International Journal of Computational Vision and Robotics (39 papers in press)
A Vision Based Non-Contact Area and Volume Estimation of Irregular Structures towards Applications in Wound Measurement
by Shubhangi Shrivastava, Alex Noel Joseph Raj
Abstract: Area and volume computation of irregular structures can be done using several techniques, but accuracy and the speed of computation become an issue. But when wounds are considered for measurements, another factor comes into picture which is the risk of contamination due to direct contact with wounds and this can lead to catastrophic situations. In order to combat such issues, there is a requirement of non-contact measurement technique and thus, the paper discusses about a vision based non-contact method for area and volume computation of irregular structures. The methodology presented here, models the irregular structured object using Delaunay triangulation and thus computes area and volume from the model formed. The new method is experimented on certain regular and irregular shaped objects and the results obtained along with their accuracy are provided.
Keywords: Irregular structures, Area and volume estimation, Vision based method, Non-contact method,Intel ToF camera
Design and analysis of active vibration and stability control of a Bio-Inspired Quadruped Robot
by D. Vishal, P.V. Manivannan
Abstract: The main aim of this research work is to design and develop an Bio-inspired Quadruped Robot for operating in an uneven terrain. The present work focus on development of a gear-train power transmission mechanism, which is more efficient, as the gear assemblies can be optimized for generating different locomotion patterns. The kinematic and dynamic analysis of the limb analysis has been carried out. The modeling of mechanism and measurement & motion analysis has been carried out using ProE Wildfire software and the results have been presented in this paper
Keywords: Bio-inspired; gear-train; Locomotion pattern; kinematic and dynamic analysis.
Classification Improvement Using an Unscented Kalman Filter in Brain Computer Interface Systems
by Yeon-Mo Yang
Abstract: In this paper, we propose an enhanced classification technique using an Unscented Kalman Filter (UKF) for Brain Computer Interface (BCI) signal processing. Since the UKF estimates the state of a nonlinear dynamic system and parameters for nonlinear system identification, the UKF can significantly improve the performance of classification in BCI systems. As a result, we confirm the performance improvement when using the UKF in motor imagery classification in terms of accuracy, Kappa value, and confidence interval.
Keywords: Brain Computer Interface; Unscented Kalman Filter; Classification, Statistical Signal Processing
Edge Based Singular Value Decomposition for Full Reference Color Image Quality Assessment
by Manisha Jadhav, Yogesh Dandawate, Narayan Pisharoty
Abstract: Today due to intense use of digital visual aids, image quality plays a crucial role in multimedia and internet based applications. Images are subjected to degradations during image acquisition and image processing which affect their naturalness and usefulness in different applications. Since last few decades literature shows efforts made to develop an ideal image quality measure that is consistent with human visual system. New image quality measuring techniques, extension of existing image quality algorithms and their applications are been developed and implemented. Singular value decomposition is used to measure amount of distortion across different distortion types at different distortion levels. Based on hypothesis that the human eye is adapted to extract edge information from any natural scene, this paper presents a novel approach of introducing edge information in SVD based image quality metric. Results are compared with SVD based metric available in related literature. Proposed metric outperforms the existing metric.
Keywords: Image quality, singular value decomposition, colorspace, human visual system, full reference image quality metric, edge detection
A Hybrid Scheme of Image Compression employing Wavelets and 2D-PCA
by Manoj Mishra, Susanta Mukhopadhyay, Rohit Ghosh
Abstract: In this paper, we have presented a method of compressing 2D gray-scalernimages employing wavelets and 2-dimensional principal component analysis(2D-PCA). Principal component analysis (PCA) is an already established technique for image compression which primarily aims at exploiting inter pixel redundancies present in the image, while wavelet is a tool widely used in multi-resolution image processing. In the proposed method the image is subjectedrnto a multi-resolution decomposition using wavelet. Subsequently, 2D-PCA is applied on the set of detail images at each level of resolution. The compressed form of the image is constituted by representative pairs of principal components and projection vectors from each level of resolution along with the approximate image at the coarsest resolution. The proposed method requires relatively few number of principal components(of varied dimension) to produce improved compression ratio with acceptable peak signal to noise ratio(PS NR). The method has been implemented and tested on a set of real 2D gray-scale images and the results have been assessed on both qualitative andrnquantitative basis by measuring parameters like compression ratio (CR), PS NR, structural similarity index measurement (S S I M) and the overall performance is found to be satisfactory.
Keywords: 2D-PCA, feature matrix, projection vector, image compression, wavelet structuralrnsimilarity index
Human Action Recognition by Fuzzy Hidden Markov Model
by Jalal A. Nasiri
Abstract: Abstract: Hidden Markov Model (HMM) has been widely applied in human
action recognition. In this paper an extension of HMM called Fuzzy Hidden
Markov Model (Fuzzy HMM) is used for action recognition. It tries to increase
the classification performance and decrease the information loss due to feature
vector quantization. Using fuzzy concepts with HMM leads to better recognition
of similar actions such as walking, jogging and running. Two feature extraction
methods including Skeleton and space-time approaches are used for action
representation. Actions could be represented efficiently using Skeleton features
where scene background is plain. Space-time features are extracted directly from
video, and therefore avoid possible failures of other pre-processing methods.
We propose space-time based features by considering temporal relation between
them. Experimental results show the effectiveness of Fuzzy HMM in human
action recognition. Moreover, it is shown that Fuzzy HMM leads to significant
improvement in recognition of similar actions. The accuracy rates of Fuzzy HMM
in comparison to HMM are incremented 3.33% and 5.59% in Weizmann and
KTH datasets respectively.
Keywords: Human action recognition; Fuzzy Hidden Markov Model; Skeletonrnfeatures; Space-time features
Efficient Image Retrievals using Generalized Gaussian Mixture Model
by P. Anuradha, Srinivas Yarramalle, M.H.M. Krishna Prasad
Abstract: The advancements in technology drifted the individuals towards the usage of modern devices and as a result the usage of web services has increased exponentially. As a result, the quantity of multimedia data accessed and stored across the Internet is growing swiftly. Online money transactions, purchases, tenders and many other applications, such as messaging among specific groups were made possible. The usage of social networking sites have gained popularity due to its capability of sharing the videos and audios along with messaging, however, retrieving the most relevant videos is a challenging task among these networking groups. In this paper a concept of audio tagging is used and the relevant videos are retrieved efficiently. The generalized GMM distribution is used as classifier and MFCC features are used to identify the voices associated with the videos.
Keywords: Voice Tagging, Retrieval, Generalized GMM, MFCC, Flicker
Efficient Local Adaptive Thresholding for Night-time Vehicle Candidate Detection
by Yeongyu Choi, Hyojin Lim, Cuong Nguyen Khac, Ju H. Park, Ho-Youl Jung
Abstract: In the current commercial automotive market, the need for intelligent headlight control systems has increased more and more. Camera-based night-time vehicle detection has become a crucial issue in determining the performance of such control systems. The purpose of this paper is to offer an answer to the question, Which thresholding method is suitable in terms of detection performance for a night-time vehicle candidate selection process? For such purposes, two local adaptive thresholding methods are introduced and tested. One is local maximum-based thresholding, and the other is local mean-based thresholding. Efficient implementation methodologies are also introduced for real-time processing. Through the simulations tested on road image sequences with different exposure times, we prove that local adaptive thresholding methods have better performance than other well-known global thresholding methods. In particular, the simulations show that the proposed mean-based thresholding method has better performance on both long- and short-exposure sequences.
Keywords: Adaptive thresholding; Vehicle detection; Computer vision.
A Context Based Algorithm for Sentiment Analysis
by Srishti Sharma, Shampa Chakraverty, Akhil Sharma, Jasleen Kaur
Abstract: With netizens continuing to express a range of opinions and making assessments online, it has become a challenge to mine sentiments accurately from the ever-multiplying Big Data. We present a context-driven Sentiment Analysis scheme with the objective of refining the degree of subjectivity during Sentiment Analysis. The essence of our scheme is to capture in a stable manner, the mutual influence of the sentiments of neighboring words on the sentiment of each word in a document. A parametric Influence Function combines the native sentiment score of each word with the context-derived sentiment score obtained from surrounding words. We apply a Genetic Algorithm to fine tune the parameters of the Influence Function so as to obtain the best possible accuracy for a given corpus. Experimental results on hotel reviews extracted from Tripadvisor.com show an average accuracy of 73.2% which is 3.6% more than the results obtained from the Baseline Sentiment Analysis approach using native scores obtained from SentiWordNet. Though the improvement is small, it re-affirms our belief that contextual information provides valuable reinforcement of sentiment scores especially with regard to the borderline cases where words show near neutral sentiments. We also present a comparison with alternative Sentiment Analysis approaches that shows the strength of our proposed Context Based Sentiment Analysis approach.
Keywords: Sentiment Analysis; Opinion Mining; Context Based Sentiment Analysis; SentiWordNet; Word Sense Disambiguation.
Odia Character Recognition using Backpropagation Network with Binary Features
by Mamata Nayak, Ajit Nayak
Abstract: Automatic recognition of printed characters has been an area of active research since last few decades. Though considerable work has been reported for Latin, CJK, and many other popular languages, but a few work has been reported for many Indian languages. In this work we propose a complete model with effective techniques to recognise printed off-line Odia language characters. First we develop technique to segment the image to extract characters, then features of these segmented characters are extracted using a binary feature extraction, as well as structural feature extraction .Finally these extracted features are used to classify character symbols using a modified backpropagation network. In each of these phases it is found that the proposed techniques outperforms existing techniques and yields high accuracy rate. Further, this work has tried to include all the possible characters (approx. 2500 characters) in training/testing unlike other works, where a small set of characters are normally considered for testing.
Keywords: optical character recognition (OCR); Artificial Neural Network (ANN); Binary feature; Odia language.
A New Method for 3-Dimensional Magnetic Resonance Images Denoising
by Feriel Romdhane, Faouzi Benzarti, Hamid Amiri
Abstract: Removing noise in Magnetic Resonance Images (MRI) is a crucial issue in the field of medical image processing. These images are infected by Rician noise which is a non additive noise, allows to reduce the image contrast and causes random fluctuations. Our paper proposed a new method for 3D MRI denoising based on new combination between Non-local Means filter and the Diffusion Tensor with adaptative MAD estimator Rician noise. The performance of our proposed algorithm was evaluated with respect to different quantitative measures, compared to other denoising methods which illustrate that our proposed denoising algorithm efficiently removes noise and preserves more details.
Keywords: 3D MRI; 3D denoising method; Non-Local Mean filter; Diffusion Tensor; Rician noise.
A Novel Incremental Topological Mapping Using Global Visual Features
by Nabila Zrira, El Houssine Bouyakhf
Abstract: Mapping is fundamental in the navigation task of autonomous mobile robots. In appearance based mapping, the process of detecting visual loop closing determines whether the current observation comes from a previously visited location or a new one. The purpose of this paper is to present a new method of exploring indoor environments by an autonomous mobile robot, as well as building topological maps based on global visual attributes. This method takes advantage of the small size of the GIST descriptors, and the ease of their calculation. We also make use of omnidirectional images to build a single global visual descriptor showing an entire room. Furthermore, in order to handle the problem of a visual loop closing, we have employed a formula that correctly assigns each global descriptor to its location.
Keywords: Topological mapping, Gist descriptor, visual loop closing,omnidirectional images, navigation, mobile robots, indoor environments.
Car Manufacturer and Model Recognition based on Scale Invariant Feature Transform
by Yongbin Gao, Hyo Jong Lee
Abstract: Vehicle analysis involves lisence plate recognition, vehicle type recognition, and car manufacturer and model recognition. Car manufacturer and model recognition plays an important role to provide complementary information for lisence plate recognition for unique identification of a car. In this paper, we propose a framework to recognition car manufacturer and its model based on Scale Invariant Feature Transform (SIFT). We first detect the moving car using frame differences; the resultant binary image is used to detect the frontal view of a car by a symmetry filter. The detected frontal view is used to identify a car based on SIFT algorithm. Experiment results show that our proposed framework achieves favorable recognition accuracy.
Keywords: Moving car detection; Car model recognition; SIFT
Image compression based multiple description transform coding using NSCT and OMP approximation.
by Amina NAIMI, Kamel BELLOULATA
Abstract: In this paper we present a novel Multiple Description Transform Image Coding architecture, which uses an attractive transform called Nonsubsampled Contourlet Transform NSCT. The proposed scheme combines NSCT and orthogonal matching pursuit algorithm OMP to give a sparse representation of images, aiming at solving the compression problem due to the redundancy property of NSCT. Apply a redundant transform in coding is a challenge. In this way, the well-known algorithm OMP turns to give a solution to remove the redundancies of NSCT subbands. We evaluate the performance of our multiple description image coder in the case of four descriptions that are dispatched over different channels. The experimentations show that the proposed method is efficient and the potential using NSCT than DWT in multiple description image coding, is evaluated by PSNR in each case of packet loss, where every description can reconstruct the image with acceptable fidelity, the later is much better if all descriptions are available. Simulation results have proved the efficiency of the suggested algorithm.
Keywords: Multiple description coding (MDC), Nonsubsampled contourlet transform (NSCT), Discrete wavelet transform (DWT), Orthogonal matching pursuit (OMP).
Original strategy for avoiding over-smoothing in SFS problem resolution
by Rocco Furferi, Lapo Governi, Yary Volpe, Luca Puggelli, Monica Carfagni
Abstract: With the aim of retrieving 3D surfaces starting from single shaded images, i.e. for solving the widely known shape from shading problem, an important class of methods is based on minimization techniques where the expected surface to be retrieved is supposed to be coincident with the one that minimize a properly developed functional, consisting of several contributions. Despite several different contributes can be explored to define a functional, the so called smoothness constraint is a cornerstone since it is the most relevant contribute to guide the convergence of the minimization process towards a more accurate solution. Unfortunately, in case input shaded image is characterized by areas where actual brightness changes rapidly, such a constraint introduces an undesired over-smoothing effect for the retrieved surface. The present work proposes an original strategy for avoiding such a typical over-smoothing effect, with regards to the image regions in which this is particularly undesired such as, for instance, zones where surface details are to be preserved in the reconstruction. The proposed strategy is tested against a set of case studies and compared with other traditional SFS-based methods to prove its effectiveness.
Keywords: Shape from Shading; variational approach; 3D model; over-smoothing; minimization; smoothness constraint.
A new method for automatic date fruit classification
by Oussama Aiadi, Mohammed Lamine Kherfi
Abstract: Date fruit classification by human is tedious, slow and requires several workers. In this paper, we propose a method for automatic classification of dates. Because dates of the same variety may considerably vary in terms of hardness, maturity level and shape, we represent each variety with a Gaussian mixture model (GMM). Calinski-Harabasz index has been adopted to estimate the optimal number of components for each GMM. Furthermore, the normality of samples belonging to each component is checked using Mardia's multivariate tests. Our method is able to accurately classify dates in spite of the large variation within some varieties and the small variation between some varieties. Moreover, it doesnt require any human intervention. To validate our method and as, to our knowledge, no date benchmark is publicly available, we introduce a new benchmark of 5,000 images from 10 varieties. Experimental results demonstrate the effectiveness and the strength of our method.
Keywords: date fruit, date classification, GMM, date benchmark
Efficient Holistic Feature Basis Learning For Pedestrian Detection
by Kyaw Kyaw Htike
Abstract: Pedestrian detection is an important research area in computer vision and Artificial Intelligence due to its potential applications in pedestrian safety, elderly monitor and care, surveillance, image retrieval and video compression. Many pedestrian detection systems have been proposed and it has been pointed out in state-of-the-art research that feature extraction is one of the significant factors in improving the performance of a pedestrian detector. Therefore, much work has focused on proposing novel feature extraction schemes to improve pedestrian detection. Moreover, most are end-to-end pedestrian detection systems, making it unclear about the contribution of classifiers in the detection pipeline. In this paper, we fill in some of this gap and focus on the classification process and propose feature basis learning for holistic high dimensional feature vectors that are common in pedestrian detection. We experimentally show that it is possible to obtain superior performance by our proposed feature basis learning algorithms even on high dimensional datasets.
Keywords: Pedestrian Detection; Feature Extraction; Classifiers; Computer Vision; Feature Learning.
Road traffic sign recognition algorithm based on computer vision
by Huiming Dai, Xin Zhang, Dacheng Yang
Abstract: As road traffic sign recognition is a crucial component for automatic driver assistance systems, and it is a key problem in computer vision as well. Therefore, in this paper, we study on the problem of road traffic sign recognition utilizing the computer vision technology. The main innovation of this paper is to propose an improved convolutional neural network, and then use it to tackle the road traffic sign recognition problem. Convolutional neural network can learn features from training data set, and a convolutional network contains alternating layers of convolution and pooling. Particularly, RGB traffic images are transformed to gray scale images, and then gray scale images are input to the improved convolutional neural network. Furthermore, the fixed layers are utilized to discover region of interests, and the learnable layers are used to extract features. In general, output information of the proposed two learnable layers are input to the classifier separately, and parameters of learnable layers and the classifier are trained at the same time. Finally, GTSDB data set is chosen to make performance evaluation, among which 600 images and 300 images are regarded as training and testing data set respectively. Experimental results demonstrate that the improved CNN based traffic sign recognition performs better than the traditional CNN.
Keywords: Road traffic sign; Object recognition; Computer vision; Convolutional neural network.
Obstacle Detection System Based on Colour Segmentation Using Monocular Vision for an Unmanned Ground Vehicle
by Auday Al-Mayyahi, Weiji Wang, Phil Birch, Alaa Hussien
Abstract: A vision system-based obstacle detection system for an autonomous ground vehicle An obstacle detection system based on vision approach is introduced for an indoor unmanned ground vehicle (UGV). Coloured and solid obstacles were placed randomly in an indoor field as obstacles. These obstacles are then captured in an image by using a monocular vision to develop an obstacle detection algorithm. The obstacles are detected by analysing and processing the captured images using computer vision and image processing techniques. A camera calibration is conducted to determine the relative position and orientation of the UGV with respect to the obstacles. The camera calibration was used to find the intrinsic and extrinsic matrices. These two matrices are then combined and used to produce the perspective projection matrix. Based on the calibration process, the relative position and the offset distance in addition to the steering angle of the UGV, from the obstacles, were derived. The field geometry was used to obtain a mapped environment in the coordinates world. In this paper, a proposed algorithm was accomplished to identify the existence of the obstacles in the field, using bounding boxes around the detected obstacle. That allows the determination of the obstacles locations in a pixel coordinate frame. Thus, the depth perception was determined by using the pixel coordinates and the camera projection matrix. In this work, real-time experiments in an indoor environment are carried out using a four wheeled UGV system to demonstrate the validity and efficiency of the proposed algorithm. The outcome shows that the actual distances between the camera and the obstacles can be obtained using this technique.
Keywords: Unmanned Ground Vehicle (UGV); Obstacle Detection; Colour Segmentation; Depth Perception; Camera Calibration.
An enhanced texture-based image retrieval approach with features selected from integration of feature extraction techniques
by Akanksha Juneja, Bharti Rana, R.K. Agrawal
Abstract: Texture is vital in characterising images for effective content-based image retrieval. Integrating features from various feature extraction techniques improves the performance of decision system in comparison to individual techniques as it provides complimentary information as a whole. However, this integration creates a large feature vector which may contain irrelevant and redundant features and hence degrade the performance. Therefore, we propose a three-phase texture-based image retrieval approach for enhanced performance. In the first phase, pool of texture features from seven feature extraction techniques is created. In the second phase, some popular feature selection techniques are applied to this pool to obtain a reduced set of relevant and non-redundant features. In the third phase, three well-known distance measures are utilised to retrieve images based on the reduced features set. The performance of the proposed approach is evaluated on Brodatz dataset. The proposed approach outperforms individual feature extraction techniques.
Keywords: texture; content-based image retrieval; feature extraction; feature selection; computational vision; robotics.
Neuro-curvelet-based image compression technique for noisy images
by Arun Vikas Singh, K. Srikanta Murthy
Abstract: The main aim of image compression is to represent an image with minimum number of bits, so that the storage requirement can be reduced, thereby increasing the transmission rate without losing significant features of the image. The compression ratio is affected by noise, as it degrades the correlation between pixels. During capture, processing or transmission of the image, noise can occur. The noise possibly can be independent of or dependent on image content. On lossy image compression algorithms, the effect of noise has been studied in this paper. In order to study the effect of noise, the original images act as a reference to the reconstructed images. The reconstructed images are compared with the original images in terms of PSNR. The proposed image encoder integrates the features of curvelet transform with both radial basis function neural network (RBFNN) and back-propagation neural network (BPNN) separately and results are presented for both the cases. The case studies which consider images with noise prove the superiority of the techniques in terms of highly acceptable PSNR values. The merits of the proposed technique are further exemplified by comparing the results with those of JPEG and JPEG 2000.
Keywords: curvelet transform; radial basis function neural network; RBFNN; back-propagation neural network; BPNN; vector quantisation.
Modelling of ink-colour degradation on old printed documents
by Biswajit Halder, Abhoy Chand Mondal
Abstract: Determination of ink age in documents is crucial for many applications including the forensic ones. The degradation in ink-colour has an important role for this purpose. This paper attempts to model the degradation of ink-colour in documents. Such a model is not only helpful for determination of ink age and it gives a better insight about how colour of a document changes over time. The mathematical foundation for the proposed model is borrowed from an optimisation technique called ant colony optimisation. From a set of 200 documents distributed over five decades (30s to 70s decades) the model, under certain assumptions, tries to find an optimised path through which the majority of documents starting at 30s pass through intermediate decades to reach at 70s. Estimation of the model parameters and use of this model for: 1) determination of ink age; 2) prediction of a document's condition after certain years are discussed in details. Two types of digitised old magazines cover pages are considered for our experiment. The first one is used for construct the model and the second one is used for verification.
Keywords: ant colony optimisation; ACO; meta-heuristic model; image processing; ink-age.
Comparative analysis of video compression mechanisms using 3D-DWT based video encoding along with EZW
by V.R. Satpute, K.D. Kulat, A.G. Keskar
Abstract: In this paper, two compression mechanisms based on 3D-discrete wavelet transform (DWT) and 2D embedded zero wavelet (EZW) are proposed and compared depending on the mathematical parameters, i.e., peak signal-to-noise ratio (PSNR) and compression ratio (CR). In this paper, we are using Haar wavelet decomposition for compression, as it has shown improved compression in recent years when used along with the techniques like EZW, SPIHT, etc. Haar wavelet is chosen because of its ease implementation and has inherent properties and EZW is chosen for compression. We apply EZW with frame-by-frame basis on the encoded video as EZW is meant for 2D-data only. Here we are adding the extra blocks for video encoding and decoding before and after the existing compression technique, i.e., EZW. So, these mechanisms are very easy to implement by just adding the extra blocks of encoding and decoding.
Keywords: 3D-discrete wavelet transform; 3D-DWT; embedded zero wavelet; EZW; Haar wavelet; video encoding; video compression; computational vision.
Emotion recognition using MLP and GMM for Oriya language
by Hemanta Kumar Palo, Mahesh Chandra, Mihir Narayan Mohanty
Abstract: Emotion recognition of human beings is one of the major challenges in the modern complicated world of political and criminal scenario. In this paper an attempt is taken to recognise two classes of speech emotions as high arousal like angry, surprise and low arousal like sad and bore. Linear prediction coefficients (LPC), Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features are used for emotions recognition using multilayer perceptron (MLP) and Gaussian mixture model (GMM) classifier. Two different databases of four emotions, one of five children and other one of a professional actor has been used in this work. Emotion recognition performance of LPC, PLP and MFCC features has been compared with two classifiers, MLP and GMM. MFCC features with MLP classifier and PLP features with GMM classifier has performed best in their respective categories.
Keywords: emotion recognition; Mel-frequency cepstral coefficient; MFCC; linear prediction coefficients; LPCs; perceptual linear prediction; PLP; neural network; multilayer perceptron; MLP; radial basis function; Gaussian mixture model; GMM.
Multi-modal motion dictionary learning for facial expression recognition
by Jin-Chul Kim, SungYong Chun, Chan-Su Lee
Abstract: Recently, dictionary learning is actively investigated in image and signal processing. A variety of dictionary learning methods for the classification problems have been proposed. In this paper, we propose a facial expression recognition system using a multi-modal motion dictionary based on motion flow composed of motion flow intensity and motion flow direction. At the dictionary learning stage, two dictionaries having different modalities are learned from motion flow intensity and from motion flow angle data of facial expression image sequences. We made a feature vector by concatenating two weight vectors of individual dictionaries from each image sequence for classification. Experimental result shows higher accuracy than conventional reconstruction-based approaches with extremely reduced classification time. The proposed approach is a promising method for real-time facial expression recognition from motion flow data.
Keywords: dictionary learning; facial expression recognition; motion flow estimation.
An efficient interpretation of hand gestures to control smart interactive television
by Dinesh Kumar Vishwakarma, Rajiv Kapoor
Abstract: In the recent era of smart world, smarter technologies are gaining focus on human computer interaction (HCI) systems, and traditional ways like remote control, mouse, keyboard, etc. are becoming less popular. This paper presents a framework of simple yet efficient approach for future applications of the new age smart and intelligent technologies that shall enhance the HCI specifically for smart television. Hand gesture recognition (HGR)-based model is proposed for wireless control of smart interactive television (SITV), which includes controlling volume and selecting channels. The proposed framework undergoes three steps: 1) the hand gesture of the person is detected by using shape, colour and skin similarity; 2) the extracted features are classified by using rule-based classification and a gesture code is generated; 3) the classified gesture is interpreted by a novel interpreter. The performance of the proposed framework is evaluated with different people's hand gestures and compared with the techniques of others.
Keywords: hand gesture recognition; HGR; interpreter; remote control; television.
Fingerprint matching and correlation checking using level 2 features
by Devarasan Ezhilmaran, Manickam Adhiyaman
Abstract: Fingerprint matching is one of the most important problems in automatic fingerprint identification system (AFIS). It has emerged as an effective tool for human recognition due to its uniqueness, universality and invariability. The significance of this work is to monitor the matching and correlation checking for two or more fingerprint images simultaneously using fuzzy logic system. The fuzzy logic system provides the more adequate description of the proposed algorithm. The algorithm has been formulated based on minutiae (level 2) points which examine n number of images.
Keywords: biometric; fingerprint image; minutiae points; Euclidean distance; matching; fuzzy inference system; similarity.
Special Issue on: Recent Advances and Emerging Topics in Computer Vision Methods and Image Analytics
Exploring Necessity and Utility of Lightweight Android Chatting Application
by Ekbal Rashid
Abstract: In this paper there is elaborate discussion about the methods and results of a field research which led to the understanding that a lightweight chatting android application would be extremely useful for people using social networking apps. The paper details the steps how the app was developed, its salient features, its use cases and finally the findings about what people thought about it once they began using it. It also discusses the usability testing and resulting iteration. The paper attempts to highlight how such an app can help in using the resources of an Android device in a better way.
Keywords: Android; application; chatting; lightweight; mobile; communication
Effective Image Retrieval Based on Hybrid Features with Weighted Similarity Measure and Query Image Classification
by Vibhav Prakash Singh, Rajeev Srivastava
Abstract: Content Based Image Retrieval (CBIR) is a wide research area in computer vision, in which unknown query image yield similar images as per the query content. An effective CBIR needs efficient extraction of low-level features, and for this many different methods have been recently proposed using colour, texture, and shape features. Most of these methods use the histogram or some variation for representing colour and other descriptors. So, all these features may require a significant amount of space and more similarity calculation. Also, the CBIR performance is not so encouraging due to gap between low-level visual features and high-level understanding. Here, an efficient CBIR system is proposed, which is based on the fusion of chromaticity-colour moments, and colour co-occurrence based small dimension features using inverse variance weighted similarity measure. In this measure, weight of a feature with high variance is low, while the weight of a feature with low variance is high. This interesting property of the varying weights, effectively retrieves relevant images. In addition, this paper also proposes a supervised query image classification and retrieval model by filtering out irrelevant class images using a multiclass support vector machine (SVM) classifier. Basically, this model categorises and recovers the category of a query image based on its visual content, and this successful categorization of images significantly enhances the performance and searching time of retrieval system. Descriptive comparative analyses confirm the effectiveness of this work. Here, we have obtained 83.83 % and 76.9 % average precision for 12 and 20 image retrieval, respectively using weighted similarity measure together with 85.6% average precision and 84.4 % recall for the query image classification framework.
Keywords: classification; chromaticity moment; colour co-occurrence; feature fusion; content based image retrieval; variance.
Facial Expression Recognition based on Eigenspaces and Principle Component Analysis
by Ashim Saha
Abstract: Facial Expression detection or Emotion Recognition is one of the risingrnfields of research on intelligent systems. Emotion plays a significant role in nonverbal communication. An efficient face and facial feature detection algorithms are required to detect emotion at that particular moment. In this work authors implemented a system that recognizes the users facial expressions from the input images, using the algorithm of Eigenspaces and Principle Component Analysis (PCA). Eigenspaces are the face images which projected onto a feature space that encodes the variation among known face images. Authors used PCA to make dimensional reduction of images in order to obtain a reduced representation of face images. The implementation is been applied on three different Facial Expressions databases, Extended Cohn-Kanade facial expression database, Japanese Female Facial Expression database and self made database in order to find out therneffectiveness of proposed method.
Keywords: Facial expression; Eigenspaces; Principle component analysis; Emotion detection; Image Processing.
Content Based Image Retrieval with Pachinko Allocation Model and a Combination of Color, Texture and Text Features
by Ahmed Boulemden, Yamina Tlili, Hamid Jalab
Abstract: Probabilistic topic models are a set of algorithms which aim to learn and discover hidden concepts responsible of generating words of documents in large archives. These models have been also used for image processing tasks such as object recognition, image annotation and image retrieval. rnWe present in this paper a content based image retrieval system (CBIR) based on pachinko allocation model (PAM) and employing a combination of color, texture and textual features. PAM has presented more efficiency compared with other topic models by the way in which it captures correlation not only between words in documents but also between different topics (concepts) responsible of their generation. Although this advantage of PAM, there is no works which explore its utility for content based image retrieval tasks and using single and multimodal image features.rnWe aim to evaluate the use of PAM for CBIRs by implementing a system based on it. We are interested also in evaluating PAM with single and multimodal image features (i.e. the use of single and combined features). In this context, PAM was applied with two different modalities of features, image global features (color and texture) and textual indexes (from associate texts with images) separately and combined.rnMean Average Precision is evaluated. The use of PAM with combination of features has slightly improved results of using it with just one modality, this opens more perspective in order to enhance results. Images from the ImageCLEF IAPR 2012 dataset have been used for experiments.rn
Keywords: Pachinko allocation;image retrieval;color moments;texture features;global feature extraction;textual modality;features combination
An automatic natural feature selection system for indoor tracking - Application to Alzheimer patient support
by Mohamed Badeche, Frederic Bousefsaf, Abdelhak Moussaoui, Mohamed Benmohammed, Alain Pruski
Abstract: In this paper, we propose an automatic selection and natural feature tracking method that uses a monocular camera for path capturing and guides the user showing him the path to be followed. The application targets Alzheimer patients for helping them in their indoor moves. By offering an automatic selection of features, the user intervention and prior knowledge of the working environment would not be required to assure the good working of the system. The general principle of the proposed method is to record the path to be followed, and then recognize it in real time using purely visual methods, using only a single camera as an acquisition sensor. The devised system could be implemented on augmented-reality glasses with one single built-in camera. The experimental results have shown that the proposed method is very promising and the application could follow accurately the required path in real time, with a satisfying robustness in a fully-contrasted and static environment.
Keywords: natural features; matching; local descriptor; optical flow; Alzheimer disease; augmented reality glasses.
Combining Zernike moment and Complex wavelet transform for Human object Classification
by Manish Khare, Om Prakash, Rajneesh Kumar Srivastava
Abstract: Human object classification is an important problem for smart video surveillance, where we classify human object in real scenes. Even though different features have been used for human object classification task, most of the existing methods adopt a single feature to classify the objects. In this paper, we have proposed a new method for human object classification, which classify the object present in a scene into one of the two classes: human and non-human. The proposed method uses combination of Daubechies complex wavelet transform and Zernike moment as a feature of object. The motivation behind using combination of these two as a features of object, because shift-invariance and better edge representation property makes Daubechies complex wavelet transform suitable for locating object as compared to real valued wavelet transform, whereas rotation invariance property of Zernike moment is also helpful for correct object identification. Therefore, combination of these two features brings about significant synthesized benefits over each single feature and other widely used features. The proposed method matches Zernike moments of Daubechies complex wavelet coefficients of objects. We have used Adaboost as a classifier for classification of the objects. The proposed method has been tested on standard dataset like INRIA person dataset. Quantitative experimental evaluation results shows that the proposed method is better than other state-of-the-art methods and gives better performance for human object classification.
Keywords: Human object classification, feature selection, Daubechies complex wavelet transform, Zernike moment, Adaboost classifier, Video Surveillance
Stairways Detection Based on Approach Evaluation and Vertical Vanishing Point
by Md. Khaliluzzaman, Kaushik Deb
Abstract: Detecting stair region and estimating the distance from a camera to stair in a stair image is the fundamental step in the implementation of autonomous stair climbing navigation, as well as alarm systems for vision impaired people. In this paper, a framework is proposed for detecting the stair region from a stair image utilizing some natural properties of a stair. One unique property of them is, every stair steps beginning and ending horizontal edge point intersects with two vertical edge points creating three connected point. These vertical edges are stair steps height and its width edge. Another property is steps of a stair appear gradually increasing order from top to bottom of a stair in a parallel arrangement. For that initially, directional Gabor filter and Canny edge detector are employed on the stair image to eliminate the influence of illumination and for detecting stair edges. Non-candidate stair edges are eliminated by performing filtering operation. Then longest horizontal edges are extracted by using a proposed edge linking method on the edge image. After that, a search method is applied for finding stair step height and its width edge point at the beginning and ending point of the longest horizontal edges. This operation is performed for detecting three connected points to validate the stair edge segments. In the next step, these validated edge segments are used to calculate the vertical vanishing point to justify the stair edges. This justification ensures that validated edge segments are arranged in an increasing parallel order from top to bottom of a stair. Finally, these increasing edge segments are verified from other stairs similar patterns. This verification is performed by utilizing the y coordinate value of the vanishing point and confirmed the detection of stair candidate region. In addition, the triangular similarity is used for distance estimation from camera to stair. The proposed framework is tested using various stair images under a variety of conditions and results are presented to demonstrate the efficiency and effectiveness.
Keywords: autonomous stair climbing navigation; alarm system; Gabor filter; illumination; vanishing point; three connected point; triangular similarity.
Special Issue on: Advances in Mathematical Computational Sciences
Local convergence of Cauchy-type methods under hypotheses on the first derivative
by Ioannis Argyros, Daniel González
Abstract: We present a local convergence analysis of Cauchy-type methods free of the second derivative using hypotheses only on the first derivative. In earlier studies such as [1, 2, 15, 16, 17, 19, 20, 21] hypotheses up to the fourth derivative have been used to show convergence although the method requires evaluations of the function and its derivative. This way we extend the applicability of these method. Numerical examples are provided in this study where earlier results cannot apply but the new results can apply to solve equations.
Keywords: Cauchy's method; Newton's method; local convergence.
A new fast motion estimation algorithm using adaptive size diamond pattern search with early search termination
by Shaifali Madan Arora, Navin Rajpal, Ravindra Purwar
Abstract: In this paper, a new dynamic zero motion prejudgment with adaptivediamond pattern search based algorithm is suggested to enhance the search efficiency and accuracy of motion estimation (ME). Firstly, a dynamic stationary block prejudgment technique is suggested to identify the zero motion blocks. For estimating the motion vectors (MV) of non-stationary blocks, a new initial search center prediction technique is suggested. This new calculated search center has very high probability to be near actual MV. The distortion at this new search center is compared against a dynamically predicted threshold to check if this location could be the position of actual MV. If so search is terminated thereafter saving large computations. Otherwise a variable size diamond pattern is suggested to swiftly attain the global minima for fast as well as slow motion sequences. Extensive simulations have been conducted for comparing the performance of the proposed algorithm with other suitable algorithms. Experimental results show 95-99% search speed gain with only 0.007-0.7dB PSNR and 0.0001-0.0073 SSIM degradation over full search. Also proposed algorithm shows very promising resultsover other search algorithms in terms of reduced search points with improved PSNR, SSIM and average bits per pixel to represent residual frame.
Keywords: Fast motion estimation; Early search termination; Dynamic search pattern; Stationary block detection; Block matching
Visually Lossless Coder for Volumetric MRI and CT Image Data using Wavelet Transform
by B.K. Chandrika, P. Aparna, David S. Sumam
Abstract: Medical imaging modalities produce large volume of digital data
each day in modern health care. Several techniques have been proposed for
volumetric medical image data compression. In this paper, we present a novel
wavelet based visually lossless coding scheme for the compression of volumetric
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images.
A visual model is incorporated in the coder to identify and measure visually
irrelevant information. Performance of the compression scheme is further
improved by eliminating the slice redundancy. The obtained results show better
compression ratio compared to results obtained with pixel based visually lossless
compression technique, without any degradation in visual quality. We compared
the performance of proposed technique with standard state of the art compression
codecs such as Joint Photographic Experts Group-Lossless (JPEG-LS), JPEG-
2000, JPEG-3D, H.264/MPEG-4 AVC, Differential Pulse Code Modulation
(DPCM) and Medical Image Lossless Compression (MILC). Results show better
compression ratio over that of standard lossless compression schemes without
any perceivable distortion.
Keywords: Visually lossless image coding; Visual model; MRI and CT images; Quality metrics
Discriminative Analysis of Lip Features for Emotion Recognition
by Neeru Rathee, Dinesh Ganotra
Abstract: The emotion recognition techniques based on facial images relies on facial features like eyes, eyebrows, nose, cheeks, chin and mouth. Out of which, either a few or almost all the features have been explored collectively, thus ignoring the discriminative ability of the individual features. In the proposed approach, we have analyzed the discriminative ability of lip features. We investigated three important facts about lip based emotion recognition: 1. Lip based emotion recognition give results comparable to the approaches using complete face. 2. Lip geometric features possess higher discrimination power for emotion recognition as compared to lip texture feature. 3. There is significant improvement in recognition accuracy by combining both the features rather than using them separately.
Keywords: Emotion recognition, Facial expression recognition, Active appearance model, Support vector machine.rn
A Look-Ahead-Bases Meta-Heuristics for Optimizing Continous Functions
by Noureddine Bouhmala
Abstract: Continuous optimization problems are abundant in many scientific engineering
problems. For simple non-convex functions with only a few dimensions and well separated local
minima, deterministic methods might be the appropriate tools to use. However, multi-
modal character of objective functions, deterministic methods tend to trap the system
within a local minimum. As many real-world optimization problems become increasingly
complex, better optimization algorithms are always needed In recent years, there has been
a great deal of interest in emerging some artificial intelligence tools called meta-heuristics
in the area of continuous optimization. In this paper, the simulated annealing and
genetic algorithm are combined with a Look-Ahead local search heuristic. The promising
performances achieved by the two hybrid approaches are demonstrated by comparisons
made to solve conventional benchmark problems.
Keywords: Continuous optimization ; simulated annealing; genetic algorithm