scholarly journals Formalizing Type Operations Using the “Image” Type Constructor

2006 ◽  
Vol 165 ◽  
pp. 121-132 ◽  
Author(s):  
Aleksey Nogin ◽  
Alexei Kopylov
Keyword(s):  
Type Constructor ◽  
Image Type

scholarly journals Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation

2021 ◽  
Vol 11 (15) ◽  
pp. 7046
Author(s):  
Jorge Francisco Ciprián-Sánchez ◽  
Gilberto Ochoa-Ruiz ◽  
Lucile Rossi ◽  
Frédéric Morandini
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
State Of The Art ◽  
Fire Detection ◽  
Loss Functions ◽  
Wildfire Spread ◽  
Combine Information ◽  
The Impact ◽  
Image Type ◽  
Segmentation Models

Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models.

scholarly journals Image Denoising Using Discrete Wavelet Transform : A Theoretical Framework

2018 ◽  
Vol 7 (2.16) ◽  
pp. 120
Author(s):  
Praveen Bhargava ◽  
Shruti Choubey ◽  
Rakesh Kumar Bhujade ◽  
Nilesh Jain
Keyword(s):  
Image Denoising ◽  
Ultra Sound ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Hybrid Filter ◽  
Edge Sharpness ◽  
In Series ◽  
Salt And Pepper ◽  
Image Type

Noise is a random variation in brightness and color in image or simply we can say that unwanted signals are called noise. The noise is mixed with original signal and cause may troubles. Due to the presence of noise, quality of image is reduced and other features like edge sharpness and pattern recognition are badly affected. In image denoising methods to improve the results a hybrid filter is used for better visualization. The hybrid filter is composed with the combination of three filters connected in series. The hybridization has performed much better in case of salt and pepper type of noise and for most of the medical image type, either MRI, CT, SPECT, Ultra Sound. PSNR values show major improvement in comparison of other existing methods. Future, the results obtained from the presented denoising experiments would be tried to be improved further by using this method with other transform domain methods. Finally, the results are concluded that the proposed approach in terms of PSNR, MSE improvement is outperformed. 

scholarly journals IMPLEMENTATION OF ALGORITHMS BASED ON ENSEMBLES OF ADJOINT FUNCTIONS FOR PROCESSING MONITORING DATA

2019 ◽  
Vol 4 (1) ◽  
pp. 121-125
Author(s):  
Alexey Penenko
Keyword(s):  
Measurement Data ◽  
Adjoint Equations ◽  
Monitoring Data ◽  
Diffusion Reaction ◽  
Transport And Transformation ◽  
Wide Range ◽  
Object Oriented Approach ◽  
Type Data ◽  
Main Components ◽  
Image Type

On the basis of the approach with the use of ensembles of solutions of adjoint equations, it is possible to solve a wide range of inverse modeling problems in a uniform way with the processing of monitoring data of various types, including is situ measurement data and image-type data. The corresponding computational system is implemented within the object-oriented approach. The article provides a brief description of the main components of the system developed for solving inverse problems for advection-diffusion-reaction models. Such problems arise, for example, when studying the processes of transport and transformation of impurities in the atmosphere and the processes of development of the living systems.

Medical Image Encryption

2019 ◽  
pp. 278-304 ◽  
Author(s):  
Sundararaman Rajagopalan ◽  
Siva Janakiraman ◽  
Amirtharajan Rengarajan
Keyword(s):  
Image Encryption ◽  
Medical Image ◽  
Diffusion Processes ◽  
Medical Images ◽  
Logistic Map ◽  
Healthcare Industry ◽  
Encryption Scheme ◽  
Dna Coding ◽  
Simultaneous Processing ◽  
Image Type

The healthcare industry has been facing a lot of challenges in securing electronic health records (EHR). Medical images have found a noteworthy position for diagnosis leading to therapeutic requirements. Millions of medical images of various modalities are generally safeguarded through software-based encryption. DICOM format is a widely used medical image type. In this chapter, DICOM image encryption implemented on cyclone FPGA and ARM microcontroller platforms is discussed. The methodology includes logistic map, DNA coding, and LFSR towards a balanced confusion – diffusion processes for encrypting 8-bit depth 256 × 256 resolution of DICOM images. For FPGA realization of this algorithm, the concurrency feature has been utilized by simultaneous processing of 128 × 128 pixel blocks which yielded a throughput of 79.4375 Mbps. Noticeably, the ARM controller which replicated this approach through sequential embedded “C” code took 1248 bytes in flash code memory and Cyclone IV FPGA consumed 21,870 logic elements for implementing the proposed encryption scheme with 50 MHz operating clock.

scholarly journals PERAN TIPE GAMBAR, TAGAR, JUMLAH LIKES DAN INFORMASI HARGA PADA INSTAGRAM TERHADAP INTENSI MEMBELI

2019 ◽  
Vol 14 (2) ◽  
pp. 71
Author(s):  
Hasrini Sari ◽  
Lidia Anggraeni
Keyword(s):  
Visual Attention ◽  
Design Method ◽  
Statistic Analysis ◽  
Eye Tracker ◽  
Price Information ◽  
Subject Design ◽  
Inferential Statistic ◽  
Image Type ◽  
Picture Type

Penelitian ini bertujuan untuk mengetahui pengaruh desain visual post Instagram dalam hal tipe gambar, tagar, jumlah likes, dan caption berupa informasi harga, terhadap tingkat atensi visual pemirsa, dan intensi pembelian. Keempat elemen tersebut dihipotesiskan akan mempengaruhi atensi visual pemirsa, dan pada akhirnya mempengaruhi intensi membeli. Metode desain eksperimen digunakan untuk menguji hipotesis dengan alat bantu eye tracker untuk mengukur atensi visual. Instagram dari sebuah perusahaan UKM yang menjual tas tangan (pouch) digunakan sebagai objek penelitian. Delapan stimulus berpasangan (gambar produk dan konsumen) yang merupakan dummy post Instagram dirancang berdasarkan hipotesis penelitian, dan satu pasang stimulus post Instagram yang ada saat ini digunakan sebagai kontrol. Empat puluh partisipan dipaparkan kesembilan stimulus menggunakan pendekatan within subject design. Dari hasil statistik deskriptif, ditemukan bahwa partisipan lebih menyukai tipe gambar produk, dan stimulus yang paling disukai yang menampilkan caption keterangan harga, hashtag serta jumlah likes yang banyak. Uji statistik inferensial menunjukkan adanya perbedaan tipe gambar (gambar produk dan gambar konsumen) dan jumlah likes mempengaruhi tingkat atensi visual pemirsa, serta tidak ada hubungan antara tingkat atensi terhadap tampilan Instagram dengan intensi membeli.  Abstract[Role Of Image Type, Hashtag, Number Of Likes And Pricing Information On Instagram Against Intensi Buying] This research is intended to investigate the influence of visual design of Instagram concerning picture type, hashtag, number of likes, and price information, on intention to buy. These elements are suggested to influence visual attention of the audience, and ultimately influence the intention to buy. Experiment design method is implemented to test the hypotheses using an eye tracker device to measure visual attention. An Instagram post from an SME offering pouch is used as a research object. Eight paired of dummy Instagram posts (the product picture and the consumer picture) are designed to test the research hypotheses, and a pair of existing Instagram post acts as a control stimulus. Forty participants are exposed to all of the stimuli using a within-subject design approach. Descriptive statistic analysis shows that participants like product picture more than consumer, and stimulus containing price information, hashtag, and many likes. Inferential statistic analysis shows that picture type and likes all influence visual attention and no significant relationship between visual attention and intention to buy.Keywords: Intention to buy; visual attention; statistic inferensia; desain eksperimen

scholarly journals Model Fusion for Building Type Classification from Aerial and Street View Images

2019 ◽  
Vol 11 (11) ◽  
pp. 1259 ◽  
Author(s):  
Eike Jens Hoffmann ◽  
Yuanyuan Wang ◽  
Martin Werner ◽  
Jian Kang ◽  
Xiao Xiang Zhu
Keyword(s):  
Early Stage ◽  
Destructive Effect ◽  
Street View ◽  
Learning Techniques ◽  
The Common ◽  
Diverse Ensemble ◽  
Type Classification ◽  
Level Fusion ◽  
Very High ◽  
Image Type

This article addresses the question of mapping building functions jointly using both aerial and street view images via deep learning techniques. One of the central challenges here is determining a data fusion strategy that can cope with heterogeneous image modalities. We demonstrate that geometric combinations of the features of such two types of images, especially in an early stage of the convolutional layers, often lead to a destructive effect due to the spatial misalignment of the features. Therefore, we address this problem through a decision-level fusion of a diverse ensemble of models trained from each image type independently. In this way, the significant differences in appearance of aerial and street view images are taken into account. Compared to the common multi-stream end-to-end fusion approaches proposed in the literature, we are able to increase the precision scores from 68% to 76%. Another challenge is that sophisticated classification schemes needed for real applications are highly overlapping and not very well defined without sharp boundaries. As a consequence, classification using machine learning becomes significantly harder. In this work, we choose a highly compact classification scheme with four classes, commercial, residential, public, and industrial because such a classification has a very high value to urban geography being correlated with socio-demographic parameters such as population density and income.

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