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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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New corner detector using non-cornerness measure

New corner detector using non-cornerness measure

引用

IEEE international conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Woon Cho Seokmok Park Jacob D'avy Joonki Paik The University of Tennessee Knoxville TN USA Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science and Film Chung-Ang University Seoul Korea University of Tennessee Knoxville Knoxville TN US

In this paper, we present a novel corner detection method based on a new non-cornerness measure. The non-cornerness function is comprised of two steps: 1) eliminate any pixel located in a flat region and 2) remove a pixel that is positioned along an edge in any orientation. The experimental results show that our proposed method outperforms previous corner detectors.

关键词： Detectors image edge detection Indexes Boundary conditions computer vision Real-time systems Information and communication technology

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Recognition Based Segmentation of Connected Characters in Text Based CAPTCHAs

Recognition Based Segmentation of Connected Characters in Te...

引用

2016 8th IEEE international conference on Communication Software and Networks(ICCSN 2016)

作者： Rafaqat Hussain Hui Gao Riaz Ahmed Shaikh Shazia Parveen Soomro School of Computer Science and Engineering University of Electronics Science and Technology of China

Text based CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)is the most widely used mechanism adopted by numerous popular web sites in order to differentiate between machines and humans,however due to extensive research carried out by computer vision researchers,it is now a days vulnerable against automated *** is the most difficult task in automatic recognition of CAPTCHAs,therefore contemporary Text based CAPTCHAs try to combine the characters together in order to make them as segmentation resistant against these attacks as *** this research,we have found vulnerabilities in such CAPTCHAs,a novel mechanism,*** recognition based segmentation is applied to crop such connected characters,a sliding window based neural network classifier is used to recognize and segment the connected *** results have proved 95.5%recognition success rate and 58.25%segmentation success rate on our dataset of tmall CAPTCHAs,this algorithm is further tested on two other datasets of slightly different implementations and promising results were achieved.

关键词： CAPTCHA intelligent character recognition computer vision image processing pattern recognition machine learning

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Noise robust speech recognition using recent developments in neural networks for computer vision

Noise robust speech recognition using recent developments in...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Takuya Yoshioka Katsunori Ohnishi Fuming Fang Tomohiro Nakatani NTT Corporation Japan University of Tokyo Japan Tokyo Institute of Technology Japan

ISBN: (纸本)9781479999897

Convolutional Neural Networks (CNNs) are superior to fully connected neural networks in various speech recognition tasks and the advantage is pronounced in noisy environments. In recent years, many techniques have been proposed in the computer vision community to improve CNN's classification performance. This paper considers two approaches recently developed for image classification and examines their impacts on noisy speech recognition performance. The first approach is to increase the depth of convolution layers. Different approaches to deepening the CNNs are compared. In particular, the usefulness of learning dynamic features with small convolution layers that perform convolution in time is shown along with a modulation frequency analysis of the learned convolution filters. The second approach is to use trainable activation functions. Specifically, the use of a Parametric Rectified Linear Unit (PReLU) is investigated. Experimental results show that both approaches yield significant improvements in performance. Combining the two approaches further reduces recognition errors, producing a word error rate of 11.1% in the Aurora4 task, the best published result for this corpus, with a standard one-pass bi-gram decoding set-up.

关键词： Convolution Neural networks Speech recognition Acoustics Hidden Markov models Noise measurement Training

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Human detection and tracking in video surveillance system

Human detection and tracking in video surveillance system

引用

IEEE international conference on Computational Intelligence and Computing Research

作者： K. V. Sriram R. H. Havaldar Department of E&C Engineering Visvesvaraya Technological University Belagavi Karnataka India KLE Dr. M.S.Sheshgiri College of Engineering and Technology Belagavi Karnataka India

ISBN: (纸本)9781509006137

The human detection and tracking in a video plays major roll in security systems. This paper proposes an approach to detect and track the persons in a video. This approach uses Gaussian Mixture Model to detect the person and Kalman filter to track the detected person. The processing time to detect the person is reduced by performing the detection operation on down-sampled video. After detecting the person, the original size of the video is reconstructed using Papoulis-Gerchberg method. The performance analysis is carried out by comparing with the state-of-the-art-algorithms. The experimental results show that the proposed method is well suited for detecting and tracking the person in lower processing time.

关键词： image resolution conferences Computational modeling computer vision Kalman filters image color analysis Video sequences

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MedianStruck for long-term tracking applications

MedianStruck for long-term tracking applications

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Florian Baumann Enes Dayangac Josep Aulinas Matthias Zobel Adasens Automotive GmbH Lindau Germany

ISBN: (纸本)9781467389112

In this paper, we propose a mutual framework that combines two state-of-the-art visual object tracking algorithms. Both trackers benefit from each other's advantage leading to an efficient visual tracking approach. Many state-of-the-art trackers have poor performance due to rain, fog or occlusion in real-world scenarios. Often, after several frames, objects are getting lost, only leading to a short-term tracking capability. In this paper, we focus on long-term tracking, preserving real-time capability and very accurate positioning of tracked objects. The proposed framework is capable to track arbitrary objects, leading to decreased labeling efforts and an improved positioning of bounding boxes. This is especially interesting for applications such as semi-automatic labeling. The benefit of our proposed framework is demonstrated by comparing it with the related algorithms using own sequences as well as a well-known and publicly available dataset.

关键词： Target tracking computer vision Visualization image motion analysis Training Trajectory

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Predicting Visual Attention using Gamma Kernels

Predicting Visual Attention using Gamma Kernels

引用

IEEE international conference on Acoustics, Speech and Signal processing

作者： Ryan Burt Eder Santana Jose C. Principe Nina Thigpen Andreas Keil Computational NeuroEngineering Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville FL 32601

ISBN: (纸本)9781479999897

Saliency measures are a popular way to predict visual attention. However, saliency is normally tested on sets of single resolution images that are unlike what the human vision system sees. We propose a new saliency measure based on convolving images with 2D gamma kernels which function as a comparison between a center and a surrounding neighborhood. The two parameters in the gamma kernel provide an ideal way to change the size of both the center and the surrounding neighborhood, which makes finding saliency at different scales simple and fast. We test the new saliency measure on both the CAT2000 database and the Toronto database and compare the results with other simple saliency methods. In addition, we test the methods on a foveated version of the Toronto database to test whether these methods perform well in a fixation system similar to the human vision system. Gamma saliency is shown to both perform better and compute faster than the competing methods in both the standard databases and the foveated version.

关键词： Foveation Gamma kernel image processing Saliency

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image retrieval and classification on deep convolutional SparkNet

Image retrieval and classification on deep convolutional Spa...

引用

IEEE international conference on Signal processing, Communications and Computing (ICSPCC)

作者： Hongyang Li Peng Su Zhizhen Chi Jingjing Wang Department of Electronic Engineering The Chinese University of Hong Kong New Territories Hong Kong School of Information and Communications Engineering Dalian University of Technology Liaoning China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9781509027095

image retrieval and classification are hot topics in computer vision and have attracted great attention nowadays with the emergence of large-scale data. We propose a new scheme to use both deep learning models and large-scale computing platform and jointly learn powerful feature representations in image classification and retrieval. We achieve a superior performance on the imageNet dataset, where the framework is easy to be embedded for daily user experience. First we conduct the classification task using deep convolutional neural networks with several novel techniques, including batch normalization and multi-crop testing to obtain a better performance. Then we transfer the network's knowledge to image retrieval task by comparing the feature codebook of the query image with those feature database extracted from the deep model. Such a search pipeline is implemented in a MapReduce framework on the Spark platform, which is suitable for large-scale and real-time data processing. At last, the system outputs to users some textual information of the predicted object searching from Internet as well as similar images from the retrieval stage, making our work a real application.

关键词： Feature extraction Sparks Training Machine learning image retrieval Convolutional codes

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ROBSTRUCK: IMPROVING OCCLUSION HANDLING OF STRUCTURED TRACKING-BY-DETECTION USING ROBUST KALMAN FILTER

ROBSTRUCK: IMPROVING OCCLUSION HANDLING OF STRUCTURED TRACKI...

引用

IEEE international conference on image processing

作者： Ivan Bogun Eraldo Ribeiro Computer Vision Laboratory Florida Institute of Technology

ISBN: (纸本)9781467399623

Starting from an object's location in a video frame, tracking-by-detection methods find the location of that object in a subsequent video frame. The tracker's detection step may produce multiple false positives during short-term occlusions, which can result in loss of track. We propose a tracking-by-detection method that is robust to short-term occlusions and false positives. Here, we extend the Struck tracker, which is based on structured SVM, to output bounding boxes at multiple scales. In addition to predicting scale changes, we use the Robust Kalman filter to decrease false-positive detections and to increase the tracker resilience to short-time occlusions. Here, we develop a special strategy for the tracker's update step, which is designed to decrease over fitting and to allow for the tracker to recover from loss of track. We thoroughly evaluate our method on a publicly available video dataset and show that it outperforms the state-of-the-art.

关键词： Visual tracking Model-free tracking Structured support vector machine Robust Kalman filter visual tracking Occlusion inspection methods False-positives VIDEO FRAMES bounding boxes Filters Steps

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An original face anti-spoofing approach using partial convolutional neural network

An original face anti-spoofing approach using partial convol...

引用

Workshops on image processing Theory, Tools and Applications, IPTA

作者： Lei Li Xiaoyi Feng Zinelabidine Boulkenafet Zhaoqiang Xia Mingming Li Abdenour Hadid School of Electronics and Information Northwestern Polytechnical University Xi'an Shaanxi China Center for Machine Vision Research (CMV) University of Oulu Finland

ISBN: (纸本)9781467389112

Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep learning into face anti-spoofing. However, most approaches just use the final fully-connected layer to distinguish the real and fake faces. Inspired by the idea of each convolutional kernel can be regarded as a part filter, we extract the deep partial features from the convolutional neural network (CNN) to distinguish the real and fake faces. In our prosed approach, the CNN is fine-tuned firstly on the face spoofing datasets. Then, the block principle component analysis (PCA) method is utilized to reduce the dimensionality of features that can avoid the over-fitting problem. Lastly, the support vector machine (SVM) is employed to distinguish the real the real and fake faces. The experiments evaluated on two public available databases, Replay-Attack and CASIA, show the proposed method can obtain satisfactory results compared to the state-of-the-art methods.

关键词： Face Feature extraction Neural networks Training Support vector machines Kernel Databases

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An analysis of vision based techniques for quality assessment and enhancement of camera captured document images

An analysis of vision based techniques for quality assessmen...

引用

international conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Pooja Sharma Shanu Sharma Department of Computer Science & Engineering Amity University Uttar Pradesh India

ISBN: (纸本)9781467382045

Now a days with the advancement of technology, there has been a tremendous rise in the volume of captured and distributed content. image acquisition can be done with the help of scanners, cameras, smart phones, tablets etc. Document retrieval and recognition systems require high quality document images but most of the time, the images acquired suffer from various degradations like blur, uneven illumination, low resolution etc. To reduce the processing time and get good results, we require methods to evaluate and improve the quality of such images. This paper reviews the quality assessment methods and enhancement techniques for document images as well as represents a survey of the work that has been performed in the field of document image quality assessment and enhancement.

关键词： Optical character recognition software Cameras Degradation Measurement image enhancement Quality assessment image quality

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