Median-Tree: An Efficient Counterpart of Tree-of-Shapes

2021 ◽  
Vol 5 (1) ◽  
pp. 108-127
Author(s):  
Behzad Mirmahboub ◽  
Deise Santana Maia ◽  
François Merciol ◽  
Sébastien Lefèvre

Abstract Representing an image through a tree structure as provided with a morphological hierarchy enables efficient image analysis and processing methods operating directly on the tree structure. Max-tree and min-tree can be built with efficient algorithms but they only focus on brighter and darker components of the image respectively. Conversely, the Tree-of-Shapes is a self-complementary image representation that provides access to all regional extrema of the image (both brighter and darker components), but its computation is more time-consuming. In this paper, we introduce a new, simple and efficient tree structure called median-tree. It relies on a median image that is straightforwardly constructed by subtracting the median pixel value from an image to decompose it into positive and negative parts. The median tree can then be obtained by applying the efficient max-tree algorithms available in the literature on this median image. We show through theoretical and experimental studies that the median-tree offers similar characteristics to the Tree-of-Shapes, but comes with a considerably lower construction complexity.

2007 ◽  
Vol 534-536 ◽  
pp. 1529-1532 ◽  
Author(s):  
Celine Pascal ◽  
Jean Marc Chaix ◽  
A. Dutt ◽  
Sabine Lay ◽  
Colette H. Allibert

A steel/cemented carbide couple is selected to generate a tough/hard two layers material. The sintering temperature and composition are chosen according to phase equilibria data. The choice of optimal sintering conditions needs experimental studies. First results evidence liquid migration from the hard layer to the tough one, leading to porosity in the hard region. The study of microstructure evolution during sintering of the tough material (TEM, SEM, image analysis) evidences the coupled mechanisms of pore reduction and WC dissolution, and leads to temperature and time ranges suitable to limit liquid migration. The sintering of the two layer material is then shown to need further compromises to avoid interface crack formation due to differential densification.


2003 ◽  
Vol 03 (01) ◽  
pp. 119-143 ◽  
Author(s):  
ZHIYONG WANG ◽  
ZHERU CHI ◽  
DAGAN FENG ◽  
AH CHUNG TSOI

Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.


2013 ◽  
Vol 58 (2) ◽  
pp. 371-375 ◽  
Author(s):  
J. Bidulská ◽  
T. Kvačkaj ◽  
I. Pokorný ◽  
R. Bidulský ◽  
M. Actis Grande

The main aim of this paper is to investigate, by means of comparison of experimental studies and mathematical models, the evolution of porosity as consequence of pressing, sintering and ECAPping an aluminium based powder (6xxx). After applying the compacting pressure, specimens were dewaxed in a ventilated furnace at 400º for 60 min. Sintering was carried out in a vacuum furnace at 610ºC for 30 min. The specimens were then ECAPed for 1 pass. The 2-dimensional quantitative image analysis was carried out by means of SEM and OM for the evaluation of the aforementioned characteristics. Results show the effect of processing parameters on the fracture/microstructure behaviour of the studied aluminium PM alloy. Quantitative image analysis, as well as fractographic interpretation and microstructure identification of weak sites in the studied aluminium PM alloy, provide a reliable and reproducible statistical procedure for the identification of the critical pore sizes.


2021 ◽  
pp. 1-13
Author(s):  
Yulong Zhang ◽  
Chaofei Zhang ◽  
Jian Tan ◽  
Frank Lim ◽  
Menglan Duan

Deep learning (DL) algorithms, especially the convolutional neural network (CNN), have been proven as a newly developed tool in machinery intelligent diagnosis. However, the current CNN-based fault diagnosis studies usually consider features or images extracted from a single domain as model input. This single domain information may not reflect fault patterns comprehensively, leading to low modeling accuracy and inaccurate diagnostic results. To overcome this limitation, this paper proposes a new CNN-based fault diagnosis approach using image representation considering multi-domain features of vibration signals. First, multi-domain features of vibration signals are extracted. These extracted features are then used to construct a n × n matrix, and subsequently to form images by RGB color transformations. This image transformation technique allows for capturing complementary and rich diagnostic information from multiple domains. At last, these images associated with different mechanical defects are fed into a CNN model that is improved based on the classic LeNet-5 CNN architecture for fault diagnosis and identification. Comparative experiments with the traditional feature extraction methods as well as state-of-the-art CNN-based methods are also investigated. Experimental studies on rolling bearings validate the effectiveness and superiorities of the proposed approach.


2018 ◽  
Vol 6 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Wael E. Fathy ◽  
◽  
Amr S. Ghoneim ◽  
Sameh Zarif ◽  
Aliaa A. Youssif

2013 ◽  
Vol 12 (1) ◽  
pp. 51 ◽  
Author(s):  
Robert Koprowski ◽  
Slawomir Wilczyński ◽  
Arkadiusz Samojedny ◽  
Zygmunt Wróbel ◽  
Anna Deda

2009 ◽  
Vol 21 (1) ◽  
pp. 253
Author(s):  
E. K. N. Arashiro ◽  
J. H. M. Viana ◽  
J. F. Fonseca ◽  
L. G. B. Siqueira ◽  
J. H. Bruschi ◽  
...  

Computer-assisted image analysis is a technological extension of reproductive ultrasonography and allows the quantitative assessment of the luteal echotexture, which is related to changes in histological features and, consequently, to steroidogenesis. The aim of this study was to determine the efficiency of luteal echotexture evaluation as a tool to assess luteal function in different phases of the estrous cycle in Toggenburg goats. Nulliparous goats (n = 21), 8 months in age, 33.52 ± 1.22 kg of body weight, and body score condition of 3.5 ± 0.07 (1 to 5 scale), which showed estrus within a 48-h period during the natural breeding season (March and April), were used. After estrous detection (Day 0) and mating, ovarian sonographic evaluations were performed daily using a portable ultrasound device (Aloka SSD 500, Aloka Co.) equipped with an adapted linear transrectal 5-MHz probe. The examinations were preceded by blood sample collections, which were stored until radioimmunoassay for progesterone (P4). Images were recorded in VHS tapes, then digitized to TIFF files (resolution of 1500 × 1125 pixels) using a video capture board. A representative elementary area of 5625 pixels (0.31 cm2) was defined for the luteal tissue according to the criterion proposed by Van den Bygaart and Protz 1999. Computer-assisted analyses were performed using custom-developed software (Quantporo®). Each pixel received a numeric value ranging from 0 (black) to 255 (white). Luteal echotexture and plasma P4 data were analyzed by ANOVA, and differences among means were determined by Tukey’s test. Correlations were established by Pearson’s correlation method. Results are shown as mean ± SEM. Corpora lutea size increased progressively (P < 0.001) until Day 9, when it reached the maximum area (1.26 ± 0.32 cm2). No increase in size was detected on the subsequent days (P > 0.05). Plasma P4 levels increased until a maximum value on Day 9 (6.31 ± 0.46 ng mL–1), and no increase was observed further (P > 0.05). In nonpregnant animals (n = 7), luteolysis was characterized by an abrupt decrease in plasma P4 concentration, which dropped to values lower than 1 ng mL–1 24 h after the onset of the process, whereas luteal area decreased gradually. Plasma P4 concentration was correlated to luteal area during luteogenesis and luteolysis (r = 0.63 and r = 0.50, respectively; P < 0.05). Mean pixel value showed a progressive increase during luteogenesis and reached the maximum value on Day 13 (54.33 ± 1.83). During corpus luteum (CL) regression, mean pixel value decreased to lower values 48 h after the onset of natural luteolysis (P < 0.05). Through both luteogenesis and luteolysis, positive correlations were observed between mean pixel values and luteal area (r = 0.34 and r = 0.26, respectively; P < 0.05) and also between mean pixel values and plasma P4 concentration (r = 0.24 and r = 0.37, respectively; P < 0.05). Pixel heterogeneity was not correlated to luteal area nor plasma P4 levels. These results suggest an association between CL echotexture and steroidogenic function; therefore, the quantitative assessment of the pixel brightness has a potential to be used for luteal function evaluation in goats. FAPEMIG and CAPES.


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