Generic Feature Learning in Computer Vision

Nithin, D; Sivakumar, P.

doi:10.1016/j.procs.2015.08.054

Cited by 14 publications

(3 citation statements)

References 11 publications

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“…Convolution Neural Network is generally used with a deep learning framework [9,10], with application ranging from object recognition and object tracking [11], pose estimation [12], text detection and recognition [13], visual saliency detection [14] and action recognition [15] to scene labelling [16] and many other contexts [17].…”

Section: Proposed Workmentioning

confidence: 99%

Alzheimer’s Disease Detection in MRI images using Deep Convolutional Neural Network Model

Naganandhini,

Shanmugavadivu

2024

EAI Endorsed Trans Perv Health Tech

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Alzheimer's disease (AD) is a neurodegenerative disease that affects cognitive abilities (thinking and memory etc) primarily among the elderly, due to which collective cognitive skills deteriorate, ultimately leading to death. Early detection of Alzheimer's disease is crucial for determining appropriate therapeutic options. This research investigates the use of a Deep Convolutional Neural Network (CNN) for detecting Alzheimer's disease. Due to similar brain patterns and pixel intensities, CNN demonstrates promising results in diagnosing AD through automated feature extraction and characterization. Deep Learning algorithms are designed to perform automated feature extraction and categorization of input image datasets. In this study, a two-way classifier categorizes each image as either Healthy Control (HC) or Alzheimer's disease (AD). Experiments were carried out with the MIRIAD dataset, and the accuracy of disease classification into binary categories was evaluated. The recorded results of CNN with 4- and 5 -layer architectures confirms the effectiveness of the proposed method for AD detection.

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Section: Proposed Workmentioning

confidence: 99%

Alzheimer’s Disease Detection in MRI images using Deep Convolutional Neural Network Model

Naganandhini,

Shanmugavadivu

2024

EAI Endorsed Trans Perv Health Tech

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“…The commonly used deep learning architecture known as a convolutional neural network [33] was modelled after the visual brain of animals [34]. As seen in fig 2, Originally it had been used extensively for tasks involving object recognition, but it is currently also being investigated in areas such as object tracking [35], pose estimation [36], text detection and recognition [37], visual saliency detection [38], action recognition [39], scene labelling , and many more [40].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Review on deep learning based techniques for person re-identification

Khobragade¹

2022

3C TIC

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In-depth study has recently been concentrated on human re-identification, which is a crucial component of automated video surveillance. Re-identification is the act of identifying someone in photos or videos acquired from other cameras after they have already been recognized in an image or video from one camera. Re-identification, which involves generating consistent labelling between several cameras, or even just one camera, is required to reconnect missing or interrupted tracks. In addition to surveillance, it may be used in forensics, multimedia, and robotics.Re-identification of the person is a difficult problem since their look fluctuates across many cameras with visual ambiguity and spatiotemporal uncertainty. These issues can be largely caused by inadequate video feeds or lowresolution photos that are full of unnecessary facts and prevent re-identification. The geographical or temporal restrictions of the challenge are difficult to capture. The computer vision research community has given the problem a lot of attention because of how widely used and valuable it is. In this article, we look at the issue of human re-identification and discuss some viable approaches.

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“…The architecture of deep convolutional neural networks is an artificial neural network which differentiates from multi-layer perceptron networks and which is widely used for various pattern recognition tasks. According to studies conducted by Nithin et al [12] and Aloysius et al [2] in deep learning and convolutional neural networks are more suitable and preferably used for recognition and classification when dealing with variations in data and automatic learning of domain specific features. Local connections, layers and spatial invariances inspire the architecture.…”

Section: Architecture Of Convolutional Neural Networkmentioning

confidence: 99%

Deformed character recognition using convolutional neural networks

Rani

Chandan²,

Jain³

et al. 2018

IJET

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Realization of high accuracies towards south Indian character recognition is one the truly interesting research challenge. In this paper, our investigation is focused on recognition of one of the most widely used south Indian script called Kannada. In particular, the proposed exper-iment is subject towards the recognition of degraded character images which are extracted from the ancient Kannada poetry documents and also on the handwritten character images that are collected from various unconstrained environments. The character images in the degraded documents are slightly blurry as a result of which character image is imposed by a kind of broken and messy appearances, this particular aspect leads to various conflicting behaviors of the recognition algorithm which in turn reduces the accuracy of recognition. The training of degraded patterns of character image samples are carried out by using one of the deep convolution neural networks known as Alex net.The performance evaluation of this experimentation is subject towards the handwritten datasets gathered synthetically from users of age groups between 18-21, 22-25 and 26-30 and also printed datasets which are extracted from ancient document images of Kannada poetry/literature. The datasets are comprised of around 497 classes. 428 classes include consonants, vowels, simple compound characters and complex com-pound characters. Each base character combined with consonant/vowel modifiers in handwritten text with overlapping/touching diacritics are assumed as a separate class in Kannada script for our experimentation. However, for those compound characters that are non-overlapping/touching are still considered as individual classes for which the semantic analysis is carried out during the post processing stage of OCR. It is observed that the performance of the Alex net in classification of printed character samples is reported as 91.3% and with reference to handwritten text, and accuracy of 92% is recorded.

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Generic Feature Learning in Computer Vision

Cited by 14 publications

References 11 publications

Alzheimer’s Disease Detection in MRI images using Deep Convolutional Neural Network Model

Alzheimer’s Disease Detection in MRI images using Deep Convolutional Neural Network Model

Review on deep learning based techniques for person re-identification

Deformed character recognition using convolutional neural networks

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