A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Filters
Learning Hybrid Part Filters for Scene Recognition
[chapter]
2012
Lecture Notes in Computer Science
Preserved Fulltext
This paper introduces a new image representation for scene recognition, where an image is described based on the response maps of object part filters. ...
The part filters are learned from existing datasets with object location annotations, using deformable part-based models trained by latent SVM [1]. ...
Very recently, DPM was employed by Pandey and Lazebnik [20] directly for scene recognition and the learned scene parts from DPM may correspond to recurring elements or objects in a scene class. ...
doi:10.1007/978-3-642-33715-4_13
fatcat:wv3ocxzyondi5mlo2izlwqz4l4
A Study on Machine Learning Approach for Fingerprint Recognition System
2019
SMART MOVES JOURNAL IJOSCIENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This paper reviews the fingerprint classification including feature extraction methods and learning models for proper classification to label different fingerprints. ...
The result and accuracy of fingerprint recognition depends on the presence of valid minutiae. ...
Rezaei [6] explored an efficient algorithm for fingerprint recognition based on hybrid features of Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and moment methods. ...
doi:10.24113/ijoscience.v5i11.234
fatcat:gpcjty5w7fgbxcwupoqogr77qm
Learning Important Spatial Pooling Regions for Scene Classification
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification. ...
It is often caused by the complexity of latent image structure when convolving part filters with input images. ...
We set the number of part filters to 8 for each scene category. The number of ISPRs is set to 4 for each part filter. ...
doi:10.1109/cvpr.2014.476
dblp:conf/cvpr/LinLLJ14
fatcat:4z22ugkoebewnayulp4qomi44y
Arabic Cursive Text Recognition from Natural Scene Images
2019
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. ...
This paper presents a comprehensive survey on Arabic cursive scene text recognition. ...
Acknowledgments: The authors would like to thank the Center for Artificial Intelligence and RObotics (CAIRO-ikhoza) lab under MJIIT, Universiti Teknologi Malaysia for arranging funds to conduct this research ...
doi:10.3390/app9020236
fatcat:jowekkffqrelvj3mk4324uj2v4
Channel surfing in the visual brain
2006
Trends in Cognitive Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Specifically, we discuss the information available for categorisation from an analysis of different spatial scales by a bank of flexible, interacting spatial-frequency (SF) channels. ...
Instead, we argue that following perceptual learning a specification of the diagnostic, object-based, SF information dynamically influences the "top-down" processing of retina-based SF information by these ...
Thanks to Iona Alexander for comments on an earlier draft of this manuscript. ...
doi:10.1016/j.tics.2006.10.007
pmid:17071128
fatcat:bwx66kgotfdtrjaidickyvgvie
AUTOMATIC DETECTION AND RECOGNITION OF MAN-MADE OBJECTS IN HIGH RESOLUTION REMOTE SENSING IMAGES USING HIERARCHICAL SEMANTIC GRAPH MODEL
2013
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Besides, discriminative learning and generative learning are combined interleavely in the inference procedure, to improve the training error and recognition efficiency. ...
This multi-levels structure is promising to enable a more comprehensive understanding of natural scenes. ...
Many approaches have been proposed for object detection and recognition, using textural features, wavelet filters, and so on. ...
doi:10.5194/isprsarchives-xl-1-w1-333-2013
fatcat:xczqmq3ut5h7zddmvd24h5o7u4
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification
2015
2015 IEEE International Conference on Computer Vision (ICCV)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper, we present a novel pipeline built upon deep CNN features to harvest discriminative visual objects and parts for scene classification. ...
Then we perform both unsupervised and weakly supervised learning to screen these patches and discover discriminative ones representing category-specific objects and parts. ...
The remaining patches correspond to the objects and parts that frequently occur in the images for this scene category. ...
doi:10.1109/iccv.2015.152
dblp:conf/iccv/WuWWY15
fatcat:qfitgh2winaz3hq7ncsrjgofam
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification
[article]
2015
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In this ICCV 2015 paper, we present a novel pipeline built upon deep CNN features to harvest discriminative visual objects and parts for scene classification. ...
Then we perform both unsupervised and weakly supervised learning to screen these patches and discover discriminative ones representing category-specific objects and parts. ...
The remaining patches correspond to the objects and parts that frequently occur in the images for this scene category. ...
arXiv:1510.01440v1
fatcat:pn7dh67gtjdd7es4ruroy32iri
Soft Computing Techniques for Various Image Processing Applications: A Survey
2020
Journal Electrical and Electronic Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The techniques of image processing stem from two principal applications namely, improvement of pictorial information for human interpretation and processing of scene data for automatic machine perception ...
There are many hybridized approaches like neuro-fuzzy system (NFS), fuzzy-neural network (FNN), genetic-fuzzy systems, neuro-genetic systems, neuro-fuzzy-genetic system exist for various image processing ...
These applications include image filtering/ enhancement, edge extraction, segmentation, object/ face/ target recognition, compression, handwritten digit recognition, motion estimation etc. ...
doi:10.11648/j.jeee.20200803.11
fatcat:ko47gevpuvavjnptb2j6xtltje
A Novel Classification Approach Capable Indoor Scene Picture Identification with Hybrid Feature Selection Algorithm
2018
International Journal of Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
color illuminations. many experiments has been conducted over the projected model for the performance analysis of the indoor scene recognition system within the planned model. ...
Image scene classification is an integral part of several aspects of image process. ...
SVM is supervised machine learning approach specifically designed for pattern matching. ...
doi:10.5120/ijca2018917055
fatcat:kzjif4w22jbndh7u2iqamhdqfa
Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation
2017
IEEE transactions on circuits and systems for video technology (Print)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2016 pre-print arXiv:1601.07977v1 fatcat:6lmzmiuyojgibhcgatng5x4g7e
To further improve the performance, in this paper, we propose to combine CNN with dictionarybased models for scene recognition and visual domain adaptation. ...
After that, the part dictionary is used to operate with the multi-scale image inputs for generating midlevel representation. ...
With the part dictionary D cs (D cm ) learned, we can consider it as a group of local discriminative filter banks. ...
doi:10.1109/tcsvt.2015.2511543
fatcat:3yl3qi3lszhj5e6gwnw7acarju
Detection and Recognition of Moving Video Objects: Kalman Filtering with Deep Learning
2021
International Journal of Advanced Computer Science and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This paper presents an approach for object recognition in videos by combining Kalman filter with CNN. Kalman filter is first applied for detection, removing the background and then cropping object. ...
The proposed hybrid algorithm has been applied to 8 different videos with total duration of is 15.4 minutes, containing 23100 frames. ...
COMPARISONS WITH EXISTING METHODS This paper proposed a hybrid system of Kalman filtering and CNN for detection (with background removal) and recognition of moving objects in videos. ...
doi:10.14569/ijacsa.2021.0120118
fatcat:psv6ckdcd5ffpfsqutslockn7u
From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this paper, inspired by the intuitive differences between scenes and objects, we propose Adi-Red, an adaptive approach to discriminative region discovery for scene recognition. ...
As deep learning approaches to scene recognition emerge, they have continued to leverage discriminative regions at multiple scales, building on practices established by conventional image classification ...
ACKNOWLEDGMENTS The author Zhengyu Zhao thanks the China Scholarship Council for providing financial support. ...
arXiv:1807.08624v1
fatcat:5yxiurpio5dmfoitgf6cnpa4mi
A Survey On Video Scene Text Deblurring Using Text-Specific Multiscale Dictionaries
2015
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This paper is concerned with the study of scene text detec tion and recognition from blurry natural video scene i.e. blurred images extracted from video which is imp ortant for image related purposes. ...
In this survey,we extend an existing end- to-end solution for text detection and recognition in natural images to video https://www.ijiert.org/paper-details?paper_id=140426 ...
A series of textspecific multi-scale dictionaries (TMD) and a natural scene dictionary is learned for separately modeling the priors on the text and non-text fields. ...
doi:10.5281/zenodo.1473903
fatcat:fn7v527luzbbxjwr5tnfc4jyc4
Object recognition within cluttered scenes employing a hybrid optical neural network filter
2004
Optical Engineering: The Journal of SPIE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We have recently proposed a hybrid filter, which we call the Hybrid Optical Neural Network (HONN) filter. ...
This paper presents the design and implementation of the HONN filter architecture and assesses its object recognition performance in clutter. ...
Acknowledgement The author would like to thank everyone in the 'Laser and Photonics Systems Research Group' to contribute for the completion of this research paper. ...
doi:10.1117/1.1767194
fatcat:pohrasjt6jfrfl5osxh3vxdzje
« Previous
Showing results 1 — 15 out of 16,340 results