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Automatic Detection of Malaria Infected RBCs from a Focus Stack of Bright Field Microscope Slide images 16

Automatic Detection of Malaria Infected RBCs from a Focus St...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Gopakumar, G. Swetha, M. Siva, Gorthi. Sai Subrahmanyam, G. R. K. S. Indian Inst Space Sci & Technol Dept ESS Trivandrum 695547 Kerala India Indian Inst Sci Dept Instrumentat & Appl Phys Bangalore 560012 Karnataka India Indian Inst Space Sci & Technol Dept Avion Trivandrum 695547 Kerala India

ISBN: (纸本)9781450347532

Malaria is a deadly infectious disease affecting red blood cells in humans due to the protozoan of type Plasmodium. In 2015, there is an estimated death toll of 438, 000 patients out of the total 214 million malaria cases reported worldwide. thus, building an accurate automatic system for detecting the malarial cases is beneficial and has huge medical value. this paper addresses the detection of Plasmodium Falciparum infected RBCs from Leishman's stained microscope slide images. Unlike the traditional way of examining a single focused image to detect the parasite, we make use of a focus stack of images collected using a bright field microscope. Rather than the conventional way of extracting the specific features we opt for using Convolutional Neural Network that can directly operate on images bypassing the need for hand-engineered features. We work with image patches at the suspected parasite location there by avoiding the need for cell segmentation. We experiment, report and compare the detection rate received when only a single focused image is used and when operated on the focus stack of images. Altogether the proposed novel approach results in highly accurate malaria detection.

关键词： Malaria Diagnosis Plasmodium Falciparum CNN

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Bangla Online Handwriting Recognition Using Recurrent Neural Network Architecture 16

Bangla Online Handwriting Recognition Using Recurrent Neural...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Chakraborty, Bappaditya Mukherjee, Partha Sarathi Bhattacharya, Ujjwal Univ Calcutta AKCSIT Kolkata W Bengal India Indian Stat Inst CVPR Unit Kolkata W Bengal India

ISBN: (纸本)9781450347532

Recognition of unconstrained handwritten texts is always a difficult problem, particularly if the style of handwriting is a mixed cursive one. Among various indian scripts, only Bangla has this additional difficulty of tackling mixed cursiveness of its handwriting style in the pipeline of a method towards its automatic recognition. A few other common recognition difficulties of handwriting in an indian script include the large size of its alphabet and the extremely cursive nature of the shapes of its alphabetic characters. these are among the reasons of achieving only limited success in the study of unconstrained handwritten Bangla text recognition. Artificial Neural Network (ANN) models have often been used for solving difficult real-life pattern recognition problems. Recurrent Neural Network models (RNN) have been studied in the literature for modeling sequence data. In this study, we consider Long Short Term Memory (LSTM) network model, a useful member of this family. In fact, Bidirectional Long Short-Term Memory (BLSTM) neural networks is a special kind of RNN and have recently attracted special attention in solving sequence labelling problems. In this article, we present a BLSTM architecture based approach for unconstrained online handwritten Bangla text recognition.

关键词： RNN BLSTM-CTC Online Handwriting Recognition

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Statistical video tracking of pomegranate fruits

Statistical video tracking of pomegranate fruits

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2011 3rd National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2011

作者： Roy, Ankush Banerjee, Suvadeep Roy, Debayan Mukhopadhyay, Anupam Indian Statistical Institute CVPR Unit 203 BT Road Kolkata 700108 India Dept. of Electrical Engg. Jadavpur University Kolkata 700032 India

ISBN: (纸本)9780769545998

Efficient location of fruits in the trees is the most important criterion of an automatic robotic harvesting system. the main challenges faced in the development of the robotic harvesting arm are accurate identification of the fruits in dense foliages and detection of the occluded fruits. this paper proposes a statistical technique that accurately detects and tracks pomegranates in trees. A k-means clustering methodology is implemented as a preprocessor step followed by image analysis algorithms that finally locate the fruit accurately under varying conditions of illumination, human intervention, camera view etc. the simplicity of the technique is attributed to its real-time capability to detect and track fruits in a video of fruit-bearing trees. the ground truth data were computed on 25 video sequences and the system was tested on another 25 videos. the detection efficiency is as high as 96.3%. © 2011 IEEE.

关键词： computer vision

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On-Demand Augmentation of Contour Trees 11

On-Demand Augmentation of Contour Trees

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sharma, Mohit Natarajan, Vijay Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781450366151

the contour tree represents the topology of level sets of a scalar function. Nodes of the tree correspond to critical level sets and arcs of the tree represent a collection of topologically equivalent level sets connecting two critical level sets. the augmented contour tree contains degree-2 nodes on the arcs that represent regular level sets. the degree-2 nodes correspond to regular points of the scalar function and other critical points that do not affect the number of level set components. the augmented contour tree is significantly larger in size and requires more effort to compute when compared to the contour tree. Applications of the contour tree to data exploration and visualization require the augmented contour tree. Current approaches propose algorithms to compute the contour tree and the augmented contour tree from scratch. Precomputing and storing the large augmented contour tree will not be necessary if the contour tree can be augmented on-demand. this paper poses the problem of computing the augmented contour tree given a contour tree as input. Computational experiments demonstrate that the on-demand augmentation can be computed fast while resulting in good memory savings.

关键词： Data visualization

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LEDNet: Deep Network for Single image Haze Removal 11

LEDNet: Deep Network for Single Image Haze Removal

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Dudhane, Akshay Murala, Subrahmanyam Dhall, Abhinav Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar India Indian Inst Technol Ropar Learning Affect & Semant Image Anal Grp Rupnagar India

ISBN: (纸本)9781450366151

Haze during the bad weather, degrades the visibility of the scene drastically. Degradation of scene visibility varies with respect to the transmission coefficient/map (T-c) of the scene. Estimation of accurate T-c is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the T-c. We, on the other hand, propose integration of local and global approaches to learn both point level and object level T-c. the proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level T-c using parallel convolutional filters and spatial invariance filtering. the second stage comprises of a two level encoder-decoder architecture which anticipates the object level T-c. We also propose, local air-light estimation (LAE) algorithm, which is able to obtain the air-light component of the outdoor scene. Combination of LEDNet and LAE improves the accuracy of haze model to recover the scene radiance. Structural similarity index, mean square error and peak signal to noise ratio are used to evaluate the performance of the proposed approach for single image haze removal. Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal.

关键词： Haze removal CNN transmission map

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SegFast : A Faster SqueezeNet based Semantic image Segmentation Technique using Depth-wise Separable Convolutions 11

SegFast : A Faster SqueezeNet based Semantic Image Segmentat...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Pal, Anisha Jaiswal, Shourya Ghosh, Swarnendu Das, Nibaran Nasipuri, Mita Manipal Inst Technol Dept Informat & Commun Technol Manipal Karnataka India Manipal Inst Technol Dept Elect & Commun Engn Manipal Karnataka India Jadavpur Univ Dept Comp Sci & Engn Kolkata W Bengal India

ISBN: (纸本)9781450366151

Recent trends in image segmentation algorithms have shown various large scale networks with impressive performance for natural scene images. However most of the networks come with costly overheads such has large memory requirements or dependence on huge number of parallel processing units. In most cases costly graphics processing units or GPUs are used to boost computational capability. However for creating products in the real world we need to consider speed, performance as well as cost of deployment. We propose a novel "spark" module which is a combination of the "fire" module of SqueezeNet and depth-wise separable convolutions. Along with this modified SqueezeNet as an encoder we also propose the use of depth-wise separable transposed convolution for a decoder. the resultant encoder-decoder network has approximately 49 times lesser number of parameters than SegNet and almost 223 times lesser number of parameters than fully convolutional networks(FCN). Even in a CPU the network completes a forward pass for a single sample in approximately 0.39 seconds which is almost 5.1 times faster as compared to SegNet and almost 8.7 times faster compared to FCN.

关键词： Deep Learning image Segmentation Squeeze Net Depthwise separable convolution

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Objects from Animacy: Discovery in Joint Shape and Haar Feature Space

Objects from Animacy: Discovery in Joint Shape and Haar Feat...

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6th indian conference on computer vision, graphics and image processing

作者： Nandi, Sudipto Guha, Prithwijit Venkatesh, K. S. Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781424442195

We propose that appearance descriptors derived from the complete animacy of an object during its scene presence more comprehensively capture the essence of an object than descriptors that merely encode uncorrelated sets of its instantaneous appearances. During its frame presence, an object presents itself in many poses with differing frequencies, thus generating multiple modes of varying strengths in the appearance feature space. Further we utilize tracking information to extract the set of all appearances of the object, while excluding those intervals where the object is partly or fully occluded by other objects or back-ground entities. this allows for completely unsupervised computation of the descriptors that consist of time-indexed vectors from shape and Haar feature templates which are then clustered to obtain appearance modes. these lead to the construction of object-animacy models as probability distributions over the space of co-occurrent shape and Haar templates. these object models are clustered further in an unsupervised manner by using different spatial clustering algorithms with a Bhattacharya distance metric between object models. Unsupervised categorization results on simple (PETS2000) and complex traffic scenes consisting of a wide variety of objects show robust performance of the proposed approach.

关键词： Clustering algorithms

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Fast Frontier Detection in Indoor Environment for Monocular SLAM 16

Fast Frontier Detection in Indoor Environment for Monocular ...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Upadhyay, Sarthak Krishna, K. Madhava Kumar, Swagat IIIT Hyderabad KCIS RRC Hyderabad Telangana India TCS Innovat Labs Bombay Maharashtra India

ISBN: (纸本)9781450347532

Frontier detection is a critical component in autonomous exploration, wherein the robot decides the next best location to move in order to continue its mapping process. the existing frontier detection methods require dense reconstruction which is difficult to attain in a poorly textured indoor environment using a monocular camera. In this effort, we present an alternate method of detecting frontiers during the course of robot motion that circumvents the requirement of dense mapping. Based on the observation that frontiers typically occur around areas with sudden change in texture (zero-crossings), we propose a novel linear chain Conditional Random Field(CRF) formulation that is able to detect the presence or absence of frontier regions around such areas. We use cues like spread of 3D points and scene change around these areas as an observation to CRF. We demonstrate that this method gives us more relevant frontiers compared to other monocular camera based methods in the literature. Finally, we present results in an indoor environment, wherein frontiers are reliably detected around walls leading to new corridors, doors leading to new rooms or corridors and tables and other objects that open up to a new space in rooms.

关键词： Mapping

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CRF based method for Curb Detection using semantic cues and stereo depth 16

CRF based method for Curb Detection using semantic cues and ...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sodhi, Danish Upadhyay, Sarthak Bhatt, Dhaivat Krishna, K. Madhava Swarup, Shanti IIIT Hyderabad KCIS RRC Hyderabad Andhra Pradesh India Uurmi Syst Hyderabad Andhra Pradesh India

ISBN: (纸本)9781450347532

Curb detection is a critical component of driver assistance and autonomous driving systems. In this paper, we present a discriminative approach to the problem of curb detection under diverse road conditions. We define curbs as the intersection of drivable and non-drivable area which are classified using dense Conditional random fields(CRF). In our method, we fuse output of a neural network used for pixelwise semantic segmentation with depth and color information from stereo cameras. CRF fuses the output of a deep model and height information available in stereo data and provides improved segmentation. Further we introduce temporal smoothness using a weighted average of Segnet output and output from a probabilistic voxel grid as our unary potential. Finally, we show improvements over the current state of the art neural networks. Our proposed method shows accurate results over large range of variations in curb curvature and appearance, without the need of retraining the model for the specific dataset.

关键词： Stereovision Conditional Random Field Deep learning Curbs

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Speeding up SIFT using GPU

Speeding up SIFT using GPU

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2013 4th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2013

作者： Aniruddha Acharya, K. Venkatesh Babu, R. Video Anaytics Laboratory Supercomputer Education and Research Center Indian Institute of Science Bangalore India

ISBN: (纸本)9781479915880

Scale Invariant Feature Transform (SIFT) is one of the widely used interest point features. It has been successfully applied in various computer vision algorithms like object detection, object tracking, robotic mapping and large scale image retrieval. Although SIFT descriptors are highly robust towards scale and rotation variations, the high computational complexity of the SIFT algorithm inhibits its use in applications demanding real time response, and in algorithms dealing with very large scale databases. this paper presents a parallel implementation of SIFT on a GPU, where we obtain a speed of around 55 fps for a 640×480 image. One of the main contributions of our work is the novel combined kernel optimization that has led to a significant improvement of 21.79% in the execution speed. We compare our results with the existing implementations in the literature that accelerate SIFT, and find that our implementation has better speedup than the most of them. © 2013 IEEE.

关键词： graphics processing unit

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