Enhancing Pix2Pix for Remote Sensing Image Classification

Yan, Hongping; Huo, Chunlei; Yu, Jiayuan; Pant, Chunhong

doi:10.1109/icpr.2018.8545870

Cited by 32 publications

(17 citation statements)

References 9 publications

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“…An approach for segmenting and converting the clothes in images is proposed in [39] and a compact end-to-end cGAN network is proposed for real-time pixel-level segmentation of insulators in high-voltage lines [42]. Detection and segmentation of roads, rivers, and areas for remote sensing are addressed via cGAN models in [40], [43], [46], [47]. Body parts are segmented by GAN to improve person identification [41] while cell membrane and nucleus are segmented by a cGAN in [44].…”

Section: Methodsmentioning

confidence: 99%

Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Eslami

Tabarestani

Albarqouni

et al. 2020

IEEE Trans. Med. Imaging

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Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widelyused for the diagnosis, screening and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. Although image processing could facilitate computer aided diagnosis, machine learning seems more amenable in dealing with the many parameters one would have to contend with to yield an near optimal classification or decision-making process. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously the bonesuppressed image and the organ segmented image, minimizing as a consequence the number of parameters the model has to deal with and optimizing the processing time as well; while at the same time exploiting the interplay in these parameters so as to benefit the performance of both tasks. The design architecture of this model, which relies on a conditional generative adversarial network, reveals the process on how we managed to modify the well-established pix2pix network to fit the need for multitasking and hence extending the standard image-to-image network to the new image-to-images architecture. Dilated convolutions are also used to improve the results through a more effective receptive field assessment. A comparison of the proposed approach to state-of-the-art algorithms is provided to gauge the merits of the proposed approach.

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Section: Methodsmentioning

confidence: 99%

Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Eslami

Tabarestani

Albarqouni

et al. 2020

IEEE Trans. Med. Imaging

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show abstract

“…GAN-based image-to-image translation refers to the task of converting images from one scene to another different scene. Pix2pix [39], [40] uses paired images as supervision to achieve image-to-image translation. However, it is difficult to obtain pairs of both day and night image samples in fixed places because the vehicle move too much to capture traffic images.…”

Section: Gan: Generative Adversarial Networkmentioning

confidence: 99%

Feature Enhancement Based on CycleGAN for Nighttime Vehicle Detection

et al. 2021

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“…Pix2Pix'in geleneksel GAN ile önemli farklılıkları olduğunu belirtmekte fayda var. Örneğin, Pix2Pix ayrımcısının girişi tek bir görüntü yerine bir çift görüntüdür, Pix2Pix'in ayrıştırıcısı gerçek görüntüyü sahte görüntülerden ayırmak için PatchGAN yapısını kullanır (Wang et al, 2018) .…”

Section: şEkil 1 Hücre çEkirdeği Oluşturulurken Pix2pix'in Genel Yapısıunclassified

Sentetik Pap-Smear Hücre Çekirdeği Üretiminde Üretici Çekişmeli Ağların Kullanılması

Altun¹,

Talu²

2021

European Journal of Science and Technology

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Enhancing Pix2Pix for Remote Sensing Image Classification

Cited by 32 publications

References 9 publications

Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Feature Enhancement Based on CycleGAN for Nighttime Vehicle Detection

Sentetik Pap-Smear Hücre Çekirdeği Üretiminde Üretici Çekişmeli Ağların Kullanılması

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