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MultiDIAS: A Hierarchical Multi-layered Document image Annotation System 4th

MultiDIAS: A Hierarchical Multi-layered Document Image Annot...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Poddar, Arnab Mukherjee, Rohan Mukhopadhyay, Jayanta Biswas, Prabir Kumar Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9789811393600

Content of the document images are often shows hierarchical multi-layered tree structure. Further, the algorithms for document image applications like line detection, paragraph detection, word recognition, layout analysis etc. require pixel level annotation. In this paper, a Multi-layered Document image Annotation System (MultiDIAS) has been introduced. The proposed system simultaneously provide a platform for hierarchical and pixel level annotation of document. MultiDIAS label the document image in four hierarchical layers (layout type, entity type, line type, word type) assigned by the user. The output generated are four ground-truth images and an XML file representing the metadata information. The MultiDIAS is tested on a complex handwritten manuscript written by renowned film director Satyajit Ray for the movie ‘Goopi Gyne Bagha Byne’. This annotated data generated using MultiDIAS can further be used in a wide range of applications of document image understanding and analysis. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image annotation

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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...

引用

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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Deep Learning Models for Medical image Analysis: Challenges and Future Directions 1

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7th International conference on Big Data analytics (BDA)

作者： Agrawal, R. K. Juneja, Akanksha Jawaharlal Nehru Univ New Delhi 110067 India

ISBN: (数字)9783030371883

ISBN: (纸本)9783030371883;9783030371876

Artificial neural network (ANN) introduced in the 1950s, is a machine learning framework inspired by the functioning of human neurons. However, for a long time the ANN remained inadequate in solving real problems, because of - the problems of overfitting and vanishing gradient while training a deep architecture, dearth of computation power, and non-availability of enough data for training the framework. This concept has lately re-emerged, in the form of Deep Learning (DL) which initially developed for computer vision and became immensely popular in several other domains. It gained traction in late 2012, when a DL approach i.e. convolutional neural network won in the imageNet Classification - an acclaimed worldwide computer vision competition. Thereafter, researchers in practically every domain, including medical imaging, started vigorously contributing in the massively progressing field of DL. The success of DL methods can be owed to the availability of data, boosted computation power provided by the existing graphics processing units (GPUs), and ground-breaking training algorithms. In this paper, we have overviewed the area of DL in medical imaging, including (1) machine learning and DL basics, (2) cause of power of DL, (3) common DL models, (4) their applications to medical imaging and (5) challenges and future work in this field.

关键词： Medical image analysis Machine learning Deep Learning computer-aided diagnosis

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Unsupervised Detection of Surface Mine Sites using Sentinel Multi-spectral imagery and Dual-polarimetric SAR Data 18

Unsupervised Detection of Surface Mine Sites using Sentinel ...

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

作者： Shashaank M. Aswatha Vishnu Saini Jayanta Mukherjee Prabir K. Biswas Subhas Aikat Arundhati Misra Indian Institute of Technology Kharagpur West Bengal India Indian Institute of Technology Kharagpur India Space Applications Centre Indian Space Research Organization Ahmedabad India

ISBN: (纸本)9781450366151

Detecting surface mine activities has been one of the major challenges of remote sensing community to monitor ecological balance in the vicinity of mine area. Towards addressing this issue, we propose an unsupervised mine-site detection technique using multi-modal remotely sensed data from Sentinel missions. We use Sentinel-1's dual polarized SAR data, and Sentinel-2's multi-spectral data for this purpose. An initial set of seed pixels is sampled using spectral slopes based rules on multi-spectral images, which are tailored to capture mining area pixels that exhibit common characteristics. Then, we generate features using multi-spectral image indices and dual polarized SAR data Stokes' parameters. These features over locations sampled by slope based rules are used to train a one-class support vector machine to detect the land anomalies (mine activities). As observed from the experiments, multi-spectral features are efficient in separating mine region and built-up region from the rest of the land surface, and SAR image features discriminate built-up region from the rest of the image. We fuse both of these imagery features to encash their advantage in segregating mine sites in the considered study area. We demonstrate the efficiency of the proposed technique in detecting active coal mines over two study sites, Asansol region (in West bengal, India) and Jharia region (in Jharkhand, India), both of which exhibit common geographical characteristics. The average detection accuracy in our results is found to be around 94.8%.

关键词： Multi-spectral imagery

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Video Dehazing using LMNN with respect to Augmented MRF 18

Video Dehazing using LMNN with respect to Augmented MRF

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

作者： Kushal Borkar Snehasis Mukherjee Indian Institute of Information Technology Sricity Chittoor Andhra Pradesh

ISBN: (纸本)9781450366151

The presence of haze within the atmospheric medium degrades the quality of videos captured by camera sensors. The expulsion of haze, referred to as dehazing, is typically performed subject to the physical degradation display, that involves an explanation of an ill-posed inverse drawback. A few efforts have been made for image dehazing, whereas, video dehazing still remains an unexplored area of research. This paper proposes an approach for video dehazing combining the concepts of single image dehazing, optical stream estimation and Markov Random Field (MRF). The proposed method enhances the temporal and spatial coherence of the hazy video. Assuming that the dark channel of the haze-free picture is zero, we acquire the raw transmission map. In the proposed approach, we focus on the raw transmission map obtained from the dark channel prior using guided filter. We assess the forward and reverse optical streams between the neighboring frames to locate individual pixels using Linear Discriminant Analysis. The color of the haze-free pixels in the frames is approximated by a few hundred discrete colors, which generate a fixed cluster in space and the directions of the pixel. The pixels at a given cluster are spread and can be determined by analyzing the forward and in reverse optical frames to predict its value after haze removal. Largest Margin Nearest Neighbor (LMNN) algorithm is applied to get the smooth transmission map of the foggy frames of the video to approximate the pixel value in the RGB space. The stream fields are utilized in an augmented MRF model on the transmission guide obtained to enhance the temporal and the spatial coherence of the transmission. The proposed method is compared against the state-of-the-art on both real and synthetic videos to preserve the information optimally.

关键词： bayes

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Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian 18

Seek and You Will Find: A New Optimized Framework for Effici...

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

作者： Sudip Das Partha Sarathi Mukherjee Ujjwal Bhattacharya Indian Statistical Institute Kolkata India

ISBN: (纸本)9781450366151

Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance, autonomous cars, etc. Also, a significant trend of latest research studies in related problem areas is the use of sophisticated Deep Learning based approaches to improve the benchmark performance on various standard datasets. A trade-off between the speed (number of video frames processed per second) and detection accuracy has often been reported in the existing literature. In this article, we present a new but simple deep learning based strategy for pedestrian detection that improves this trade-off. Since training of similar models using publicly available sample datasets failed to improve the detection performance to some significant extent, particularly for the instances of pedestrians of smaller sizes, we have developed a new sample dataset consisting of more than 80K annotated pedestrian figures in videos recorded under varying traffic conditions. Performance of the proposed model on the test samples of the new dataset and two other existing datasets, namely Caltech Pedestrian Dataset (CPD) and CityPerson Dataset (CD) have been obtained. Our proposed system shows nearly 16% improvement over the existing state-of-the-art result.

关键词： Convolutional Neural Networks

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Hierarchical Deep Network for Group Discovery and Multi-level Activity Recognition 18

Hierarchical Deep Network for Group Discovery and Multi-leve...

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

作者： Ashish Goyal Neha Bhargava Subhasis Chaudhuri Rajbabu Velmurugan Indian Institute of Technology Bombay Mumbai Maharashtra

ISBN: (纸本)9781450366151

We present a deep network based hierarchical framework to recognize activities performed by people collectively from videos at various levels of granularity - individual person, group and overall (or scene level). Individual person analysis, which includes person detection, tracking, pose estimation and individual activity recognition, has been studied extensively. Most of the existing work on collective activity recognition has focused on overall scene activity estimation. However, in scenarios where multiple groups perform different group activities, overall scene activity recognition in isolation paints an incomplete picture of various activities. Identifying groups and recognizing their activities is therefore important to understand a scene in it's completeness. To this end, we add an extra layer in existing methods that finds the groups (or clusters) of people present in a scene and their activities. We then utilize these group activities along with the scene context to recognize the scene activity. To discover these groups, we propose a min-max criteria within the framework to train a sub-network which learns pairwise similarity between any two individuals, used by a clustering algorithm for identification of groups. The group activity is captured by an LSTM module whereas the individual and scene activities are captured by CNN-LSTM based modules. These modules along with the grouping layer form the proposed network. We evaluate the network on a publicly available dataset to indicate the usefulness of our approach.

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An Efficient Character Segmentation Algorithm for Connected Handwritten Documents 4th

An Efficient Character Segmentation Algorithm for Connected ...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Rajput, Vishal Jayanthi, N. Indu, S. Department of Electronics and Communication Engineering PDPM-Indian Institute of Information Technology Design and Manufacturing Jabalpur JabalpurMP482005 India Department of Electronics and Communication Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789811393600

This paper proposes an efficient method of character segmentation for handwritten text. The main challenge in character segmentation of hand-written text is the varied size of each letter in different documents, connected alphabets in a word in cursive writing and the presence of ligatures within an open character. Hence, this paper proposes an adaptive vertical pixel count algorithm to solve the problem of over-segmentation due to the presence of open characters such as ‘w’, ‘v’ and ‘m’. Proposed algorithm works effectively against both the handwritten and standard text. The proposed method is evaluated on IAM and self-created data set. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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Leveraging information from imperfect examples: Common action sequence mining from a mix of incorrect performances 18

Leveraging information from imperfect examples: Common actio...

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

作者： Hiteshi Jain Gaurav Harit Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9781450366151

As much as good representation and theory are needed to explain human actions, so are the action videos used for learning good segmentation techniques. To accurately model complex actions such as diving, figure skating, and yoga practices, videos depicting action by human experts are required. Lack of experts in any domain leads to reduced number of videos and hence an improper learning. In this work we attempt to utilize imperfect amateur performances to get more confident representations of human action sequences. We introduce a novel Community Detection based unsupervised framework that provides mechanisms to interpret video data and address its limitations to produce better action representation. Human actions are composed of distinguishable key poses which form dense communities in graph structures. Anomalous poses performed for a longer duration can also form such dense communities but can be identified based on their rare occurrence across action videos and be rejected. Further, we propose a technique to learn the temporal order of these key poses from these imperfect videos, where the inter community links help reduce the search space of many possible pose sequences. Our framework is seen to improve the segmentation performance of complex human actions with the help of some imperfect performances. The efficacy of our approach has been illustrated over two complex action datasets - Sun Salutation and Warm-up exercise, that have been developed using random executions from amateur performers.

关键词： Unsupervised Anomaly Detection

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Anomaly Detection using Context Dependent Optical Flow 2018

Anomaly Detection using Context Dependent Optical Flow

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

作者： Ranjan Mondal Bhabatosh Chanda Indian Statistical Institute Kolkata India

ISBN: (纸本)9781450366151

Anomaly detection is a major task in crowd management through video surveillance. It refers to the events which are deviated from normal events. We have introduced an unsupervised method to detect motion anomaly in surveillance video. In this work we have considered only optical flow as feature. First, for each frame we compute magnitude of optical flow of motion using flownet2 [10]. Then, mean of magnitude of flow due to regular normal motion (given in training data) is computed at each pixel where such motion exists in the training video frames. Our strategy is to compare the motion under consideration against this mean flow magnitude, and we expect that the anomalous motion would differ significantly from normal motion. An autoencoder type network is trained to detect this anomaly. Training data patch is constructed by Interleaving the columns of mean optical flow patch and the corresponding flow patch from each frame. This interleaving is done to incorporate context dependency. The autoencoder is trained to minimize mean-square reconstruction error between input column wise interleaved patch and output (i.e., reconstructed patch) of the autoencoder. During testing, a patch is declared anomalous if the reconstruction error is high compared to the training error. Experiments have been carried out on UCSD and UMN dataset and are compared with other methods. Our method gives comparable results with other state-of-the-art methods.

关键词： Motion Representation Anomaly Detection Activity Recognition Unsupervised Method

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