Search results for: moving object recognition

Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

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Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: Feature recognition, automation, sheet metal manufacturing, CAM, CAD.

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Authors: Michal Mardiak, Jaroslav Polec

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In this paper we proposed comparison of four content based objective metrics with results of subjective tests from 80 video sequences. We also include two objective metrics VQM and SSIM to our comparison to serve as “reference” objective metrics because their pros and cons have already been published. Each of the video sequence was preprocessed by the region recognition algorithm and then the particular objective video quality metric were calculated i.e. mutual information, angular distance, moment of angle and normalized cross-correlation measure. The Pearson coefficient was calculated to express metrics relationship to accuracy of the model and the Spearman rank order correlation coefficient to represent the metrics relationship to monotonicity. The results show that model with the mutual information as objective metric provides best result and it is suitable for evaluating quality of video sequences.

Keywords: Objective quality metrics, mutual information, region recognition, content based metrics

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Authors: Rafiullah Chamlawi, Asifullah Khan, Adnan Idris, Zahid Munir

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Authentication of multimedia contents has gained much attention in recent times. In this paper, we propose a secure semi-fragile watermarking, with a choice of two watermarks to be embedded. This technique operates in integer wavelet domain and makes use of semi fragile watermarks for achieving better robustness. A self-recovering algorithm is employed, that hides the image digest into some Wavelet subbands to detect possible malevolent object manipulation undergone by the image (object replacing and/or deletion). The Semi-fragility makes the scheme tolerant for JPEG lossy compression as low as quality of 70%, and locate the tempered area accurately. In addition, the system ensures more security because the embedded watermarks are protected with private keys. The computational complexity is reduced using parameterized integer wavelet transform. Experimental results show that the proposed scheme guarantees the safety of watermark, image recovery and location of the tempered area accurately.

Keywords: Integer Wavelet Transform (IWT), Discrete Cosine Transform (DCT), JPEG Compression, Authentication and Self- Recovery.

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Authors: Kayun Chantarasathaporn, Chonawat Srisa-an

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Low power consumption is a major constraint for battery-powered system like computer notebook or PDA. In the past, specialists usually designed both specific optimized equipments and codes to relief this concern. Doing like this could work for quite a long time, however, in this era, there is another significant restraint, the time to market. To be able to serve along the power constraint while can launch products in shorter production period, objectoriented programming (OOP) has stepped in to this field. Though everyone knows that OOP has quite much more overhead than assembly and procedural languages, development trend still heads to this new world, which contradicts with the target of low power consumption. Most of the prior power related software researches reported that OOP consumed much resource, however, as industry had to accept it due to business reasons, up to now, no papers yet had mentioned about how to choose the best OOP practice in this power limited boundary. This article is the pioneer that tries to specify and propose the optimized strategy in writing OOP software under energy concerned environment, based on quantitative real results. The language chosen for studying is C# based on .NET Framework 2.0 which is one of the trendy OOP development environments. The recommendation gotten from this research would be a good roadmap that can help developers in coding that well balances between time to market and time of battery.

Keywords: Low power consumption, object oriented programming, power conscious system, software.

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Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

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The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: Public emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining.

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Authors: Fathi Taibi

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Measuring the reusability of Object-Oriented (OO) program code is important to ensure a successful and timely adaptation and integration of the reused code in new software projects. It has become even more relevant with the availability of huge amounts of open-source projects. Reuse saves cost, increases the speed of development and improves software reliability. Measuring this reusability is not s straight forward process due to the variety of metrics and qualities linked to software reuse and the lack of comprehensive empirical studies to support the proposed metrics or models. In this paper, a conceptual model is proposed to measure the reusability of OO program code. A comprehensive set of metrics is used to compute the most significant factors of reusability and an empirical investigation is conducted to measure the reusability of the classes of randomly selected open-source Java projects. Additionally, the impact of using inner and anonymous classes on the reusability of their enclosing classes is assessed. The results obtained are thoroughly analyzed to identify the factors behind lack of reusability in open-source OO program code and the impact of nesting on it.

Keywords: Code reuse, Low Complexity, Empirical Analysis, Modularity, Software Metrics, Understandability.

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Authors: Manasjyoti Bhuyan, Kandarpa Kumar Sarma, Hirendra Das

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Signature represents an individual characteristic of a person which can be used for his / her validation. For such application proper modeling is essential. Here we propose an offline signature recognition and verification scheme which is based on extraction of several features including one hybrid set from the input signature and compare them with the already trained forms. Feature points are classified using statistical parameters like mean and variance. The scanned signature is normalized in slant using a very simple algorithm with an intention to make the system robust which is found to be very helpful. The slant correction is further aided by the use of an Artificial Neural Network (ANN). The suggested scheme discriminates between originals and forged signatures from simple and random forgeries. The primary objective is to reduce the two crucial parameters-False Acceptance Rate (FAR) and False Rejection Rate (FRR) with lesser training time with an intension to make the system dynamic using a cluster of ANNs forming a multiple classifier system.

Keywords: offline, algorithm, FAR, FRR, ANN.

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Authors: Heeseung Choi, Raechoong Kang, Kyungtaek Choi, Jaihie Kim

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Fake finger submission attack is a major problem in fingerprint recognition systems. In this paper, we introduce an aliveness detection method based on multiple static features, which derived from a single fingerprint image. The static features are comprised of individual pore spacing, residual noise and several first order statistics. Specifically, correlation filter is adopted to address individual pore spacing. The multiple static features are useful to reflect the physiological and statistical characteristics of live and fake fingerprint. The classification can be made by calculating the liveness scores from each feature and fusing the scores through a classifier. In our dataset, we compare nine classifiers and the best classification rate at 85% is attained by using a Reduced Multivariate Polynomial classifier. Our approach is faster and more convenient for aliveness check for field applications.

Keywords: Aliveness detection, Fingerprint recognition, individual pore spacing, multiple static features, residual noise.

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Authors: Hsinfu Huang Li-Hao Chen

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In order to enhance the usability of the human computer interface (HCI) on the touchscreen, this study explored the optimal tactile depth and effect of visual cues on the user-s tendency to touch the touchscreen icons. The experimental program was designed on the touchscreen in this study. Results indicated that the ratio of the icon size to the tactile depth was 1:0.106. There were significant effects of experienced users and novices on the tactile feedback depth (p < 0.01). In addition, the results proved that the visual cues provided a feedback that helped to guide the user-s touch icons accurately and increased the capture efficiency for a tactile recognition field. This tactile recognition field was 18.6 mm in length. There was consistency between the experienced users and novices under the visual cue effects. Finally, the study developed an applied design with touch feedback for touchscreen icons.

Keywords: HCI, Touchscreen icon, Touch feedback, Optimaltactile depth, Visual cues.

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Authors: Xiang Zhang, Daohong Yang, Ziyuan Wu, Lei Li, Wanting Zhou

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Visual Simultaneous Localization and Mapping (VSLAM) uses cameras to gather information in unknown environments to achieve simultaneous localization and mapping of the environment. This technology has a wide range of applications in autonomous driving, virtual reality, and other related fields. Currently, the research advancements related to VSLAM can maintain high accuracy in static environments. But in dynamic environments, the presence of moving objects in the scene can reduce the stability of the VSLAM system, leading to inaccurate localization and mapping, or even system failure. In this paper, a robust VSLAM method was proposed to effectively address the challenges in dynamic environments. We proposed a dynamic region removal scheme based on a semantic segmentation neural network and geometric constraints. Firstly, a semantic segmentation neural network is used to extract the prior active motion region, prior static region, and prior passive motion region in the environment. Then, the lightweight frame tracking module initializes the transform pose between the previous frame and the current frame on the prior static region. A motion consistency detection module based on multi-view geometry and scene flow is used to divide the environment into static regions and dynamic regions. Thus, the dynamic object region was successfully eliminated. Finally, only the static region is used for tracking thread. Our research is based on the ORBSLAM3 system, which is one of the most effective VSLAM systems available. We evaluated our method on the TUM RGB-D benchmark and the results demonstrate that the proposed VSLAM method improves the accuracy of the original ORBSLAM3 by 70%˜98.5% under a high dynamic environment.

Keywords: Dynamic scene, dynamic visual SLAM, semantic segmentation, scene flow, VSLAM.

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Authors: Mahdad M’hamed, Belaidi Idir

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This paper develops a meshless approach, called Element Free Galerkin (EFG) method, which is based on the weak form Moving Least Squares (MLS) of the partial differential governing equations and employs the interpolation to construct the meshless shape functions. The variation weak form is used in the EFG where the trial and test functions are approximated bye the MLS approximation. Since the shape functions constructed by this discretization have the weight function property based on the randomly distributed points, the essential boundary conditions can be implemented easily. The local weak form of the partial differential governing equations is obtained by the weighted residual method within the simple local quadrature domain. The spline function with high continuity is used as the weight function. The presently developed EFG method is a truly meshless method, as it does not require the mesh, either for the construction of the shape functions, or for the integration of the local weak form. Several numerical examples of two-dimensional static structural analysis are presented to illustrate the performance of the present EFG method. They show that the EFG method is highly efficient for the implementation and highly accurate for the computation. The present method is used to analyze the static deflection of beams and plate hole

Keywords: Numerical computation, element-free Galerkin, moving least squares, meshless methods.

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Authors: Javaid Butt, Habtom Mebrahtu, Hassan Shirvani

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The paper presents an additive manufacturing process for the production of metal and composite parts. It is termed as composite metal foil manufacturing and is a combination of laminated object manufacturing and brazing techniques. The process has been described in detail and is being used to produce dissimilar aluminum to copper foil single lap joints. A three dimensional finite element model has been developed to study the thermo-mechanical characteristics of the dissimilar Al/Cu single lap joint. The effects of thermal stress and strain have been analyzed by carrying out transient thermal analysis on the heated plates used to join the two 0.1mm thin metal foils. Tensile test has been carried out on the foils before joining and after the single Al/Cu lap joints are made, they are subjected to tensile lap-shear test to analyze the effect of heat on the foils. The analyses are designed to assess the mechanical integrity of the foils after the brazing process and understand whether or not the heat treatment has an effect on the fracture modes of the produced specimens.

Keywords: Brazing, Laminated Object Manufacturing, Tensile Lap-Shear Test, Thermo-Mechanical Analysis.

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Authors: Ricardo Mestre

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The soundscape stands out as an agglomeration of sounds available in the world, associated with different contexts and origins, being a theme studied by various areas of knowledge, seeking to guide their benefits and their consequences, contributing to the welfare of society and other ecosystems. With the objective for a greater recognition of sound reality, through the selection and differentiation of sounds, the soundscape studies focus on the contribution for a better tuning of the world and to the balance and well-being of humanity. Sound environment, produced and created in various ways, can provide various sources of information, contributing to the orientation of the human being, alerting and manipulating him during his daily journey, like small notifications received on a cell phone or other device with these features. In this way, it becomes possible to give sound its due importance in relation to the processes of individual representation, in manners of social, professional and emotional life. Ensuring an individual representation means providing the human being with new tools for the long process of reflection by recognizing his environment, the sounds that represent him, and his perspective on his respective function in it. In order to provide more information about the importance of the sound environment inherent to the individual reality, one introduces the term sound instance, in order to refer to the whole sound field existing in the individual's life, which is divided into four distinct subfields, but essential to the process of individual representation, called sound matrix, sound cycles, sound traces and sound interference. Alongside volunteers we were able to create six representations of sound instances, based on the individual perception of his/her life, focusing on the present, past and future. With this investigation it was possible to determine that sound instance has a tool for self-recognition, considering the statements of opinion about the experience from the volunteers, reflecting about the three time lines, based on memories, thoughts and wishes.

Keywords: Sound instance, soundscape, sound art, self-recognition.

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Authors: Pratap Sriram Sundar, Chandan Chowdhury, Sagar Kamarthi

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Nature has perfected her designs over 3.5 billion years of evolution. Research fields such as biomimicry, biomimetics, bionics, bio-inspired computing, and nature-inspired designs have explored nature-made artifacts and systems to understand nature’s mechanisms and intelligence. Learning from nature, the researchers have generated sustainable designs and innovation in a variety of fields such as energy, architecture, agriculture, transportation, communication, and medicine. Axiomatic design offers a method to judge if a design is good. This paper analyzes design aspects of one of the nature’s amazing object: chicken egg. The functional requirements (FRs) of components of the object are tabulated and mapped on to nature-chosen design parameters (DPs). The ‘independence axiom’ of the axiomatic design methodology is applied to analyze couplings and to evaluate if eggs’ design is good (i.e., uncoupled design) or bad (i.e., coupled design). The analysis revealed that eggs design is a good design, i.e., uncoupled design. This approach can be applied to any nature’s artifacts to judge whether their design is a good or a bad. This methodology is valuable for biomimicry studies. This approach can also be a very useful teaching design consideration of biology and bio-inspired innovation.

Keywords: Uncoupled design, axiomatic design, nature design, design evaluation.

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Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

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Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: Autonomous vehicles, deformable part model, dpm, pedestrian recognition.

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence/pattern recognition/classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: Hybrid systems, Hidden Markov Models, Recurrent neural networks, Deterministic finite state automata.

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Authors: Ahmad Attiq Al-Ogaibi, Ahmad Sharieh, Moh’d Belal Al-Zoubi, R. Bremananth

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In pattern clustering, nearest neighborhood point computation is a challenging issue for many applications in the area of research such as Remote Sensing, Computer Vision, Pattern Recognition and Statistical Imaging. Nearest neighborhood computation is an essential computation for providing sufficient classification among the volume of pixels (voxels) in order to localize the active-region-of-interests (AROI). Furthermore, it is needed to compute spatial metric relationships of diverse area of imaging based on the applications of pattern recognition. In this paper, we propose a new methodology for finding the nearest neighbor point, depending on making a virtually grid of a hexagon cells, then locate every point beneath them. An algorithm is suggested for minimizing the computation and increasing the turnaround time of the process. The nearest neighbor query points Φ are fetched by seeking fashion of hexagon holistic. Seeking will be repeated until an AROI Φ is to be expected. If any point Υ is located then searching starts in the nearest hexagons in a circular way. The First hexagon is considered be level 0 (L0) and the surrounded hexagons is level 1 (L1). If Υ is located in L1, then search starts in the next level (L2) to ensure that Υ is the nearest neighbor for Φ. Based on the result and experimental results, we found that the proposed method has an advantage over the traditional methods in terms of minimizing the time complexity required for searching the neighbors, in turn, efficiency of classification will be improved sufficiently.

Keywords: Hexagon cells, k-nearest neighbors, Nearest Neighbor, Pattern recognition, Query pattern, Virtually grid

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

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Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: Mayank Kumar, D B Mahesh Kumar, Ashwin S Kumar, N K Srinath

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In this era of technology, fueled by the pervasive usage of the internet, security is a prime concern. The number of new attacks by the so-called “bots", which are automated programs, is increasing at an alarming rate. They are most likely to attack online registration systems. Technology, called “CAPTCHA" (Completely Automated Public Turing test to tell Computers and Humans Apart) do exist, which can differentiate between automated programs and humans and prevent replay attacks. Traditionally CAPTCHA-s have been implemented with the challenge involved in recognizing textual images and reproducing the same. We propose an approach where the visual challenge has to be read out from which randomly selected keywords are used to verify the correctness of spoken text and in turn detect the presence of human. This is supplemented with a speaker recognition system which can identify the speaker also. Thus, this framework fulfills both the objectives – it can determine whether the user is a human or not and if it is a human, it can verify its identity.

Keywords: CAPTCHA, automatic speech recognition, keyword spotting.

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Authors: Kwan Hee Han, Jun Woo Park, Seock Kyu Yoo, Geon Lee

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As business environments are rapidly changing, the manufacturing system must be reconfigured to adapt to various customer needs. In order to cope with this challenge, it is quintessential to test industrial control logic rapidly and easily in the design time, and monitor operational behavior in the run time of automated manufacturing system. Proposed integrated model for virtual prototyping and operational monitoring of industrial control logic is to improve limitations of current ladder programming practices and general discrete event simulation method. Each plant layout model using HMI package and object-oriented control logic model is designed independently and is executed simultaneously in integrated manner to reflect design practices of automation system in the design time. Control logic is designed and executed using UML activity diagram without considering complicated control behavior to deal with current trend of reconfigurable manufacturing. After the physical installation, layout model of virtual prototype constructed in the design time is reused for operational monitoring of system behavior during run time.

Keywords: automated manufacturing system, HMI, monitoring, object-oriented, PLC, virtual prototyping

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Authors: Mongkorn Klingajay, Tawatchai Jitson

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The pipe inspection operation is the difficult detective performance. Almost applications are mainly relies on a manual recognition of defective areas that have carried out detection by an engineer. Therefore, an automation process task becomes a necessary in order to avoid the cost incurred in such a manual process. An automated monitoring method to obtain a complete picture of the sewer condition is proposed in this work. The focus of the research is the automated identification and classification of discontinuities in the internal surface of the pipe. The methodology consists of several processing stages including image segmentation into the potential defect regions and geometrical characteristic features. Automatic recognition and classification of pipe defects are carried out by means of using an artificial neural network technique (ANN) based on Radial Basic Function (RBF). Experiments in a realistic environment have been conducted and results are presented.

Keywords: Artificial neural network, Radial basic function, Curve fitting, CCTV, Image segmentation, Data acquisition.

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Authors: Yi-Shiang Shiu, Meng-Lung Lin, Kang-Tsung Chang, Tzu-How Chu

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Most paddy rice fields in East Asia are small parcels, and the weather conditions during the growing season are usually cloudy. FORMOSAT-2 multi-spectral images have an 8-meter resolution and one-day recurrence, ideal for mapping paddy rice fields in East Asia. To map rice fields, this study first determined the transplanting and the most active tillering stages of paddy rice and then used multi-temporal images to distinguish different growing characteristics between paddy rice and other ground covers. The unsupervised ISODATA (iterative self-organizing data analysis techniques) and supervised maximum likelihood were both used to discriminate paddy rice fields, with training areas automatically derived from ten-year cultivation parcels in Taiwan. Besides original bands in multi-spectral images, we also generated normalized difference vegetation index and experimented with object-based pre-classification and post-classification. This paper discusses results of different image classification methods in an attempt to find a precise and automatic solution to mapping paddy rice in Taiwan.

Keywords: paddy rice fields; multi-temporal; FORMOSAT-2images, normalized difference vegetation index, object-basedclassification.

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Authors: S. H. Mohades Kasaei, S. M. Mohades Kasaei, S. A. Mohades Kasaei, M. Taheri, M. Rahimi, H. Vahiddastgerdi, M. Saeidinezhad

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Omni directional mobile robots have been popularly employed in several applications especially in soccer player robots considered in Robocup competitions. However, Omni directional navigation system, Omni-vision system and solenoid kicking mechanism in such mobile robots have not ever been combined. This situation brings the idea of a robot with no head direction into existence, a comprehensive Omni directional mobile robot. Such a robot can respond more quickly and it would be capable for more sophisticated behaviors with multi-sensor data fusion algorithm for global localization base on the data fusion. This paper has tried to focus on the research improvements in the mechanical, electrical and software design of the robots of team ADRO Iran. The main improvements are the world model, the new strategy framework, mechanical structure, Omni-vision sensor for object detection, robot path planning, active ball handling mechanism and the new kicker design, , and other subjects related to mobile robot

Keywords: Mobile robot, Machine vision, Omni directional movement, Autonomous Systems, Robot path planning, Object Localization.

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Authors: Irshad Ahmad, Abdul Muhamin Naeem, Muhammad Islam, Shahid Nawaz

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Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Maxima with threshold of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.

Keywords: Image processing, real-time recognition, weeddetection.

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Authors: A. Ouanane, A. Serir

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In this paper, a novel algorithm based on Ridgelet Transform and support vector machine is proposed for human action recognition. The Ridgelet transform is a directional multi-resolution transform and it is more suitable for describing the human action by performing its directional information to form spatial features vectors. The dynamic transition between the spatial features is carried out using both the Principal Component Analysis and clustering algorithm K-means. First, the Principal Component Analysis is used to reduce the dimensionality of the obtained vectors. Then, the kmeans algorithm is then used to perform the obtained vectors to form the spatio-temporal pattern, called set-of-labels, according to given periodicity of human action. Finally, a Support Machine classifier is used to discriminate between the different human actions. Different tests are conducted on popular Datasets, such as Weizmann and KTH. The obtained results show that the proposed method provides more significant accuracy rate and it drives more robustness in very challenging situations such as lighting changes, scaling and dynamic environment

Keywords: Human action, Ridgelet Transform, PCA, K-means, SVM.

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Authors: Randa Ibrahim Elanwar, Mohsen Rashwan, Samia Mashali

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In this paper we propose a segmentation system for unconstrained Arabic online handwriting. An essential problem addressed by analytical-based word recognition system. The system is composed of two-stages the first is a newly special designed hidden Markov model (HMM) and the second is a rules based stage. In our system, handwritten words are broken up into characters by simultaneous segmentation-recognition using HMMs of unique design trained using online features most of which are novel. The HMM output characters boundaries represent the proposed segmentation points (PSP) which are then validated by rules-based post stage without any contextual information help to solve different segmentation errors. The HMM has been designed and tested using a self collected dataset (OHASD) [1]. Most errors cases are cured and remarkable segmentation enhancement is achieved. Very promising word and character segmentation rates are obtained regarding the unconstrained Arabic handwriting difficulty and not using context help.

Keywords: Arabic, Hidden Markov Models, online handwriting, word segmentation

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Authors: Zhou Yu, Jiang Feng

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An adaptive Chinese hand-talking system is presented in this paper. By analyzing the 3 data collecting strategies for new users, the adaptation framework including supervised and unsupervised adaptation methods is proposed. For supervised adaptation, affinity propagation (AP) is used to extract exemplar subsets, and enhanced maximum a posteriori / vector field smoothing (eMAP/VFS) is proposed to pool the adaptation data among different models. For unsupervised adaptation, polynomial segment models (PSMs) are used to help hidden Markov models (HMMs) to accurately label the unlabeled data, then the "labeled" data together with signerindependent models are inputted to MAP algorithm to generate signer-adapted models. Experimental results show that the proposed framework can execute both supervised adaptation with small amount of labeled data and unsupervised adaptation with large amount of unlabeled data to tailor the original models, and both achieve improvements on the performance of recognition rate.

Keywords: sign language recognition, signer adaptation, eMAP/VFS, polynomial segment model.

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Authors: Juliana Barcelos Cordeiro, Khashayar Mahani, Farbod Farzan, Mohsen A. Jafari

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Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.  

Keywords: Energy consumption forecasting, energy efficiency, load disaggregation, pattern recognition approach.

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Authors: Siddhant Rao

Abstract:

Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming. It usually requires a trained pathologist to manually examine histopathological images and identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F- measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.

Keywords: Object detection, histopathology, breast cancer, mitotic count, deep learning, computer vision.

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Authors: Md. Asraful Haque, Babbar Imam

Abstract:

Information Security is the most describing problem in present times. To cop up with the security of the information, the passwords were introduced. The alphanumeric passwords are the most popular authentication method and still used up to now. However, text based passwords suffer from various drawbacks such as they are easy to crack through dictionary attacks, brute force attacks, keylogger, social engineering etc. Graphical Password is a good replacement for text password. Psychological studies say that human can remember pictures better than text. So this is the fact that graphical passwords are easy to remember. But at the same time due to this reason most of the graphical passwords are prone to shoulder surfing. In this paper, we have suggested a shoulder-surfing resistant graphical password authentication method. The system is a combination of recognition and pure recall based techniques. Proposed scheme can be useful for smart hand held devices (like smart phones i.e. PDAs, iPod, iPhone, etc) which are more handy and convenient to use than traditional desktop computer systems.

Keywords: Authentication, Graphical Password, Text Password, Information Security, Shoulder-surfing.

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