Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier

Digital understanding of Indian classical dance is least studied work, though it has been a part of Indian Culture from around 200BC. This work explores the possibilities of recognizing classical dance mudras in various dance forms in India. The images of hand mudras of various classical dances are collected form the internet and a database is created for this job.  Histogram of oriented (HOG) features of hand mudras input the classifier. Support vector machine (SVM) classifies the HOG features into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices

Sign Language Recognition System Simulated for Video Captured with Smart Phone Front Camera

This works objective is to bring sign language closer to real time implementation on mobile platforms. A video database of Indian sign language is created with a mobile front camera in selfie mode. This video is processed on a personal computer by constraining the computing power to that of a smart phone with 2GB ram. Pre-filtering, segmentation and feature extraction on video frames creates a sign language feature space. Minimum distance classification of the sign feature space converts signs to text or speech. ASUS smart phone with 5M pixel front camera captures continuous sign videos containing around 240 frames at a frame rate of 30fps. Sobel edge operator’s power is enhanced with morphology and adaptive thresholding giving a near perfect segmentation of hand and head portions. Word matching score (WMS) estimates performance of the proposed method with an average WMS of around 90.58%

Performance of active contour models in train rolling stock part segmentation on high-speed video data

Rolling stock examination is performed to identify the defects during train movements at speeds <30 kmph. In this study, this process was automated using computer vision models. Parts on a moving train were segmented using four types of active contour-level set models: Chan–Vese (CV), CV-based morphological differential gradient (CV-MDG), CV with shape priors (CV-SP), and CV with shape invariance (CV-SI). CV level sets with shape invariance model enables the adjustment of contour according to scale, rotation, and location of the shape prior object in the rolling stock frame. Train rolling stock video data were captured at a high speed of 240 fps by using a sports action camera with 52° wide angle lenses. The level sets yielded optimal segmentation results compared with traditional segmentation methods. The performance indicators of segmented parts from the proposed four algorithms are structural similarity index measure and peak signal-to-noise ratio (in dB). A total of 10 parts were extracted from a bogie by using the proposed models and compared against the ground truth models to test the performance of the methods. The train had 15 passenger cars with 30 bogies. Furthermore, the models were tested under various lighting conditions for five trains. The CV shape invariance model yielded more efficient segmentations both qualitatively and quantitatively

Selfie video based continuous Indian sign language recognition system

This paper introduces a novel method to bring sign language closer to real time application on mobile platforms. Selfie captured sign language video is processed by constraining its computing power to that of a smart phone. Pre-filtering, segmentation and feature extraction on video frames creates a sign language feature space. Minimum Distance and Artificial Neural Network classifiers on the sign feature space is trained and tested iteratively. Sobel edge operator's power is enhanced with morphology and adaptive thresholding giving a near perfect segmentation of hand and head portions compensating for the small vibrations of the selfie stick. Word matching score (WMS) gives the performance of the proposed method with an average WMS of around 85.58% for MDC and 90% for ANN with a small variation of 0.3 s in classification times. Neural network classifiers with fast training algorithms will certainly make this novel selfie sign language recognizer application into app stores. Keywords: Indian sign language, Sobel adaptive threshold, Morphological differencing, Mahalanobis distance, Multi layered artificial neural network

Flower segmentation with level sets evolution controlled by colour, texture and shape features

This work proposes a pre-informed Chan vese based level sets algorithm. Pre information includes objects colour, texture and shape fused features. The aim is to use this algorithm to segment flower images and extract meaningful features that will help is classification of floral content. Shape pre-information modelling is handled manually using advance image processing tools. Local binary patterns features makeup texture pre-information and Red, Green and Blue colour channels of the object provide colour pre-information. All pre-defined object information is fused together to for high dimension subspace defining object characteristics. Testing of the algorithm on flower images datasets show a jump in information content in the resulting segmentation output compared to other models in the category. Segmentation of flowers is important for recognition, classification and quality assessment to ever increasing volumes in floral markets

Flower image segmentation with PCA fused colored covariance and gabor texture features based level sets

This paper presents a framework for segmenting flower images captured with a digital camera. Segmenting flowers from images is a complex problem attributed to translation, scaling, rotation with variable backgrounds in each captured image. We propose to solve this problem using principle component analysis based color texture fusion as a prior parameter for level set evolution (FCTAC). First, Color Gabor textures (CGT) and Color Level Covariance Matrix (CLCM) texture features are extracted. Principle component analysis based fusion constructs a color discriminative texture as a knowledge base with convex energy function for active contours without edges. The proposed global segmentation framework with fused textures will avoid the local minimums during curve evolution. We test the proposed segmentation model on the benchmark oxford flower image dataset and our own dataset. The results of FCTAC were tested against the state-of-the-art methods in accuracy and efficiency. Keywords: Flower image segmentation, Color Level Covariance matrix, Gabor textures, Colored texture Fusion, Principle component analysis, Active contour