International Journal of Computational Vision and Robotics (36 papers in press)
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.
APPLICATIONS OF HYPERSPECTRAL AND OPTICAL SCATTERING IMAGING TECHNIQUE IN THE DETECTION OF FOOD MICROORGANISM
by Xu Jing, Ma Long, Wu Jie, Xu Xiaomeng, Sun Ye, Pan Leiqing, Tu Kang
Abstract: Food is very easy to contaminate microorganism during production, processing, storage and transportation, the mass propagation of microorganism can cause food deterioration so that the food-borne pollution and food poisoning will be caused, with a serious threat to human health. However, the traditional methods for microorganism detection are complicated in process, poor in timeliness or low in sensitivity and are hard to meet the increasing requirements of the rapid and accurate detection, becoming the bottleneck for food quality and safety detection. With collecting the relevant information, then algorithm processing information and finally the relevant models, the modern optical imaging technique can achieve the rapid detection of food quality information. This article reviews in detail the the latest developments of hyperspectral imaging and optical scattering techniques in the nondestructive detection of the food microbial contamination, and discusses the advantages and deficiency of the various techniques.
Keywords: hyperspectral imaging; optical scattering; food microorganism; detection.
Weighted Feature Voting Technique for Content Based Image Retrieval
by Walaa Elhady, Abdulwahab Alsammak, Shady Elmashad
Abstract: A content-based image retrieval process is used to retrieve most
similar images to a query from a large database of images on the basis of
extracted features. Matching measures are used to find similar images by
measuring how the query features are close to the features of other images in
the database. In this paper, a multi-features system is proposed which
incorporates more than one feature in the retrieval process. The weights of
these features are calculated based on the precision of each feature to reflect its
importance in the retrieval process. These weights are used in a weighted
feature voting technique to incorporate the role of each feature in extracting the
relevant images. Also, different distance measures are used to get the highest
precision of each feature. The results of applying the multi-features and
multi-distances measures technique outperform other existing methods with
accuracy 86.5% for Wang database, 86.5% for UW database and 85% for
Keywords: content based image retrieval; computational vision; feature
extraction; hierarchical annular histogram; weighted average; matching
measures; weighted feature voting.
Fusion Strategy based multimodal human-computer interaction
by Shu Yang, Ye-peng Guan
Abstract: Human-computer interaction (HCI) has great potential for applications in many fields. The diversity of interaction habits and low recognition rate are main factors to limit its development. In this paper, a framework of multi-modality based human-computer interaction is constructed. Interactive target can be determined by different modalities including gaze, hand pointing and speech in a non-contact and non-wearable way. The corresponding response is feedback timely to users in the form of audio-visual sense with an immersive experience. Besides, the decision matrix based fusion strategy is proposed to improve the systems accuracy and adapt to different interaction habits which are considered in an ordinary hardware from a crowded scene without any hypothesis that the interactive user and his corresponding actions are known in advance. Experimental results have highlighted that the proposed method has better robustness and real-time performance in the actual scene by comparisons.
Keywords: human-computer interaction (HCI); multi-modality; audio-visual feedback; interaction habits; fusion strategy.
Channel Estimation for High Speed Unmanned Aerial Vehicle (UAV) with STBC in MIMO Radio Links
by Amirhossein Fereidountabar, Luca Di Nunzio, Rocco Fazzolari, GianCarlo Cardarilli
Abstract: This paper proposes a channel estimation method based on Kalman filter and adaptive estimation with Space Time Block Code (STBC) and multiple antenna systems (Multiple Input Single Output, MISO, and Multiple Input Multiple Output, MIMO) for high speed UAVs. Simulations have been done in time-varying Rayleigh faded channels for BPSK and QPSK. The proposed technique seems to obtain an error performance closer to the known channel information case in severely faded channel considerations. Application of Alamouti STBCs with diversity based on multiple antennas provides improved performance in faded wireless channels. Alamouti transmit diversity scheme, however, relies on the availability of accurate Channel State Information (CSI) for Unmanned Aerial Vehicles (UAVs). The simulation results show that our proposed method achieves accurate estimation for the SNR and Doppler shift in a wide range of velocities and SNRs.
Keywords: Doppler Shift; Radio Propagation; SNR; MIMO; STBC; Adaptive Estimation.
New Spatiotemporal Method for Assessing Video Quality
by David Bong, Woei-Tan Loh
Abstract: The existence of temporal effects and temporal distortions in a video differentiate the way it is assessed from an image. Temporal effects and distortions can enhance or depress the visibility of spatial effects in a video. Thus, the temporal part of videos plays a significant role in determining the video quality. In this study, a spatiotemporal video quality assessment (VQA) method is proposed due to the importance of temporal effects and distortions in assessing video quality. Instead of measuring the frame quality on a frame basis, the quality of several averaged frames is measured. The proposed spatiotemporal VQA method is significantly improved compared with image quality assessment (IQA) methods applied on a frame basis. When combined with IQA methods, the proposed spatiotemporal VQA method has comparable performance with state-of-the-art VQA methods. The computational complexity of the proposed temporal method is also lower when compared with current VQA methods.
Keywords: video; frames; video quality; spatial effects; temporal effects; temporal distortions; spatiotemporal; average; video quality assessment; image quality assessment; computational complexity.
Performance Evaluation on Image Fusion Techniques for Face Recognition
by Aniruddha Dey, Shiladitya Chowdhury, Jamuna Kanta Sing
Abstract: In face recognition, a feature vector usually represents the salient characteristics that best describe a face image. However, these characteristics vary quite substantially while looking into a face image from different directions. Therefore, by accumulating these directional features into a single feature vector will certainly lead to superior performance. This paper addresses this issue by means of image fusion and presents a comprehensive performance analysis of different image fusion techniques for face recognition. Image fusion is done between the original captured image and its true/partial diagonal images. The fusion is made by three different ways by placing the images (i) one-over-other (superimposed), (ii) side-by-side (horizontally) and (ii) up-and-down (vertically). The empirical results on publicly available AT&T, UMIST and FERET face databases collectively demonstrate that superimposed image between the original and its true diagonal images actually provides superior discriminant features for face recognition as compared to either original or its diagonal image.
Keywords: Face recognition; Generalized 2DFLD; Image fusion; Projection vector; Diagonal image.
Random Neighborhood Dynamic Clustering
by Gaurav Tyagi, Nilesh Patel, Pawel Marcinek
Abstract: Recognition of arbitrary shaped clusters is highly active research topic in data mining and cluster analysis. In this paper we consider the problem of data clustering of arbitrary shaped clusters as a random evolutionary process. We propose a new algorithm RNDC which uses the random process for cluster analysis. RNDC assumes that an object contains information only about the characteristic values of its local neighborhood. It explores the local cluster structures to determine the global partitions of data set. It is of significance that RNDC evolve randomly among the objects of data set, while other well known partitioning clustering algorithms used the techniques of sequential propagation through the nearest connected/reachable objects. Our method is, in principle, applicable for any arbitrary shaped clusters. Since randomness is an essential part of RNDC, it makes this algorithm suitable for multiprocessing parallel computation.
Keywords: Dynamic Clustering; Neighborhood Based Method; Random propagation.
Motion Planning and Coordination of Multi-Agent Systems
by Nirmal B. Hui, Buddhadeb Pradhan, Diptendu Sinha Roy
Abstract: Development of navigation scheme for a group of cooperative mobile robots is attempted in this paper. Potential field method is used to generate collision-free movement of a robot while it encounters other robots as obstacles. Finally, a cooperation scheme is proposed based on human behaviour to optimize motion strategies of robots treating each other as their obstacles. Computer simulations have been conducted for three different cases with four, eight and twelve robots negotiating a common dynamic environment. In each case, 100 different scenarios are considered and travelling times of all the robots are computed separately. The scenes in which the robots meeting collision is treated as a failure scenario and travelling time is not calculated. Performance of cooperation scheme has improved with the increase in a number of robots.
Keywords: Multi Agent Systems; Coordination; Potential Field Method; Navigation.
Labeling and evaluation of 3D objects segmentation using multiclass boosting algorithm
by Omar Herouane, Lahcen MOUMOUN, Mohamed CHAHHOU, Taoufiq GADI
Abstract: 3D objects segmentation has become a fundamental task in computer vision and digital multimedia. Evaluating the automatic 3D segmentation algorithms quality and comparison of their performances are important topics, some metrics are proposed in the literature. However, these metrics are not too prominent for evaluating the automatic segmentation algorithms in general. The aim of this paper is to introduce a simple and efficient approach to evaluate the automatic segmentation of a 3D object. This new evaluation scheme is based on learning 3D mesh and on the Minkowski metric. Machine learning is used to learn a function that assigns the appropriate label to each part of a segmented 3D object of the database; then, the error committed by each labeled segment is computed using the Minkowski norm. The best performance and high quality of the quantitative results demonstrated the efficiency of the proposed approach.
Keywords: Boosting algorithm; Adaboost; Machine learning; Minkowski metric; Segmentation; Labeling; Evaluation; Shape index; Shape Spectrum Descriptor; 3D object; computer vision.
A neural-based method for optical flow estimation using phase correlation
by Khalid Ghoul, Mohamed Berkane, Mohamed Chaouki Batouche
Abstract: Motion estimation for image sequences is one of the most important tasks in computer vision. Thus, many methods have been proposed to solve this problem, but even so, it still lacks a generic method that determines motion in all situations and for all types of objects. In this work, we propose a two-phase connectionist neural method for motion estimation in the frequency domain that takes discontinuities into account. In the first phase, the most probable motion of each pixel is estimated using self-organizing maps principles and the phase correlation method. The second phase consists in regularizing the displacement field that considers the discontinuities. When tested and compared with other approaches on both synthetic and real image sequences, our method showed good performances according to the following criteria: precision, regularity, resistence to noise and running-time. Moreover, it could estimate the motions in cases where rotation and scaling are required.
Keywords: Keywords: Motion estimation; neural network; frequency domain; Fourier transform; phase correlation method.
Script invariant Handwritten Digit Recognition using a Simple Feature Descriptor
by Pawan Kumar Singh, Supratim Das, Ram Sarkar, Mita Nasipuri
Abstract: Handwritten digit recognition is still considered as a difficult task because of the large variability of the digits shapes written by individuals. A lot of work has been done towards digit identification with excellent performance but mostly these works have been made focusing on digits written in a particular script. Hence, in a multilingual country like India, where different scripts are prevalent, methods which recognize numerals written in single script may not always serve the purpose. To address this issue, we propose a script invariant handwritten digit recognition scheme in this paper. A novel feature extraction technique named as Quadrangular transition count has been introduced. Experimentations performed using five conventional classifiers advocate that Multi Layer Perceptron (MLP) is best among them which yields recognition accuracies of 98.13%, 97.85%, 96.72%, and 95.35% on four popularly used scripts of the world namely, Arabic, Bangla, Devanagari, and Roman respectively.
Keywords: Handwritten digit recognition; Quadrangular transition count features; Arabic script; Bangla script; Devanagari script; Roman script; ADBase; HDRC 2013.
Intelligent Plankton Image Classification with Deep Learning
by Abhishek Verma
Abstract: Plankton are extremely diverse groups of organisms that exist in large water columns. They are sources of food for fishes and many other marine life animals. The plankton distribution is essential for the survival of many ocean lives and plays a critical role in marine ecosystem. In recent years, intelligent image classification systems were developed to study plankton distribution through classification of the plankton images taken by underwater imaging devices. Due to the significant differences in both shapes and sizes of the plankton population, accurate classification poses a daunting challenge. The mixed quality of the collected images adds more difficulty to the task. In this paper, we present an intelligent machine learning system built on convolutional neural networks (CNN) for plankton image classification. Unlike most of the existing image classification algorithms, CNN based systems do not depend on features engineering and they can be efficiently extended to encompass newclasses. The experimental results on SIPPER image datasets show that the proposed system achieves higher accuracy compared with the state-of-the-art approaches. The new system is also capable of learning a much larger number of plankton classes.
Keywords: SIPPER Plankton Image; Convolutional Neural Network; Machine Learning; Image Classification.
Configuration of a Min-Cost Flow Network for Data Association in Multi-object Tracking
by Chanuk Lim, Jeonghwan Gwak, Moongu Jeon
Abstract: In this paper, we mainly describe how to formulate a network flows optimally for multi-object tracking. The network flows can be used to construct trajectories of objects (between frames) to achieve multi-object tracking. The most important issue to establish such network is to design nodes and edges in the network. In this work, we propose a method to fuse the object detector with object trackers in order to efficiently design the nodes and edges. The object trackers can give the information on robust classifiers or features of objects through training, which helps to design the edges. This approach is significant when a detector fails due to occluded objects. If an object is failed to be detected, the object tracker will be substituted to the object detector. In this way, we employ the object tracker and the object detector to formulate a sophisticated network depending on the condition. The proposed approach enables to eliminate the clutters and thus overcome the heavy occlusion situations. We evaluated performance of the proposed method through several experiments using real-world video sequences. The experimental results demonstrated good performance of the proposed approach compared to state-of-the-art methods.
Keywords: Multi-object Tracking; Data association; Min-Cost Flow Network.
Normalization of handwriting speed for online Arabic characters recognition
by Bougamouza Fateh, Hazmoune Samira, Benmohammed Mohamed
Abstract: In this paper, we propose a new solution for the problem of the unevenly distributing points along the stroke curve, in online Arabic handwriting recognition, due to the variation in writing speed. An algorithm based on linear interpolation is generally used to solve this problem. The main weakness of this algorithm is the missing of some information related to point density distributions in different stroke parts. This limitation is due to the use of only one resampling distance. In our approach, we propose to segment the stroke trajectory into several parts according to their densities and to classify them into three classes: high, medium and low density. For this purpose, two thresholds are chosen: the first one defines the maximum distance between two successive points belonging to high-density class, and the second threshold characterizes the minimum distance between two successive points into low-density class. The interval from the first to the second threshold covers distances between any two successive points in the medium-density class. Three resampling distances, instead of one, are also suggested, each of them is associated to one class of densities. This solution is evaluated using NOUN dataset and it gives an excellent improvement in the recognition rate, up to 4%, compared to linear interpolation method.
Keywords: online Arabic handwriting recognition; HMM; writing speed variation; linear interpolation; trace segmentation; point density distributions.
Features selection for offline handwritten signature verification: state of the art.
by Anwar Yahya Ebrahim, Hoshang Kolivand, Amjad Rehman, Mohd Shafry Mohd Rahim, Tanzila Saba
Abstract: This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verifications accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification.
Keywords: Handwritten Signature Verification; Feature Extraction; Feature Reduction Methods; Feature Selection.
3D OBJECTS CLASSIFICATION BASED ON $P RECOGNISER
by Safae El Houfi, Maha Jazouli, Aicha Majda, Arsalane Zarghili
Abstract: In this paper, we propose a method for 3-dimensional (3D) model recognition based on 2-dimensional (2D) views. The goal of this method is to provide a selection of 2D views from a 3D model, by using the $P method for 3D model retrieval from these views. So, in order to extract the necessary information, we study the different multi-view indexing methods; characterizing the shape of the 3D image using 2D projection. With regard to the shape descriptor, we propose using the Fast Fourier Transform to provide spectral rendering for each extracted view. The method is based on the $P point-cloud recognizer. Our approach allows comparing either directly with a query image or with another 3D object by comparing their sets of views. We demonstrate the potential of this approach in a set of experiments, which prove that our system achieves a recognition rate ranging from 91.5% to 93.5%.
Keywords: $P; classification; 3D/2D indexing; 3D retrieval; views; VRML.
An Image analogy approach for multi-scale Image segmentation
by Asma Bellili, Slimane Larabi
Abstract: Image segmentation is one of the challenging tasks in image processing. This paper introduces a novel method for image segmentation based on image analogy principle. First, contours of source image are located using 14 pairs of stereo patches. Next, elementary regions, considered as areas between contours located using to successive pairs of patches. Finally, we define borders stability of neighbouring regions which constitutes the measure for regions merging. Three scales are defined giving three segmentation at three resolutions (low, intermediate and high). Experiments conducted on Wiezmann dataset, the obtained results are presented and compared to the state-of-the-art.
Keywords: image analogy; image segmentation; merging; contour detection; computational vision; multi scale; Wiezmann.
Enhancing the Laws filter descriptor on DTCWT coefficients by thresholding approach for texture classification
by Sonali Dash, Uma Ranjan Jena
Abstract: In this paper, we propose a new approach of combining dual-tree complex wavelet transform with traditional Laws filter descriptor for texture classification using thresholding method. The dual-tree complex wavelet transform (DTCWT) is a recent technique to discrete wavelet transform, with important additional properties. It has been observed that the thresholding is a method to keep significant information of the image while discarding the unimportant part. On this basis for further enhancement of texture classification, we have acquired the texture images by applying thresholding technique to the entire texture database. These thresholded images are then applied to the fusion model of DTCWT with Laws filter descriptor. We verify the effectiveness of the proposed method by utilizing two texture databases such as Brodatz and UIUC. The proposed methods are also compared with the classical laws filter descriptor. Results demonstrate that the proposed method greatly enhanced the classification accuracies by using k-NN as classifier.
Keywords: Texture feature; Texture classification; Laws’ mask; Dual-tree complex wavelet; Thresholding;.
Automated Identification and Counting of Proliferating Mesenchymal Stem Cells in Bone Callus
by Samer Awad, Rula Abdallat, Othman Smadi, Thakir AlMomani
Abstract: The assessment of cell count is essential for the evaluation of biological cell proliferation development. Manual counting can be time consuming and subject to human error as it depends on visual inspection. On the other hand, automated counting using software based morphological analysis can eliminate or reduce these disadvantages and provide statistical reliability. In this study, we employ a software-based method for the automated counting of mesenchymal stem cells (MSCs) proliferation in the bone callus of Wistar rats to evaluate fracture healing. The proposed method started with extracting the green component of the digital image acquired using a light microscope. The subsequent stages involved: contrast enhancement, adaptive thresholding and false detection reduction. This method was tested using 48 MSCs images and the results were evaluated by a specialist. The average of precision, recall and F-measure were found to be 87.14%, 88.04%, and 87.50% respectively.
Keywords: Automated cell counting; Biological cell counting; Image processing; Image segmentation; Pattern recognition; Automated thresholding; Light microscopic images; Mesenchymal stem cells; MSC; False detection minimization.
Content-Based Image Retrieval (CBIR): A deep look at features prospectus.
by Mohammed Suliman Haji, Amjad Rehman, Tanzila Saba
Abstract: Currently rapid growth of digital images on the internet is observed, accordingly, the need for content-based image retrieval systems are in high demand. Content-Based Image Retrieval (CBIR) is an image search technique that does not depend on manually assigned annotations; rather, CBIR uses discriminative features to search an image. By refining features, an efficient retrieval mechanism could be achieved. The aim of this research is to review features extraction and selection that have an impact on Content-Based Image Retrieval (CBIR) and information extraction from images using global and local features such as shape, texture, and color. In order to extract most appropriate features for Content-Based Image Retrieval (CBIR), several feature extraction and selection techniques are analyzed and their efficiency is compared. Additionally, shortcomings of current content-based image retrieval techniques are addressed and possible solutions are suggested to enhance accuracy.
Keywords: CBIR; Discrete Wavelet Transform; Low-level Features and High-level Features.
Bio-inspired visual attention process using spiking neural networks controlling a camera
by André Cyr, Frédéric Thériault
Abstract: This study introduces virtual and physical implementations of a bottom-up visual attention mechanism using a spiking neural network(SNN) controlling a camera. The SNN is able to focus simple stimuli of various length that appear randomly in the camera's view. This is accomplished with an overt process based on a competitive choice according to a stimulus quadrant location. After focusing a selected stimulus toward the center of its view, the SNN scans it from one edge to the other. Since the spike train of dedicated neurons reflects the duration of each scan, it allows the extraction of the stimulus length. Upon the completion of a scan, the SNN has the ability to switch to another stimulus. This preliminary work on spatial visual attention intends to be a step toward the study of the concept size learning process in a robotic context.
Keywords: Spiking neurons; Robotics; Overt process; Visual Attention.
Fast Binary Shape Categorization for Intelligent Vehicles
by Insaf Setitra, Slimane Larabi
Abstract: In this paper we propose a novel method for shape categorization suitable
for video surveillance and intelligent vehicles applications. Binary shape is
convoluted with a Gaussian filter at different scales and curvatures are detected
for each scale. Shape is then described using arclength and radius of curvatures.
This descriptor allows differentiating between shapes particularly for objects
that may appear in front of intelligent vehicle or in monitored scene such as
pedestrian, car, cyclist, animal (horse, cow, dog, cat). Conducted experiments
show that our method compete the state-of-the-art methods in term of accuracy
and surpasses them in term of time processing.
Keywords: Binary shape; categorisation; Intelligent vehicles; Matching; Categorisation; Curvature; Scale space.
Enhancing Proximity Measure Between Residual and Noise for Image Denoising
by Gulsher Baloch, Junaid Ahmed
Abstract: Sparse representation and dictionary learning based image denoising algorithms approximate the clean image patch by linear combination of few dictionary atoms. Clearly, residue after completion of denoising must be similar to the contaminating noise. Ideally, clean image patch is perfectly recovered if residue is exactly contaminating noise. Hence, for better denoising residue must be enforced to possess characteristics similar to the contaminating noise. In this paper, we model residue such that proximity between residue and contaminating noise is increased. The proposed mathematical model makes sure that the residue is as random in nature as contaminating noise. This is achieved by unique sparse coding and dictionary update stages developed based on modeling of randomness in residue. The proposed algorithm is tested on Additive White Gaussian Noise (AWGN), Additive Colored Gaussian Noise (ACGN) and Laplacian Noise. Since performance of the image denoising algorithms also depend on image effective bandwidth, therefore, in this paper we have generated synthetic images with known effective image bandwidths. These images are generated using the Discrete Cosine Transform (DCT). The proposed algorithm is also tested on these images. The proposed algorithm is compared with state-of-the-art algorithms. The comparison on the bases of peak signal-to-noise ratio (PSNR), structure similarity index measure (SSIM) and feature similarity index measure (FSIM) indicate that the proposed algorithm is able to produce often better and competitive results.
Keywords: Additive Colored Noise; Laplacian Noise; Residual Correlation; Image Denoising.
Special Issue on: Recent Advances in Theory and Applications of Visual Intelligence
Calibration and using a laser profile scanner for 3D robotic welding
by Michal Chalus, Jindrich Liska
Abstract: This paper describes first functions of a developed cognitive module for 3D robotic welding using TIG or laser technology. This area is constantly evolving with the needs to solve complex problems of welding automatization and robotization, and also thanks to the continuous advances in measurement technology and robotics. Besides the use of welding robots for serial production with dedicated tightly defined trajectories, systems for automatic welding of a previously undefined path or paths, which the operator cant manually define because of its complexity, are developed. This paper covers the general description of the cognitive module and its required functions. Then necessary knowledge about a pose representation and transformation is presented. After that, procedures for a calibration of a profile scanner and its using for 3D model construction based on a depth map are described in more detail. The cognitive module prototype is tested in the task of automatic cavity repair.
Keywords: 3D robotic welding; tungsten inert gas welding; TIG welding; laser welding; laser profile scanner; hand-eye calibration; 3D model construction; depth map; image processing; trajectory identification; cognitive robot.
Content-Based Image Retrieval Using Multiresolution Speeded-Up Robust Feature
by Prashant Srivastava, Ashish Khare
Abstract: The advent of numerous low cost image capturing devices has led to the proliferation of huge amount of images in the present world. The images have grown more complex day-by-day and in order to access them easily, there is a need of efficient indexing and retrieval of these images. The field of Content-Based Image Retrieval (CBIR) tends to achieve this goal. This paper proposes the concept of multiresolution Speeded-Up Robust Feature (SURF) descriptor which combines Discrete Wavelet Transform and SURF descriptor to extract interest points at multiple resolutions of image for CBIR. The feature vector has been constructed through Gray-Level Co-occurrence Matrix (GLCM). The advantage of this technique is that it exploits multiple resolutions of image to extract interest points which single resolution processing techniques fail to do. Performance of the proposed method is tested on two benchmark datasets Corel-1K and GHIM-10K and measured in terms of precision and recall. The performance of the proposed method is measured with other state-of-the-art feature descriptors. Experimental results demonstrate that the proposed method outperforms other state-of-the-art descriptors in terms of precision and recall.
Keywords: Content-Based Image Retrieval; Speeded Up Robust Transform; Gray-Level Co-occurrence Matrix; Multiresolution SURF.
An image encryption algorithm using logarithmic function and henon-chaotic function
by PURUSHOTHAM REDDY M
Abstract: This paper proposes a natural logarithmic and chaotic-based encryption algorithm for securing images. It has two important steps. In the first step, a natural logarithmic function of the image to reduce the intensity of the pixel values and image fusion are used for encrypting the image using the key. In the second step, the Henon chaotic function is used for shuffling the pixel values. Here, logarithmic function scatters the pixel values differently and image matrix is a key used to create image fusion. In the resulting matrix, neighboring values with naturally close will take on appreciably different values, making it difficult to crack the resulting image. The proposed method creates complexity against differential attacks. The different types of the tests with analysis have been performed to prove the validity and the security of the algorithm.
Keywords: Natural logarithmic function; henon chaotic function; image fusion; key image.
Support Vector Machine Based Approach for Text Description from the Video
by Vishakha Wankhede, Ramesh M. Kagalkar
Abstract: Human uses communication, language either by written or spoken to describe visual the world around them. So the study of text description for any video goes increasing. In this paper, we are representing a framework that gives output as a description for any long length video using natural language processing. The framework is divided into two sections called training and testing section. The training section is used to train the video with its description like activities of objects present in that video.Another section is testing section. The testing section is used to test the video and retrieve the output as description of video comparing videos stored into database (i.e. in training section). Using natural language processing, sentences are generated from objects and their activities. For the evaluation, maximum 50 second videos are used.
Keywords: natural-language processing; NLP; video processing; video recognition.
Improved Eigenspectrum Regularization for Human Activity Recognition
by FESTUS OSAYAMWEN, Jules-Raymond Tapamo
Abstract: A within-class subspace regularization approach is proposed for eigenfeatures extraction and regularization in human activity recognition. In this approach, the within-class subspace is modeled using more eigenvalues from the reliable subspace to obtain a four parameter modelling scheme. This model enables a better and true estimation of the eigenvalues that are distorted by small sample size effect. This regularization is done in one piece, thereby avoiding undue complexity of modeling eigenspectrum differently. The whole eigenspace is used for performance evaluation because feature extraction and dimensionality reduction is done at later stage of the evaluation process. Results show that the proposed approach has better discriminative capacity than several other subspace approaches for human activity recognition.
Keywords: Feature extraction; human activity recognition; linear discriminant analysis.
DETECTION OF DEFECTIVE PRINTED CIRCUIT BOARDS USING IMAGE PROCESSING
by Beant Kaur, Gurmeet Kaur, Amandeep Kaur
Abstract: Manufacturing of Printed Circuit Boards involves three stages (printing, component fabrication over surface of printed circuit boards, soldering of components), where inspection at every stage is very important to improve the quality of production. Image subtraction method is widely used for finding the difference between any two images. Using this method, defects have been detected by finding the difference between reference (defect free) and test image (to be inspected). The major limitation of image subtraction is that both the images should have same size and same orientation. The proposed method removes the above explained limitation of image subtraction method and also calculates total number of defects on printed circuit boards. The proposed method is tested on six test images. Experimental results show that proposed method is simple, economical and easy to implement in small and medium scale industries where most of the inspection is still done by humans.
Keywords: Printed Circuit boards; inspection system; image registration; phase correlation; mean; image subtraction and connected components.
Special Issue on: Research in Virtual Reality
Real time vision based hand gesture recognition using depth sensor and a stochastic context free grammar
by Jayesh Gangrade
Abstract: This paper presents a new algorithm in computer vision for the recognition of hand gestures. In the proposed system, kinect sensor is used to track and segment hand in the clutter background and feature extracted by finger and an angle between them. Classify the hand posture using multi-class support vector machine. The hand gesture is recognized by stochastic context free grammar (SCFG). Stochastic context free grammar uses syntactic structure analysis and by this method recognizes hand gestures by set of production rules which consists of a combination of hand postures. The proposed algorithm is able to recognize various hand postures in real time with more than 97% accuracy.
Keywords: Kinect sensor; hand gesture; SCFG; Multi-class support vector machine.
Real time sign language recognition using depth sensor
by Jayesh Gangrade, Jyoti Bharti
Abstract: Communication via gestures is a visual dialect utilized by deaf and Hard-of-Hearing (HoH) people group. This paper proposed a system for sign language recognition utilizing human skeleton data provided from Microsofts Kinect sensor to recognizing sign gestures. The Kinect sensor generates the skeleton of a human body and distinguishes 20 joints in it. The proposed method utilizes 11 out of 20 joints and extracts 35 novel features per frame, based on distances, angles and velocity involving upper body joints. Multi-class Support Vector Machine classified the 35 Indian sign gestures in real time with accuracy of 87.6%. The proposed method is robust in cluttered environment and viewpoint variation.
Keywords: Kinect sensor; Indian sign gesture; Multi class support vector machine; Human computer interaction; Pattern recognition.
Adaptive Multi-Threshold Based De-noising Filter for Medical Image Applications
by Ramya A, Murugan D, Murugeswari G, Nisha Joseph
Abstract: Medical image processing is the emerging research area and many researchers contributed to medical image processing by proposing new techniques for medical image enhancement and abnormality detection. Interpretation of medical images is a challenging problem because of the unavoidable noise produced by the medical imaging devices and interference. In this work, a new framework is proposed for noise detection and reduction. This framework comprises two phases. First phase is the noise detection phase which is performed using the newly proposed Adaptive Multi-Threshold scheme (AMT). In second phase, modification of noisy pixel is done using Edge Preserving Median filter (EPM), which conserves the edge component and controls the blurring effect with preservation of fine details of interior region. The proposed work is tested with benchmark images and few medical images. It produces promising result and the results are compared with existing two-stage noise reduction techniques. Popular performance metrics such PSNR and SSIM are used for evaluation. Quantitative analysis and experimental results demonstrate that the proposed method is more efficient and suitable for medical image pre-processing.
Keywords: Noise removal; Noise Detection; Impulse noise; Multi-Threshold; Edge Preserving.
Crowd detection and counting using a static and dynamic platform: State of the Art
by Huma Chaudhry, Mohd Shafry Mohd Rahim, Tanzila Saba, Amjad Rehman
Abstract: Automated object detection and crowd density estimation are popular and important topics in visual surveillance research area. The last decades witnessed many significant publications in this field and it has been and still is a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms.
Keywords: Crowd; Counting; Holistic and Local Motion Features; Estimation; Visual Surveillance; Moving Platform.
Special Issue on: MIWAI 2017 Computational Intelligence and Deep Learning for Computer Vision
Attention-Based Argumentation Mining
by Derwin Suhartono, Aryo Pradipta Gema, Suhendro Winton, Theodorus David, Mohamad Ivan Fanany, Aniati Murni Arymurthy
Abstract: This paper is intended to make a breakthrough in argumentation
mining field. Current trends in argumentation mining research use handcrafted
features and traditional machine learning (e.g., Support Vector Machine).
We worked on two tasks: identifying argument components and recognizing
insufficiently supported arguments. We utilize deep learning approach and
implement attention mechanism on top of it to gain the best result. We do also
implement Hierarchical Attention Network (HAN) in this task. HAN is a neural
network that gives attention to two levels, which are word-level and sentencelevel.
Deep learning with attention mechanism models can achieve better result
compared with other deep learning methods. This paper also proves that on
research task with hierarchically-structured data, HAN will perform remarkably
good. We do present our result on using XGBoost instead of a regular nonensemble
classifier as well.
Keywords: argumentation mining; hand-crafted features; deep learning; attention mechanism; hierarchical attention network; word-level; sentence-level; XGBoost.
SEGMENTATION AND RECOGNITION OF CHARACTERS ON TULU PALM LEAF MANUSCRIPTS
by Savitha C.K.
Abstract: This paper proposes an efficient method for segmentation and recognition of handwritten characters from Tulu palm leaf manuscript images. The proposed method uses an automated tool with a combination of thresholding and edge detection technique to binarize the image. Further projection profile with connected component analysis is used to line and character segmentation. Deep convolution neural network (DCNN) model used here to extract features and recognize segmented Tulu characters efficiently with a recognition rate of 79.92 %. The results are verified using benchmark dataset, the AMADI_LontarSet to generalize our model to handwritten character recognition task. The results showed that our method outperforms from the existing state of art models.
Keywords: Handwritten Character Recognition; Palm Leaf; Segmentation; DCNN; Tulu.