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Near-infrared spectroscopy and multivariate analysis techniques were employed to nondestructively evaluate the rancidity of perilla seed oil by developing prediction models for the acid and peroxide values. The acid, peroxide value, and transmittance spectra of perilla seed oil stored in two different environments for 96 and 144 h were obtained and used to develop prediction models for different storage conditions and time periods. Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. Titration analysis shows that the free fatty acids in an oil oxidation process were more affected by relative humidity than temperature, whereas peroxides in an oil oxidation process were more significantly affected by temperature than relative humidity for the two different environments in this study. Also, the prediction results of ANN models for both acid and peroxide values were the highest among the developed models. These results suggest that the proposed near-infrared spectroscopy technique with multivariate analysis can be used for the nondestructive evaluation of the rancidity of perilla seed oil, especially the acid and peroxide values.

Discoloration and dehydration are crucial problems associated with quality of bell pepper (Capsicum annuum L.). Color and weight loss of the bell pepper were measured at different vibration levels, storage environments and storage periods. Vibration tests were carried out at different 'g' levels from normal to the worst-case for 180 sec on the initial day. The effect of two different storage environments (10°C, 90 % RH and 18°C, 40 % RH) for four time periods (1, 4, 7 and 10 days) were observed. At 18°C, 40 % RH storage environment, more weight loss and duller color were observed. In the 10°C, 90 % RH storage environment, the group of bell pepper sample having 12 g level vibration had significantly much more weight loss compared to other groups. In this storage environment, most of bell pepper samples maintained vivid color. Storage life and fruit quality of bell pepper were affected by storage temperatures, humidity and duration. The results showed that high g level vibration, high temperature, and low humidity environment during harvesting and storage process of the bell pepper should be avoided. [ABSTRACT FROM AUTHOR]

Labeling children's social play behavior is an important process in children's peer-play analysis which is traditionally done by experienced coders. With the growing volume of data, automatic methods for labeling are increasingly required. This paper presents a novel method to classify children's social play behavior in peer-play scenarios into three categories (solitary play, parallel play and group play). Based on the two key cues -- attentiveness and proximity proposed in "The Play Observation Scale", unary features and pairwise features are calculated to describe the relationships between a child and the whole context, and the interactions between two children. Inspired by the recent studies in social behavior analysis and interaction recognition, children's activities are classified by support vector machine (SVM) and hidden conditional random field (HCRF). This method is evaluated by a dataset of children's peer-play scenarios collected by psychology researchers and the results show this method has a good performance in the dataset. [ABSTRACT FROM AUTHOR]

Fresh fruit bruising caused by mechanical impact is the most problematic effect of mechanical postharvest processing of tomatoes. The conventional postharvest process of tomato fruit (Solanum lycopersicum L.) handling was surveyed from harvest to shipping preparation at a farm in Toechon in the Republic of Korea. Acceleration sensors located on the harvest boxes and fruit skin showed that the most severe impact forces (>10 g) occurred while fruit was moved in and out of the harvest box. Next, multivariate analysis was used to evaluate nondestructively the susceptibility of tomatoes to bruising by developing five estimation models incorporating quality factors and the presence of bruising resulting from peak contact forces. Linear regression models, artificial neural network (ANN) regression models, and a logistic regression model were built; the primary dependent variables were the rates of weight loss and firmness loss, and impactinduced bruising. The impact force was controlled by using a pendulum, which produced four levels of impact force on tomato fruit. Increasing the relative humidity (RH) and firmness, and decreasing the temperature and degree of weight loss decreased tomato fruit bruise damage. In addition, the proposed drop--impact measurement technique, with multivariate analysis, can be used to evaluate tomato quality nondestructively. [ABSTRACT FROM AUTHOR]

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain (QAHSS), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge. [ABSTRACT FROM AUTHOR]

This issue of JAISE is composed of five papers. The review process for the papers in this issue was supervised by our editors Bjoern Gottfried, Andrea Prati, Jinshi Cui, and Vincent Tam, whom we thank for their service. However, the data involved in the monitoring system and the automation processes can be huge and heterogeneous, complicating the system integration processes at the deployment and operation of such systems. [Extracted from the article]

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn and intelligently transfers the learned generic object knowledge from the common heavy-tailed to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented. [ABSTRACT FROM AUTHOR]

Theoretically, ink rubbings are secondary to the steles they come from, but they served as primary sources for epigraphers who were more interested in literati inscriptionality than they were in the three-dimensional monumentality of the steles. In this shifting status of the two media, textual form and calligraphic style play a subtle yet critical role in the appreciation of inscriptions. Beginning in the late eighteenth and early nineteenth centuries, scholars in China and Korea exchanged rubbings of ancient steles to build friendships. Conflicting interpretations, however, arose when discussing the calligraphic style of Korean inscriptions. Chinese fascination with Korean steles is understood as part of their investigation into the remnants of Chinese heritage on the Korean peninsula. Korean scholars, in turn, attempted to suggest that Korean culture kept abreast with that of China from early times onward. Using two case studies of the Mujangsa and Hwangch'oryŏng Steles, this article discusses how self-serving agendas motivated scholars of both countries. [ABSTRACT FROM AUTHOR]

Recently, concerning the challenge of collecting large-scale explicitly annotated videos, weakly supervised video object segmentation (WSVOS) using video tags has attracted much attention. Existing WSVOS approaches follow a general pipeline including two phases, i.e., a pseudo masks generation phase and a refinement phase. To explore the intrinsic property and correlation buried in the video frames, most of them focus on the later phase by introducing optical flow as temporal information to provide more supervision. However, these optical flow-based studies are greatly affected by illumination and distortion and lack consideration of the discriminative capacity of multi-level deep features. In this article, with the goal of capturing more effective temporal information and investigating a temporal information fusion strategy accordingly, we propose a unified WSVOS model by adopting a two-branch architecture with a multi-level cross-branch fusion strategy, named as dual-attention cross-branch fusion network (DACF-Net). Concretely, the two branches of DACF-Net, i.e., a temporal prediction subnetwork (TPN) and a spatial segmentation subnetwork (SSN), are used for extracting temporal information and generating predicted segmentation masks, respectively. To perform the cross-branch fusion between TPN and SSN, we propose a dual-attention fusion module that can be plugged into the SSN flexibly. We also pose a cross-frame coherence loss (CFCL) to achieve smooth segmentation results by exploiting the coherence of masks produced by TPN and SSN. Extensive experiments demonstrate the effectiveness of proposed approach compared with the state-of-the-arts on two challenging datasets, i.e., Davis-2016 and YouTube-Objects. [ABSTRACT FROM AUTHOR]

In spread spectrum induced polarization (SSIP) data processing, attenuation of background noise from the observed data is the essential step that improves the signal-to-noise ratio (SNR) of SSIP data. The time-domain spectral induced polarization based on pseudorandom sequence (TSIP) algorithm has been proposed to improve the SNR of these data. However, signal processing in background noise is still a challenging problem. We propose an enhanced correlation identification (ECI) algorithm to attenuate the background noise. In this algorithm, the cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Then the frequency-domain IP (FDIP) method is used for extracting the frequency response of the observation system. Experiments on both synthetic and real SSIP data show that the ECI algorithm will not only suppress the background noise but also better preserve the valid information of the raw SSIP data to display the actual location and shape of adjacent high-resistivity anomalies, which can improve subsequent steps in SSIP data processing and imaging. [ABSTRACT FROM AUTHOR]

The procedure of procurement the original image from the degraded image assumed the knowledge of the debasing factors is called as Image Restoration. The corrupted image will be considered as input and provided to the soft computing method for decreasing the noisy information from the input image in our suggested method. Nonetheless, the established output image obtained from the soft computing method will seem to be blurry with less superiority in the contrast level. This blurred information damages the function of image reconstruction. Thus, into overawed this disadvantage, we deed an Extended Kalman filter method to provide great quality reconstructed the image, in that an optimization algorithm Grey Wolf Optimization (GWO) will be used for a greater reconstructed image. The main contribution of the proposed work is to improve the image quality of reconstruction image by means of optimal Extended Kalman filter. For proving the function of our suggested method, the reconstructed image quality will be associated with the conventional methods. The restoration method was tested with different image modalities such as MRI, CT and also Ultra Sound images of the Human abdomen. The method will be applied to the functioning platform of MATLAB. The implemented restoration technique achieves the maximum Peak Signal to Noise Ratio (PSNR) and structural similarity index (SSIM) value for liver CT image are 35.10db and 0.999 and minimum Mean square error (MSE) and Mean absolute error (MAE) value for liver CT image are 7.90E-05 and 1.1281 respectively. [ABSTRACT FROM AUTHOR]

The recognition frameworks are highly vulnerable to spoofing attacks and this vulnerability generates an effective security concerned issues in biometric domain. Moreover, some of the earlier proposed approaches have attained attractive results with intra test (i.e. by training and testing the system on same database) evaluation done to detect the face spoofing attack. Consequently, most of these techniques generate incorrect decision on the recognition of genuine faces with unseen attacks in case of inter test evaluation (i.e. the system is trained on one database and then tested on another database). However, this impact is considered as a major difficulty in the highly focused biometric anti-spoofing research domain. In this work, we propose a multimodal biometric framework for the accurate recognition of fake face from genuine face. Initially, face image features which are coupled to the color spaces HSV and YCbCr are extracted with EDGHM-SURF (Enhanced Discrete Gaussian-Hermite Moment based Speed-up Robust Feature) descriptor. Then, a newly developed method of feature-level fusion using OGWO (FLFO) is used to fuse these extracted features. This method utilized the OGWO (Oppositional Gray Wolf Optimization) algorithm due to its excellent exploitation and exploration behavior in the identification of optimal weight score from the solution space, without allowing the solutions to stick in the local optimum. Finally, the fused features are fed into the Layered k-SVM (k-support vector machine) classifier for the recognition of fake face. The experimental results of our proposed approach are evaluated on three traditional benchmark face spoofing databases, namely the Replay-Attack, the CASIA Face Anti-Spoofing, and the MSU Mobile Face Spoof database. The outcome of our proposed approach exhibited steady and robust performance across all the three datasets. More commonly, our proposed approach executes well in the inter database tests and yields high performance, even though when only operated with minimized training data. [ABSTRACT FROM AUTHOR]

In visual tracking topic, developing a robust tracking method is very challenging, seen that there are many issues to look at, particularly, fast motion, target appearance changing, background clutter and camera motion. To override these problems, we present a new object tracking method with the fusion of interacting multiple models (IMM) and the particle filter (PF). First, the IMM is applied with a bank of parallel H∞ filter to estimate the global motion, the target motion is efficiently represented using only two parametric single models, and an adaptive strategy is preformed to adjust automatically the parameters of the two sub models at each recursive time step. Second, the particle filter is performed to estimate the local motion, we fuse the color and texture features to describe the appearance of the tracked object, we use the alpha Gaussian mixture model (α-GMM) to model the color feature distribution, the parameter α allows the probability function to possesses a flatter distribution, and the texture feature is represented by the distinctive uniform local binary pattern histogram (DULBP) based on the uniform local binary pattern (ULBP) operator; we fuse then the two features to represent the target’s appearance under the particle filter framework. We conduct quantitative and qualitative experiments on a variety of challenging public sequences; the results show that our method performs robustly and demonstrates strong accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a method to capture human pose from individual real-world RGB images using a deep learning technique. The current works on estimating human pose by deep learning are designed in a detection or a regression framework, and in a part-based manner. As a new perspective, we introduce a classification scheme for this problem, which reasons the pose holistically. To the best of our knowledge, this is the first work for holistic human pose classification task that owes its feasibility to the great power of convolutional neural networks in feature learning. After training a convolutional neural network to classify the input image to one of the KeyPoses, the final pose is computed as a linear combination of several KeyPoses. In this new holistic classification attitude, the vast and high degree of freedom human pose space is divided into a finite number of subspaces and the convolutional neural network shows promising results in learning the features of each subspace. Empirical results (PCP and PCK rates) demonstrate that the proposed scheme is successfully able to understand human pose (i.e., predict a valid, true and coarse pose) in real-world unconstrained images with challenges like severe occlusion, high articulation, low quality and cluttered background. Furthermore, using the proposed method, the need for defining a complex model (such as appearance model or joints pairwise relations) is relieved. We have also verified a potential application of our proposed method in semantic image retrieval based on human pose. [ABSTRACT FROM AUTHOR]

In the present paper, an advanced encryption technique commonly known as Elliptic Curve Cryptography (ECC) is used to embed a binary image as a watermark in five grayscale host images in a semi-blind manner. The ECC algorithm is a fast encryption technique which successfully encrypts the subject with significantly less number of bits as compared to other popular encryption algorithms such as Rivest-Shamir-Adleman (RSA) and Direct Selling Association (DSA). In the proposed watermarking scheme, embedding in the grayscale host images is carried out in DWT-SVD domain. First, entropy based Human Visual System (HVS) parameters are computed block wise to identify the most appropriate blocks in spatial domain. First level DWT is computed for these selected blocks and watermark embedding is carried out by using the calculated Singular Value Decomposition (SVD) parameters. Preliminary results of this work show that proposed scheme outperforms the other similar schemes carried out in DCT-SVD domain without using any encryption method. It is concluded that the use of DWT-SVD hybrid architecture along with the fast encryption technique ECC is responsible for better performance in present case. In the second part of this simulation, an established HVS model working in DCT domain is implemented and compared with the entropy based HVS model implemented in transform domain to embed the ECC encrypted binary watermark in images. In this case also, proposed scheme performs better both in terms of visual imperceptibility and robustness as compared to other scheme. It is concluded that HVS parameters - Luminance, Contrast and Edge Sensitivity are better placed in comparison to entropy parameters to examine image features and characteristics for watermarking purpose. [ABSTRACT FROM AUTHOR]