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Sketch-based Simulated Draping for indian Garments 16

Sketch-based Simulated Draping for Indian Garments

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

作者： Muralikrishnan, Sanjeev Chaudhuri, Parag Indian Inst Technol Dept Comp Scince & Engn Bombay 400076 Maharashtra India

ISBN: (纸本)9781450347532

Virtual garments like shirts and trousers are created from 2D patterns stitched over 3D models. However, indian garments, like dhotis and saris, pose a unique draping challenge for physically-simulated garment systems, as they are not stitched garments. We present a method to intuitively specify the parameters governing the drape of an indian garment using a sketch-based interface. We then interpret the sketch strokes to procedural, physically-simulated draping routines to wrap, pin and tuck the garments around the body mesh as needed. After draping, the garments are ready to be simulated and used during animation as required. We present several examples of our draping technique.

关键词： Automated garment draping indian garment draping sketch-based

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Fast Content Aware image Retargeting

Fast Content Aware Image Retargeting

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

作者： Srivastava, Awadhesh Biswas, K. K. Indian Inst Technol Delhi Dept Comp Sci & Engn Delhi India

ISBN: (纸本)9781424442195

this paper addresses the problem of retargeting, namely adapting large source images for effective viewing at a smaller size with possible applications to PDAs, or dynamic page layouts. Instead of extracting regions of interest for retargeting, the uninteresting parts are removed from the scene [8]. this is done by computing the RGB variance within non-overlapping 3x3 blocks and removing the block path with minimal variance cost using dynamic programming. It is shown that transformation to CIELAB space is more effective for visual interpretation of image content. the implementations are shown to be much faster than the seam carving approach of [8]. Schemes are also presented for speeding up the seam carving scheme itself.

关键词： Dynamic programming

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Hierarchical Spectral Clustering based Large Margin Classification of Visually Correlated Categories 16

Hierarchical Spectral Clustering based Large Margin Classifi...

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

作者： Bose, Digbalay Chaudhuri, Subhasis IBM Res Bangalore Karnataka India Indian Inst Technol Dept Elect Engn Bombay Maharashtra India

ISBN: (纸本)9781450347532

Object recognition is one of the challenging tasks in computer vision and the problem becomes increasingly difficult when the image categories are visually correlated among themselves i.e. they are visually similar and only fine differences exist among the categories. this paper has a two-fold objective which involves organization of the image categories in a hierarchical tree like structure using self tuning spectral clustering for exploiting the correlations among them. the organization phase is followed by a node specific large margin nearest neighbor classification scheme, where a Mahalnobis distance metric is learnt for each non-leaf node. Further a procedure for hyperparameters selection has been discussed w.r.t two strategies i.e. grid search and Bayesian optimization. the proposed algorithm's effectiveness is tested on selected classes of the popular imagenet dataset.

关键词： Object recognition Visually correlated categories Large margin nearest neighbor classification Self Tuning Spectral Clustering Hierarchical Organization

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PD-Shift: Patch Detector Shift based Tracker 5

PD-Shift: Patch Detector Shift based Tracker

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Francis, Mathew Rajesh, Ratnakaram Guha, Prithwijit IIT Guwahati Dept EEE Gauhati India

ISBN: (纸本)9781467385640

We identify two important directions of algorithm development in the tracking literature - first, the older and more established mean-shift based approaches;and second, more recent formulations of viewing tracking as detection by parts (aka tracking-learning-detecting). Introductory versions of both these approaches had their own weaknesses due to the main challenges of tracking like illumination variations, abrupt motions, deformations/articulations and occlusions. Both these approaches have developed independently with a number of follow-ups in the literature and even the matured versions were never integrated. We propose to integrate these two different viewpoints of single object tracking where the contribution lies in the construction of the target model as a collection of object part/patch detectors integrated with a kernel. Similarly, the kernel weighted patch detector responses form the candidate model. the Bhattacharyya coefficient based similarity measure between the target and candidate model is maximized in a gradient ascent framework to deduce a mean-shift like centroid update rule to iteratively track the target in the subsequent frames. We call this unified one the Patch Detector Shift based Tracker (PD-Shift). Experimental results of PD-Shift based tracking performance analysis is presented for the BoBoT dataset.

关键词： Iterative methods

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Automated Detection and Classification of Mass from Breast Ultrasound images 5

Automated Detection and Classification of Mass from Breast U...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Menon, Radhika V. Raha, Poulami Kothari, Shweta Chakraborty, Sumit Chakrabarti, Indrajit Karim, Rezaul IIT Kharagpur Dept E & ECE Kharagpur W Bengal India IPGMER SSKM Kolkata India CMSDH Kolkata India

ISBN: (纸本)9781467385640

this paper introduces a computer aided diagnosis (CAD) technique for segmentation of mass in breast ultrasound (BUS) images followed by an efficient classification of the image into benign or malignant one. the presence of speckle noise, low contrast and blurred boundary of mass in a BUS image makes it challenging to determine the mass, which is the region of interest (ROI) in the current work. Detecting an accurate ROI in turn results in efficient feature extraction and classification. In current work, image enhancement and speckle noise reduction are implemented for preprocessing in a simple but efficient way through filtering techniques. the results of the preprocessing stage are as effective as those obtained using traditional speckle reduction anisotropic diffusion (SRAD) algorithm. ROI is then accurately determined on preprocessed image by employing local region based active contour method. BUS images are classified through textural, morphological and histogram oriented feature metrics in this work. the obtained features are dimensionally reduced using principal component analysis (PCA) and classified through support vector machine (SVM) method. the proposed method is tested on several images and found to be very effective having an accuracy of 95.7% with very high specificity and positive predictive value (PPV).

关键词： breast ultrasound CAD active contours geometric features PCA classification SVM

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A novel floor segmentation algorithm for mobile robot navigation 5

A novel floor segmentation algorithm for mobile robot naviga...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Bhowmick, Soumabha Pant, Abhishek Mukherjee, Jayanta Deb, Alok Kanti IIT Kharagpur Adv Technol Dev Ctr Kharagpur W Bengal India IIT Kharagpur Comp Sci & Engn Kharagpur W Bengal India IIT Kharagpur Elect Engn Kharagpur W Bengal India

ISBN: (纸本)9781467385640

the task of detection of floor area for mobile robot navigation has received immense importance over the years. the main challenging problem as long as the path planning of robots is concerned, is the obstacle avoidance. Obstacle detection and avoidance in real time is a complex and computationally expensive process as a result of which the robotics researchers opted to segment out floors, which is comparatively easier process and at the same time very much feasible in real time applications. the proposed floor segmentation algorithm detects floor from a scene irrespective of the change of illumination as well as shadows. A conventional breadth first search based region growing technique has been used with histogram based features in YCbCr color space and floor junction masking to detect the floor pixels from the scene. the algorithm has been compared with various other existing techniques to showcase the improved results of the proposed one.

关键词： Histogram floor junction masking breadth first search

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GraphVL: Graph-Enhanced Semantic Modeling via vision-Language Models for Generalized Class Discovery 24

GraphVL: Graph-Enhanced Semantic Modeling via Vision-Languag...

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15th indian conference on computer vision graphics and image processing

作者： Solanki, Bhupendra Nair, Ashwin R. Singha, Mainak Mukhopadhyay, Souradeep Jha, Ankit Banerjee, Biplab Indian Inst Technol Mumbai Maharashtra India Indian Inst Sci Educ & Res Thiruvananthapuram Thiruvananthapuram Kerala India Indian Inst Sci Bangalore Karnataka India LNM Inst Informat Technol Jaipur Rajasthan India

ISBN: (纸本)9798400710759

Generalized Category Discovery (GCD) aims to cluster unlabeled images into known and novel categories using labeled images from known classes. To address the challenge of transferring features from known to unknown classes while mitigating model bias, we introduce GraphVL, a novel approach for vision-language modeling in GCD, leveraging CLIP. Our method integrates a graph convolutional network (GCN) with CLIP's text encoder to preserve class neighborhood structure. We also employ a lightweight visual projector for image data, ensuring discriminative features through margin-based contrastive losses for image-text mapping. this neighborhood preservation criterion effectively regulates the semantic space, making it less sensitive to known classes. Additionally, we learn textual prompts from known classes and align them to create a more contextually meaningful semantic feature space for the GCN layer using a contextual similarity loss. Finally, we represent unlabeled samples based on their semantic distance to class prompts from the GCN, enabling semi-supervised clustering for class discovery and minimizing errors. Our experiments on seven benchmark datasets consistently demonstrate the superiority of GraphVL when integrated with the CLIP backbone.

关键词： Class Discovery Contrastive Learning Graph Convolutional Networks Unsupervised learning vision-Language models

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CHILD: A Robust Computationally-Efficient Histogram-based image Local Descriptor

CHILD: A Robust Computationally-Efficient Histogram-based Im...

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

作者： Anamandra, Sai Hareesh Chandrasekaran, V. Sri Sathya Sai Inst Higher Learning Dept Math & Comp Sci Puttaparthi 515134 India

ISBN: (纸本)9781479915880

Designing a robust image local descriptor for the purpose of pattern recognition and classification has been an active area of research. Towards this end, a number of local descriptors based on Weber's law have been proposed recently. Notable among them are Weber Local Descriptor (WLD), Weber Local Binary Pattern (WLBP) and Gabor Weber Local Descriptor (GWLD). Experiments reveal their inability to classify patterns under noisy environments. Our analysis indicates that the components of the WLD: differential excitation and orientation are to be redesigned for robustness and computational efficiency. In this paper, we propose a local Descriptor, called Computationally-Efficient Histogram-based image Local Descriptor (CHILD), which implements the differential excitation component based on LoG as in WLBP and the orientation component based on fractional order derivatives. the novelty of CHILD stems from the fact that both the LoG and the fractional order derivatives are robust against noise. Further, we have used Wasserstein distance metric to compute the distance between the two histograms and a nearest neighbour classifier which are computationally efficient. We have demonstrated that on the benchmark texture database Kth-TIPS2-a, under both noiseless and noisy conditions, the average classification accuracies of the CHILD consistently outperform the popular local descriptors for texture classification quoted in the literature until 2013.

关键词： useful area Descriptors Frontotemporal Dementia Computational efficiency Pattern recognition Noisy environment Robustness imagery (Psychotherapy) image images

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Greedy Partitioning based Tree Structured Multiclass SVM for Odia OCR 5

Greedy Partitioning based Tree Structured Multiclass SVM for...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sahu, Sandeep Kumar Pujari, Arun K. Kumar, Vikas Kagita, Venkateswara Rao Padmanabhan, Vineet Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

ISBN: (纸本)9781467385640

there have been many proposals to extend the basic two-class SVM classifier for multiclass classification and it is established that among these extensions binary-structured hierarchical SVMs is the most efficient computationally. However, determining an effective binary structure by recursively dividing the classes is a major research issue. We describe a new classifier, GP-SVM, based on greedy partitioning of classes and demonstrate that GP-SVM gives better classification accuracy than all major combinational techniques besides having the computational advantages. the advantages of GP-SVM is better realized when the number of classes is large. We demonstrate this advantage in recognition of printed Odia character. We built a corpus of 10025 tagged Odia aksharas collected over multiple printed documents of different fonts. We used a very modest number of features. GP-SVM with 133 classes yielded 95% accuracy of recognition. During the learning process of GP-SVM, the proposed system could learn the taxonomy of character-shapes of Odia script.

关键词： Optical character recognition

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Kernelization of Locality Preserving Discriminant Projection 5

Kernelization of Locality Preserving Discriminant Projection

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Shikkenawis, Gitam Mitra, Suman K. Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar 382007 India

ISBN: (纸本)9781467385640

In recent years, non-linear dimensionality reduction approaches have become popular due to their suitability to capture the non-linearities present in the data. However, these are not applicable for recognition related applications as representation of new data points is not defined in the reduced subspace. Hence, to explore the non-linearities of the data, local structure preserving approaches received considerable attention. these approaches keep the local information of the data points intact in the lower dimensional space as well. Locality Preserving Discriminant Projection (LPDP) not only preserves the neighborhood information for each data point, but also tries to discriminate data points from different classes using their class labels. the performance of such neighborhood information preserving dimensionality reduction techniques do not guarantee to capture complex non-linearities present in the data. To address this issue, kernel functions that map the data in a non-linear feature space before applying the dimensionality reduction approach are widely used. this article discusses Kernelization of LPDP which explores complex non-linear changes of face images due to facial expression, illumination, pose and environmental changes. the proposed approach is applied for face recognition on some of the benchmark databases with such variability.

关键词： Face recognition

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