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Regularized Random Walk Ranking for Co-Saliency Detection in images 2018

Regularized Random Walk Ranking for Co-Saliency Detection in...

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

作者： Sayanti Bardhan Shibu Jacob Indian Institute of Technology Madras Madras India National Institute of Ocean Technology Madras India

ISBN: (纸本)9781450366151

Co-saliency detection refers to the computational process for identification of common but prominent and salient foreground regions in an image. However most of the co-saliency detection methods suffer from the following two limitations. First, co-saliency detection models largely generate superpixel level co-saliency maps that leads to sacrifice of significant information from the pixel level input images. Second, co-saliency detection frameworks mostly involve redesigned models for detection of co-salient objects in an image group, instead of utilization of the existing single image saliency detection models. To address these problems, we propose a novel framework, Co-saliency via Regularized Random Walk Ranking (CR2WR), which provides highly efficient pixel level co-saliency maps and utilizes existing saliency models on a single image to detect co-salient objects in an image sequence. this is achieved by: (1) Introducing Regularized random walk as the ranking function for a two-stage co-saliency detection framework. (2) Novel weighting function to incorporate more image information in graph construction and utilization of normalized Laplacian matrix for efficient cosaliency maps. (3) Generated saliency maps are fused further with high level priors namely, Location and Objectness priors, that enhances detection of co-salient regions. Suitably designed novel objective functions provide an enriched solution. the proposed model is evaluated on challenging benchmark co-saliency datasets. It is demonstrated that the proposed method outperforms prominent state-of-the-art methods in terms of efficiency and computational time.

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Jointly Learning Convolutional Representations to Compress Radiological images and Classify thoracic Diseases in the Compressed Domain 18

Jointly Learning Convolutional Representations to Compress R...

引用

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

作者： Ekagra Ranjan Soumava Paul Siddharth Kapoor Aupendu Kar Ramanathan Sethuraman Debdoot Sheet Indian Institute of Technology Guwahati India Indian Institute of Technology Kharagpur India National Institute of Technology Karnataka India Intel Technology India Pvt. Ltd. Bangalore India

ISBN: (纸本)9781450366151

Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain. Generally, very high dimensional medical images are down-sampled by using interpolation techniques before feeding them to deep learning models that are imageNet compliant and accept only low-resolution images of size 224 x 224 px. this popular technique may lead to the loss of key information thus hampering the classification. Significant pathological features in medical images typically being small sized and highly affected. To combat this problem, we introduce a convolutional neural network (CNN) based classification approach which learns to reduce the resolution of the image using an autoencoder and at the same time classify it using another network, while both the tasks are trained jointly. this algorithm guides the model to learn essential representations from high-resolution images for classification along with reconstruction. We have used the publicly available dataset of chest x-rays to evaluate this approach and have outperformed state-of-the-art on test data. Besides, we have experimented with the effects of different augmentation approaches in this dataset and report baselines using some well known imageNet class of CNNs.

关键词： Convolutional autoencoder

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Crowd Detection and Tracking in Surveillance Video Sequences 6

Crowd Detection and Tracking in Surveillance Video Sequences

引用

6th IEEE International conference on Smart Instrumentation, Measurement and Application, ICSIMA 2019

作者： Salim, Sohail Khalifa, Othman O. Rahman, Farah Abdul Lajis, Adidah Jalan Gombak ECE Dept Kuala Lumpur 53100 Malaysia Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9781728139524

the importance for video-based monitoring systems is on the rise leading to the growth of interest in the field of computer vision. With the increase of human population, crowd needs to be monitored, be it in a public place or in a building. Human monitoring can be quite tiresome and expensive, making way for the upcoming of automated crowd monitoring systems. Crowd analysis comprises of detection, tracking, behavioral analysis, etc. In this paper a framework for the detection of crowd, tracking and counting is proposed. the goal is to create a robust system with utmost accuracy in its results. Contrasted with sensor-based arrangements and human-based, the video-based ones take into account more adaptable functionalities, enhanced execution with lower costs. In this work, the dataset PETS2009 was used. the results showed that the proposed system has the capability to count all the people passing through the field of view of the surveillance camera. the system was tested for different types of crowd and the average efficiency of various scenarios is 83.14%. © 2019 IEEE.

关键词： image processing

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Automatic image Segmentation Using PCNN and Quantum Geese Swarm Optimization 6th

Automatic Image Segmentation Using PCNN and Quantum Geese Sw...

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6th International conference on Communications, Signal processing, and Systems (ICCSPS)

作者： Gao, H. Y. Su, X. Liang, Y. S. Harbin Engn Univ Harbin Heilongjiang Peoples R China

ISBN: (纸本)9789811065712;9789811065705

image segmentation is a very important aspect in the fields of computer vision and pattern recognition. Although Pulse-coupled Neural Network (PCNN) is an effective method for image segmentation, the optimal parameters of PCNN are difficult to be decided. In order to effectively find the optimal parameters of the PCNN, Quantum Geese Swarm Optimization (QGSO) is proposed to evolve parameters of PCNN. the proposed QGSO applies quantum computing theory to Geese Swarm Optimization (GSO) for continuous optimization problems. Minimal combined weighting entropy which considers of Shannon-entropy and Cross-entropy is used as the fitness function of QGSO. Experiment results show that the proposed method can obtain better segmented image and has an excellent performance.

关键词： PCNN image segmentation Quantum Geese Swarm Optimization

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DP-GAN: Dual Pathway Generative Adversarial Network for Face Recognition in Degraded Scenarios 2018

DP-GAN: Dual Pathway Generative Adversarial Network for Face...

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

作者： Avishek Bhattacharjee Samik Banerjee Sukhendu Das Indian Institute of Technology Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

Face Recognition (FR) using Convolutional Neural Network (CNN) based models have achieved considerable success in constrained environments. they however fail to perform well in unconstrained scenarios, especially when the images are captured using surveillance cameras. these probe samples suffer from degradations such as noise, poor illumination, low resolution, blur as well as aliasing, when compared to the rich training (gallery) set, comprising mostly of mugshot images captured in laboratory settings. these images in the training (gallery) set are crisp and have high contrast, compared to the probe samples. To cope with this scenario, we propose a novel dual-pathway generative adversarial network (DP-GAN) which maps low resolution images captured using surveillance camera into their corresponding high resolution images, which are gallery-like, using a novel combination of multi-scale reconstruction and Jensen-Shannon divergence based loss. these images thus obtained are then used to train a deep domain adaptation (deep-DA) network to perform the task of FR. the proposed network achieves superior results (>90%) on four benchmark surveillance face datasets, evident from the rank-1 recognition rates when compared with recent state-of-the-art CNN-based techniques.

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Activity Recognition in Egocentric Videos Using Bag of Key Action Units 2018

Activity Recognition in Egocentric Videos Using Bag of Key A...

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

作者： K. Sai Suma G. Aditya Snehasis Mukherjee Indian Institute of Information Technology Sricity Chittoor Andhra Pradesh

ISBN: (纸本)9781450366151

In this paper we present a novel methodology for recognizing human activity in Egocentric video based on the Bag of Visual Features. the proposed technique is based on the assumption that, only a portion of the whole video can be sufficient to identify an activity. Rather, we argue that, for activity recognition in egocentric videos, the proposed approach performs better than any deep learning based method. Because, in egocentric videos, often the person wiring the sensor, becomes static for long time, or moves his head frequently. In both the cases, it becomes difficult to learn the spatiotemporal pattern of the video during action. the proposed approach divides the video into smaller video segments called Video Units. Spatio-temporal features extracted from the units, are clustered to construct the dictionary of Action Units (AU). the AUs are ranked based upon their score of likeliness. the scores are obtained by constructing a weighted graph with the AUs as vertices and edge weights calculated based on the frequencies of occurrences of the AUs during the activity. the less significant AUs are pruned out from the dictionary, and the revised dictionary of key AUs are used for activity classification. We test our approach on benchmark egocentric dataset and achieve a good accuracy.

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Moving Average Recurrent Neural Network Model for Video-based Person Re-Identification 2018

Moving Average Recurrent Neural Network Model for Video-base...

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

作者： Muhammed Fasil C. Subhasis Chaudhuri Dept. of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9781450366151

Person re-identification has great applications in video surveillance. It can be viewed as recognizing the same person across non-overlapping cameras. Video-based person re-identification methods are gaining increased attention due to the better discriminative nature of spatio-temporal feature representations. Current video-based methods make use of RNN to extract temporal information. In this paper, we propose a novel Moving Average Recurrent Neural Network (MA-RNN) model that can build a strong feature representation by taking both previous and present inputs at each time stamp. Specifically, here the recurrent layer produces a better sequential information by looking back directly in to the past values where as general RNNs has only an indirect dependence on the previous values in the form of hidden-state information. the proposed model is tested on two publicly available datasets: iLIDS-VID and PRID-2011 and it performed better in comparison with the state-of-the-art methods with a significant margin. We also analyze the effect of the depth of previous input dependence of the MA-RNN model on the matching accuracy.

关键词： MA-RNN RNN person re-identification

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Nrityantar: Pose oblivious indian classical dance sequence classification system 18

Nrityantar: Pose oblivious Indian classical dance sequence c...

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

作者： Vinay Kaushik Prerana Mukherjee Brejesh Lall Dept. of Electrical Engineering IIT Delhi

ISBN: (纸本)9781450366151

In this paper, we attempt to advance the research work done in human action recognition to a rather specialized application namely indian Classical Dance (ICD) classification. the variation in such dance forms in terms of hand and body postures, facial expressions or emotions and head orientation makes pose estimation an extremely challenging task. To circumvent this problem, we construct a pose-oblivious shape signature which is fed to a sequence learning framework. the pose signature representation is done in two-fold process. First, we represent person-pose in first frame of a dance video using symmetric Spatial Transformer Networks (STN) to extract good person object proposals and CNN-based parallel single person pose estimator (SPPE). Next, the pose basis are converted to pose flows by assigning a similarity score between successive poses followed by non-maximal suppression. Instead of feeding a simple chain of joints in the sequence learner which generally hinders the network performance we constitute a feature vector of the normalized distance vectors, flow, angles between anchor joints which captures the adjacency configuration in the skeletal pattern. thus, the kinematic relationship amongst the body joints across the frames using pose estimation helps in better establishing the spatio-temporal dependencies. We present an exhaustive empirical evaluation of state-of-the-art deep network based methods for dance classification on ICD dataset.

关键词： Action Recognition

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Yield Estimation of Chilli Crop using image processing Techniques

Yield Estimation of Chilli Crop using Image Processing Techn...

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International conference on Advanced Computing and Communication Systems (ICACCS)

作者： Nageswararao Naik Bhookya R. Malmathanraj P. Palanisamy National Institute of Technology Thiruchirapalli India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

In agriculture, determining the total number of chilli fruits to estimate the amount of crop yield plays an important role. Determining manually the total number of chilli fruits in an orchard is a tedious job, also it requires huge human resource, cost and has low accuracy. In this research work, a novel approach is proposed, for detecting and counting ripened chilli fruit from the plant images. It helps farmers to plan the manual labor to harvest the crop, shipment, sales and operations related to the post harvest. computer vision techniques can help to precisely count the chilli fruits of the orchard. therefore, an automated determination and counting the number of chilli fruits is introduced in the agricultural farms. the proposed technique achieves fine accuracy when compared towards ground truth chilli fruit images.

关键词： image color analysis Color Morphological operations Mathematical model Shape Agriculture Manuals

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Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and Rotation Invariant Character Recognition: SRC-CNN for Scale and Rotation Invariant Character Recognition 2018

Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and R...

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

作者： Swetha V. C. Deepak Mishra Sai Subrahmanyam Gorthi Indian Institute of Space Science and Technology Thiruvananthapuram Kerala Indian Institution of Technology Tirupati Andra Pradesh

ISBN: (纸本)9781450366151

Last decade has witnessed rapid growth for the popularity of Convolutional Neural Networks (CNNs), in detecting and classifying objects. the self trainable nature of CNNs makes them the strongest candidate as a classifier and a feature extractor. However, many of the existing CNN architectures fail recognizing texts or objects under input rotation and scaling. this paper introduces an elegant approach, 'Scale and Rotation Corrected CNN (SRC-CNN)' for scale and rotation invariant text recognition, exploiting the concept of principal component of characters. Prior to training and testing with baseline CNN, 'SRC-CNN' maps each character image to a reference orientation and scale, which is again derived from the character image itself. SRC-CNN is capable of recognizing characters in a document, even though they differ in orientation and scale greatly. the proposed method does not demand any training with samples which are scaled or rotated. the performance of proposed approach is validated on different character data sets like MNIST, MNIST_rot_12k and English alphabets and compared with state of the art rotation invariant classification networks. SRC-CNN is a generalized approach and can be extended for rotation and scale invariant classification of many other datasets as well, choosing any appropriate baseline CNN. Here we have demonstrated the generality of the proposed SRC-CNN on MNIST Fashion data set and found to perform well in rotation and scale invariant classification of objects as well. this paper demonstrates how the basic PCA based rotation and scale invariant image recognition can be integrated to CNN for achieving better rotational and scale invariances in classification.

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