The weave pattern (texture) of woven fabric is considered to be an important factor of the design and production of high-quality fabric Traditionally, the recognition of woven fabric has a lot of challenges due to its manual visual inspection Moreover, the approaches based on early machine learning algorithms directly depend on handcrafted features, which are time-consuming and error-prone processes Hence, an automated system is needed for classification of woven fabric to improve productivity In this paper, we propose a deep learning model based on data augmentation and transfer learning approach for the classification and recognition of woven fabrics The model uses the residual network (ResNet), where the fabric texture features are extracted and classified automatically in an end-to-end fashion We evaluated the results of our model using evaluation metrics such as accuracy, balanced accuracy, and F1-score The experimental results show that the proposed model is robust and achieves state-of-the-art accuracy even when the physical properties of the fabric are changed We compared our results with other baseline approaches and a pretrained VGGNet deep learning model which showed that the proposed method achieved higher accuracy when rotational orientations in fabric and proper lighting effects were considered

https://www.mdpi.com/2079-9292/9/6/1048/pdf

Woven Fabric Pattern Recognition and Classification Based on Deep Convolutional Neural Networks

The invention relates to a traffic scene participant identification method based on a vehicle-mounted binocular camera The method overcomes the defects of the prior art According to the technical scheme, data are acquired through a binocular system The method is characterized by comprising the steps that 1, the vehicle-mounted binocular camera acquires a current road image and conducts precise image pretreatment and binocular parallax matching cost computation; 2, longitudinal road surface parameter estimation is conducted, and horizon line and road surface range information is acquired; 3, the position and dimension information of a potential obstacle are determined by means of parallax matching cost; 4, the characteristic information of the obstacle is described by means of a deep convolutional neural network, an obstacle detection area is verified based on output of a characteristic chromatogram by means of a softmax classifier, and the category of the obstacle is determined Besides profile and texture information, parallax information can also be utilized, so that identification accuracy is far higher than that of a superficial zone identification method

Traffic scene participant identification method based on vehicle-mounted binocular camera

The recognition of woven fabric pattern is a crucial task for mass manufacturing and quality control in the textile industry Traditional methods based on image processing have some limitations on accuracy and stability In this paper, an automatic method is proposed to jointly realize yarn location and weave pattern recognition First, a new big fabric dataset is established by a portable wireless device The dataset contains wide kinds of fabrics and detailed fabric structure parameters Then, a novel multi-task and multi-scale convolutional neural network (MTMSnet) is proposed to predict the location maps of yarns and floats By adopting the multi-task structure, the MTMSnet can better learn the related features between yarns and floats Finally, the weave pattern and basic weave repeat are recognized by combining the yarn and float location maps Extensive experimental results on various kinds of fabrics indicate that the proposed method achieves high accuracy and quality in weave pattern recognition

A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern

The present invention discloses a facial expression recognition method and device The method comprises the following steps that: the local features and overall features of a face in a detection image are extracted; the local features, overall features and detection image are inputted into a deep learning model, so that the classification results of the detection image belonging to different facial expressions are obtained, wherein the deep learning model is obtained through training a deep neural network in advance according to the training images of different facial expressions and the local features and overall features of the training images; and the facial expression of the detection image is determined according to the classification results With the facial expression recognition method and device of the invention adopted, the accuracy of facial expression recognition can be improved

Facial expression recognition method and device

The invention discloses a face expression identification method and device. The method comprises steps that a to-be-identified face image sequence is acquired, and the face image sequence comprises one or more than two face images; each face image of the face image sequence is pre-processed; the pre-processed face images are inputted to a trained training model to carry out expression identification to acquire expression identification results of the face image sequence; a convolutional neural network model, a long-time short-time memory cycle neural network model, a first pooling layer and a logic regression model are sequentially constructed from an input end to an output end of the training model, and the training model is acquired through training a continuous-image set marked with expression categories. The method is advantaged in that face expression identification performance can be effectively improved.

Face expression identification method and device

The invention provides a vehicle license plate recognition method, and relates to a method for image recognition. The method comprises the following steps of preprocessing an image; partitioning a vehicle region according to color and texture characteristics; extracting a remarkable factor graph of a vehicle region diagram; extracting candidate vehicle license plates by an Adaboost classifier based on expanded Harr-like characteristic; determining the position of a real vehicle license plate from the candidate vehicle license plates; partitioning the marked vehicle license plate from the corresponding vehicle region original diagram; carrying out character segmentation according to structural characteristic; and carrying out character recognition based on the improved template matching method. With the method provided by the invention, the defects that the application scene of the traditional vehicle license plate recognition method is relatively single, some traditional vehicle license plate recognition methods are only suitably used for single vehicle license plate recognition of the single scene and difficultly used for multiple-vehicle license plate recognition of multiple scenes, and the recognition rate is easily affected by strong light, haze and weak light environments are overcome.

Vehicle license plate recognition method

The invention provides a small-sample face recognition method which relates to a method for face recognition by means of electronic equipment. According to the face recognition method, a single-layer multi-scale convolutional neural network structure is used for performing multi-characteristic fusion and classification. The method comprises the steps of preprocessing a face image; extracting a multi-characteristic graph of the face image; extracting a DWT low-frequency subband graph, an Sobel edge characteristic graph and an LBP pattern characteristic graph; performing multi-characteristic fusion by means of the single-layer multi-scale convolutional neural network structure; and predicating a classification result by means of a Softmax classifier, thereby realizing face recognition. The small-sample face recognition method overcomes a defect of low classification recognition rate caused by a classification method in settling a small-sample face recognition problem.

Small-sample face recognition method

The invention discloses a method for recognizing human face micro-expressions in a video sequence and relates to the treatment on a recording carrier for recognized patterns. A method for the extracting dynamic spatial-temporal textural characteristics of human face micro-expression sequences based on the HLACLF-TOP algorithm is provided. The method comprises the steps of amplifying the video of human face micro-expressions based on the Euler function, preprocessing the images of human face micro-expressions, extracting the dynamic spatial-temporal textural characteristics of human face micro-expression sequences based on the HLACLF-TOP algorithm, training and predicting by utilizing an ELM classifier. Based on the above method, the defect in the prior art that human face micro-expressions are hard to recognize due to the small variation amplitude of human face micro-expressions can be overcome.

Method for recognizing human face micro-expressions in video sequence

The invention relates to an image scaling method based on content awareness, and relates to graphic image conversion in an image plane. An energy function is obtained by adopting a hybrid feature model fusing a salient map, an edge line map and a gradient map, and according to the energy function, a line clipping operation is carried out to complete image scaling. The image scaling method comprises the steps of: inputting a color image to carry out preprocessing; simultaneously carrying out extraction of the salient map and a salient target image of an original color image, extraction of the edge map of a gray scale image, which is fused with line information, and extraction of the gradient map of the gray scale image; fusing three feature maps by utilizing an HFPM (hybrid feature model) algorithm so as to obtain the energy function; and clipping the original image by using a line clipping algorithm. The method disclosed by the invention overcomes the defects that an existing line clipping method adopts a gradient map definition energy function of an image and still causes distortion and loss of partial image information in the image scaling process.

Image scaling method based on content awareness

The present invention discloses a binocular visual image stereo matching method, relating to image data processing. The method comprises the steps of the acquisition and pre-processing of a binocular image, the image gradient matrix solution of the binocular image, the obtainment of an initial disparity map, and the obtainment and output of a final disparity map and the completion of the binocular visual image stereo matching. According to the method, the defect of low matching precision and insufficient real-time performance of the existing binocular stereo matching technology is overcome.

Liu Yi

Papers

Vehicle license plate recognition method

Small-sample face recognition method

Method for recognizing human face micro-expressions in video sequence

Image scaling method based on content awareness

Binocular visual image stereo matching method