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Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.

This paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition.

The proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition.

Experimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively.

The good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.

This paper aims to propose a novel methodology for classifying the gestures using support vector machine (SVM) classification method. Initially, the Red Green Blue color hand gesture image is converted into YC_bC_r image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point (centroid) of palm region is detected and the fingertips are detected using SVM classification algorithm based on the detected centroids of the detected palm region.

Gesture is a physical indication of the body to convey information. Though any bodily movement can be considered a gesture, generally it originates from the movement of hand or face or combination of both. Combined gestures are quiet complex and difficult for a machine to classify. This paper proposes a novel methodology for classifying the gestures using SVM classification method. Initially, the color hand gesture image is converted into YC_bC_r image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point of the palm region is detected and the fingertips are detected using SVM classification algorithm. The proposed hand gesture image classification system is applied and tested on “Jochen Triesch,” “Sebastien Marcel” and “11Khands” data set hand gesture images to evaluate the efficiency of the proposed system. The performance of the proposed system is analyzed with respect to sensitivity, specificity, accuracy and recognition rate. The simulation results of the proposed method on these different data sets are compared with the conventional methods.

This paper proposes a novel methodology for classifying the gestures using SVM classification method. Distance transform method is used to detect the center point of the segmented palm region. The proposed hand gesture detection methodology achieves 96.5% of sensitivity, 97.1% of specificity, 96.9% of accuracy and 99.3% of recognition rate on “Jochen Triesch” data set. The proposed hand gesture detection methodology achieves 94.6% of sensitivity, 95.4% of specificity, 95.3% of accuracy and 97.8% of recognition rate on “Sebastien Marcel” data set. The proposed hand gesture detection methodology achieves 97% of sensitivity, 98% of specificity, 98.1% of accuracy and 98.8% of recognition rate on “11Khands” data set. The proposed hand gesture detection methodology consumes 0.52 s as recognition time on “Jochen Triesch” data set images, 0.71 s as recognition time on “Sebastien Marcel” data set images and 0.22 s as recognition time on “11Khands” data set images. It is very clear that the proposed hand gesture detection methodology consumes less recognition rate on “11Khands” data set when compared with other data set images. Hence, this data set is very suitable for real-time hand gesture applications with multi background environments.

The modern world requires more numbers of automated systems for improving our daily routine activities in an efficient manner. This present day technology emerges touch screen methodology for operating or functioning many devices or machines with or without wire connections. This also makes impact on automated vehicles where the vehicles can be operated without any interfacing with the driver. This is possible through hand gesture recognition system. This hand gesture recognition system captures the real-time hand gestures, a physical movement of human hand, as a digital image and recognizes them with the pre stored set of hand gestures.

Purpose – The advancement of multimedia technology has spurred the use of multimedia in business practice. The adoption of audio and visual data will accelerate as marketing scholars become more aware of the value of audio and visual data and the technologies required to reveal insights into marketing problems. This chapter aims to introduce marketing scholars into this field of research.Design/methodology/approach – This chapter reviews the current technology in audio and visual data analysis and discusses rewarding research opportunities in marketing using these data.Findings – Compared with traditional data like survey and scanner data, audio and visual data provides richer information and is easier to collect. Given these superiority, data availability, feasibility of storage, and increasing computational power, we believe that these data will contribute to better marketing practices with the help of marketing scholars in the near future.Practical implications: The adoption of audio and visual data in marketing practices will help practitioners to get better insights into marketing problems and thus make better decisions.Value/originality – This chapter makes first attempt in the marketing literature to review the current technology in audio and visual data analysis and proposes promising applications of such technology. We hope it will inspire scholars to utilize audio and visual data in marketing research.

The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to automatically localize the face in the image and, if necessary, identify the person in the face. Interests in the procedures underlying the process of localization and individual’s recognition are quite significant in connection with the variety of their practical application in such areas as security systems, verification, forensic expertise, teleconferences, computer games, etc. This paper aims to recognize facial images efficiently. An averaged-feature based technique is proposed to reduce the dimensions of the multi-expression facial features. The classifier model is generated using a supervised learning algorithm called a back-propagation neural network (BPNN), implemented on a MatLab R2017. The recognition rate and accuracy of the proposed methodology is comparable with other methods such as the principle component analysis and linear discriminant analysis with the same data set. In total, 150 faces subjects are selected from the Olivetti Research Laboratory (ORL) data set, resulting 95.6 and 85 per cent recognition rate and accuracy, respectively, and 165 faces subjects from the Yale data set, resulting 95.5 and 84.4 per cent recognition rate and accuracy, respectively.

Averaged-feature based approach (dimension reduction) and BPNN (generate supervised classifier).

The recognition rate is 95.6 per cent and recognition accuracy is 85 per cent for the ORL data set, whereas the recognition rate is 95.5 per cent and recognition accuracy is 84.4 per cent for the Yale data set.

Averaged-feature based method.

With the continuous development of aerospace technology, space exploration missions have been increasing year by year, and higher requirements have been placed on the upper level rocket. The purpose of this paper is to improve the ability to identify and detect potential targets for upper level rocket.

Aiming at the upper-level recognition of space satellites and core components, this paper proposes a deep learning-based spatial multi-target recognition method, which can simultaneously recognize space satellites and core components. First, the implementation framework of spatial multi-target recognition is given. Second, by comparing and analyzing convolutional neural networks, a convolutional neural network model based on YOLOv3 is designed. Finally, seven satellite scale models are constructed based on systems tool kit (STK) and Solidworks. Multi targets, such as nozzle, star sensor, solar,etc., are selected as the recognition objects.

By labeling, training and testing the image data set, the accuracy of the proposed method for spatial multi-target recognition is 90.17%, which is improved compared with the recognition accuracy and rate based on the YOLOv1 model, thereby effectively verifying the correctness of the proposed method.

This paper only recognizes space multi-targets under ideal simulation conditions, but has not fully considered the space multi-target recognition under the more complex space lighting environment, nutation, precession, roll and other motion laws. In the later period, training and detection can be performed by simulating more realistic space lighting environment images or multi-target images taken by upper-level rocket to further verify the feasibility of multi-target recognition algorithms in complex space environments.

The research in this paper validates that the deep learning-based algorithm to recognize multiple targets in the space environment is feasible in terms of accuracy and rate.

The paper helps to set up an image data set containing six satellite models in STK and one digital satellite model that simulates spatial illumination changes and spins in Solidworks, and use the characteristics of spatial targets (such as rectangles, circles and lines) to provide prior values to the network convolutional layer.

This study aims to monitor and guide the assembly process. The operators need to change the assembly process according to the products’ specifications during manual assembly of mass customized production. Traditional information inquiry and display methods, such as manual lookup of assembly drawings or electronic manuals, are inefficient and error-prone.

This paper proposes a projection-based augmented reality system (PBARS) for assembly guidance and monitoring. The system includes a projection method based on viewpoint tracking, in which the position of the operator’s head is tracked and the projection images are changed correspondingly. The assembly monitoring phase applies a method for parts recognition. First, the pixel local binary pattern (PX-LBP) operator is achieved by merging the classical LBP operator with the pixel classification process. Afterward, the PX-LBP features of the depth images are extracted and the randomized decision forests classifier is used to get the pixel classification prediction image (PCPI). Parts recognition and assembly monitoring is performed by PCPI analysis.

The projection image changes with the viewpoint of the human body, hence the operators always perceive the three-dimensional guiding scene from different viewpoints, improving the human-computer interaction. Part recognition and assembly monitoring were achieved by comparing the PCPIs, in which missing and erroneous assembly can be detected online.

This paper designed the PBARS to monitor and guide the assembly process simultaneously, with potential applications in mass customized production. The parts recognition and assembly monitoring based on pixels classification provides a novel method for assembly monitoring.

The paper aims to propose a novel method based on deep sparse convolutional neural network (CNN) for clothing recognition. A CNN based on inception module is applied to bridge pixel-level features and high-level category labels. In order to improve the robustness accuracy of the network, six transformation methods are used to preprocess images. To avoid representational bottlenecks, small-sized convolution kernels are adopted in the network. This method first pretrains the network on ImageNet and then fine-tune the model in clothing data set.

The paper opts for an exploratory study by using the control variable comparison method. To verify the rationality of the network structure, lateral contrast experiments with common network structures such as VGG, GoogLeNet and AlexNet, and longitudinal contrast tests with different structures from one another are performed on the created clothing image data sets. The indicators of comparison include accuracy, average recall, average precise and F-1 score.

Compared with common methods, the experimental results show that the proposed network has better performance on clothing recognition. It is also can be found that larger input size can effectively improve accuracy. By analyzing the output structure of the model, the model learns a certain “rules” of human recognition clothing.

Clothing analysis and recognition is a meaningful issue, due to its potential values in many areas, including fashion design, e-commerce and retrieval system. Meanwhile, it is challenging because of the diversity of clothing appearance and background. Thus, this paper raises a network based on deep sparse CNN to realize clothing recognition.

This study aims at a biometric verification based on facial profile images for mobile security. The modern technology of mobile Internet devices and smart phones such as the iPhone series and Galaxy phone series has revealed the development of information technology of input and output devices as high-definition multimedia interface. The development of information technology requires novel biometric verification for personal identification or authentication in mobile security, especially in Internet banking and mobile Internet access. Our study deals with a biometric verification based on facial profile images for mobile security.

The product of cellphones with built-in cameras gives us the opportunity of the biometric verification to recognize faces, fingerprints and biological features without any other special devices. Our study focuses on recognizing the left and right facial profile images as well as the front facial images as a biometric verification of personal identification and authentication for mobile security, which can be captured by smart phone devices such as iPhone 4 and Galaxy S2.

As the recognition technique of the facial profile images for a biometric verification in mobile security is a very simple, relatively easy to use and inexpensive, it can be easily applied to personal mobile phone identification and authentication instead of passwords, keys or other methods. The biometric system can also be used as one of multiple verification techniques for personal recognition in a multimodal biometric system. Our experimental data are taken from persons of all ages, ranging from children to senior citizens.

As the recognition technique of the facial profile images for a biometric verification in mobile security is very simple, relatively easy to use and inexpensive, it can be easily applied to personal mobile phone identification and authentication instead of passwords, keys or other methods. The biometric system can also be used as one of multiple verification techniques for personal recognition in a multimodal biometric system. Our experimental data are taken from persons of all ages, ranging from children to senior citizens.