do defeito, em função dos critérios de continuidade e intensidade. O mapeamento foi realizado por meio da análise das características individuais, como comprimento, largura e área, dos elementos segmentados que compõem a linha segregada. A avaliação do desempenho do algoritmo foi realizada em dois momentos específicos, de acordo com sua fase de implementação. Para a realização da avaliação, foram analisadas 255 imagens de amostras reais, oriundas de duas usinas siderúrgicas, distribuídas nos diferentes graus de severidade. Os resultados da primeira fase de implementação mostram que a identificação da linha segregada apresenta acurácia de 93%. As classificações oriundas do mapeamento realizado para as classes de criticidade do defeito, na segunda fase de implementação, apresentam acurácia de 92% para o critério de continuidade e 68% para o critério de intensidade.

Improved bounds for subband-adaptive iterative shrinkage/thresholding algorithms.

This paper presents new methods for computing the step sizes of the subband-adaptive iterative shrinkage-thresholding algorithms proposed by Bayram & Selesnick and Vonesch & Unser. The method yields tighter wavelet-domain bounds of the system matrix, thus leading to improved convergence speeds. It is directly applicable to non-redundant wavelet bases, and we also adapt it for cases of redundant frames. It turns out that the simplest and most intuitive setting for the step sizes that ignores subband aliasing is often satisfactory in practice. We show that our methods can be used to advantage with reweighted least squares penalty functions as well as L1 penalties. We emphasize that the algorithms presented here are suitable for performing inverse filtering on very large datasets, including 3D data, since inversions are applied only to diagonal matrices and fast transforms are used to achieve all matrix-vector products.