Soumitra Samanta

A Simple and Fast Algorithm to Detect the Fovea Region in Fundus Retinal Image

Retinal image analysis is one of the crucial topics in medical image processing. During the last three decades, people are trying to extract the different features (like blood vessels, optic disk, macula, fovea etc.) automatically from retinal image. Fovea is one of the important feature of a fundus retinal image. This paper present a simple and fast algorithm using Mathematical Morphology to find the fovea region. Proposed algorithm is based on the structure of the blood vessels and little bit information of the optic disk. We have tested our result on a publicly available DRIVE database and got a comparable results with a state of the art in this area.

Space-Time Facet Model for Human Activity Classification

This paper presents a novel space-time feature-based human activity analysis system. We detect Space Time Interest Points (STIP) and generate their description based on the facet model. The proposed approach detects interest points in video data using the three-dimensional facet model efficiently. Then we describe each interest point by three-dimensional Haar wavelet transform and time derivatives of different order obtained from said facet model. Here we represent each video clip following the bag-of-words approach by learning feature specific dictionary. Finally, classification is done using non-linear SVM with χ2-kernel. We evaluate the performance of our system on standard datasets like Weizmann, KTH, UCF sports, ICD, UCF YouTube, and UCF50 and get better, or at least comparable results compared to other state-of-the-art systems.

Indian Classical Dance classification by learning dance pose bases

In this paper, we address an interesting application of computer vision technique, namely classification of Indian Classical Dance (ICD). With the best of our knowledge, the problem has not been addressed so far in computer vision domain. To deal with this problem, we use a sparse representation based dictionary learning technique. First, we represent each frame of a dance video by a pose descriptor based on histogram of oriented optical flow (HOOF), in a hierarchical manner. The pose basis is learned using an on-line dictionary learning technique. Finally each video is represented sparsely as a dance descriptor by pooling pose descriptor of all the frames. In this work, dance videos are classified using support vector machine (SVM) with intersection kernel. Our contribution here are two folds. First, to address dance classification as a new problem in computer vision and second, to present a new action descriptor to represent a dance video which overcomes the problem of the “Bags-of-Words” model. We have tested our algorithm on our own ICD dataset created from the videos collected from YouTube. An accuracy of 86.67% is achieved on this dataset. Since we have proposed a new action descriptor too, we have tested our algorithm on well known KTH dataset. The performance of the system is comparable to the state-of-the-art.

FaSTIP: a new method for detection and description of space-time interest points for human activity classification

This paper presents a new method to detect space time interest point (STIP) from video data. We use three dimensional facet model to detect STIP and call it as facet space-time interest point or FaSTIP. The proposed algorithm detects all the desired interest points efficiently in different scales compared to other existing methods. A video clip is described as a collection of 3D wavelet base features computed at these interest points. Finally, multi-channel SVM with χ2- kernel is used to classify human actions. Our contribution here are two fold: first, we present a new algorithm for interest point detection in video data, and second, we propose a new descriptor for general human activity classification. Experimental results show the accuracy of the detected interest points and the power of descriptor compared to the state-of-the-art.

A Novel Technique for Space-Time-Interest Point Detection and Description for Dance Video Classification

This paper presents a different type of video analysis problem which is cultural activity analysis in general and Indian Classical Dance ICD classification in particular. To tackle this problem we propose a novel method for space time interest point STIP detection and description using differential geometry. Each video is represented by sparse code of STIP descriptors in each frame and then classification is done by a non-linear SVM with i¾ź 2-kernel. We have created a ICD dataset of six classes Bharatanatyam, Kathak, Kuchipudi, Mohiniyattam, Manipuri and Odissi from YouTube and got on an average 68.18% accuracy which is better than the performance of state-of-the-art general human activity classification methods. We also have tested our algorithm on the benchmark datasets, like UCF sports and KTH, and the accuracy is comparable to that of the state-of-the-art.

Ensemble classifier-based off-line handwritten word recognition system in holistic approach

This study presents a novel ensemble classifier-based off-line handwritten word recognition system following a holistic approach. Here each handwritten word is recognised using two handcrafted features, namely (i) Arnold transform-based feature that addresses local directional feature which depends on the stroke orientation distribution of cursive word and (ii) oriented curvature-based feature which is basically the histogram of curvelet index and one machine generated feature using deep convolution neural network (DCNN). In this study, a new architecture of DCNN is proposed for handwritten word recognition. These features are recognised by three classifiers separately. Finally, the decision of three classifiers is combined to predict the ultimate word class level. To fuse the decision of individual classifiers, the authors have explored three strategies: (i) vote for strongest decision, (ii) vote for majority decision and (iii) vote for the sum of the decisions. The proposed handwritten word recognition system is tested on three handwritten word databases: (i) CENPARMI database, (ii) IAM database and (iii) ISIHWD database. The performance of the proposed system is promising and comparable to state-of-the-art handwriting recognition systems.

A Patch-Based Constrained Inpainting for Damaged Mural Images

Heritage artefacts and monuments are important components of social science. Those are under constant threat of decaying and degrading due to exposition to unfriendly natural environment and hooliganism. Restoration of heritage artefacts such as murals and paintings is an important task for preservation of social, cultural and political history of a nation. As being in the temples in India, a significant share of murals and paintings are not accessible for physical restoration. This motivates many researchers to put effort in restoration of such priceless paintings and reliefs digitally in augmented reality domain. In this work, we have proposed an exemplar based coherent texture synthesis technique to inpaint the digital image of damaged portion of murals and paintings. Inpainting method, while maintaining the spatial coherency, usually introduces blurring as well as structured noise to the inpainted regions. To overcome this problem, we have combined the proposed patch-based diffusion technique with a novel technique for high-frequency generation that leads to edge sharpening and denoising simultaneously. Finally, the proposed constraint and interactive nature of the method is found efficient to handle rich variety of such paintings. The experimental results with empirical evaluation show the efficacy of the proposed method.

Scale-Invariant Image Inpainting Using Gradient-Based Image Composition

In this paper, we propose a novel scale-invariant image inpainting algorithm that combines several inpainted images obtained from multiple pyramids of different coarsest scales. To achieve this, first we build the pyramids and then we run an image inpainting algorithm individually on each of the pyramids to obtain different inpainted images. Finally, we combine those inpainted images by a gradient based approach to obtain the final inpainted image. The motivation of this approach is to solve the problem of appearing artifacts in traditional single pyramid-based approach since the results depend on the starting scale of the pyramid. Here we assume that most of the inpainted images produced by the pyramids are quite good. However, some of them may have artifacts and these artifacts are eliminated by gradient based image composition. We test the proposed algorithm on a large number of natural images and compare the results with some of the existing methods to demonstrate the efficacy and superiority of the proposed method.

Bishnupur heritage image dataset (BHID): a resource for various computer vision applications

Bishnupur is an attractive tourist place in West Bengal, India and is known for its terracotta temples. The place is one of the prospective candidates to be included in the list of UNESCO World Heritage sites. We intend to preserve this heritage site digitally and also to present some virtual interaction for the tourist and researchers. In this paper, we present an image dataset of different temples (namely, Jor Bangla, Kalachand, Madan Mohan, Radha Madhav, Rasmancha, Shyamrai and Nandalal) in Bishnupur for evaluating different types of computer vision and image processing algorithms (like 3D reconstruction, image inpainting, texture classification and content specific image retrieval). The dataset is captured using four different cameras with different parameter settings. Some datasets are extracted and earmarked for certain applications such as texture classification, image inpainting and content specific image retrieval. Example results of baseline methods are also shown for these applications. Thus we evaluate the usefulness of this dataset. To the best of our knowledge, probably this is the first attempt of combined dataset for evaluating various types of problems for a heritage site in India. The dataset is publicly available at http://www.isical.ac.in/~bsnpr/ for research purpose only.

Indian Classical Dance Classification on Manifold Using Jensen-Bregman LogDet Divergence

Due to occlusion, lighting condition, variation in clothing dance video classification is a challenging problem in computer vision domain. In this paper we present a local spatiotemporal feature model on manifold for Indian Classical Dance (ICD) classification. We represent features at each space-time interest point as a covariance matrix by fusing different order spatial and temporal derivatives. Each video clip is then represented in bag-of-words framework on manifold using Jensen-Bregman LogDet Divergence. Classification is done by popular non-linear SVM with ?2-kernel. We evaluate our system on a ICD dataset created from YouTube and get 69.39% accuracy which is better than that of the state-of-the-art human activity classification algorithms. We have also tested our algorithms on human activity benchmark datasets like KTH, and UCF50 and get promising results compared to the state-of-the-art methods.