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VidSeg-GAN: Generative Adversarial Network for Video Object Segmentation Tasks 2018

VidSeg-GAN: Generative Adversarial Network for Video Object ...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Saptakatha Adak Sukhendu Das Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9781450366151

Video object segmentation aims to segment objects in a video sequence, given some user annotation which indicates the object of interest. Although Convolutional Neural Networks (CNNs) have been used in the recent past for the purpose of foreground segmentation in videos, adversarial training methods have not been used effectively to solve this problem, in spite of its extensive use for solving many other problems in computer vision. Earlier, flow features and motion trajectories have been extensively used to capture the temporal consistency between subsequent frames to segment moving objects in videos. However, we show that our proposed framework of processing the video frames independently using a deep generative adversarial network (GAN), is able to maintain the temporal coherency across frames without the use of any explicit trajectory based information, to provide superior results. Our main contribution lies in introducing a GAN based framework along with the incorporation of an Intersection-over-Union score based novel cost function for training the model, to solve the problem of foreground object segmentation in videos. the proposed method, when evaluated on popular real-world video segmentation datasets viz. DAVIS, SegTrack-v2 and YouTube-Objects, exhibits substantial performance gain over the recent state-of-the-art methods.

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Perfectly Secure Shamir's Secret Sharing Scheme for Privacy Preserving image processing Over Cloud 2018

Perfectly Secure Shamir's Secret Sharing Scheme for Privacy ...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Debanjan Sadhya Akshay Agarwal Balasubramanian Raman Indian Institute of Technology Roorkee Roorkee India Manipal Institute of Technology Manipal India

ISBN: (纸本)9781450366151

Cloud computing is a highly prospective paradigm in which computational resources from third parties are used for processing outsourced data. Nonetheless, the distributed architecture of this concept poses many security and privacy threats for the data owners. Shamir's secret sharing is an effective technique for distributing and processing secret images over the encrypted domain. However, it has got some critical limitations primarily due to the presence of correlated information between the image pixels. Our study addresses this problem by proposing a perfectly secure Shamir's secret sharing scheme for images. Our work builds upon the formal notion of perfect secrecy for encoding the Shamir's shares in a particular manner such that they (i.e. encoded shares) do not reveal any additional information about the original image. Importantly, we have provided both theoretical and empirical validation of our proposed approach. We have also performed several image filtering operations on the stored shares and found the resulting PSNR and NCC values to be similar in the plain and encrypted domains. Hence our work provides a privacy-preserving and secure framework for working with images over a cloud-based architecture.

关键词： Perfect Secrecy Encrypted Domain Cloud Computing Shamir's Secret Sharing

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MultiDIAS: A Hierarchical Multi-layered Document image Annotation System 4th

MultiDIAS: A Hierarchical Multi-layered Document Image Annot...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Poddar, Arnab Mukherjee, Rohan Mukhopadhyay, Jayanta Biswas, Prabir Kumar Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9789811393600

Content of the document images are often shows hierarchical multi-layered tree structure. Further, the algorithms for document image applications like line detection, paragraph detection, word recognition, layout analysis etc. require pixel level annotation. In this paper, a Multi-layered Document image Annotation System (MultiDIAS) has been introduced. the proposed system simultaneously provide a platform for hierarchical and pixel level annotation of document. MultiDIAS label the document image in four hierarchical layers (layout type, entity type, line type, word type) assigned by the user. the output generated are four ground-truth images and an XML file representing the metadata information. the MultiDIAS is tested on a complex handwritten manuscript written by renowned film director Satyajit Ray for the movie ‘Goopi Gyne Bagha Byne’. this annotated data generated using MultiDIAS can further be used in a wide range of applications of document image understanding and analysis. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image annotation

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An Enhanced Approach in Detecting Object Applied to Automotive Traffic Roads Signs

An Enhanced Approach in Detecting Object Applied to Automoti...

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International conference on Optimization and Applications (ICOA)

作者： Anass Barodi Abdrrahim Bajit Mohammed Benbrahim Ahmed Tamtaoui Laboratory of Electrical Systems and Telecommunication Engineering National School of Applied Sciences Ibn Tofail University Kenitra Morocco National Institute of Posts and Telecommunications (INPT-Rabat) Mohammed V University Rabat Morocco

ISBN: (数字)9781728166544

ISBN: (纸本)9781728166551

In this article, we use among and the best-known library is Open computer vision we call it for short OpenCV. It is used for image processing, to do all operations we want, to isolate and detect a specific object, which in our case are traffic road signs. We process to find the most efficient methods of detection of traffic road signs. Our objective is to demonstrate the links the elements for optimized and powerful to computer vision algorithms that are easy to use as typing in an image and video processing. Most of the techniques they employed the color selection, edge detection, a region of interest selection, and shapes transformation of detection. Many applications require the recognition of traffic road signs in urban areas. the automation of this task is necessary, for example, ADAS systems, and the application of vision robotics or an autonomous vehicle, consist of recognizing and identifying traffic road signs as quickly as possible with errors to be minimized, in images of their type acquired by an embedded camera on board a vehicle.

关键词： image color analysis image edge detection Roads Information filters Cameras

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BatchOut: Batch-level feature augmentation to improve robustness to adversarial examples 18

BatchOut: Batch-level feature augmentation to improve robust...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Akshayvarun Subramanya Konda Reddy Mopuri R. Venkatesh Babu Video Analytics Lab Indian Institute of Science Bangalore India

ISBN: (纸本)9781450366151

Machine Learning models are known to be susceptible to small but structured changes to their inputs that can result in wrong inferences. It has been shown that such samples, called adversarial samples, can be created rather easily for standard neural network architectures. these adversarial samples pose a serious threat for deploying state-of-the-art deep neural network models in the real world. We propose a feature augmentation technique called BatchOut to learn robust models towards such examples. the proposed approach is a generic feature augmentation technique that is not specific to any adversary and handles multiple attacks. We evaluate our algorithm on benchmark datasets and architectures to show that models trained using our method are less susceptible to adversaries created using multiple methods.

关键词： Adversarial examples

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Real-Time HDTV-to-8K TV Hardware Upconverter 12th

Real-Time HDTV-to-8K TV Hardware Upconverter

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12th International Joint conference on computer vision, Imaging and computer graphics theory and Applications (VISIGRAPP)

作者： Gohshi, Seiichi Kogakuin Univ Shinjuku Ku 1-24-2 Nishi Shinjuku Tokyo Japan

ISBN: (纸本)9783030122096;9783030122089

8K is the pinnacle of the video systems and 8K broadcasting service will be started in December 2018. However, the availability of content for 8K TV is still insufficient, a situation similar to that of HDTV in the 1990s. Upconverting analogue content to HDTV content was important to supplement the insufficient HDTV content. this upconverted content was also important for news coverage as HDTV equipment was heavy and bulky. the current situation for 8K TV is similar wherein covering news with 8K TV equipment is very difficult as this equipment is much heavier and bulkier than that required for HDTV in the 1990s. the HDTV content available currently is sufficient, and the equipment has also evolved to facilitate news coverage;therefore, an HDTV-to-8K TV upconverter can be a solution to the problems described above. However, upconversion from interlaced HDTV to 8K TV results in an enlargement of the images by a factor of 32, thus making the upconverted images very blurry. Super resolution (SR) is a technology to solve the enlargement blur issue. One of the most common SR technologies is super resolution image reconstruction (SRR). However, SRR has limitations to use for the HDTV-to-8K TV upconverter. In this paper an HDTV-to-8K TV upconverter with nonlinear processing SR has been proposed in this study in order to fix this issue.

关键词： 8KTV 4KTV HDTV Up-convert Super resolution with non-linear processing Super resolution image reconstruction Learning based super resolution Non-linear signal processing

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Geometric image Synthesis 14th

Geometric Image Synthesis

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14th Asian conference on computer vision (ACCV)

作者： Abu Alhaija, Hassan Mustikovela, Siva Karthik Geiger, Andreas Rother, Carsten Heidelberg Univ Visual Learning Lab Heidelberg Germany MPI Intelligent Syst Autonomous Vis Grp Tubingen Germany Univ Tubingen Tubingen Germany

ISBN: (纸本)9783030208769;9783030208752

the task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image generation method that leverages various types of scene information, including geometry and segmentation, to create realistic looking natural images that match the desired scene structure. Our geometrically-consistent image synthesis method is a deep neural network, called Geometry to image Synthesis (GIS) framework, which retains the advantages of a trainable method, e.g., differentiability and adaptiveness, but, at the same time, makes a step towards the generalizability, control and quality output of modern graphics rendering engines. We utilize the GIS framework to insert vehicles in outdoor driving scenes, as well as to generate novel views of objects from the Linemod dataset. We qualitatively show that our network is able to generalize beyond the training set to novel scene geometries, object shapes and segmentations. Furthermore, we quantitatively show that the GIS framework can be used to synthesize large amounts of training data which proves beneficial for training instance segmentation models.

关键词： Geometry

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AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral image Classification 1

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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

作者： Patel, Heena Upla, Kishor P. Sardar Vallabhbhai National Institute of Technology SuratGujarat India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

this paper addresses an approach for classification of hyperspectral imagery (HSI). In remote sensing, the HSI sensor acquires hundreds of images with very narrow but continuous spectral width in visible and near-infrared regions of the electromagnetic (EM) spectrum. Due to the nature of data acquisition with contiguous bands, the HS images are very useful in classification and/or the identification of materials present in the captured geographical area. However, the low spatial resolution and large volume of HS images make the classification of those images as a challenging task. In the proposed approach, we use an autoencoder with convolutional neural network (AECNN) for classification of HS image. Pre-processing procedure with autoencoder leads to obtain optimized weights in the initial layer of CNN model. Moreover, features are enhanced in the HS images by utilizing the autoencoder. the CNN is used for efficient extraction of the features and same is also utilised for the classification of HS data. the potential of the proposed approach has been verified by conducting the experiments on three recent datasets. the experimental results are compared with the results obtained in the geoscience and remote sensing society (GRSS) image Fusion Contest-2018 held at IEEE International Geoscience and Remote Sensing Symposium (IGARSS)-2018 and other existing frameworks for HSI classification. the testing accuracy of classification for the proposed approach is better than that of the other existing deep learning based methods. © Springer Nature Singapore Pte Ltd, 2019.

关键词： Remote sensing

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An Efficient Character Segmentation Algorithm for Connected Handwritten Documents 4th

An Efficient Character Segmentation Algorithm for Connected ...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Rajput, Vishal Jayanthi, N. Indu, S. Department of Electronics and Communication Engineering PDPM-Indian Institute of Information Technology Design and Manufacturing Jabalpur JabalpurMP482005 India Department of Electronics and Communication Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789811393600

this paper proposes an efficient method of character segmentation for handwritten text. the main challenge in character segmentation of hand-written text is the varied size of each letter in different documents, connected alphabets in a word in cursive writing and the presence of ligatures within an open character. Hence, this paper proposes an adaptive vertical pixel count algorithm to solve the problem of over-segmentation due to the presence of open characters such as ‘w’, ‘v’ and ‘m’. Proposed algorithm works effectively against both the handwritten and standard text. the proposed method is evaluated on IAM and self-created data set. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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SACIC: A Semantics-Aware Convolutional image Captioner Using Multi-level Pervasive Attention 26th

SACIC: A Semantics-Aware Convolutional Image Captioner Using...

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26th International conference on Neural Information processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Parameswaran, Sandeep Narayan Das, Sukhendu Indian Inst Technol Madras Dept Comp Sci & Engn Visualizat & Percept Lab Chennai Tamil Nadu India

ISBN: (纸本)9783030367183;9783030367176

Attention mechanisms alongside encoder-decoder architectures have become integral components for solving the image captioning problem. the attention mechanism recombines an encoding of the image depending on the state of the decoder, to generate the caption sequence. the decoder is predominantly recurrent in nature. In contrast, we propose a novel network possessing attention-like properties that are pervasive through its layers, by utilizing a convolutional neural network (CNN) to refine and combine representations at multiple levels of the architecture for captioning images. We also enable the model to use explicit higher-level semantic information obtained by performing panoptic segmentation on the image. the attention capability of the model is visually demonstrated, and an experimental evaluation is shown on the MS-COCO dataset. We exhibit that the approach is more robust, efficient, and yields better performance in comparison to the state-of-the-art architectures for image captioning.

关键词： image captioning Convolutional neural networks Deep learning computer vision

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