This paper presents a proposed model for content-based image retrieval (CBIR) which depends only on extracting the most relevant features according to a feature selection technique. The suggested feature selection technique aims at selecting the optimal features that not only maximize the detection rate but also simplify the computation of the image retrieval process. The proposed model is divided into three main techniques, the first one is concerned with the features extraction from images database, the second is performing feature discrimination, and the third is concerned with the feature selection from the original ones. As for the first technique, the 3D color histogram and the Gabor filter algorithm are used to extract the color and texture features respectively. While the second technique depends on a genetic algorithm (GA) for replacing numerical features with nominal features that represent intervals of numerical domains with discrete values. The GA is utilized in this technique to obtain the optimal boundaries of these intervals, and consequently to reduce the complexity in feature space. In the third technique, the feature selection performs two successive functions which are called preliminary and deeply reduction for extracting the most relevant features from the original features set. Indeed, the main contribution of the proposed model is providing a precise image retrieval in a short time.

A novel image retrieval model based on the most relevant features

This paper presents an algorithm of Hand Gesture Recognition by using Dynamic Time Warping methodology. The system consists of three modules: real time detection of face region and two hand regions, tracking the hands trajectory both in terms of direction among consecutive frames as well as distance from the centre of the frame and gesture recognition based on analyzing variations in the hand locations along with the centre of the face. The proposed technique of ours overcomes not only the limitations of a glove based approach but also most of the vision based approach concerning different illumination conditions, background complexity and distance from camera which is up to 2 meters. Also by using Dynamic Time Warping Algorithm which finds the optimal alignment between the stored database features and query features, improvement in recognition accuracy is observed compared to conventional methods. Experimental results show that the accuracy is 90% in recognizing 24 gestures based on Indian Sign Language.

Vision based Hand Gesture Recognition using Dynamic Time Warping for Indian Sign Language

The main objective of the paper is to implement Savitzky Golay Smoothing Filter (SGSF) so as to apply in pre-processing of real time smart medical diagnostic systems. As very important information of EEG and ECG waveforms lies in the peak of the signal, hence it becomes absolutely necessary to filter noise and artifacts from the signal. The implemented filter should be able to reject the noise efficiently along with the least distortion from the original signal. The shape preserving characteristics of the filter are determined by introducing different noise levels in the signal. The designed filter is tested on synthetic signals of EEG and ECG by adding different types of noise and the performance is analysed on various parameters, i.e., SNR, SSNR, SNRI, MSE, COR and signal distortion of the final output. The smoothing performance comparison of SGSF with the most commonly used Moving Average Filter (MAF) proves that SGSF is more efficient. Hence it is suggested that MAF can be replaced by SGSF. For real time issues, it is further implemented on reconfigurable architectures so as to achieve high speed, low cost, low power consumption and less area. Therefore SGSF is realized on FPGA platform to combine the advantages of both. Real time EEG and ECG signals are also considered for experimentation. The experimental results show that the proposed methodology (FPGA-SGSF) significantly reduces the processing time and preserves the actual features of the signal.

Performance Evaluation and Implementation of FPGA Based SGSF in Smart Diagnostic Applications

The use of hand gestures provides an attractive alternative to cumbersome interface devices for Human-Computer Interaction (HCI). However, in dynamic gesture recognition area, hand tracking under a complicated environment and gesture spotting namely detecting the start and end point are the two most challenging topics. In our work, a realtime Kinect-based dynamic hand gesture recognition (HGR) system which contains hand tracking, data processing, model training and gesture classification is proposed. In the first stage, two states of the performed hand including open and closed are utilized to achieve gesture spotting and 3D motion trajectories of gestures are captured by Kinect sensor. Further, motion orientation is extracted as the unique feature and Support Vector Machine (SVM) is used as the recognition algorithm in the proposed system. The results of experiments conducted in our database containing 10 Arabic numbers from 0 to 9 and the 26 characters of alphabet show efficiency with an average recognition rate of 95.42% and real-time performance of our method.

A real-time dynamic hand gesture recognition system using kinect sensor

Human–robot interaction (HRI) has become a research hotspot in computer vision and robotics due to its wide application in human–computer interaction (HCI) domain. Based on the explored algorithms of gesture recognition and limb movement recognition in somatosensory interaction, an HRI model of a robotic arm is proposed for robot arm manipulation. More specifically, 3D SSD architecture is used for the location and identification of gesture and arm movement. Then, DTW template matching algorithm is adopted to trace the dynamic gestures. The interactive scenarios and interactive modes are designed for experiment and implementation. Virtual interactive experimental results have demonstrated the usefulness of our method.

Human–robot interaction based on gesture and movement recognition

Summary Content Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many indexing techniques are based on global features distribution such as Gabor Wavelets. [1]. In this paper we present a new approach for global feature extraction using an emerging technique known as Independent Component Analysis (ICA). A comparative study is done between ICA feature vectors and Gabor feature vectors for 180 different texture and natural images in a databank. Result analysis show that extracting color and texture information by ICA provides significantly improved results in terms of retrieval accuracy, computational complexity and storage space of feature vectors as compared to Gabor approaches.

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Content Based Image Retrieval Using Independent Component Analysis

Classification of hand gesture is crucial for the development of hand gesture based system for human machine interaction. Gesture recognition system consists of hand gesture acquisition, segmentation, morphological filtering, contour extraction, and classification. This paper aims at classification of hand gesture as a similarity measure using Dynamic Time Warping and Piecewise Dynamic Time Warping. Experiments and evaluation on a subset of American Sign Language (ASL) hand gesture show that, by using Dynamic Time Warping hand gesture can be classified. Additionally, it is also estimated that Piecewise DTW can be efficiently used to speed up the computations of DTW.

http://ieeexplore.ieee.org/iel7/6881541/6892507/06892646.pdf

Gesture recognition using DTW & piecewise DTW

Automatic hand gesture recognition using hybrid meta-heuristic-based feature selection and classification with Dynamic Time Warping

Early pest detection and identification in agriculture is necessary for good quality and quantity of crop production. This will help to reduce the effect of pest and the use of pesticides. Detection of pest is necessary to get the information and location of the pest and this can be achieved only if temperature is controlled in the room like in the greenhouse. There are various conventional techniques which are used to detect the pest, but the proposed method is very fast, easy and convenient. This project proposes pest detection and movement by using the video processing. Video processing involves capturing the video and processing on it to find the presence and location of pest.

Agriculture pest detection using video processing technique

Independent component analysis is a generative model for observed multivariate data, which are assumed to be mixtures of some unknown latent variables. It is a statistical and computational technique for revealing hidden factors that underlies set of random variable measurements of signals. A common problem faced in the disciplines such as statistics, data analysis, signal processing and neural network is finding a suitable representation of multivariate data. The objective of ICA is to represent a set of multidimensional measurement vectors in a basis where the components are statistically independent. In the present paper we deal with a set of images that are mixed randomly. We apply the principle of uncorrelatedness and minimum entropy to find ICA. The original images are then retrieved using fixed point algorithm known as FastICA algorithm and compared with the original images with the help of estimated error. The outputs from the intermediate steps of algorithm such as PCA, Whitening matrix, Convergence of algorithm and dewhitening matrix are also discussed.

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Arti Khaparde

Papers

Content Based Image Retrieval Using Independent Component Analysis

Gesture recognition using DTW & piecewise DTW

Automatic hand gesture recognition using hybrid meta-heuristic-based feature selection and classification with Dynamic Time Warping

Agriculture pest detection using video processing technique

FastICA algorithm for the separation of mixed images