domain transformation
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2021 ◽  
Vol 6 (4) ◽  
pp. 46-59
Author(s):  
Godwin Poi ◽  
Bobby Chime Elechi

Purpose: This study examined the relationship between business model transformation and business process reengineering of information technology companies in Nigeria. Methodology: The study adopted a cross-sectional research survey. The population of the study was the 4 major information technology companies that met the capitalization base condition as listed in Nigeria Stock Exchange. Questionnaire was the major instrument for data collection and the pilot survey was distributed to 50 respondents to help ascertain the possible response outcome on the long run if the entire respondents are examined. A Cronbach alpha of 0.7 was used to determine the level of reliability of the research instrument.  The hypotheses were tested using the Spearman Rank Order Correlation Coefficient with the aid of Statistical Package for Social Sciences version 23.0. Results The findings revealed that there is a significant relationship between business model transformation and business process reengineering of information technology companies in Nigeria. The study specifically found that there is a significant relationship between business model transformation and process formation in information technology companies in Nigeria. Also, the study found that there is a significant relationship between business model transformation and enhanced capabilities in information technology companies in Nigeria. Finally, the study found that that there is a significant relationship between business model transformation and efficiency improvements in information technology companies in Nigeria. Based on the study findings, the researchers conclude that domain transformation significantly relate with business process reengineering in information technology companies in Nigeria. Unique contribution: The study recommends that preference to expertise can be emphasized through the acknowledgement and appreciation of skill and creativity within the workplace in a manner that recognizes and encourages knowledge development and skill upgrades within the organization, thereby driving competence in the workforce of the organization.


Author(s):  
Chang-M. Liu ◽  
Yan-J. Sun ◽  
Yu Shi

With the raising popularity of digital devices in the current society, the present image detection system is becoming a great threaten. Especially the appearance of the recaptured images. It can be used in traditional invalid digital image detection algorithm. There is a new algorithm in this paper is presented to detect the recaptured and real image. The algorithm obtains low-frequency images, directional filtering images and high-frequency images by multiple application frequency domain filtering. Then the proposed algorithm analyzes the directional filtering images and high-frequency images by means of LBP algorithm to extract features. At last, the recaptured images were classified by the SVM. The experimental results demonstrated the algorithm in this paper could be effectively identify in the recaptured images.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7623
Author(s):  
Byeongjun Yu ◽  
Dongkyu Lee ◽  
Jae-Seol Lee ◽  
Seok-Cheol Kee

Although numerous road segmentation studies have utilized vision data, obtaining robust classification is still challenging due to vision sensor noise and target object deformation. Long-distance images are still problematic because of blur and low resolution, and these features make distinguishing roads from objects difficult. This study utilizes light detection and ranging (LiDAR), which generates information that camera images lack, such as distance, height, and intensity, as a reliable supplement to address this problem. In contrast to conventional approaches, additional domain transformation to a bird’s eye view space is executed to obtain long-range data with resolutions comparable to those of short-range data. This study proposes a convolutional neural network architecture that processes data transformed to a bird’s eye view plane. The network’s pathways are split into two parts to resolve calibration errors in the transformed image and point cloud. The network, which has modules that operate sequentially at various scaled dilated convolution rates, is designed to quickly and accurately handle a wide range of data. Comprehensive empirical studies using the Karlsruhe Institute of Technology and Toyota Technological Institute’s (KITTI’s) road detection benchmarks demonstrate that this study’s approach takes advantage of camera and LiDAR information, achieving robust road detection with short runtimes. Our result ranks 22nd in the KITTI’s leaderboard and shows real-time performance.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Haopeng Lei ◽  
Simin Chen ◽  
Mingwen Wang ◽  
Xiangjian He ◽  
Wenjing Jia ◽  
...  

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.


2021 ◽  
Vol 70 ◽  
pp. 102004
Author(s):  
Thomas de Bel ◽  
John-Melle Bokhorst ◽  
Jeroen van der Laak ◽  
Geert Litjens

2021 ◽  
Vol 352 ◽  
pp. 109091
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi ◽  
Saeed Mozaffari

2021 ◽  
Vol 42 (6) ◽  
pp. 1086-1091
Author(s):  
WEI Dong ◽  
◽  
◽  
SANG Mei ◽  
YU Minhui ◽  
...  

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