This research project explores a paradigm shift in perceptual enhancement by integrating a Unified Recognition Framework andvision-Language Pre-Training in three-dimensional image reconstruction. Through the synergy ...
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With the rapid change of society and the continuous development of science and technology, the application of instruments is more and more widely in all kinds of places from daily life to chemical power and aerospace....
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The computervision has become an indispensable part in the fields of biomedical application and laboratory research in which images are processed and analyzed. In this article, we have presented a non-invasive real-t...
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ISBN:
(纸本)9789811073861;9789811073854
The computervision has become an indispensable part in the fields of biomedical application and laboratory research in which images are processed and analyzed. In this article, we have presented a non-invasive real-time biomedical imageprocessing design. The aim of the current research is to propose a hybrid design by using techniques such as watershed, Eulerian Video Magnification and morphological filters for multiple biomedical applications like detection of brain tumor, lung cancer, gallbladder stone, cataract, and measurement of pulse rate and its implementation using MATLAB. The proposed design can process different category of images and manipulate them to visualize and understand the defects clearly that is not visible to the human eyes.
Cancer is a rampant phenomenon caused by uncontrollable cells that grow and spread throughout the body. Invasive Ductal Carcinoma 1 is the most common type of breast cancer, which can be fatal for females if not detec...
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This paper discusses an outdoor guidance system that can be implemented on a smartphone. The purpose of such a system would be to assist the blind or visually impaired. The system will be able to detect the ground, fi...
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ISBN:
(纸本)9781450363549
This paper discusses an outdoor guidance system that can be implemented on a smartphone. The purpose of such a system would be to assist the blind or visually impaired. The system will be able to detect the ground, find obstacles, and notify the user when the obstacle is close enough to constitute a tripping hazard. This work uses a novel semantic segmentation technique and proposes an alternate method for positioning the phone for better fall prevention techniques.
This paper addresses an approach to scene reconstruction by inferring missing range data in a partial range map based on intensity image and sparse initial range data. It is assumed that the initial known range data i...
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Geometric registration of visual images is a fundamental intermediate processing step in a wide variety of computervision applications that deal with image sequence analysis. 2D motion recovery and mosaicing, 3D scen...
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Geometric registration of visual images is a fundamental intermediate processing step in a wide variety of computervision applications that deal with image sequence analysis. 2D motion recovery and mosaicing, 3D scene reconstruction and also motion detection approaches strongly rely on accurate registration results. However, automatically assessing the overall quality of a registration is a challenging task. In particular, optimization criteria used in registration are not necessarily closely linked to the final quality of the result and often show a lack of local sensitivity. In this paper we present a new approach for an objective quality metric in 2D image registration. The proposed method is based on local structure analysis and facilitates voting-techniques for error pooling, leading to an objective measure that correlates well with the visual appearance of registered images. Since observed differences are furthermore classified in more detail according to various underlying error sources, the new measure not only yields a suitable base for objective quality assessment, but also opens perspectives towards an automatic and optimally adjusted correction of errors.
Recently the term Deep Learning has been creating a lot of interest in the fields of Artificial Intelligence, computervision and Natural Language processing. And especially the Convolution Neural Networks (CNN) are g...
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ISBN:
(纸本)9781509037049
Recently the term Deep Learning has been creating a lot of interest in the fields of Artificial Intelligence, computervision and Natural Language processing. And especially the Convolution Neural Networks (CNN) are giving state of art results in image recognition, scene understanding, object detection andimage description etc. Generally in CNN the processing of images is done in RGB colourspace even though we have many other colourspaces available. In this paper we try to understand the effect of image colourspace on the performance of CNN models in recognizing the objects present in the image. We evaluate this on CIFAR10 dataset, by converting all the original RGB images into four other colourspaces like HLS, HSV, LUV, YUV etc. To compare results we have trained AlexNet with fixed set of parameters on all five colourspaces, including RGB. We have observed that LUV colourspace is the best alternative to RGB colourspace to use with CNN models with almost equal performance on the test set of CIFAR10 dataset. While YUV colourspace is the worst to use with CNN models.
This project proposes a new method that uses fuzzy comprehensive evaluation method to integrate ResN et-50 self- supervised and Rep Vggsupervised learning. The source image dataset HWOBC oracle is taken as input, the ...
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The problem of visual question answering (VQA) is of significant importance both as a challenging research question and for the rich set of applications it enables. In this context, however, inherent structure in our ...
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The problem of visual question answering (VQA) is of significant importance both as a challenging research question and for the rich set of applications it enables. In this context, however, inherent structure in our world and bias in our language tend to be a simpler signal for learning than visual modalities, resulting in VQA models that ignore visual information, leading to an inflated sense of their capability. We propose to counter these language priors for the task of VQA and make vision (the V in VQA) matter! Specifically, we balance the popular VQA dataset (Antol et al., in: ICCV, 2015) by collecting complementary images such that every question in our balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the question. Our dataset is by construction more balanced than the original VQA dataset and has approximately twice the number of image-question pairs. Our complete balanced dataset is publicly available at http://***/ as part of the 2nd iteration of the VQA Dataset and Challenge (VQA v2.0). We further benchmark a number of state-of-art VQA models on our balanced dataset. All models perform significantly worse on our balanced dataset, suggesting that these models have indeed learned to exploit language priors. This finding provides the first concrete empirical evidence for what seems to be a qualitative sense among practitioners. We also present interesting insights from analysis of the participant entries in VQA Challenge 2017, organized by us on the proposed VQA v2.0 dataset. The results of the challenge were announced in the 2nd VQA Challenge Workshop at the IEEE conference on computervision and Pattern Recognition (CVPR) 2017. Finally, our data collection protocol for identifying complementary images enables us to develop a novel interpretable model, which in addition to providing an answer to the given (image, question) pair, also provides a counter-example ba
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